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Hospitalists' Awareness of Patient Charges
Hospitalists have been suggested to offer rational and efficient medical care through specialized knowledge about inpatient care services.1 The goal that hospitalists will use care resources more efficiently presumes hospitalists' accurate knowledge about charges and costs.
Data regarding physicians' awareness of care charges and its impact upon care is limited. An international meta‐analysis of clinicians' awareness of pharmaceutical prices demonstrated poor accuracy of physicians' estimates of charges, but effects of increasing their knowledge remained unexamined.2 Continuous exposure to education and alerts about charges have been demonstrated to diminish physicians' unnecessary use of specific laboratory assays in a single teaching hospital, in a pediatric emergency department, and an outpatient primary care system; test use declined when physicians were alerted to test charges at the point‐of‐care without negative impact upon clinical outcomes; when the notices ceased, utilization climbed back towards baseline levels. Specific to inpatient care, a single‐center study evaluated the impact of price‐alerts upon laboratory and imaging use, but showed no effects.36
Applicability of these existing data for contemporary hospitalists are limited, and most data were collected before hospitalists developed as an organized focus of practice. A review of existing literature revealed no published data demonstrating hospitalists' higher expert awareness of charges generated by inpatient care. Published comparisons of the care expense generated by hospitalists' care versus that of general internists or academic teams have shown minimal and inconsistent effects.79 Those data showing reduced costs from hospitalists were associated with small length‐of‐stay reductions, rather than more expert resource utilization.7 We measured the accuracy and precision of hospitalist's estimates of charges associated with services commonly used in inpatient care.
Setting
Two community‐based private, academic‐affiliated hospitals operated by a not‐for‐profit health system in Washington State, comprising together 895 inpatient beds. The questionnaire instrument was approved by the governing Institutional Review Board (IRB).
Methods
A list of true charges for 14 services, procedures, tests, and physician charges commonly ordered by adult medicine hospitalists was acquired directly from the responsible departments of a multi‐hospital system operated by a single non‐profit entity using a unified chargemaster. Specifically, we acquired the charge that a hypothetical self‐paying patient would receive for each service, excluding any adjustments exercised by other payer sources. The list of charges was reported to the organization's financial officers for affirmation. Physician charges were standardized to geographically‐adjusted Medicare charges obtained directly from the American Medical Assocation's online Common Procedural Terminology tool.10 A cross‐section of hospitalists (n = 25) was surveyed from a private hospitalist group and an academically‐affiliated hospitalist service. Hospitalists included US and international medical graduates, new‐graduates from residency training and clinicians with a range of prior experiences in academic centers, government hospital systems, and private primary care. Respondents were asked to estimate to the nearest dollar the billing charge that a hypothetical self‐pay patient would receive for each care item. Direct data collection was arranged through the groups' medical directors and occurred in the hospitalist groups' regular business meetings. The design gathered no data on individual characteristics of respondents such as domestic or international education, sex, age, or time in practice, nor from which practice‐group a given respondent originated, to affirm to participants that their responses could not be used to imply performance measures or quality profiles.
Findings
Hospitalists tended to rank the expense of items in essentially the correct order, as reflected by the rough trend of rising estimates compared to true‐charges (Figure 1). Of note, we did not ask hospitalists to discern the different charges of 2 appropriate competing clinical choices, but asked for estimated prices of diverse services. The range of respondents' estimates about each item was broad. The mean‐value of hospitalists' estimates for each care item was less revealing than the range and diversity of estimates about each care item. Accuracy of hospitalists' estimates of charges was poor. Only 10.8% of hospitalists' estimates were within 10% of the actual unadjusted charge, 17.8% within 20% of that charge, and 24.8% were within a 30% margin of accuracy. Summary results are presented (Table 1). Pearson's r correlation value between the unadjusted charges and the estimates made by hospitalists was 0.548, a coefficient of determination equal to 0.300. Thus, the true charges list we obtained had only a low‐grade association with hospitalists' estimates (Figure 1). Hospitalists' estimates about the charges of relatively‐expensive items (abdominal computed tomography [CT]) overlapped with their estimates about the least‐expensive items (such as a urine culture ). Inter‐hospitalist agreement about charges associated with each care item was also low; estimates for each care item charge varied over logarithmic orders of magnitude.
Care Service | Unadjusted Charge, USD$ | Mean Estimate, USD$ | Minimum Estimate, USD$ | Maximum Estimate, USD$ | % of Estimates Within 10% Accuracy | % of Estimates Within 20% Accuracy | % of Estimates Within 30% Accuracy |
---|---|---|---|---|---|---|---|
| |||||||
Complete blood count | 30 | 73 | 10 | 440 | 16 | 20 | 20 |
Complete metabolic panel | 37 | 135 | 15 | 1200 | 4 | 16 | 16 |
Urinalysis with microscopy | 37 | 53 | 15 | 105 | 12 | 20 | 24 |
Urine culture | 26 | 77 | 20 | 200 | 4 | 16 | 20 |
Ward bed, charge per night | 744 | 998 | 300 | 3000 | 20 | 20 | 20 |
ICU Bed, charge per night | 1107 | 2018 | 750 | 6000 | 8 | 12 | 12 |
Chest x‐ray | 271 | 169 | 60 | 700 | 12 | 16 | 24 |
CT scan, abdomen | 2204 | 803 | 150 | 1800 | 0 | 4 | 4 |
Methylpredisolone 125 mg IV dose | 26.63 | 63 | 3 | 200 | 4 | 20 | 24 |
Levofloxacin 500 mg IV dose | 105.41 | 114 | 10 | 500 | 24 | 28 | 36 |
Levofloxacin 500 mg oral dose | 29.78 | 25 | 4 | 70 | 12 | 12 | 20 |
Admission services (CPT code 99223) | 169.56 | 225 | 100 | 700 | 8 | 36 | 52 |
Inpatient care services (CPT code 99232) | 62.47 | 110 | 40 | 400 | 12 | 28 | 48 |
Central venous catheter placement (CPT 36569) | 286.04 | 338 | 50 | 1200 | 8 | 16 | 28 |
Average % correct | 10.8 | 17.8 | 24.8 |

Discussion
To date, hospitalist programs have shown little impact upon care costs when compared with other inpatient care staffing models. One limiting factor may be the opacity of medical care pricing. Patients have been demonstrated to have little access to knowledge of what care will cost them and complex barriers prevent them from gaining pricing information.11, 12 Hospitalists may be conjectured to serve as expert sources on the costs and values of medical care services on behalf of inpatients, but our observations suggest that hospitalists' actual knowledge of patient‐charges is lacking. The opacity of US medical prices to patients appears to extend to hospital‐care physicians as well. We observe that no widespread mechanism exists by which hospitalists would be well‐positioned to become informed about the actual charges their patients receive. Unadjusted chargemaster lists are generally restricted information, and would be difficult to access outside of participation in the charge‐notifications used in the existing studies cited above.
The inquiry was specifically limited to how closely hospitalists' estimates of the unadjusted charges for some commonly‐ordered items compare to the actual unadjusted chargemaster at their own institutions. We did not assess the hospitalists' perceptions about the accuracy of their estimates, nor the impact of specific hospitalist characteristics upon accuracy. Our sample's representation of the larger national population of hospital physicians is not established, but engenders no expectation that these clinicians' charge‐awareness is substantively different from that of hospitalists in most other institutions. It is not known what specific clinician or practice‐setting characteristics will direct charge‐awareness, or will influence the impact of charge‐awareness upon clinical practices.
The range of estimates different hospitalists made about the same care items in the same facilities was very broad, which argues that respondents did not estimate charges based upon a different knowledge base of which the investigators were unaware. This is important because our use of unadjusted charges to self‐pay patients as true prices is necessarily somewhat arbitrary. Chargemaster price may not reflect the institution's cost of performing the service, the different prices paid for a single service by different payer sources, nor reflect services' true value based upon outcomes. However, recognizing that these actual prices are somewhat artificial, the use of these prices suffices for the current inquiry, and does not negate our findings of hospitalists' low accuracy and low agreement. Also noteworthy, an unadjusted chargemaster can often represent the charges received by those uninsured US patients for whom payer‐source adjustment is inaccessible, and informs downstream accounting such as the value of unpaid care a hospital delivers annually.
The most immediate matter for examination among hospitalists is what effects increased charge‐awareness may exert upon clinical decisions and practice processes. It appears that the premise that hospitalists' exercise expert knowledge of costs is likely not valid; but it is unknown whether accurate charge‐awareness among hospitalists will improve cost‐reductions by hospitalists. In some payer‐arrangements, accurate charge‐awareness might engender reduced care quantity, rather than increased efficiency or quality. The impact of upgrading hospitalists' knowledge about the charges and costs they generate, and the most effective method to do so, is worthy of investigation; based upon this initial data we encourage and are undertaking a larger‐scale study and exploration of the effects of enhanced hospitalist charge‐awareness.
Conclusion
Hospitalists have low awareness of the charges associated with inpatient care. The opacity of hospital care pricing to patient populations extends also to hospitalist physicians. Hospitalists likely do not improve cost‐efficiency through expert knowledge of services' costs to patients. Education and reminder systems to apprise hospitalists of charges should be examined as possible tools to optimize the use of inpatient care resources.
- The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19(4):293–201. , , et al.
- Physician awareness of drug cost: a systematic review.PLoS Med.2007;4(9):e283. , , .
- The effect of price information on test‐ordering behavior and patient outcomes in a pediatric emergency department.Pediatrics.1999;103(4):877–882. , , , , .
- The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests.N Engl J Med1990;322:1499–1504. , , .
- Factors contributing to inappropriate ordering of tests in an academic medical department and the effect of an educational feedback strategy.Postgrad Med J.2006;82:823–829. , , , .
- Tanasijevic does the computerized display of charges affect inpatient ancillary test utilization?Arch Intern Med.1997;157(21):2501–2508. , , , et al.
- The impact of hospitalists on the cost and quality of inpatient care in the united states: a research synthesis.Med Care Res Rev.2005;62:379–406. , .
- Comparison of hospital costs and length of stay for community internists, hospitalists, and academicians.J Gen Intern Med.2007;22:662–667. , , .
- Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357(25):2589–2600. , , , , , .
- American Medical Association, CPT and RVU Search utility. Available at: https://catalog.ama‐assn.org/Catalog/cpt/cpt_search.jsp. Accessed December 2009.
- Does price tranparency improve market efficiency? Implications of empirical evidence in other markets for the health sector. 2007, Congressional Research Service Report RL34101. Available at: http://ftp.fas.org/sgp/crs/secrecy/RL34101. Accessed December2009. , .
- The pricing of US hospital services.Health Aff.2006;25(1):57–69. .
Hospitalists have been suggested to offer rational and efficient medical care through specialized knowledge about inpatient care services.1 The goal that hospitalists will use care resources more efficiently presumes hospitalists' accurate knowledge about charges and costs.
Data regarding physicians' awareness of care charges and its impact upon care is limited. An international meta‐analysis of clinicians' awareness of pharmaceutical prices demonstrated poor accuracy of physicians' estimates of charges, but effects of increasing their knowledge remained unexamined.2 Continuous exposure to education and alerts about charges have been demonstrated to diminish physicians' unnecessary use of specific laboratory assays in a single teaching hospital, in a pediatric emergency department, and an outpatient primary care system; test use declined when physicians were alerted to test charges at the point‐of‐care without negative impact upon clinical outcomes; when the notices ceased, utilization climbed back towards baseline levels. Specific to inpatient care, a single‐center study evaluated the impact of price‐alerts upon laboratory and imaging use, but showed no effects.36
Applicability of these existing data for contemporary hospitalists are limited, and most data were collected before hospitalists developed as an organized focus of practice. A review of existing literature revealed no published data demonstrating hospitalists' higher expert awareness of charges generated by inpatient care. Published comparisons of the care expense generated by hospitalists' care versus that of general internists or academic teams have shown minimal and inconsistent effects.79 Those data showing reduced costs from hospitalists were associated with small length‐of‐stay reductions, rather than more expert resource utilization.7 We measured the accuracy and precision of hospitalist's estimates of charges associated with services commonly used in inpatient care.
Setting
Two community‐based private, academic‐affiliated hospitals operated by a not‐for‐profit health system in Washington State, comprising together 895 inpatient beds. The questionnaire instrument was approved by the governing Institutional Review Board (IRB).
Methods
A list of true charges for 14 services, procedures, tests, and physician charges commonly ordered by adult medicine hospitalists was acquired directly from the responsible departments of a multi‐hospital system operated by a single non‐profit entity using a unified chargemaster. Specifically, we acquired the charge that a hypothetical self‐paying patient would receive for each service, excluding any adjustments exercised by other payer sources. The list of charges was reported to the organization's financial officers for affirmation. Physician charges were standardized to geographically‐adjusted Medicare charges obtained directly from the American Medical Assocation's online Common Procedural Terminology tool.10 A cross‐section of hospitalists (n = 25) was surveyed from a private hospitalist group and an academically‐affiliated hospitalist service. Hospitalists included US and international medical graduates, new‐graduates from residency training and clinicians with a range of prior experiences in academic centers, government hospital systems, and private primary care. Respondents were asked to estimate to the nearest dollar the billing charge that a hypothetical self‐pay patient would receive for each care item. Direct data collection was arranged through the groups' medical directors and occurred in the hospitalist groups' regular business meetings. The design gathered no data on individual characteristics of respondents such as domestic or international education, sex, age, or time in practice, nor from which practice‐group a given respondent originated, to affirm to participants that their responses could not be used to imply performance measures or quality profiles.
Findings
Hospitalists tended to rank the expense of items in essentially the correct order, as reflected by the rough trend of rising estimates compared to true‐charges (Figure 1). Of note, we did not ask hospitalists to discern the different charges of 2 appropriate competing clinical choices, but asked for estimated prices of diverse services. The range of respondents' estimates about each item was broad. The mean‐value of hospitalists' estimates for each care item was less revealing than the range and diversity of estimates about each care item. Accuracy of hospitalists' estimates of charges was poor. Only 10.8% of hospitalists' estimates were within 10% of the actual unadjusted charge, 17.8% within 20% of that charge, and 24.8% were within a 30% margin of accuracy. Summary results are presented (Table 1). Pearson's r correlation value between the unadjusted charges and the estimates made by hospitalists was 0.548, a coefficient of determination equal to 0.300. Thus, the true charges list we obtained had only a low‐grade association with hospitalists' estimates (Figure 1). Hospitalists' estimates about the charges of relatively‐expensive items (abdominal computed tomography [CT]) overlapped with their estimates about the least‐expensive items (such as a urine culture ). Inter‐hospitalist agreement about charges associated with each care item was also low; estimates for each care item charge varied over logarithmic orders of magnitude.
Care Service | Unadjusted Charge, USD$ | Mean Estimate, USD$ | Minimum Estimate, USD$ | Maximum Estimate, USD$ | % of Estimates Within 10% Accuracy | % of Estimates Within 20% Accuracy | % of Estimates Within 30% Accuracy |
---|---|---|---|---|---|---|---|
| |||||||
Complete blood count | 30 | 73 | 10 | 440 | 16 | 20 | 20 |
Complete metabolic panel | 37 | 135 | 15 | 1200 | 4 | 16 | 16 |
Urinalysis with microscopy | 37 | 53 | 15 | 105 | 12 | 20 | 24 |
Urine culture | 26 | 77 | 20 | 200 | 4 | 16 | 20 |
Ward bed, charge per night | 744 | 998 | 300 | 3000 | 20 | 20 | 20 |
ICU Bed, charge per night | 1107 | 2018 | 750 | 6000 | 8 | 12 | 12 |
Chest x‐ray | 271 | 169 | 60 | 700 | 12 | 16 | 24 |
CT scan, abdomen | 2204 | 803 | 150 | 1800 | 0 | 4 | 4 |
Methylpredisolone 125 mg IV dose | 26.63 | 63 | 3 | 200 | 4 | 20 | 24 |
Levofloxacin 500 mg IV dose | 105.41 | 114 | 10 | 500 | 24 | 28 | 36 |
Levofloxacin 500 mg oral dose | 29.78 | 25 | 4 | 70 | 12 | 12 | 20 |
Admission services (CPT code 99223) | 169.56 | 225 | 100 | 700 | 8 | 36 | 52 |
Inpatient care services (CPT code 99232) | 62.47 | 110 | 40 | 400 | 12 | 28 | 48 |
Central venous catheter placement (CPT 36569) | 286.04 | 338 | 50 | 1200 | 8 | 16 | 28 |
Average % correct | 10.8 | 17.8 | 24.8 |

Discussion
To date, hospitalist programs have shown little impact upon care costs when compared with other inpatient care staffing models. One limiting factor may be the opacity of medical care pricing. Patients have been demonstrated to have little access to knowledge of what care will cost them and complex barriers prevent them from gaining pricing information.11, 12 Hospitalists may be conjectured to serve as expert sources on the costs and values of medical care services on behalf of inpatients, but our observations suggest that hospitalists' actual knowledge of patient‐charges is lacking. The opacity of US medical prices to patients appears to extend to hospital‐care physicians as well. We observe that no widespread mechanism exists by which hospitalists would be well‐positioned to become informed about the actual charges their patients receive. Unadjusted chargemaster lists are generally restricted information, and would be difficult to access outside of participation in the charge‐notifications used in the existing studies cited above.
The inquiry was specifically limited to how closely hospitalists' estimates of the unadjusted charges for some commonly‐ordered items compare to the actual unadjusted chargemaster at their own institutions. We did not assess the hospitalists' perceptions about the accuracy of their estimates, nor the impact of specific hospitalist characteristics upon accuracy. Our sample's representation of the larger national population of hospital physicians is not established, but engenders no expectation that these clinicians' charge‐awareness is substantively different from that of hospitalists in most other institutions. It is not known what specific clinician or practice‐setting characteristics will direct charge‐awareness, or will influence the impact of charge‐awareness upon clinical practices.
The range of estimates different hospitalists made about the same care items in the same facilities was very broad, which argues that respondents did not estimate charges based upon a different knowledge base of which the investigators were unaware. This is important because our use of unadjusted charges to self‐pay patients as true prices is necessarily somewhat arbitrary. Chargemaster price may not reflect the institution's cost of performing the service, the different prices paid for a single service by different payer sources, nor reflect services' true value based upon outcomes. However, recognizing that these actual prices are somewhat artificial, the use of these prices suffices for the current inquiry, and does not negate our findings of hospitalists' low accuracy and low agreement. Also noteworthy, an unadjusted chargemaster can often represent the charges received by those uninsured US patients for whom payer‐source adjustment is inaccessible, and informs downstream accounting such as the value of unpaid care a hospital delivers annually.
The most immediate matter for examination among hospitalists is what effects increased charge‐awareness may exert upon clinical decisions and practice processes. It appears that the premise that hospitalists' exercise expert knowledge of costs is likely not valid; but it is unknown whether accurate charge‐awareness among hospitalists will improve cost‐reductions by hospitalists. In some payer‐arrangements, accurate charge‐awareness might engender reduced care quantity, rather than increased efficiency or quality. The impact of upgrading hospitalists' knowledge about the charges and costs they generate, and the most effective method to do so, is worthy of investigation; based upon this initial data we encourage and are undertaking a larger‐scale study and exploration of the effects of enhanced hospitalist charge‐awareness.
Conclusion
Hospitalists have low awareness of the charges associated with inpatient care. The opacity of hospital care pricing to patient populations extends also to hospitalist physicians. Hospitalists likely do not improve cost‐efficiency through expert knowledge of services' costs to patients. Education and reminder systems to apprise hospitalists of charges should be examined as possible tools to optimize the use of inpatient care resources.
Hospitalists have been suggested to offer rational and efficient medical care through specialized knowledge about inpatient care services.1 The goal that hospitalists will use care resources more efficiently presumes hospitalists' accurate knowledge about charges and costs.
Data regarding physicians' awareness of care charges and its impact upon care is limited. An international meta‐analysis of clinicians' awareness of pharmaceutical prices demonstrated poor accuracy of physicians' estimates of charges, but effects of increasing their knowledge remained unexamined.2 Continuous exposure to education and alerts about charges have been demonstrated to diminish physicians' unnecessary use of specific laboratory assays in a single teaching hospital, in a pediatric emergency department, and an outpatient primary care system; test use declined when physicians were alerted to test charges at the point‐of‐care without negative impact upon clinical outcomes; when the notices ceased, utilization climbed back towards baseline levels. Specific to inpatient care, a single‐center study evaluated the impact of price‐alerts upon laboratory and imaging use, but showed no effects.36
Applicability of these existing data for contemporary hospitalists are limited, and most data were collected before hospitalists developed as an organized focus of practice. A review of existing literature revealed no published data demonstrating hospitalists' higher expert awareness of charges generated by inpatient care. Published comparisons of the care expense generated by hospitalists' care versus that of general internists or academic teams have shown minimal and inconsistent effects.79 Those data showing reduced costs from hospitalists were associated with small length‐of‐stay reductions, rather than more expert resource utilization.7 We measured the accuracy and precision of hospitalist's estimates of charges associated with services commonly used in inpatient care.
Setting
Two community‐based private, academic‐affiliated hospitals operated by a not‐for‐profit health system in Washington State, comprising together 895 inpatient beds. The questionnaire instrument was approved by the governing Institutional Review Board (IRB).
Methods
A list of true charges for 14 services, procedures, tests, and physician charges commonly ordered by adult medicine hospitalists was acquired directly from the responsible departments of a multi‐hospital system operated by a single non‐profit entity using a unified chargemaster. Specifically, we acquired the charge that a hypothetical self‐paying patient would receive for each service, excluding any adjustments exercised by other payer sources. The list of charges was reported to the organization's financial officers for affirmation. Physician charges were standardized to geographically‐adjusted Medicare charges obtained directly from the American Medical Assocation's online Common Procedural Terminology tool.10 A cross‐section of hospitalists (n = 25) was surveyed from a private hospitalist group and an academically‐affiliated hospitalist service. Hospitalists included US and international medical graduates, new‐graduates from residency training and clinicians with a range of prior experiences in academic centers, government hospital systems, and private primary care. Respondents were asked to estimate to the nearest dollar the billing charge that a hypothetical self‐pay patient would receive for each care item. Direct data collection was arranged through the groups' medical directors and occurred in the hospitalist groups' regular business meetings. The design gathered no data on individual characteristics of respondents such as domestic or international education, sex, age, or time in practice, nor from which practice‐group a given respondent originated, to affirm to participants that their responses could not be used to imply performance measures or quality profiles.
Findings
Hospitalists tended to rank the expense of items in essentially the correct order, as reflected by the rough trend of rising estimates compared to true‐charges (Figure 1). Of note, we did not ask hospitalists to discern the different charges of 2 appropriate competing clinical choices, but asked for estimated prices of diverse services. The range of respondents' estimates about each item was broad. The mean‐value of hospitalists' estimates for each care item was less revealing than the range and diversity of estimates about each care item. Accuracy of hospitalists' estimates of charges was poor. Only 10.8% of hospitalists' estimates were within 10% of the actual unadjusted charge, 17.8% within 20% of that charge, and 24.8% were within a 30% margin of accuracy. Summary results are presented (Table 1). Pearson's r correlation value between the unadjusted charges and the estimates made by hospitalists was 0.548, a coefficient of determination equal to 0.300. Thus, the true charges list we obtained had only a low‐grade association with hospitalists' estimates (Figure 1). Hospitalists' estimates about the charges of relatively‐expensive items (abdominal computed tomography [CT]) overlapped with their estimates about the least‐expensive items (such as a urine culture ). Inter‐hospitalist agreement about charges associated with each care item was also low; estimates for each care item charge varied over logarithmic orders of magnitude.
Care Service | Unadjusted Charge, USD$ | Mean Estimate, USD$ | Minimum Estimate, USD$ | Maximum Estimate, USD$ | % of Estimates Within 10% Accuracy | % of Estimates Within 20% Accuracy | % of Estimates Within 30% Accuracy |
---|---|---|---|---|---|---|---|
| |||||||
Complete blood count | 30 | 73 | 10 | 440 | 16 | 20 | 20 |
Complete metabolic panel | 37 | 135 | 15 | 1200 | 4 | 16 | 16 |
Urinalysis with microscopy | 37 | 53 | 15 | 105 | 12 | 20 | 24 |
Urine culture | 26 | 77 | 20 | 200 | 4 | 16 | 20 |
Ward bed, charge per night | 744 | 998 | 300 | 3000 | 20 | 20 | 20 |
ICU Bed, charge per night | 1107 | 2018 | 750 | 6000 | 8 | 12 | 12 |
Chest x‐ray | 271 | 169 | 60 | 700 | 12 | 16 | 24 |
CT scan, abdomen | 2204 | 803 | 150 | 1800 | 0 | 4 | 4 |
Methylpredisolone 125 mg IV dose | 26.63 | 63 | 3 | 200 | 4 | 20 | 24 |
Levofloxacin 500 mg IV dose | 105.41 | 114 | 10 | 500 | 24 | 28 | 36 |
Levofloxacin 500 mg oral dose | 29.78 | 25 | 4 | 70 | 12 | 12 | 20 |
Admission services (CPT code 99223) | 169.56 | 225 | 100 | 700 | 8 | 36 | 52 |
Inpatient care services (CPT code 99232) | 62.47 | 110 | 40 | 400 | 12 | 28 | 48 |
Central venous catheter placement (CPT 36569) | 286.04 | 338 | 50 | 1200 | 8 | 16 | 28 |
Average % correct | 10.8 | 17.8 | 24.8 |

Discussion
To date, hospitalist programs have shown little impact upon care costs when compared with other inpatient care staffing models. One limiting factor may be the opacity of medical care pricing. Patients have been demonstrated to have little access to knowledge of what care will cost them and complex barriers prevent them from gaining pricing information.11, 12 Hospitalists may be conjectured to serve as expert sources on the costs and values of medical care services on behalf of inpatients, but our observations suggest that hospitalists' actual knowledge of patient‐charges is lacking. The opacity of US medical prices to patients appears to extend to hospital‐care physicians as well. We observe that no widespread mechanism exists by which hospitalists would be well‐positioned to become informed about the actual charges their patients receive. Unadjusted chargemaster lists are generally restricted information, and would be difficult to access outside of participation in the charge‐notifications used in the existing studies cited above.
The inquiry was specifically limited to how closely hospitalists' estimates of the unadjusted charges for some commonly‐ordered items compare to the actual unadjusted chargemaster at their own institutions. We did not assess the hospitalists' perceptions about the accuracy of their estimates, nor the impact of specific hospitalist characteristics upon accuracy. Our sample's representation of the larger national population of hospital physicians is not established, but engenders no expectation that these clinicians' charge‐awareness is substantively different from that of hospitalists in most other institutions. It is not known what specific clinician or practice‐setting characteristics will direct charge‐awareness, or will influence the impact of charge‐awareness upon clinical practices.
The range of estimates different hospitalists made about the same care items in the same facilities was very broad, which argues that respondents did not estimate charges based upon a different knowledge base of which the investigators were unaware. This is important because our use of unadjusted charges to self‐pay patients as true prices is necessarily somewhat arbitrary. Chargemaster price may not reflect the institution's cost of performing the service, the different prices paid for a single service by different payer sources, nor reflect services' true value based upon outcomes. However, recognizing that these actual prices are somewhat artificial, the use of these prices suffices for the current inquiry, and does not negate our findings of hospitalists' low accuracy and low agreement. Also noteworthy, an unadjusted chargemaster can often represent the charges received by those uninsured US patients for whom payer‐source adjustment is inaccessible, and informs downstream accounting such as the value of unpaid care a hospital delivers annually.
The most immediate matter for examination among hospitalists is what effects increased charge‐awareness may exert upon clinical decisions and practice processes. It appears that the premise that hospitalists' exercise expert knowledge of costs is likely not valid; but it is unknown whether accurate charge‐awareness among hospitalists will improve cost‐reductions by hospitalists. In some payer‐arrangements, accurate charge‐awareness might engender reduced care quantity, rather than increased efficiency or quality. The impact of upgrading hospitalists' knowledge about the charges and costs they generate, and the most effective method to do so, is worthy of investigation; based upon this initial data we encourage and are undertaking a larger‐scale study and exploration of the effects of enhanced hospitalist charge‐awareness.
Conclusion
Hospitalists have low awareness of the charges associated with inpatient care. The opacity of hospital care pricing to patient populations extends also to hospitalist physicians. Hospitalists likely do not improve cost‐efficiency through expert knowledge of services' costs to patients. Education and reminder systems to apprise hospitalists of charges should be examined as possible tools to optimize the use of inpatient care resources.
- The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19(4):293–201. , , et al.
- Physician awareness of drug cost: a systematic review.PLoS Med.2007;4(9):e283. , , .
- The effect of price information on test‐ordering behavior and patient outcomes in a pediatric emergency department.Pediatrics.1999;103(4):877–882. , , , , .
- The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests.N Engl J Med1990;322:1499–1504. , , .
- Factors contributing to inappropriate ordering of tests in an academic medical department and the effect of an educational feedback strategy.Postgrad Med J.2006;82:823–829. , , , .
- Tanasijevic does the computerized display of charges affect inpatient ancillary test utilization?Arch Intern Med.1997;157(21):2501–2508. , , , et al.
- The impact of hospitalists on the cost and quality of inpatient care in the united states: a research synthesis.Med Care Res Rev.2005;62:379–406. , .
- Comparison of hospital costs and length of stay for community internists, hospitalists, and academicians.J Gen Intern Med.2007;22:662–667. , , .
- Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357(25):2589–2600. , , , , , .
- American Medical Association, CPT and RVU Search utility. Available at: https://catalog.ama‐assn.org/Catalog/cpt/cpt_search.jsp. Accessed December 2009.
- Does price tranparency improve market efficiency? Implications of empirical evidence in other markets for the health sector. 2007, Congressional Research Service Report RL34101. Available at: http://ftp.fas.org/sgp/crs/secrecy/RL34101. Accessed December2009. , .
- The pricing of US hospital services.Health Aff.2006;25(1):57–69. .
- The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19(4):293–201. , , et al.
- Physician awareness of drug cost: a systematic review.PLoS Med.2007;4(9):e283. , , .
- The effect of price information on test‐ordering behavior and patient outcomes in a pediatric emergency department.Pediatrics.1999;103(4):877–882. , , , , .
- The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests.N Engl J Med1990;322:1499–1504. , , .
- Factors contributing to inappropriate ordering of tests in an academic medical department and the effect of an educational feedback strategy.Postgrad Med J.2006;82:823–829. , , , .
- Tanasijevic does the computerized display of charges affect inpatient ancillary test utilization?Arch Intern Med.1997;157(21):2501–2508. , , , et al.
- The impact of hospitalists on the cost and quality of inpatient care in the united states: a research synthesis.Med Care Res Rev.2005;62:379–406. , .
- Comparison of hospital costs and length of stay for community internists, hospitalists, and academicians.J Gen Intern Med.2007;22:662–667. , , .
- Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357(25):2589–2600. , , , , , .
- American Medical Association, CPT and RVU Search utility. Available at: https://catalog.ama‐assn.org/Catalog/cpt/cpt_search.jsp. Accessed December 2009.
- Does price tranparency improve market efficiency? Implications of empirical evidence in other markets for the health sector. 2007, Congressional Research Service Report RL34101. Available at: http://ftp.fas.org/sgp/crs/secrecy/RL34101. Accessed December2009. , .
- The pricing of US hospital services.Health Aff.2006;25(1):57–69. .
A Novel Approach to Physician Shortages
The demand for physician talent is intensifying as the US healthcare system confronts an unprecedented confluence of demographic pressures. Not only will 78 million retiring baby‐boomers require significant healthcare resources, but tens of thousands of practicing physicians will themselves reach retirement age within the next decade.1 At the same time, factors like the large increase in the percentage of female physicians (who are more likely to work part time), the growth of nonpractice opportunities for MDs, and generational demands for greater worklife balance are creating a major supply‐demand mismatch within the physician workforce.2 In this demographic atmosphere, the ability to recruit and retain physician leaders confers tremendous value to healthcare enterprisesboth public and private. Recruiting and retaining strategies already weigh heavily in the most palpable shortage areas, like primary care, but the system faces widespread unmet demand for a variety of specialist and generalist practitioners.36
This article does not address the public policy implications of the upcoming physician shortage, recognition of which will lead to the largest increase in new medical school slots in decades.7 Rather, we set out to illustrate how successful nonmedical businesses are embracing a thoughtful, systematic approach to retaining talent, based on the philosophy that keeping and engaging valued employees is more efficient than recruiting and orienting replacements. We posit that the innovations used by progressive companies could apply to recruitment and retention challenges confronting medicine.
How Industry Approaches the Talent Vacuum: Talent Facilitation
Leaders outside of medicine have long acknowledged that changing demographics and a global economy are driving unprecedented employee turnover.8 In confronting a talent vacuum, forward‐thinking managers have prioritized retaining key talent (rather than hiring anew) in planning for the future.9, 10 Doing so begins with attempts to understand the relationship between workers and the workplace, with a particular emphasis on appreciating workers' priorities. Indeed, new executive positions with titles like Chief Learning Officer and Chief Experience Officer are appearing as companies realize a need for focused expertise beyond traditional human resource departments. These companies understand that offering higher salaries is not the only retention strategyand often not even the most effective one.
The Four Actions of Talent Facilitation
The talent facilitation process centers on four actions: attract, engage, develop, and retain. None of these actions can stand alone, and all should be present, to some degree, at all stages of a worker's tenure. To attract or engage an employee or practice partner is not a 1‐time hook, but a constant and dynamic process.
Importantly, the concepts addressed here are not specific to 1 type of corporate system, size, or management level. Although the early business focus had been on upper‐level and executive talent within large corporate settings, there is an increasing recognition that a dedicated talent strategy is useful wherever recruiting and retaining talented people is important (and where is it not?).
The ideas presented here may seem most applicable to leaders of large physician corporations, hospital‐owned physician groups, or large integrated healthcare systems (such as Kaiser) that employ physicians. However, we also believe that the ideas apply across‐the‐board in medicine, including entities such as small, private, physician‐owned groups. We argue that regardless of the exact practice structure, a limited pool of resources must be dedicated to the attraction and retention of talented partners or employees, or to the cost of replacing those people if they pursue other opportunities (or the cost of inefficient and disengaged physicians). While an integrated health system may have the resources and scale to hire a Chief Experience Officer, we do not anticipate that a 5‐partner private practice would. Rather, we point to examples to illustrate the talent facilitation paradigm as a tool to systematically frame the allocation of those resources. Undoubtedly, the specific shape of a thoughtful talent facilitation effort will vary when applied in a large urban academic medical center vs. an integrated healthcare system vs. a small physician private practice, but the basic principles remain the same.
Attract
Increasingly, companies approach talented prospects with dedicated marketing campaigns to convey the value of a work environment.11 Silicon Valley employee lounges with free massages and foosball tables are the iconic example of attraction, but the concept runs deeper. Today's workers seek access to state‐of‐the‐art ideas and technology and often want to be part of a larger vision. Many seek opportunities to integrate their own professional and personal aspirations into a particular job description.
Hospital executives have long recognized the importance of attracting physicians to their facility (after all, the physicians draw patients and thus generate revenue). The traditional approach has surrounded perks, from comfortable doctor's lounges to the latest in surgical technology. But, the stakes seem greater now than before, and successful talent facilitation strategies are going beyond the tried and true.
Clearly, physicians seek financially stable practice settings with historical success. But they may also seek evidence of a defined strategic plan focused on more than mere profitability. Physicians may gravitate to practice environments that endorse progressive movements like the No One Dies Alone campaign.12 Similarly, recognition of movements beyond healthcarea commitment to Leadership in Energy and Environmental Design (LEED) (Green Building Rating System; US Green Building Council [USGBC];
The current recruitment campaign of California's prison healthcare system offers an unlikely source of inspiration. The prison system was placed in receivership to address a shortage of competent physician staff and other inadequacies. A central feature of the campaign is an attractive starting salary and good benefits. But, the campaign does not rely on money alone. For example, the campaign's website (

Engage
The corporate tool being employed at this stage is a strategy called on‐boarding, which emphasizes a streamlined integration of newcomers with existing workers and culture, and prioritizes aligning organizational roles with a worker's specific skills and interests. On‐boarding also emphasizes the value of early and frequent provision of constructive feedback from same‐level peers or managers with advanced coaching skills.
Many companies use formal survey tools to measure employee engagement and regularly evaluate the proficiency of system leaders in the ability to engage their employees. An engineering firm executive recently told us (P.K., C.K.) that he performs detailed and frequent on‐the‐job interviews, even with company veterans. The primary goal of these interviews is to ensure that engineers spend at least 85% of their time on work that: (1) they find interesting and (2) allows for the application of their best skills. Wherever possible, traditional job descriptions are altered to achieve this. Inevitably, there is still work (15% in this particular corporate vision) that no one prefers but needs to get done, but this process of active recalibration minimizes this fraction to the degree possible.
Even within a small physician practice group, one can imagine how a strategic approach of inviting and acknowledging individual physician's professional goals and particular talents may challenge the long‐held belief that everyone within a group enjoys and must do the exact same job. Once these goals and talents are articulated, groups may find that allowing for more customized roles within the practice enhances professional satisfaction.
Social networking, collaboration, and sharing of best practices are staples of engaging companies. The Cisco and Qualcomm companies, for example, utilize elaborate e‐networks (rough corporate equivalents to Facebook) to foster collegial interaction within and across traditional hierarchical boundaries so that managers and executives directly engage the ideas of employees at every level.14 The premise is logical: engaged employees will be more likely to contribute innovative ideas which, when listened to, are more likely to engage employees.
Most physicians will recognize the traditional resident report as a model for engagement. Beyond its educational value, interaction with program leadership, social bonding, collaborative effort, and exploration of best practices add tremendous value. Many companies would jump at the chance to engrain a similar cultural staple. Enhancing this type of interaction in a postresidency setting may promote engagement in a given system, especially if it facilitates interactions between physicians and senior hospital leaders. Absent these types of interactions, ensuring regular provision of peer review and/or constructive feedback can help systematically enhance 2‐way communication and enhance engagement.
Develop
Talent development relies on mentorship reflecting a genuine interest in an individual's future. Development strategies include pairing formal annual talent reviews or (in the case of practice partners) formal peer review with strategic development plans. Effective development strategy may include transparent succession planning so that individuals are aware they are being groomed for future roles.
A well‐known adage suggests, People quit the manager or administrator, not the job. Development in this sense relies on presenting new opportunities and knowing that people flourish when allowed to explore multiple paths forward. In many companies, the role of Chief Experience or Chief Learning Officers is to enhance development planning. Consider how career coaching of young hospitalists could transform an infinitely portable and volatile commodity job, prone to burnout, into an engaged specialist of sorts with immense value to a hospital. Hospitalists have already demonstrated their potential as quality improvement leaders.15 Imagine if hospital leadership enlisted a young hospitalist in a relevant quality improvement task force, such as one working on preventing falls. With appropriate support, the physician could obtain skills for quality improvement evaluation that would not only enhance his or her engagement with the hospital system but also provide a valuable analysis for the hospital.
As an example of development strategy within a small practice setting, consider the following real‐life anecdote: a group of 4 physicians recently completed a long and expensive recruitment of a new partner. The new partner, intrigued by the local hospital's surgical robot technology, sought the support of her partners (who are not currently using the technology) to partake in an expensive robotics training program. The partners decided not to provide the financial support. The new partner subsequently left the group for a nearby practice opportunity that would provide for the training, and the group was faced with the loss of a partner (one‐fourth of the practice!) and the cost of repeating the recruiting process. A preemptive evaluation of the value of investing in the development of the new partner and enhancing that partner's professional development may have proven wise despite the significant up‐front costs. In this case the manager the new partner quit was the inflexibility of the practice trajectory.
Retain
The economic incentive to retain talented workers is not subtle. If it was, companies would not be funneling resources into Chief Experience Officers. Likewise, the estimated cost for a medical practice to replace an individual physician is as at least $250,000.16, 17
In retention, as in attraction, salary is only part of the equation.18 People want fair and competitive compensation, and may leave if they are not getting it, but they will not stay (and will not stay engaged) only for a salary bump. Retention is enhanced when workers can advance according to skills and talent, rather than mere tenure. An effective retention policy responds to people's desire to incorporate individual professional goals into their work and allows for people to customize their career rather than simply occupy a job class. Effective retention policy respects worklife balance and recognizes that this balance might look different for 2 people with the same job. It may take the form of positive reinforcement (rather than subtle disdain) for using vacation time or allowing for participation in international service projects. Many literally feel that they need to quit their job in order to take time off or explore other interests.
Worklife balance has been a longstanding issue in medicine, and innovative augmentation strategies may well help retain top talent. Today's successful medical school applicants not only show aptitude in the classroom, they often have many well‐developed nonmedical skills. No one can expect that medical training will somehow convince them to leave everything else behind. Moreover, today's residency graduates, already with Generation Y sensibilities, have completed their entire training under the auspices of the Accreditation Council for Graduate Medical Education (ACGME) duty hours regulations, which has made residents far more comfortable with shift work and defined hours.
At the other end of the generation spectrum, as large numbers of physicians ready for retirement, effective talent facilitation strategies may evaluate how to reoffer medicine as a valid option for senior physicians who still wish to work. Retaining these physicians will require an appreciation of their lifestyle goals, as they will likely find continuing a traditional practice role untenable. A recent survey of orthopedic surgeons 50 years of age or older found that having a part‐time option was a common reason they continued practicing, and that the option to work part‐time would have the most impact on keeping these surgeons working past age 65 years.19 Those working part‐time were doing so in a wide range of practice arrangements including private practice. However, one‐third of those surveyed said a part‐time option was not available to them. Clearly, in an environment of workforce shortages, physician‐leaders must begin to think about worklife balance not only for new doctors but for those considering retirement.
Critics will point out the financial drawbacks in the provision of worklife balance. But the cost may pale in comparison to the cost of replacing physician leaders. Moreover, engaged physicians are more likely to add value in the form of intangible capital such as patient satisfaction and practice innovation. As such, we argue that effective retention strategy in medicine is likely to be cost‐effective, even if it requires significant new up‐front resources.
Lessons From Industry
Doctors frequently assume that the challenges and obstacles confronted in healthcare are unique to medicine. But, for every phrase like When I started practice, I decided how long office visits were, not the insurance company, or Young doctors just don't want to work as hard, there is a parallel utterance in the greater business world. Luckily, there are now examples of the healthcare world learning lessons from business. For example, innovators in medical quality improvement found value in the experience of other industries.20 Airlines and automakers have long honed systems for error prevention and possess expertise that may curb errors in the hospital.2123
We suspect that the ideas and practice of talent facilitation have already made their way into some medical settings. A Google search reveals multiple opportunities for hospital‐based talent managers, and websites advertise the availability of talent consultants ready to lend their expertise to the medical world. In the arena of academic medicine, the University of California, San Francisco (UCSF) Division of Hospital Medicine put some of the ideas of talent facilitation into practice over the past year, in part in response to an increasingly competitive market for academic hospitalists.24 Leaders introduced a formal faculty development program that links junior faculty with mentors and facilitates early and frequent feedback across hierarchical boundaries.25 These more intentional mentoring efforts were accompanied by a seminar series aimed at the needs of new faculty members, a research incubator program, divisional grand rounds, and other web‐based and in‐person forums for sharing best practices and innovations. Less formal social events have also been promoted. Importantly, these sweeping strategies seek to encompass the needs of both teaching and nonteaching hospitalists within UCSF.26
Clearly, an academic hospitalist group with 45 faculty physicians has unique characteristics that inform the specifics of its talent facilitation strategy. The interventions discussed above are meant to represent examples of the types of strategies that may be utilized by physician groups once a decision is made to focus on talent management. Undoubtedly, the shape of such efforts will vary in diverse practice settings, but physician leaders have much to gain through further exploration of where these core principles already exist within medicine and where they may be more effectively deployed. By examining how multinational businesses are systematically applying the concepts of talent facilitation to address a global talent shortage, the doctoring profession might again take an outside hint to help inform its future.
- Long Term Care: Aging Baby Boom Generation Will Increase Demand and Burden on Federal and State Budgets. United States General Accounting Office Testimony before the Special Committee on Aging, US Senate. Hearing Before the Special Committee on Aging of the US Senate,2002. Available at:http://www.gao.gov/new.items/d02544t.pdf. Accessed July 2009.
- Physician workforce shortages: implications and issues for academic health centers and policymakers.Acad Med.2006;81(9):782–787. , .
- New York moves to tackle shortage of primary‐care doctors.Lancet.2008;371(9615):801–802. .
- The US dermatology workforce: a specialty remains in shortage.J Am Acad Dermatol.2008;59(5):741–745. , .
- Challenges and opportunities for recruiting a new generation of neurosurgeons.Neurosurgery.2007;61(6):1314–1319. , .
- The developing crisis in the national general surgery workforce.J Am Coll Surg.2008;206(5):790–795. , .
- Medical School Enrollment Plans: Analysis of the 2007 AAMC Survey. Publication of the Association of American Medical Colleges, Center for Workforce Studies, April2008. Available at:http://www.aamc.org/workforce. Accessed July 2009.
- It's 2008: Do You Know Where Your Talent Is? Why acquisition and retention strategies don't work. Part 1 of a Deloitte Research Series on Talent Management.2008. Available at: http://www.deloitte.com/dtt/cda/content/UKConsulting_TalentMgtResearchReport.pdf. Accessed August 2009.
- The race for talent: retaining and engaging workers in the 21st century.Hum Resour Plann.2004;27(3):12–25. , , .
- Expecting sales growth, CEOs cite worker retention as critical to success. March 1,2004. Available at:http://www.barometersurveys.com/production/barsurv.nsf/89343582e94adb6185256b84006c8ffe/9672ab2f54cf99f885256e5500768232?OpenDocument. Accessed July 2009.
- Jet Blue announces aviation university gateway program for pilot candidates: airline partners with Embry‐Riddle Aeronautical University, University of North Dakota, and Cape Air to fill pilot pipeline. January 30, 2008. Available at:http://investor.jetblue.com/phoenix.zhtml?c=131045287(4):487–494.
- A review of physician turnover: rates, causes, and consequences.Am J Med Qual.2004;19(2):56–66. , , .
- The impact on revenue of physician turnover: an assessment model and experience in a large healthcare center.J Med Pract Manage.2006;21(6):351–355. , , .
- Employee motivation: a powerful new model.Harv Bus Rev.2008;86(7–8):78,84,160. , , .
- Work satisfaction and retirement plans of orthopaedic surgeons 50 years of age or older.Clin Orthop Relat Res.2008;466(1):231–238. , , .
- The long road to patient safety: a status report on patient safety systems.JAMA.2005(22);294:2858–2865. , , , .
- Error reduction through team leadership: what surgeons can learn from the airline industry.Clin Neurosurg.2007;54:195–199. .
- Applying the Toyota production system: using a patient safety alert system to reduce error.Jt Comm J Qual Patient Saf.2007;33(7):376–386. , .
- Improving Papanikolaou test quality and reducing medical errors by using Toyota production system methods.Am J Obstet Gynecol.2006;194(1):57–64. , , , .
- Society of Hospital Medicine Career Satisfaction Task Force. White Paper on Hospitalist Career Satisfaction.2006; 1–45. Available at: http://www.hospitalmedicine.org. Accessed July 2009.
- UCSF Department of Medicine, Division of Hospital Medicine, Faculty Development. Available at: http://hospsrvr.ucsf.edu/cme/fds.html. Accessed July 2009.
- Non‐housestaff medicine services in academic centers: models and challenges.J Hosp Med.2008;3(3):247–245. , , , et al.
The demand for physician talent is intensifying as the US healthcare system confronts an unprecedented confluence of demographic pressures. Not only will 78 million retiring baby‐boomers require significant healthcare resources, but tens of thousands of practicing physicians will themselves reach retirement age within the next decade.1 At the same time, factors like the large increase in the percentage of female physicians (who are more likely to work part time), the growth of nonpractice opportunities for MDs, and generational demands for greater worklife balance are creating a major supply‐demand mismatch within the physician workforce.2 In this demographic atmosphere, the ability to recruit and retain physician leaders confers tremendous value to healthcare enterprisesboth public and private. Recruiting and retaining strategies already weigh heavily in the most palpable shortage areas, like primary care, but the system faces widespread unmet demand for a variety of specialist and generalist practitioners.36
This article does not address the public policy implications of the upcoming physician shortage, recognition of which will lead to the largest increase in new medical school slots in decades.7 Rather, we set out to illustrate how successful nonmedical businesses are embracing a thoughtful, systematic approach to retaining talent, based on the philosophy that keeping and engaging valued employees is more efficient than recruiting and orienting replacements. We posit that the innovations used by progressive companies could apply to recruitment and retention challenges confronting medicine.
How Industry Approaches the Talent Vacuum: Talent Facilitation
Leaders outside of medicine have long acknowledged that changing demographics and a global economy are driving unprecedented employee turnover.8 In confronting a talent vacuum, forward‐thinking managers have prioritized retaining key talent (rather than hiring anew) in planning for the future.9, 10 Doing so begins with attempts to understand the relationship between workers and the workplace, with a particular emphasis on appreciating workers' priorities. Indeed, new executive positions with titles like Chief Learning Officer and Chief Experience Officer are appearing as companies realize a need for focused expertise beyond traditional human resource departments. These companies understand that offering higher salaries is not the only retention strategyand often not even the most effective one.
The Four Actions of Talent Facilitation
The talent facilitation process centers on four actions: attract, engage, develop, and retain. None of these actions can stand alone, and all should be present, to some degree, at all stages of a worker's tenure. To attract or engage an employee or practice partner is not a 1‐time hook, but a constant and dynamic process.
Importantly, the concepts addressed here are not specific to 1 type of corporate system, size, or management level. Although the early business focus had been on upper‐level and executive talent within large corporate settings, there is an increasing recognition that a dedicated talent strategy is useful wherever recruiting and retaining talented people is important (and where is it not?).
The ideas presented here may seem most applicable to leaders of large physician corporations, hospital‐owned physician groups, or large integrated healthcare systems (such as Kaiser) that employ physicians. However, we also believe that the ideas apply across‐the‐board in medicine, including entities such as small, private, physician‐owned groups. We argue that regardless of the exact practice structure, a limited pool of resources must be dedicated to the attraction and retention of talented partners or employees, or to the cost of replacing those people if they pursue other opportunities (or the cost of inefficient and disengaged physicians). While an integrated health system may have the resources and scale to hire a Chief Experience Officer, we do not anticipate that a 5‐partner private practice would. Rather, we point to examples to illustrate the talent facilitation paradigm as a tool to systematically frame the allocation of those resources. Undoubtedly, the specific shape of a thoughtful talent facilitation effort will vary when applied in a large urban academic medical center vs. an integrated healthcare system vs. a small physician private practice, but the basic principles remain the same.
Attract
Increasingly, companies approach talented prospects with dedicated marketing campaigns to convey the value of a work environment.11 Silicon Valley employee lounges with free massages and foosball tables are the iconic example of attraction, but the concept runs deeper. Today's workers seek access to state‐of‐the‐art ideas and technology and often want to be part of a larger vision. Many seek opportunities to integrate their own professional and personal aspirations into a particular job description.
Hospital executives have long recognized the importance of attracting physicians to their facility (after all, the physicians draw patients and thus generate revenue). The traditional approach has surrounded perks, from comfortable doctor's lounges to the latest in surgical technology. But, the stakes seem greater now than before, and successful talent facilitation strategies are going beyond the tried and true.
Clearly, physicians seek financially stable practice settings with historical success. But they may also seek evidence of a defined strategic plan focused on more than mere profitability. Physicians may gravitate to practice environments that endorse progressive movements like the No One Dies Alone campaign.12 Similarly, recognition of movements beyond healthcarea commitment to Leadership in Energy and Environmental Design (LEED) (Green Building Rating System; US Green Building Council [USGBC];
The current recruitment campaign of California's prison healthcare system offers an unlikely source of inspiration. The prison system was placed in receivership to address a shortage of competent physician staff and other inadequacies. A central feature of the campaign is an attractive starting salary and good benefits. But, the campaign does not rely on money alone. For example, the campaign's website (

Engage
The corporate tool being employed at this stage is a strategy called on‐boarding, which emphasizes a streamlined integration of newcomers with existing workers and culture, and prioritizes aligning organizational roles with a worker's specific skills and interests. On‐boarding also emphasizes the value of early and frequent provision of constructive feedback from same‐level peers or managers with advanced coaching skills.
Many companies use formal survey tools to measure employee engagement and regularly evaluate the proficiency of system leaders in the ability to engage their employees. An engineering firm executive recently told us (P.K., C.K.) that he performs detailed and frequent on‐the‐job interviews, even with company veterans. The primary goal of these interviews is to ensure that engineers spend at least 85% of their time on work that: (1) they find interesting and (2) allows for the application of their best skills. Wherever possible, traditional job descriptions are altered to achieve this. Inevitably, there is still work (15% in this particular corporate vision) that no one prefers but needs to get done, but this process of active recalibration minimizes this fraction to the degree possible.
Even within a small physician practice group, one can imagine how a strategic approach of inviting and acknowledging individual physician's professional goals and particular talents may challenge the long‐held belief that everyone within a group enjoys and must do the exact same job. Once these goals and talents are articulated, groups may find that allowing for more customized roles within the practice enhances professional satisfaction.
Social networking, collaboration, and sharing of best practices are staples of engaging companies. The Cisco and Qualcomm companies, for example, utilize elaborate e‐networks (rough corporate equivalents to Facebook) to foster collegial interaction within and across traditional hierarchical boundaries so that managers and executives directly engage the ideas of employees at every level.14 The premise is logical: engaged employees will be more likely to contribute innovative ideas which, when listened to, are more likely to engage employees.
Most physicians will recognize the traditional resident report as a model for engagement. Beyond its educational value, interaction with program leadership, social bonding, collaborative effort, and exploration of best practices add tremendous value. Many companies would jump at the chance to engrain a similar cultural staple. Enhancing this type of interaction in a postresidency setting may promote engagement in a given system, especially if it facilitates interactions between physicians and senior hospital leaders. Absent these types of interactions, ensuring regular provision of peer review and/or constructive feedback can help systematically enhance 2‐way communication and enhance engagement.
Develop
Talent development relies on mentorship reflecting a genuine interest in an individual's future. Development strategies include pairing formal annual talent reviews or (in the case of practice partners) formal peer review with strategic development plans. Effective development strategy may include transparent succession planning so that individuals are aware they are being groomed for future roles.
A well‐known adage suggests, People quit the manager or administrator, not the job. Development in this sense relies on presenting new opportunities and knowing that people flourish when allowed to explore multiple paths forward. In many companies, the role of Chief Experience or Chief Learning Officers is to enhance development planning. Consider how career coaching of young hospitalists could transform an infinitely portable and volatile commodity job, prone to burnout, into an engaged specialist of sorts with immense value to a hospital. Hospitalists have already demonstrated their potential as quality improvement leaders.15 Imagine if hospital leadership enlisted a young hospitalist in a relevant quality improvement task force, such as one working on preventing falls. With appropriate support, the physician could obtain skills for quality improvement evaluation that would not only enhance his or her engagement with the hospital system but also provide a valuable analysis for the hospital.
As an example of development strategy within a small practice setting, consider the following real‐life anecdote: a group of 4 physicians recently completed a long and expensive recruitment of a new partner. The new partner, intrigued by the local hospital's surgical robot technology, sought the support of her partners (who are not currently using the technology) to partake in an expensive robotics training program. The partners decided not to provide the financial support. The new partner subsequently left the group for a nearby practice opportunity that would provide for the training, and the group was faced with the loss of a partner (one‐fourth of the practice!) and the cost of repeating the recruiting process. A preemptive evaluation of the value of investing in the development of the new partner and enhancing that partner's professional development may have proven wise despite the significant up‐front costs. In this case the manager the new partner quit was the inflexibility of the practice trajectory.
Retain
The economic incentive to retain talented workers is not subtle. If it was, companies would not be funneling resources into Chief Experience Officers. Likewise, the estimated cost for a medical practice to replace an individual physician is as at least $250,000.16, 17
In retention, as in attraction, salary is only part of the equation.18 People want fair and competitive compensation, and may leave if they are not getting it, but they will not stay (and will not stay engaged) only for a salary bump. Retention is enhanced when workers can advance according to skills and talent, rather than mere tenure. An effective retention policy responds to people's desire to incorporate individual professional goals into their work and allows for people to customize their career rather than simply occupy a job class. Effective retention policy respects worklife balance and recognizes that this balance might look different for 2 people with the same job. It may take the form of positive reinforcement (rather than subtle disdain) for using vacation time or allowing for participation in international service projects. Many literally feel that they need to quit their job in order to take time off or explore other interests.
Worklife balance has been a longstanding issue in medicine, and innovative augmentation strategies may well help retain top talent. Today's successful medical school applicants not only show aptitude in the classroom, they often have many well‐developed nonmedical skills. No one can expect that medical training will somehow convince them to leave everything else behind. Moreover, today's residency graduates, already with Generation Y sensibilities, have completed their entire training under the auspices of the Accreditation Council for Graduate Medical Education (ACGME) duty hours regulations, which has made residents far more comfortable with shift work and defined hours.
At the other end of the generation spectrum, as large numbers of physicians ready for retirement, effective talent facilitation strategies may evaluate how to reoffer medicine as a valid option for senior physicians who still wish to work. Retaining these physicians will require an appreciation of their lifestyle goals, as they will likely find continuing a traditional practice role untenable. A recent survey of orthopedic surgeons 50 years of age or older found that having a part‐time option was a common reason they continued practicing, and that the option to work part‐time would have the most impact on keeping these surgeons working past age 65 years.19 Those working part‐time were doing so in a wide range of practice arrangements including private practice. However, one‐third of those surveyed said a part‐time option was not available to them. Clearly, in an environment of workforce shortages, physician‐leaders must begin to think about worklife balance not only for new doctors but for those considering retirement.
Critics will point out the financial drawbacks in the provision of worklife balance. But the cost may pale in comparison to the cost of replacing physician leaders. Moreover, engaged physicians are more likely to add value in the form of intangible capital such as patient satisfaction and practice innovation. As such, we argue that effective retention strategy in medicine is likely to be cost‐effective, even if it requires significant new up‐front resources.
Lessons From Industry
Doctors frequently assume that the challenges and obstacles confronted in healthcare are unique to medicine. But, for every phrase like When I started practice, I decided how long office visits were, not the insurance company, or Young doctors just don't want to work as hard, there is a parallel utterance in the greater business world. Luckily, there are now examples of the healthcare world learning lessons from business. For example, innovators in medical quality improvement found value in the experience of other industries.20 Airlines and automakers have long honed systems for error prevention and possess expertise that may curb errors in the hospital.2123
We suspect that the ideas and practice of talent facilitation have already made their way into some medical settings. A Google search reveals multiple opportunities for hospital‐based talent managers, and websites advertise the availability of talent consultants ready to lend their expertise to the medical world. In the arena of academic medicine, the University of California, San Francisco (UCSF) Division of Hospital Medicine put some of the ideas of talent facilitation into practice over the past year, in part in response to an increasingly competitive market for academic hospitalists.24 Leaders introduced a formal faculty development program that links junior faculty with mentors and facilitates early and frequent feedback across hierarchical boundaries.25 These more intentional mentoring efforts were accompanied by a seminar series aimed at the needs of new faculty members, a research incubator program, divisional grand rounds, and other web‐based and in‐person forums for sharing best practices and innovations. Less formal social events have also been promoted. Importantly, these sweeping strategies seek to encompass the needs of both teaching and nonteaching hospitalists within UCSF.26
Clearly, an academic hospitalist group with 45 faculty physicians has unique characteristics that inform the specifics of its talent facilitation strategy. The interventions discussed above are meant to represent examples of the types of strategies that may be utilized by physician groups once a decision is made to focus on talent management. Undoubtedly, the shape of such efforts will vary in diverse practice settings, but physician leaders have much to gain through further exploration of where these core principles already exist within medicine and where they may be more effectively deployed. By examining how multinational businesses are systematically applying the concepts of talent facilitation to address a global talent shortage, the doctoring profession might again take an outside hint to help inform its future.
The demand for physician talent is intensifying as the US healthcare system confronts an unprecedented confluence of demographic pressures. Not only will 78 million retiring baby‐boomers require significant healthcare resources, but tens of thousands of practicing physicians will themselves reach retirement age within the next decade.1 At the same time, factors like the large increase in the percentage of female physicians (who are more likely to work part time), the growth of nonpractice opportunities for MDs, and generational demands for greater worklife balance are creating a major supply‐demand mismatch within the physician workforce.2 In this demographic atmosphere, the ability to recruit and retain physician leaders confers tremendous value to healthcare enterprisesboth public and private. Recruiting and retaining strategies already weigh heavily in the most palpable shortage areas, like primary care, but the system faces widespread unmet demand for a variety of specialist and generalist practitioners.36
This article does not address the public policy implications of the upcoming physician shortage, recognition of which will lead to the largest increase in new medical school slots in decades.7 Rather, we set out to illustrate how successful nonmedical businesses are embracing a thoughtful, systematic approach to retaining talent, based on the philosophy that keeping and engaging valued employees is more efficient than recruiting and orienting replacements. We posit that the innovations used by progressive companies could apply to recruitment and retention challenges confronting medicine.
How Industry Approaches the Talent Vacuum: Talent Facilitation
Leaders outside of medicine have long acknowledged that changing demographics and a global economy are driving unprecedented employee turnover.8 In confronting a talent vacuum, forward‐thinking managers have prioritized retaining key talent (rather than hiring anew) in planning for the future.9, 10 Doing so begins with attempts to understand the relationship between workers and the workplace, with a particular emphasis on appreciating workers' priorities. Indeed, new executive positions with titles like Chief Learning Officer and Chief Experience Officer are appearing as companies realize a need for focused expertise beyond traditional human resource departments. These companies understand that offering higher salaries is not the only retention strategyand often not even the most effective one.
The Four Actions of Talent Facilitation
The talent facilitation process centers on four actions: attract, engage, develop, and retain. None of these actions can stand alone, and all should be present, to some degree, at all stages of a worker's tenure. To attract or engage an employee or practice partner is not a 1‐time hook, but a constant and dynamic process.
Importantly, the concepts addressed here are not specific to 1 type of corporate system, size, or management level. Although the early business focus had been on upper‐level and executive talent within large corporate settings, there is an increasing recognition that a dedicated talent strategy is useful wherever recruiting and retaining talented people is important (and where is it not?).
The ideas presented here may seem most applicable to leaders of large physician corporations, hospital‐owned physician groups, or large integrated healthcare systems (such as Kaiser) that employ physicians. However, we also believe that the ideas apply across‐the‐board in medicine, including entities such as small, private, physician‐owned groups. We argue that regardless of the exact practice structure, a limited pool of resources must be dedicated to the attraction and retention of talented partners or employees, or to the cost of replacing those people if they pursue other opportunities (or the cost of inefficient and disengaged physicians). While an integrated health system may have the resources and scale to hire a Chief Experience Officer, we do not anticipate that a 5‐partner private practice would. Rather, we point to examples to illustrate the talent facilitation paradigm as a tool to systematically frame the allocation of those resources. Undoubtedly, the specific shape of a thoughtful talent facilitation effort will vary when applied in a large urban academic medical center vs. an integrated healthcare system vs. a small physician private practice, but the basic principles remain the same.
Attract
Increasingly, companies approach talented prospects with dedicated marketing campaigns to convey the value of a work environment.11 Silicon Valley employee lounges with free massages and foosball tables are the iconic example of attraction, but the concept runs deeper. Today's workers seek access to state‐of‐the‐art ideas and technology and often want to be part of a larger vision. Many seek opportunities to integrate their own professional and personal aspirations into a particular job description.
Hospital executives have long recognized the importance of attracting physicians to their facility (after all, the physicians draw patients and thus generate revenue). The traditional approach has surrounded perks, from comfortable doctor's lounges to the latest in surgical technology. But, the stakes seem greater now than before, and successful talent facilitation strategies are going beyond the tried and true.
Clearly, physicians seek financially stable practice settings with historical success. But they may also seek evidence of a defined strategic plan focused on more than mere profitability. Physicians may gravitate to practice environments that endorse progressive movements like the No One Dies Alone campaign.12 Similarly, recognition of movements beyond healthcarea commitment to Leadership in Energy and Environmental Design (LEED) (Green Building Rating System; US Green Building Council [USGBC];
The current recruitment campaign of California's prison healthcare system offers an unlikely source of inspiration. The prison system was placed in receivership to address a shortage of competent physician staff and other inadequacies. A central feature of the campaign is an attractive starting salary and good benefits. But, the campaign does not rely on money alone. For example, the campaign's website (

Engage
The corporate tool being employed at this stage is a strategy called on‐boarding, which emphasizes a streamlined integration of newcomers with existing workers and culture, and prioritizes aligning organizational roles with a worker's specific skills and interests. On‐boarding also emphasizes the value of early and frequent provision of constructive feedback from same‐level peers or managers with advanced coaching skills.
Many companies use formal survey tools to measure employee engagement and regularly evaluate the proficiency of system leaders in the ability to engage their employees. An engineering firm executive recently told us (P.K., C.K.) that he performs detailed and frequent on‐the‐job interviews, even with company veterans. The primary goal of these interviews is to ensure that engineers spend at least 85% of their time on work that: (1) they find interesting and (2) allows for the application of their best skills. Wherever possible, traditional job descriptions are altered to achieve this. Inevitably, there is still work (15% in this particular corporate vision) that no one prefers but needs to get done, but this process of active recalibration minimizes this fraction to the degree possible.
Even within a small physician practice group, one can imagine how a strategic approach of inviting and acknowledging individual physician's professional goals and particular talents may challenge the long‐held belief that everyone within a group enjoys and must do the exact same job. Once these goals and talents are articulated, groups may find that allowing for more customized roles within the practice enhances professional satisfaction.
Social networking, collaboration, and sharing of best practices are staples of engaging companies. The Cisco and Qualcomm companies, for example, utilize elaborate e‐networks (rough corporate equivalents to Facebook) to foster collegial interaction within and across traditional hierarchical boundaries so that managers and executives directly engage the ideas of employees at every level.14 The premise is logical: engaged employees will be more likely to contribute innovative ideas which, when listened to, are more likely to engage employees.
Most physicians will recognize the traditional resident report as a model for engagement. Beyond its educational value, interaction with program leadership, social bonding, collaborative effort, and exploration of best practices add tremendous value. Many companies would jump at the chance to engrain a similar cultural staple. Enhancing this type of interaction in a postresidency setting may promote engagement in a given system, especially if it facilitates interactions between physicians and senior hospital leaders. Absent these types of interactions, ensuring regular provision of peer review and/or constructive feedback can help systematically enhance 2‐way communication and enhance engagement.
Develop
Talent development relies on mentorship reflecting a genuine interest in an individual's future. Development strategies include pairing formal annual talent reviews or (in the case of practice partners) formal peer review with strategic development plans. Effective development strategy may include transparent succession planning so that individuals are aware they are being groomed for future roles.
A well‐known adage suggests, People quit the manager or administrator, not the job. Development in this sense relies on presenting new opportunities and knowing that people flourish when allowed to explore multiple paths forward. In many companies, the role of Chief Experience or Chief Learning Officers is to enhance development planning. Consider how career coaching of young hospitalists could transform an infinitely portable and volatile commodity job, prone to burnout, into an engaged specialist of sorts with immense value to a hospital. Hospitalists have already demonstrated their potential as quality improvement leaders.15 Imagine if hospital leadership enlisted a young hospitalist in a relevant quality improvement task force, such as one working on preventing falls. With appropriate support, the physician could obtain skills for quality improvement evaluation that would not only enhance his or her engagement with the hospital system but also provide a valuable analysis for the hospital.
As an example of development strategy within a small practice setting, consider the following real‐life anecdote: a group of 4 physicians recently completed a long and expensive recruitment of a new partner. The new partner, intrigued by the local hospital's surgical robot technology, sought the support of her partners (who are not currently using the technology) to partake in an expensive robotics training program. The partners decided not to provide the financial support. The new partner subsequently left the group for a nearby practice opportunity that would provide for the training, and the group was faced with the loss of a partner (one‐fourth of the practice!) and the cost of repeating the recruiting process. A preemptive evaluation of the value of investing in the development of the new partner and enhancing that partner's professional development may have proven wise despite the significant up‐front costs. In this case the manager the new partner quit was the inflexibility of the practice trajectory.
Retain
The economic incentive to retain talented workers is not subtle. If it was, companies would not be funneling resources into Chief Experience Officers. Likewise, the estimated cost for a medical practice to replace an individual physician is as at least $250,000.16, 17
In retention, as in attraction, salary is only part of the equation.18 People want fair and competitive compensation, and may leave if they are not getting it, but they will not stay (and will not stay engaged) only for a salary bump. Retention is enhanced when workers can advance according to skills and talent, rather than mere tenure. An effective retention policy responds to people's desire to incorporate individual professional goals into their work and allows for people to customize their career rather than simply occupy a job class. Effective retention policy respects worklife balance and recognizes that this balance might look different for 2 people with the same job. It may take the form of positive reinforcement (rather than subtle disdain) for using vacation time or allowing for participation in international service projects. Many literally feel that they need to quit their job in order to take time off or explore other interests.
Worklife balance has been a longstanding issue in medicine, and innovative augmentation strategies may well help retain top talent. Today's successful medical school applicants not only show aptitude in the classroom, they often have many well‐developed nonmedical skills. No one can expect that medical training will somehow convince them to leave everything else behind. Moreover, today's residency graduates, already with Generation Y sensibilities, have completed their entire training under the auspices of the Accreditation Council for Graduate Medical Education (ACGME) duty hours regulations, which has made residents far more comfortable with shift work and defined hours.
At the other end of the generation spectrum, as large numbers of physicians ready for retirement, effective talent facilitation strategies may evaluate how to reoffer medicine as a valid option for senior physicians who still wish to work. Retaining these physicians will require an appreciation of their lifestyle goals, as they will likely find continuing a traditional practice role untenable. A recent survey of orthopedic surgeons 50 years of age or older found that having a part‐time option was a common reason they continued practicing, and that the option to work part‐time would have the most impact on keeping these surgeons working past age 65 years.19 Those working part‐time were doing so in a wide range of practice arrangements including private practice. However, one‐third of those surveyed said a part‐time option was not available to them. Clearly, in an environment of workforce shortages, physician‐leaders must begin to think about worklife balance not only for new doctors but for those considering retirement.
Critics will point out the financial drawbacks in the provision of worklife balance. But the cost may pale in comparison to the cost of replacing physician leaders. Moreover, engaged physicians are more likely to add value in the form of intangible capital such as patient satisfaction and practice innovation. As such, we argue that effective retention strategy in medicine is likely to be cost‐effective, even if it requires significant new up‐front resources.
Lessons From Industry
Doctors frequently assume that the challenges and obstacles confronted in healthcare are unique to medicine. But, for every phrase like When I started practice, I decided how long office visits were, not the insurance company, or Young doctors just don't want to work as hard, there is a parallel utterance in the greater business world. Luckily, there are now examples of the healthcare world learning lessons from business. For example, innovators in medical quality improvement found value in the experience of other industries.20 Airlines and automakers have long honed systems for error prevention and possess expertise that may curb errors in the hospital.2123
We suspect that the ideas and practice of talent facilitation have already made their way into some medical settings. A Google search reveals multiple opportunities for hospital‐based talent managers, and websites advertise the availability of talent consultants ready to lend their expertise to the medical world. In the arena of academic medicine, the University of California, San Francisco (UCSF) Division of Hospital Medicine put some of the ideas of talent facilitation into practice over the past year, in part in response to an increasingly competitive market for academic hospitalists.24 Leaders introduced a formal faculty development program that links junior faculty with mentors and facilitates early and frequent feedback across hierarchical boundaries.25 These more intentional mentoring efforts were accompanied by a seminar series aimed at the needs of new faculty members, a research incubator program, divisional grand rounds, and other web‐based and in‐person forums for sharing best practices and innovations. Less formal social events have also been promoted. Importantly, these sweeping strategies seek to encompass the needs of both teaching and nonteaching hospitalists within UCSF.26
Clearly, an academic hospitalist group with 45 faculty physicians has unique characteristics that inform the specifics of its talent facilitation strategy. The interventions discussed above are meant to represent examples of the types of strategies that may be utilized by physician groups once a decision is made to focus on talent management. Undoubtedly, the shape of such efforts will vary in diverse practice settings, but physician leaders have much to gain through further exploration of where these core principles already exist within medicine and where they may be more effectively deployed. By examining how multinational businesses are systematically applying the concepts of talent facilitation to address a global talent shortage, the doctoring profession might again take an outside hint to help inform its future.
- Long Term Care: Aging Baby Boom Generation Will Increase Demand and Burden on Federal and State Budgets. United States General Accounting Office Testimony before the Special Committee on Aging, US Senate. Hearing Before the Special Committee on Aging of the US Senate,2002. Available at:http://www.gao.gov/new.items/d02544t.pdf. Accessed July 2009.
- Physician workforce shortages: implications and issues for academic health centers and policymakers.Acad Med.2006;81(9):782–787. , .
- New York moves to tackle shortage of primary‐care doctors.Lancet.2008;371(9615):801–802. .
- The US dermatology workforce: a specialty remains in shortage.J Am Acad Dermatol.2008;59(5):741–745. , .
- Challenges and opportunities for recruiting a new generation of neurosurgeons.Neurosurgery.2007;61(6):1314–1319. , .
- The developing crisis in the national general surgery workforce.J Am Coll Surg.2008;206(5):790–795. , .
- Medical School Enrollment Plans: Analysis of the 2007 AAMC Survey. Publication of the Association of American Medical Colleges, Center for Workforce Studies, April2008. Available at:http://www.aamc.org/workforce. Accessed July 2009.
- It's 2008: Do You Know Where Your Talent Is? Why acquisition and retention strategies don't work. Part 1 of a Deloitte Research Series on Talent Management.2008. Available at: http://www.deloitte.com/dtt/cda/content/UKConsulting_TalentMgtResearchReport.pdf. Accessed August 2009.
- The race for talent: retaining and engaging workers in the 21st century.Hum Resour Plann.2004;27(3):12–25. , , .
- Expecting sales growth, CEOs cite worker retention as critical to success. March 1,2004. Available at:http://www.barometersurveys.com/production/barsurv.nsf/89343582e94adb6185256b84006c8ffe/9672ab2f54cf99f885256e5500768232?OpenDocument. Accessed July 2009.
- Jet Blue announces aviation university gateway program for pilot candidates: airline partners with Embry‐Riddle Aeronautical University, University of North Dakota, and Cape Air to fill pilot pipeline. January 30, 2008. Available at:http://investor.jetblue.com/phoenix.zhtml?c=131045287(4):487–494.
- A review of physician turnover: rates, causes, and consequences.Am J Med Qual.2004;19(2):56–66. , , .
- The impact on revenue of physician turnover: an assessment model and experience in a large healthcare center.J Med Pract Manage.2006;21(6):351–355. , , .
- Employee motivation: a powerful new model.Harv Bus Rev.2008;86(7–8):78,84,160. , , .
- Work satisfaction and retirement plans of orthopaedic surgeons 50 years of age or older.Clin Orthop Relat Res.2008;466(1):231–238. , , .
- The long road to patient safety: a status report on patient safety systems.JAMA.2005(22);294:2858–2865. , , , .
- Error reduction through team leadership: what surgeons can learn from the airline industry.Clin Neurosurg.2007;54:195–199. .
- Applying the Toyota production system: using a patient safety alert system to reduce error.Jt Comm J Qual Patient Saf.2007;33(7):376–386. , .
- Improving Papanikolaou test quality and reducing medical errors by using Toyota production system methods.Am J Obstet Gynecol.2006;194(1):57–64. , , , .
- Society of Hospital Medicine Career Satisfaction Task Force. White Paper on Hospitalist Career Satisfaction.2006; 1–45. Available at: http://www.hospitalmedicine.org. Accessed July 2009.
- UCSF Department of Medicine, Division of Hospital Medicine, Faculty Development. Available at: http://hospsrvr.ucsf.edu/cme/fds.html. Accessed July 2009.
- Non‐housestaff medicine services in academic centers: models and challenges.J Hosp Med.2008;3(3):247–245. , , , et al.
- Long Term Care: Aging Baby Boom Generation Will Increase Demand and Burden on Federal and State Budgets. United States General Accounting Office Testimony before the Special Committee on Aging, US Senate. Hearing Before the Special Committee on Aging of the US Senate,2002. Available at:http://www.gao.gov/new.items/d02544t.pdf. Accessed July 2009.
- Physician workforce shortages: implications and issues for academic health centers and policymakers.Acad Med.2006;81(9):782–787. , .
- New York moves to tackle shortage of primary‐care doctors.Lancet.2008;371(9615):801–802. .
- The US dermatology workforce: a specialty remains in shortage.J Am Acad Dermatol.2008;59(5):741–745. , .
- Challenges and opportunities for recruiting a new generation of neurosurgeons.Neurosurgery.2007;61(6):1314–1319. , .
- The developing crisis in the national general surgery workforce.J Am Coll Surg.2008;206(5):790–795. , .
- Medical School Enrollment Plans: Analysis of the 2007 AAMC Survey. Publication of the Association of American Medical Colleges, Center for Workforce Studies, April2008. Available at:http://www.aamc.org/workforce. Accessed July 2009.
- It's 2008: Do You Know Where Your Talent Is? Why acquisition and retention strategies don't work. Part 1 of a Deloitte Research Series on Talent Management.2008. Available at: http://www.deloitte.com/dtt/cda/content/UKConsulting_TalentMgtResearchReport.pdf. Accessed August 2009.
- The race for talent: retaining and engaging workers in the 21st century.Hum Resour Plann.2004;27(3):12–25. , , .
- Expecting sales growth, CEOs cite worker retention as critical to success. March 1,2004. Available at:http://www.barometersurveys.com/production/barsurv.nsf/89343582e94adb6185256b84006c8ffe/9672ab2f54cf99f885256e5500768232?OpenDocument. Accessed July 2009.
- Jet Blue announces aviation university gateway program for pilot candidates: airline partners with Embry‐Riddle Aeronautical University, University of North Dakota, and Cape Air to fill pilot pipeline. January 30, 2008. Available at:http://investor.jetblue.com/phoenix.zhtml?c=131045287(4):487–494.
- A review of physician turnover: rates, causes, and consequences.Am J Med Qual.2004;19(2):56–66. , , .
- The impact on revenue of physician turnover: an assessment model and experience in a large healthcare center.J Med Pract Manage.2006;21(6):351–355. , , .
- Employee motivation: a powerful new model.Harv Bus Rev.2008;86(7–8):78,84,160. , , .
- Work satisfaction and retirement plans of orthopaedic surgeons 50 years of age or older.Clin Orthop Relat Res.2008;466(1):231–238. , , .
- The long road to patient safety: a status report on patient safety systems.JAMA.2005(22);294:2858–2865. , , , .
- Error reduction through team leadership: what surgeons can learn from the airline industry.Clin Neurosurg.2007;54:195–199. .
- Applying the Toyota production system: using a patient safety alert system to reduce error.Jt Comm J Qual Patient Saf.2007;33(7):376–386. , .
- Improving Papanikolaou test quality and reducing medical errors by using Toyota production system methods.Am J Obstet Gynecol.2006;194(1):57–64. , , , .
- Society of Hospital Medicine Career Satisfaction Task Force. White Paper on Hospitalist Career Satisfaction.2006; 1–45. Available at: http://www.hospitalmedicine.org. Accessed July 2009.
- UCSF Department of Medicine, Division of Hospital Medicine, Faculty Development. Available at: http://hospsrvr.ucsf.edu/cme/fds.html. Accessed July 2009.
- Non‐housestaff medicine services in academic centers: models and challenges.J Hosp Med.2008;3(3):247–245. , , , et al.
Macrolides and Quinolones for AECOPD
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are responsible for more than 600,000 hospitalizations annually, resulting in direct costs of over $20 billion.1 Bacterial infections appear responsible for 50% of such exacerbations,25 and current COPD guidelines recommend treatment with antibiotics for patients with severe exacerbations or a change in sputum.1,69 These recommendations are based on a number of small randomized trials, most of which were conducted more than 20 years ago using narrow spectrum antibiotics that are no longer commonly used.10 Only 4 studies, totaling 321 subjects, included hospitalized patients, and most studies excluded patients who required steroids. Because no clinical trials have compared different antibiotic regimens for AECOPD, existing guidelines offer a range of treatment options, including amoxicillin‐clavulonate, macrolides, quinolones, cephalosporins, aminopenicillins, and tetracyclines.
Among hospitalized patients, macrolides and quinolones appear to be the most frequently prescribed antibiotics.11 Both are available in oral formulations, have excellent bioavailability, and are administered once daily. In addition to their antimicrobial activity, macrolides are believed to have antiinflammatory effects, which could be especially advantageous in AECOPD.1214 In trials of chronic bronchitis, however, fluoroquinolones have been shown to reduce the risk of recurrent exacerbation when compared to macrolides.15 The wide variation that has been observed in antibiotic selection for patients hospitalized for AECOPD suggests a high degree of uncertainty among clinicians about the benefits of different treatment options.11 Given the limited evidence from randomized trials, we sought to evaluate the comparative effectiveness of macrolides and quinolones among a large, representative sample of patients hospitalized with AECOPD.
Subjects and Methods
Setting and Subjects
We conducted a retrospective cohort study of all patients hospitalized between January 1 and December 31, 2001 for AECOPD at any 1 of 375 acute care facilities in the United States that participated in Premier's Perspective, a voluntary, fee‐supported database developed for measuring quality and healthcare utilization. Participating hospitals represent all geographical regions, and are primarily small‐sized to medium‐sized nonteaching hospitals located mostly in urban areas. In addition to the information contained in the standard hospital discharge file (Uniform Billing 92) such as patient age, International Classification of Disease, 9th Edition, Clinical Modification (ICD‐9‐CM) codes, the Perspective database includes a date‐stamped log of all billed items, including diagnostic tests, medications, and other treatments, as well as costs, for individual patients. The study was approved by the Institutional Review Board of Baystate Medical Center.
Patients were included if they had a primary diagnosis consistent with AECOPD (ICD‐9 codes 491.21 and 493.22) or a primary diagnosis of respiratory failure (ICD‐9 codes 518.81 and 518.84) paired with secondary diagnosis of AECOPD; they also had to receive at least 2 consecutive days of either a macrolide or a quinolone, started within 48 hours of hospitalization. Patients receiving both antibiotics were excluded, but patients who received additional antibiotics were included. To enhance the specificity of our diagnosis codes, we limited our study to patients age 40 years.16 Because mechanical ventilation initiated after hospital day 2 was an outcome measure, we excluded patients admitted directly to the intensive care unit. We also excluded: those with other bacterial infections, such as pneumonia or cellulitis, who might have another indication for antibiotics; those with a length of stay <2 days, because we could not ascertain whether they received a full course of antibiotics; patients with a secondary diagnosis of pulmonary embolism or pneumothorax; and those whose attending physicians were not internists, family practitioners, hospitalists, pulmonologists, or intensivists. For patients with more than 1 admission during the study period, we included only the first admission.
Data Elements
For each patient, we assessed age, gender, race, marital and insurance status, principal diagnosis, comorbidities, and specialty of the attending physician. Comorbidities were identified from ICD‐9 secondary diagnosis codes and Diagnosis Related Groups using Healthcare Cost and Utilization Project Comorbidity Software (version 3.1), based on the work of Elixhauser et al.17 In addition, to assess disease severity we recorded the presence of chronic pulmonary heart disease, the number of admissions for COPD during the 12 months prior to the index admission, and arterial blood gas testing.18,19 We also identified pharmacy or diagnostic charges for interventions that were recommended in current guidelines (beta‐adrenergic and anticholinergic bronchodilators, steroids, and noninvasive positive‐pressure ventilation); those that were not recommended or were of uncertain benefit (methylxanthine bronchodilators, spirometry/pulmonary function testing, mucolytic medications, chest physiotherapy, and sputum testing); and drugs that might be associated with severe exacerbations or end‐stage COPD (loop diuretics, morphine, and nutritional supplements).1,69 Hospitals were categorized by region (Northeast, South, Midwest, or West), bed size, setting (urban vs. rural), ownership, and teaching status.
Antibiotic Class and Outcome Variables
Our primary predictor variable was the antibiotic initiated during the first 2 hospital days and continued for at least 2 days, regardless of other antibiotics the patient may have received during the course of hospitalization. Because we anticipated low in‐hospital mortality, our primary outcome was a composite measure of treatment failure, defined as initiation of mechanical ventilation after hospital day 2, in‐hospital mortality, or readmission for COPD within 30 days of discharge.20 Secondary outcomes included hospital costs and length of stay, as well as allergic reactions identified by ICD‐9 code, and antibiotic‐associated diarrhea, defined as treatment with either metronidazole or oral vancomycin begun after hospital day 3.
Statistical Analysis
Summary statistics were computed using frequencies and percents for categorical variables; and means, medians, standard deviations, and interquartile ranges for continuous variables. Associations between antibiotic selection and patient and hospital characteristics were assessed using chi‐square tests for categorical variables and z‐tests for continuous variables.
We developed a series of multivariable models to evaluate the impact of initial antibiotic selection on the risk of treatment failure, length of stay, and total cost. In order to account for the effects of within‐hospital correlation, generalized estimating equation (GEE) models with a logit link were used to assess the effect of antibiotic selection on the risk of treatment failure, and identity link models were used for analyses of length of stay and cost. Unadjusted and covariate‐adjusted models for treatment failure were evaluated with and without adjustments for propensity score. A propensity score is the probability that a given patient would receive treatment with a macrolide, derived from a nonparsimonious model in which treatment with a macrolide was considered the outcome. The propensity model included all patient characteristics, other early treatments and tests, comorbidities, hospital and physician characteristics, and selected interaction terms.21 Length of stay and cost were trimmed at 3 standard deviations above the mean, and natural log‐transformed values were modeled due to extreme positive skew. In addition, we carried out matched analyses in which we compared the outcomes of patients who were treated with a macrolide to those with similar propensity scores (ie, with similar likelihood of receiving a macrolide) who received a quinolone.22
Finally, to reduce the threat of residual confounding by indication, which can occur if sicker patients are more likely to receive a particular antibiotic, we developed a grouped treatment model, in which all patients treated at the same hospital were assigned a probability of treatment with a macrolide equal to the overall treatment rate at that hospital.23 This is an adaptation of instrumental variable analysis, a well‐accepted technique in econometrics with growing use in health care.24,25 It attempts to assess whether patients treated at a hospital at which quinolones are used more frequently have better outcomes than patients treated at hospitals at which macrolides are used more frequently, while adjusting for other patient, physician, and hospital variables. It ignores the actual treatment the patient received, and instead substitutes the hospital's rate of macrolide use. By grouping treatment at the hospital level, this method greatly reduces the possibility of residual selection bias, unless hospitals that use a lot of macrolides have patients who differ in a consistent way from hospitals which use mostly quinolones.
All analyses were performed using SAS version 9.1 (SAS Institute, Inc., Cary, NC).
Results
Of 26,248 AECOPD patients treated with antibiotics, 19,608 patients met the inclusion criteria; of these, 6139 (31%) were treated initially with a macrolide; the median age was 70 years; 60% were female; and 78% were white. A total of 86% of patients had a primary diagnosis of obstructive chronic bronchitis with acute exacerbation, and 6% had respiratory failure. The most common comorbidities were hypertension, diabetes, and congestive heart failure. Twenty‐two percent had been admitted at least once in the preceding 12 months. Treatment failure occurred in 7.7% of patients, and 1.3% died in the hospital. Mean length of stay was 4.8 days. Hospital prescribing rates for macrolides varied from 0% to 100%, with a mean of 33% and an interquartile range of 14% to 46% (Supporting Appendix Figure 1).
Compared to patients receiving macrolides, those receiving quinolones were older, more likely to have respiratory failure, to be cared for by a pulmonologist, and to have an admission in the previous year (Table 1). They were also more likely to be treated with bronchodilators, methylxanthines, steroids, diuretics, and noninvasive positive pressure ventilation, and to have an arterial blood gas, but less likely to receive concomitant treatment with a cephalosporin (11% vs. 57%). With the exception of cephalosporin treatment, these differences were small, but due to the large sample were statistically significant. Comorbidities were similar in both groups. Patients in the quinolone group were also more likely to experience treatment failure (8.1% vs. 6.8%), death (1.5% vs. 1.0%), and antibiotic‐associated diarrhea (1.1% vs. 0.5%).
Complete Cohort | Propensity‐matched Subsample | |||||
---|---|---|---|---|---|---|
Characteristic | Quinolone (n = 13469) | Macrolide (n = 6139) | P Value | Quinolone (n = 5610) | Macrolide (n = 5610) | P Value |
| ||||||
Antibiotics received during hospitalization* [n (%)] | ||||||
Macrolide | 264 (2) | 6139 (100) | 119 (2) | 5610 (100) | ||
Quinolone | 13469 (100) | 459 (8) | 5610 (100) | 424 (8) | ||
Cephalosporin | 1696 (13) | 3579 (59) | <0.001 | 726 (13) | 3305 (59) | <0.001 |
Tetracycline | 231 (2) | 75 (2) | 0.01 | 101 (2) | 73 (2) | 0.06 |
Other antibiotics | 397 (3) | 220 (4) | 0.02 | 166 (3) | 193 (3) | 0.03 |
Age (years) (mean [SD]) | 69.1 (11.4) | 68.2 (11.8) | <0.001 | 68.6 (11.7) | 68.5 (11.7) | 0.58 |
Male sex (n [%]) | 5447 (40) | 2440 (40) | 0.36 | 2207 (39) | 2196 (39) | 0.85 |
Race/ethnic group (n [%]) | <0.001 | 0.44 | ||||
White | 10454 (78) | 4758 (78) | 4359 (78) | 4368 (78) | ||
Black | 1060 (8) | 540 (9) | 470 (8) | 455 (8) | ||
Hispanic | 463 (3) | 144 (2) | 157 (3) | 134 (2) | ||
Other | 1492 (11) | 697 (11) | 624 (11) | 653 (12) | ||
Primary diagnosis (n [%]) | <0.001 | 0.78 | ||||
Obstructive chronic bronchitis with acute exacerbation | 11650 (87) | 5298 (86) | 4884 (87) | 4860 (87) | ||
Chronic obstructive asthma/asthma with COPD | 908 (7) | 569 (9) | 466 (8) | 486 (9) | ||
Respiratory failure | 911 (7) | 272 (4) | 260 (5) | 264 (5) | ||
Admissions in the prior year (n [%]) | <0.001 | 0.84 | ||||
0 | 9846 (73) | 4654 (76) | 4249 (76) | 4231 (75) | ||
1 | 1918 (14) | 816 (13) | 747 (13) | 750 (13) | ||
2+ | 1085 (8) | 445 (7) | 397 (7) | 420 (8) | ||
Missing | 620 (5) | 224 (4) | 217 (4) | 209 (4) | ||
Physician specialty (n [%]) | <0.001 | 0.84 | ||||
Internal medicine/hospitalist | 7069 (53) | 3321 (54) | 3032 (54) | 3072 (55) | ||
Family/general medicine | 3569 (27) | 2074 (34) | 1824 (33) | 1812 (32) | ||
Pulmonologist | 2776 (21) | 727 (12) | 738 (13) | 711 (13) | ||
Critical care/emntensivist | 55 (0) | 17 (0) | 16 (0) | 15 (0) | ||
Tests on hospital day 1 or 2 (n [%]) | ||||||
Arterial blood gas | 8084 (60) | 3377 (55) | <0.001 | 3195 (57) | 3129 (56) | 0.22 |
Sputum test | 1741 (13) | 766 (13) | 0.39 | 20 (0) | 16 (0) | 0.62 |
Medications/therapies on hospital day 1 or 2 (n [%]) | ||||||
Short‐acting bronchodilators | 7555 (56) | 3242 (53) | <0.001 | 2969 (53) | 2820 (50) | 0.005 |
Long‐acting beta‐2 agonists | 2068 (15) | 748 (12) | <0.001 | 704 (13) | 719 (13) | 0.69 |
Methylxanthine bronchodilators | 3051 (23) | 1149 (19) | <0.001 | 1102 (20) | 1093 (20) | 0.85 |
Steroids | 0.04 | 0.68 | ||||
Intravenous | 11148 (83) | 4989 (81) | 4547 (81) | 4581 (82) | ||
Oral | 772 (6) | 376 (6) | 334 (6) | 330 (6) | ||
Severity indicators (n [%]) | ||||||
Chronic pulmonary heart disease | 890 (7) | 401 (7) | 0.85 | 337 (6) | 368 (7) | 0.24 |
Sleep apnea | 586 (4) | 234 (4) | 0.08 | 211 (4) | 218 (4) | 0.77 |
Noninvasive positive pressure ventilation | 391 (3) | 128 (2) | <0.001 | 128 (2) | 114 (2) | 0.40 |
Loop diuretics | 4838 (36) | 1971 (32) | <0.001 | 1884 (34) | 1862 (33) | 0.67 |
Hospital characteristics (n [%]) | ||||||
Staffed beds | <0.001 | 0.71 | ||||
6200 | 3483 (26) | 1688 (28) | 1610 (29) | 1586 (28) | ||
201300 | 3132 (23) | 1198 (20) | 1174 (21) | 1154 (21) | ||
301500 | 4265 (32) | 2047 (33) | 1809 (32) | 1867 (33) | ||
500+ | 2589 (19) | 1206 (20) | 1017 (18) | 1003 (18) | ||
Hospital region (n [%]) | <0.001 | 0.65 | ||||
South | 8562 (64) | 3270 (53) | 3212 (57) | 3160 (56) | ||
Midwest | 2602 (19) | 1444 (24) | 1170 (21) | 1216 (22) | ||
Northeast | 1163 (9) | 871 (14) | 687 (12) | 704 (13) | ||
West | 1142 (9) | 554 (9) | 541 (10) | 530 (9) | ||
Teaching hospital | <0.001 | 0.63 | ||||
No | 12090 (90) | 5037 (82) | 4896 (87) | 4878 (87) | ||
Yes | 1379 (10) | 1102 (18) | 714 (13) | 732 (13) | ||
Comorbidities (n [%]) | ||||||
Congestive heart failure | 2673 (20) | 1147 (19) | 0.06 | 1081 (19) | 1060 (19) | 0.63 |
Metastatic cancer | 134 (1) | 27 (0) | <0.001 | 34 (1) | 38 (1) | 0.72 |
Depression | 1419 (11) | 669 (11) | 0.45 | 598 (11) | 603 (11) | 0.90 |
Deficiency anemias | 1155 (9) | 476 (8) | 0.05 | 426 (8) | 432 (8) | 0.86 |
Solid tumor without metastasis | 1487 (11) | 586 (10) | 0.002 | 550 (10) | 552 (10) | 0.97 |
Hypothyroidism | 1267 (9) | 527 (9) | 0.07 | 481 (9) | 482 (9) | 1.00 |
Peripheral vascular disease | 821 (6) | 312 (5) | 0.005 | 287 (5) | 288 (5) | 1.00 |
Paralysis | 165 (1) | 46 (1) | 0.003 | 49 (1) | 51 (1) | 0.92 |
Obesity | 957 (7) | 435 (7) | 0.98 | 386 (7) | 398 (7) | 0.68 |
Hypertension | 5793 (43) | 2688 (44) | 0.31 | 2474 (44) | 2468 (44) | 0.92 |
Diabetes | 0.04 | 0.45 | ||||
Without chronic complications | 2630 (20) | 1127 (18) | 1057 (19) | 1066 (19) | ||
With chronic complications | 298 (2) | 116 (2) | 115 (2) | 97 (2) |
In the unadjusted analysis, compared to patients receiving quinolones, those treated with macrolides were less likely to experience treatment failure (OR, 0.83; 95% CI, 0.740.94) (Table 2). Adjusting for all patient, hospital, and physician covariates, including the propensity for treatment with macrolides, increased the OR to 0.89 and the results were no longer significant (95% CI, 0.781.01). Propensity matching successfully balanced all measured covariates except for the use of short‐acting bronchodilators and additional antibiotics (Table 1). In the propensity‐matched sample (Figure 1), quinolone‐treated patients were more likely to experience antibiotic‐associated diarrhea (1.2% vs. 0.6%; P = 0.0003) and late mechanical ventilation (1.3% vs. 0.8%; P = 0.02). There were no differences in adjusted cost or length of stay between the 2 groups. The results of the grouped treatment analysis, substituting the hospital's specific rate of macrolide use in place of the actual treatment that each patient received suggested that the 2 antibiotics were associated with similar rates of treatment failure. The OR for a 100% hospital rate of macrolide treatment vs. a 0% rate was 1.01 (95% CI, 0.751.35).

Treatment Failure | Cost | LOS | ||||
---|---|---|---|---|---|---|
Models | OR | 95% CI | Ratio | 95% CI | Ratio | 95% CI |
| ||||||
Unadjusted | 0.83 | 0.730.93 | 0.98 | 0.971.00 | 0.96 | 0.950.98 |
Adjusted for propensity score only* | 0.89 | 0.791.01 | 1.00 | 0.981.01 | 0.98 | 0.971.00 |
Adjusted for covariates | 0.87 | 0.770.99 | 1.00 | 0.991.02 | 0.99 | 0.971.00 |
Adjusted for covariates and propensity score | 0.89 | 0.781.01 | 1.00 | 0.991.02 | 0.98 | 0.971.00 |
Matched sample, unadjusted | 0.87 | 0.751.00 | 0.99 | 0.981.01 | 0.99 | 0.971.01 |
Matched sample, adjusted for unbalanced variables | 0.87 | 0.751.01 | 1.00 | 0.981.02 | 0.99 | 0.971.01 |
Grouped treatment model, unadjusted | 0.90 | 0.681.19 | 0.97 | 0.891.06 | 0.92 | 0.870.96 |
Group treatment model, adjusted for covariates∥ | 1.01 | 0.751.35 | 0.96 | 0.881.05 | 0.96 | 0.911.00 |
Discussion
In this large observational study conducted at 375 hospitals, we took advantage of a natural experiment in which antibiotic prescribing patterns varied widely across hospitals to compare the effectiveness of 2 common antibiotic regimens for AECOPD. Treatment with macrolides and quinolones were associated with a similar risk of treatment failure, costs, and length of stay; however, patients treated with macrolides were less likely to experience late mechanical ventilation or treatment for antibiotic‐associated diarrhea.
Despite broad consensus in COPD guidelines that patients with severe acute exacerbations should receive antibiotics, there is little agreement about the preferred empiric agent. Controversy exists regarding antibiotics' comparative effectiveness, and even over which pathogens cause COPD exacerbations. Given the frequency of hospitalization for AECOPD, understanding the comparative effectiveness of treatments in this setting could have important implications for health outcomes and costs. Unfortunately, most antibiotic studies in AECOPD were conducted >20 years ago, using antibiotics that rarely appeared in our sample.26 Consequently, clinical practice guidelines offer conflicting recommendations. For example, the National Institute for Clinical Excellence recommends empirical treatment with an aminopenicillin, a macrolide, or a tetracycline,8 while the American Thoracic Society recommends amoxicillin‐clavulonate or a fluoroquinolone.9
As might be expected in light of so much uncertainty, we found wide variation in prescribing patterns across hospitals. Overall, approximately one‐third of patients received a macrolide and two‐thirds a quinolone. Both regimens provide adequate coverage of H. influenza, S. pneumoniae, and M. catarrhalis, and conform to at least 1 COPD guideline. Nevertheless, patients receiving macrolides often received a cephalosporin as well; this pattern of treatment suggests that antibiotic selection is likely to have been influenced more by guidelines for the treatment of community‐acquired pneumonia than COPD.
Previous studies comparing antibiotic effectiveness suffer from shortcomings that limit their application to patients hospitalized with AECOPD. First, they enrolled patients with chronic bronchitis, and included patients without obstructive lung disease, and most studies included patients as young as 18 years old. Second, many either did not include treatment with steroids or excluded patients receiving more than 10 mg of prednisone daily. Third, almost all enrolled only ambulatory patients.
While there are no studies comparing quinolones and macrolides in patients hospitalized for AECOPD, a meta‐analysis comparing quinolones, macrolides, and amoxicillin‐clavulonate identified 19 trials of ambulatory patients with chronic bronchitis. That study found that all 3 drugs had similar efficacy initially, but that quinolones resulted in the fewest relapses over a 26‐week period.27 Macrolides and quinolones had similar rates of adverse effects. In contrast, we did not find a difference in treatment failure, cost, or length of stay, but did find a higher rate of diarrhea associated with quinolones. Others have also documented an association between fluoroquinolones and C. difficile diarrhea.2830 This trend, first noted in 2001, is of particular concern because the fluoroquinolone‐resistant strains appear to be hypervirulent and have been associated with nosocomial epidemics.3134
Our study has several limitations. First, its observational design leaves open the possibility of selection bias. For this reason we analyzed our data in several ways, including using a grouped treatment approach, an adaptation of the instrumental variable technique, and accepted only those differences which were consistent across all models. Second, our study used claims data, and therefore we could not directly adjust for physiological measures of severity. However, the highly detailed nature of the data allowed us to adjust for numerous tests and treatments that reflected the clinician's assessment of the patient's severity, as well as the number of prior COPD admissions. Third, we cannot exclude the possibility that some patients may have had concurrent pneumonia without an ICD‐9 code. We think that the number would be small because reimbursement for pneumonia is generally higher than for COPD, so hospitals have an incentive to code pneumonia as the principal diagnosis when present. Finally, we compared initial antibiotics only. More than one‐quarter of our patients received an additional antibiotic before discharge. In particular, patients receiving macrolides were often prescribed a concomitant cephalosporin. We do not know to what extent these additional antibiotics may have affected the outcomes.
Despite the large number of patients hospitalized annually for AECOPD, there are no randomized trials comparing different antibiotics in this population. Studies comparing antibiotics in chronic bronchitis can offer little guidance, since they have primarily focused on proving equivalence between existing antibiotics and newer, more expensive formulations.35 Because many of the patients enrolled in such trials do not benefit from antibiotics at all, either because they do not have COPD or because their exacerbation is not caused by bacteria, it is relatively easy to prove equivalence. Given that AECOPD is one of the leading causes of hospitalization in the United States, large, randomized trials comparing the effectiveness of different antibiotics should be a high priority. In the meantime, macrolides (often given together with cephalosporins) and quinolones appear to be equally effective initial antibiotic choices; considering antibiotic‐associated diarrhea, macrolides appear to be the safer of the 2.
- Evidence base for management of acute exacerbations of chronic obstructive pulmonary disease.Ann Intern Med.2001;134:595–599. , , .
- Infectious etiologies in acute exacerbation of COPD.Diagn Microbiol Infect Dis.2001;40:95–102. , , , et al.
- Bacterial infections in patients requiring admission for an acute exacerbation of COPD; a 1‐year prospective study.Respir Med.2003;97:770–777. , .
- Microbiologic determinants of exacerbation in chronic obstructive pulmonary disease.Arch Intern Med.2005;165:891–897. , , , et al.
- Infection in the pathogenesis and course of chronic obstructive pulmonary disease.N Engl J Med.2008;359:2355–2365. , .
- Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary.Am J Respir Crit Care Med.2007;176:532–555. , , , et al.
- Canadian Thoracic Society recommendations for management of chronic obstructive pulmonary disease—2008 update—highlights for primary care.Can Respir J.2008;15(suppl A):1A–8A. , , , et al.
- Chronic obstructive pulmonary disease.National clinical guideline on management of chronic obstructive pulmonary disease in adults in primary and secondary care.Thorax.2004;59(suppl 1):1–232.
- Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper.Eur Respir J.2004;23:932–946. , , , et al.
- Antibiotics for exacerbations of chronic obstructive pulmonary disease.Cochrane Database Syst Rev.2006:CD004403. , , , , .
- Quality of care for patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease.Ann Intern Med.2006;144:894–903. , , , , , .
- Modulation of neutrophil and inflammation markers in chronic obstructive pulmonary disease by short‐term azithromycin treatment.Eur J Pharmacol.2005;517:132–143. , , , et al.
- Azithromycin modulates neutrophil function and circulating inflammatory mediators in healthy human subjects.Eur J Pharmacol.2002;450:277–289. , , , et al.
- The effect of clarithromycin on inflammatory markers in chronic obstructive pulmonary disease: preliminary data.Ann Pharmacother.2004;38:1400–1405. , , , , , .
- A comparison of gemifloxacin and clarithromycin in acute exacerbations of chronic bronchitis and long‐term clinical outcomes.Clin Ther.2002;24:639–652. , , , .
- In‐hospital mortality following acute exacerbations of chronic obstructive pulmonary disease.Arch Intern Med.2003;163:1180–1186. , , , .
- Comorbidity measures for use with administrative data.Med Care.1998;36:8–27. , , , .
- Outcomes following acute exacerbation of severe chronic obstructive lung disease. The SUPPORT investigators (Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments).Am J Respir Crit Care Med.1996;154:959–967. , , , et al.
- Mortality and mortality‐related factors after hospitalization for acute exacerbation of COPD.Chest.2003;124:459–467. , , .
- Effect of systemic glucocorticoids on exacerbations of chronic obstructive pulmonary disease.N Engl J Med.1999;340:1941–1947. , , , et al.
- Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group.Stat Med.1998;17:2265–2281. .
- Reducing bias in a propensity score matched‐pair sample using greedy matching techniques. Proceedings of the Twenty‐Sixth Annual SAS Users Group International Conference. Cary, NC: SAS Institute;2001,214–216. .
- Modeling treatment effects on binary outcomes with grouped‐treatment variables and individual covariates.Am J Epidemiol.2002;156:753–760. , , , .
- Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. [see Comment].JAMA.1994;272:859–866. , , .
- Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.JAMA.2007;297:278–285. , , , , , .
- Antibiotics in chronic obstructive pulmonary disease exacerbations. A meta‐analysis.JAMA.1995;273:957–960. , , , .
- Macrolides, quinolones and amoxicillin/clavulanate for chronic bronchitis: a meta‐analysis.Eur Respir J.2007;29:1127–1137. , , , , .
- Antimicrobial‐associated risk factors for Clostridium difficile infection.Clin Infect Dis.2008;46(suppl 1):S19–S31. , , , , .
- Short‐term and long‐term outcomes of moxifloxacin compared to standard antibiotic treatment in acute exacerbations of chronic bronchitis.Chest.2004;125:953–964. , , , et al.
- Oral gemifloxacin once daily for 5 days compared with sequential therapy with i.v. ceftriaxone/oral cefuroxime (maximum of 10 days) in the treatment of hospitalized patients with acute exacerbations of chronic bronchitis.Respir Med.2003;97:242–249. , , , , .
- A large outbreak of Clostridium difficile‐associated disease with an unexpected proportion of deaths and colectomies at a teaching hospital following increased fluoroquinolone use.Infect Control Hosp Epidemiol.2005;26:273–280. , , , et al.
- An epidemic, toxin gene‐variant strain of Clostridium difficile.N Engl J Med.2005;353:2433–2441. , , , et al.
- Outbreak of Clostridium difficile infection in a long‐term care facility: association with gatifloxacin use.Clin Infect Dis.2004;38:640–645. , , , et al.
- A predominantly clonal multi‐institutional outbreak of Clostridium difficile‐associated diarrhea with high morbidity and mortality.N Engl J Med.2005;353:2442–2449. , , , et al.
- No more equivalence trials for antibiotics in exacerbations of COPD, please.Chest.2004;125:811–813. , .
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are responsible for more than 600,000 hospitalizations annually, resulting in direct costs of over $20 billion.1 Bacterial infections appear responsible for 50% of such exacerbations,25 and current COPD guidelines recommend treatment with antibiotics for patients with severe exacerbations or a change in sputum.1,69 These recommendations are based on a number of small randomized trials, most of which were conducted more than 20 years ago using narrow spectrum antibiotics that are no longer commonly used.10 Only 4 studies, totaling 321 subjects, included hospitalized patients, and most studies excluded patients who required steroids. Because no clinical trials have compared different antibiotic regimens for AECOPD, existing guidelines offer a range of treatment options, including amoxicillin‐clavulonate, macrolides, quinolones, cephalosporins, aminopenicillins, and tetracyclines.
Among hospitalized patients, macrolides and quinolones appear to be the most frequently prescribed antibiotics.11 Both are available in oral formulations, have excellent bioavailability, and are administered once daily. In addition to their antimicrobial activity, macrolides are believed to have antiinflammatory effects, which could be especially advantageous in AECOPD.1214 In trials of chronic bronchitis, however, fluoroquinolones have been shown to reduce the risk of recurrent exacerbation when compared to macrolides.15 The wide variation that has been observed in antibiotic selection for patients hospitalized for AECOPD suggests a high degree of uncertainty among clinicians about the benefits of different treatment options.11 Given the limited evidence from randomized trials, we sought to evaluate the comparative effectiveness of macrolides and quinolones among a large, representative sample of patients hospitalized with AECOPD.
Subjects and Methods
Setting and Subjects
We conducted a retrospective cohort study of all patients hospitalized between January 1 and December 31, 2001 for AECOPD at any 1 of 375 acute care facilities in the United States that participated in Premier's Perspective, a voluntary, fee‐supported database developed for measuring quality and healthcare utilization. Participating hospitals represent all geographical regions, and are primarily small‐sized to medium‐sized nonteaching hospitals located mostly in urban areas. In addition to the information contained in the standard hospital discharge file (Uniform Billing 92) such as patient age, International Classification of Disease, 9th Edition, Clinical Modification (ICD‐9‐CM) codes, the Perspective database includes a date‐stamped log of all billed items, including diagnostic tests, medications, and other treatments, as well as costs, for individual patients. The study was approved by the Institutional Review Board of Baystate Medical Center.
Patients were included if they had a primary diagnosis consistent with AECOPD (ICD‐9 codes 491.21 and 493.22) or a primary diagnosis of respiratory failure (ICD‐9 codes 518.81 and 518.84) paired with secondary diagnosis of AECOPD; they also had to receive at least 2 consecutive days of either a macrolide or a quinolone, started within 48 hours of hospitalization. Patients receiving both antibiotics were excluded, but patients who received additional antibiotics were included. To enhance the specificity of our diagnosis codes, we limited our study to patients age 40 years.16 Because mechanical ventilation initiated after hospital day 2 was an outcome measure, we excluded patients admitted directly to the intensive care unit. We also excluded: those with other bacterial infections, such as pneumonia or cellulitis, who might have another indication for antibiotics; those with a length of stay <2 days, because we could not ascertain whether they received a full course of antibiotics; patients with a secondary diagnosis of pulmonary embolism or pneumothorax; and those whose attending physicians were not internists, family practitioners, hospitalists, pulmonologists, or intensivists. For patients with more than 1 admission during the study period, we included only the first admission.
Data Elements
For each patient, we assessed age, gender, race, marital and insurance status, principal diagnosis, comorbidities, and specialty of the attending physician. Comorbidities were identified from ICD‐9 secondary diagnosis codes and Diagnosis Related Groups using Healthcare Cost and Utilization Project Comorbidity Software (version 3.1), based on the work of Elixhauser et al.17 In addition, to assess disease severity we recorded the presence of chronic pulmonary heart disease, the number of admissions for COPD during the 12 months prior to the index admission, and arterial blood gas testing.18,19 We also identified pharmacy or diagnostic charges for interventions that were recommended in current guidelines (beta‐adrenergic and anticholinergic bronchodilators, steroids, and noninvasive positive‐pressure ventilation); those that were not recommended or were of uncertain benefit (methylxanthine bronchodilators, spirometry/pulmonary function testing, mucolytic medications, chest physiotherapy, and sputum testing); and drugs that might be associated with severe exacerbations or end‐stage COPD (loop diuretics, morphine, and nutritional supplements).1,69 Hospitals were categorized by region (Northeast, South, Midwest, or West), bed size, setting (urban vs. rural), ownership, and teaching status.
Antibiotic Class and Outcome Variables
Our primary predictor variable was the antibiotic initiated during the first 2 hospital days and continued for at least 2 days, regardless of other antibiotics the patient may have received during the course of hospitalization. Because we anticipated low in‐hospital mortality, our primary outcome was a composite measure of treatment failure, defined as initiation of mechanical ventilation after hospital day 2, in‐hospital mortality, or readmission for COPD within 30 days of discharge.20 Secondary outcomes included hospital costs and length of stay, as well as allergic reactions identified by ICD‐9 code, and antibiotic‐associated diarrhea, defined as treatment with either metronidazole or oral vancomycin begun after hospital day 3.
Statistical Analysis
Summary statistics were computed using frequencies and percents for categorical variables; and means, medians, standard deviations, and interquartile ranges for continuous variables. Associations between antibiotic selection and patient and hospital characteristics were assessed using chi‐square tests for categorical variables and z‐tests for continuous variables.
We developed a series of multivariable models to evaluate the impact of initial antibiotic selection on the risk of treatment failure, length of stay, and total cost. In order to account for the effects of within‐hospital correlation, generalized estimating equation (GEE) models with a logit link were used to assess the effect of antibiotic selection on the risk of treatment failure, and identity link models were used for analyses of length of stay and cost. Unadjusted and covariate‐adjusted models for treatment failure were evaluated with and without adjustments for propensity score. A propensity score is the probability that a given patient would receive treatment with a macrolide, derived from a nonparsimonious model in which treatment with a macrolide was considered the outcome. The propensity model included all patient characteristics, other early treatments and tests, comorbidities, hospital and physician characteristics, and selected interaction terms.21 Length of stay and cost were trimmed at 3 standard deviations above the mean, and natural log‐transformed values were modeled due to extreme positive skew. In addition, we carried out matched analyses in which we compared the outcomes of patients who were treated with a macrolide to those with similar propensity scores (ie, with similar likelihood of receiving a macrolide) who received a quinolone.22
Finally, to reduce the threat of residual confounding by indication, which can occur if sicker patients are more likely to receive a particular antibiotic, we developed a grouped treatment model, in which all patients treated at the same hospital were assigned a probability of treatment with a macrolide equal to the overall treatment rate at that hospital.23 This is an adaptation of instrumental variable analysis, a well‐accepted technique in econometrics with growing use in health care.24,25 It attempts to assess whether patients treated at a hospital at which quinolones are used more frequently have better outcomes than patients treated at hospitals at which macrolides are used more frequently, while adjusting for other patient, physician, and hospital variables. It ignores the actual treatment the patient received, and instead substitutes the hospital's rate of macrolide use. By grouping treatment at the hospital level, this method greatly reduces the possibility of residual selection bias, unless hospitals that use a lot of macrolides have patients who differ in a consistent way from hospitals which use mostly quinolones.
All analyses were performed using SAS version 9.1 (SAS Institute, Inc., Cary, NC).
Results
Of 26,248 AECOPD patients treated with antibiotics, 19,608 patients met the inclusion criteria; of these, 6139 (31%) were treated initially with a macrolide; the median age was 70 years; 60% were female; and 78% were white. A total of 86% of patients had a primary diagnosis of obstructive chronic bronchitis with acute exacerbation, and 6% had respiratory failure. The most common comorbidities were hypertension, diabetes, and congestive heart failure. Twenty‐two percent had been admitted at least once in the preceding 12 months. Treatment failure occurred in 7.7% of patients, and 1.3% died in the hospital. Mean length of stay was 4.8 days. Hospital prescribing rates for macrolides varied from 0% to 100%, with a mean of 33% and an interquartile range of 14% to 46% (Supporting Appendix Figure 1).
Compared to patients receiving macrolides, those receiving quinolones were older, more likely to have respiratory failure, to be cared for by a pulmonologist, and to have an admission in the previous year (Table 1). They were also more likely to be treated with bronchodilators, methylxanthines, steroids, diuretics, and noninvasive positive pressure ventilation, and to have an arterial blood gas, but less likely to receive concomitant treatment with a cephalosporin (11% vs. 57%). With the exception of cephalosporin treatment, these differences were small, but due to the large sample were statistically significant. Comorbidities were similar in both groups. Patients in the quinolone group were also more likely to experience treatment failure (8.1% vs. 6.8%), death (1.5% vs. 1.0%), and antibiotic‐associated diarrhea (1.1% vs. 0.5%).
Complete Cohort | Propensity‐matched Subsample | |||||
---|---|---|---|---|---|---|
Characteristic | Quinolone (n = 13469) | Macrolide (n = 6139) | P Value | Quinolone (n = 5610) | Macrolide (n = 5610) | P Value |
| ||||||
Antibiotics received during hospitalization* [n (%)] | ||||||
Macrolide | 264 (2) | 6139 (100) | 119 (2) | 5610 (100) | ||
Quinolone | 13469 (100) | 459 (8) | 5610 (100) | 424 (8) | ||
Cephalosporin | 1696 (13) | 3579 (59) | <0.001 | 726 (13) | 3305 (59) | <0.001 |
Tetracycline | 231 (2) | 75 (2) | 0.01 | 101 (2) | 73 (2) | 0.06 |
Other antibiotics | 397 (3) | 220 (4) | 0.02 | 166 (3) | 193 (3) | 0.03 |
Age (years) (mean [SD]) | 69.1 (11.4) | 68.2 (11.8) | <0.001 | 68.6 (11.7) | 68.5 (11.7) | 0.58 |
Male sex (n [%]) | 5447 (40) | 2440 (40) | 0.36 | 2207 (39) | 2196 (39) | 0.85 |
Race/ethnic group (n [%]) | <0.001 | 0.44 | ||||
White | 10454 (78) | 4758 (78) | 4359 (78) | 4368 (78) | ||
Black | 1060 (8) | 540 (9) | 470 (8) | 455 (8) | ||
Hispanic | 463 (3) | 144 (2) | 157 (3) | 134 (2) | ||
Other | 1492 (11) | 697 (11) | 624 (11) | 653 (12) | ||
Primary diagnosis (n [%]) | <0.001 | 0.78 | ||||
Obstructive chronic bronchitis with acute exacerbation | 11650 (87) | 5298 (86) | 4884 (87) | 4860 (87) | ||
Chronic obstructive asthma/asthma with COPD | 908 (7) | 569 (9) | 466 (8) | 486 (9) | ||
Respiratory failure | 911 (7) | 272 (4) | 260 (5) | 264 (5) | ||
Admissions in the prior year (n [%]) | <0.001 | 0.84 | ||||
0 | 9846 (73) | 4654 (76) | 4249 (76) | 4231 (75) | ||
1 | 1918 (14) | 816 (13) | 747 (13) | 750 (13) | ||
2+ | 1085 (8) | 445 (7) | 397 (7) | 420 (8) | ||
Missing | 620 (5) | 224 (4) | 217 (4) | 209 (4) | ||
Physician specialty (n [%]) | <0.001 | 0.84 | ||||
Internal medicine/hospitalist | 7069 (53) | 3321 (54) | 3032 (54) | 3072 (55) | ||
Family/general medicine | 3569 (27) | 2074 (34) | 1824 (33) | 1812 (32) | ||
Pulmonologist | 2776 (21) | 727 (12) | 738 (13) | 711 (13) | ||
Critical care/emntensivist | 55 (0) | 17 (0) | 16 (0) | 15 (0) | ||
Tests on hospital day 1 or 2 (n [%]) | ||||||
Arterial blood gas | 8084 (60) | 3377 (55) | <0.001 | 3195 (57) | 3129 (56) | 0.22 |
Sputum test | 1741 (13) | 766 (13) | 0.39 | 20 (0) | 16 (0) | 0.62 |
Medications/therapies on hospital day 1 or 2 (n [%]) | ||||||
Short‐acting bronchodilators | 7555 (56) | 3242 (53) | <0.001 | 2969 (53) | 2820 (50) | 0.005 |
Long‐acting beta‐2 agonists | 2068 (15) | 748 (12) | <0.001 | 704 (13) | 719 (13) | 0.69 |
Methylxanthine bronchodilators | 3051 (23) | 1149 (19) | <0.001 | 1102 (20) | 1093 (20) | 0.85 |
Steroids | 0.04 | 0.68 | ||||
Intravenous | 11148 (83) | 4989 (81) | 4547 (81) | 4581 (82) | ||
Oral | 772 (6) | 376 (6) | 334 (6) | 330 (6) | ||
Severity indicators (n [%]) | ||||||
Chronic pulmonary heart disease | 890 (7) | 401 (7) | 0.85 | 337 (6) | 368 (7) | 0.24 |
Sleep apnea | 586 (4) | 234 (4) | 0.08 | 211 (4) | 218 (4) | 0.77 |
Noninvasive positive pressure ventilation | 391 (3) | 128 (2) | <0.001 | 128 (2) | 114 (2) | 0.40 |
Loop diuretics | 4838 (36) | 1971 (32) | <0.001 | 1884 (34) | 1862 (33) | 0.67 |
Hospital characteristics (n [%]) | ||||||
Staffed beds | <0.001 | 0.71 | ||||
6200 | 3483 (26) | 1688 (28) | 1610 (29) | 1586 (28) | ||
201300 | 3132 (23) | 1198 (20) | 1174 (21) | 1154 (21) | ||
301500 | 4265 (32) | 2047 (33) | 1809 (32) | 1867 (33) | ||
500+ | 2589 (19) | 1206 (20) | 1017 (18) | 1003 (18) | ||
Hospital region (n [%]) | <0.001 | 0.65 | ||||
South | 8562 (64) | 3270 (53) | 3212 (57) | 3160 (56) | ||
Midwest | 2602 (19) | 1444 (24) | 1170 (21) | 1216 (22) | ||
Northeast | 1163 (9) | 871 (14) | 687 (12) | 704 (13) | ||
West | 1142 (9) | 554 (9) | 541 (10) | 530 (9) | ||
Teaching hospital | <0.001 | 0.63 | ||||
No | 12090 (90) | 5037 (82) | 4896 (87) | 4878 (87) | ||
Yes | 1379 (10) | 1102 (18) | 714 (13) | 732 (13) | ||
Comorbidities (n [%]) | ||||||
Congestive heart failure | 2673 (20) | 1147 (19) | 0.06 | 1081 (19) | 1060 (19) | 0.63 |
Metastatic cancer | 134 (1) | 27 (0) | <0.001 | 34 (1) | 38 (1) | 0.72 |
Depression | 1419 (11) | 669 (11) | 0.45 | 598 (11) | 603 (11) | 0.90 |
Deficiency anemias | 1155 (9) | 476 (8) | 0.05 | 426 (8) | 432 (8) | 0.86 |
Solid tumor without metastasis | 1487 (11) | 586 (10) | 0.002 | 550 (10) | 552 (10) | 0.97 |
Hypothyroidism | 1267 (9) | 527 (9) | 0.07 | 481 (9) | 482 (9) | 1.00 |
Peripheral vascular disease | 821 (6) | 312 (5) | 0.005 | 287 (5) | 288 (5) | 1.00 |
Paralysis | 165 (1) | 46 (1) | 0.003 | 49 (1) | 51 (1) | 0.92 |
Obesity | 957 (7) | 435 (7) | 0.98 | 386 (7) | 398 (7) | 0.68 |
Hypertension | 5793 (43) | 2688 (44) | 0.31 | 2474 (44) | 2468 (44) | 0.92 |
Diabetes | 0.04 | 0.45 | ||||
Without chronic complications | 2630 (20) | 1127 (18) | 1057 (19) | 1066 (19) | ||
With chronic complications | 298 (2) | 116 (2) | 115 (2) | 97 (2) |
In the unadjusted analysis, compared to patients receiving quinolones, those treated with macrolides were less likely to experience treatment failure (OR, 0.83; 95% CI, 0.740.94) (Table 2). Adjusting for all patient, hospital, and physician covariates, including the propensity for treatment with macrolides, increased the OR to 0.89 and the results were no longer significant (95% CI, 0.781.01). Propensity matching successfully balanced all measured covariates except for the use of short‐acting bronchodilators and additional antibiotics (Table 1). In the propensity‐matched sample (Figure 1), quinolone‐treated patients were more likely to experience antibiotic‐associated diarrhea (1.2% vs. 0.6%; P = 0.0003) and late mechanical ventilation (1.3% vs. 0.8%; P = 0.02). There were no differences in adjusted cost or length of stay between the 2 groups. The results of the grouped treatment analysis, substituting the hospital's specific rate of macrolide use in place of the actual treatment that each patient received suggested that the 2 antibiotics were associated with similar rates of treatment failure. The OR for a 100% hospital rate of macrolide treatment vs. a 0% rate was 1.01 (95% CI, 0.751.35).

Treatment Failure | Cost | LOS | ||||
---|---|---|---|---|---|---|
Models | OR | 95% CI | Ratio | 95% CI | Ratio | 95% CI |
| ||||||
Unadjusted | 0.83 | 0.730.93 | 0.98 | 0.971.00 | 0.96 | 0.950.98 |
Adjusted for propensity score only* | 0.89 | 0.791.01 | 1.00 | 0.981.01 | 0.98 | 0.971.00 |
Adjusted for covariates | 0.87 | 0.770.99 | 1.00 | 0.991.02 | 0.99 | 0.971.00 |
Adjusted for covariates and propensity score | 0.89 | 0.781.01 | 1.00 | 0.991.02 | 0.98 | 0.971.00 |
Matched sample, unadjusted | 0.87 | 0.751.00 | 0.99 | 0.981.01 | 0.99 | 0.971.01 |
Matched sample, adjusted for unbalanced variables | 0.87 | 0.751.01 | 1.00 | 0.981.02 | 0.99 | 0.971.01 |
Grouped treatment model, unadjusted | 0.90 | 0.681.19 | 0.97 | 0.891.06 | 0.92 | 0.870.96 |
Group treatment model, adjusted for covariates∥ | 1.01 | 0.751.35 | 0.96 | 0.881.05 | 0.96 | 0.911.00 |
Discussion
In this large observational study conducted at 375 hospitals, we took advantage of a natural experiment in which antibiotic prescribing patterns varied widely across hospitals to compare the effectiveness of 2 common antibiotic regimens for AECOPD. Treatment with macrolides and quinolones were associated with a similar risk of treatment failure, costs, and length of stay; however, patients treated with macrolides were less likely to experience late mechanical ventilation or treatment for antibiotic‐associated diarrhea.
Despite broad consensus in COPD guidelines that patients with severe acute exacerbations should receive antibiotics, there is little agreement about the preferred empiric agent. Controversy exists regarding antibiotics' comparative effectiveness, and even over which pathogens cause COPD exacerbations. Given the frequency of hospitalization for AECOPD, understanding the comparative effectiveness of treatments in this setting could have important implications for health outcomes and costs. Unfortunately, most antibiotic studies in AECOPD were conducted >20 years ago, using antibiotics that rarely appeared in our sample.26 Consequently, clinical practice guidelines offer conflicting recommendations. For example, the National Institute for Clinical Excellence recommends empirical treatment with an aminopenicillin, a macrolide, or a tetracycline,8 while the American Thoracic Society recommends amoxicillin‐clavulonate or a fluoroquinolone.9
As might be expected in light of so much uncertainty, we found wide variation in prescribing patterns across hospitals. Overall, approximately one‐third of patients received a macrolide and two‐thirds a quinolone. Both regimens provide adequate coverage of H. influenza, S. pneumoniae, and M. catarrhalis, and conform to at least 1 COPD guideline. Nevertheless, patients receiving macrolides often received a cephalosporin as well; this pattern of treatment suggests that antibiotic selection is likely to have been influenced more by guidelines for the treatment of community‐acquired pneumonia than COPD.
Previous studies comparing antibiotic effectiveness suffer from shortcomings that limit their application to patients hospitalized with AECOPD. First, they enrolled patients with chronic bronchitis, and included patients without obstructive lung disease, and most studies included patients as young as 18 years old. Second, many either did not include treatment with steroids or excluded patients receiving more than 10 mg of prednisone daily. Third, almost all enrolled only ambulatory patients.
While there are no studies comparing quinolones and macrolides in patients hospitalized for AECOPD, a meta‐analysis comparing quinolones, macrolides, and amoxicillin‐clavulonate identified 19 trials of ambulatory patients with chronic bronchitis. That study found that all 3 drugs had similar efficacy initially, but that quinolones resulted in the fewest relapses over a 26‐week period.27 Macrolides and quinolones had similar rates of adverse effects. In contrast, we did not find a difference in treatment failure, cost, or length of stay, but did find a higher rate of diarrhea associated with quinolones. Others have also documented an association between fluoroquinolones and C. difficile diarrhea.2830 This trend, first noted in 2001, is of particular concern because the fluoroquinolone‐resistant strains appear to be hypervirulent and have been associated with nosocomial epidemics.3134
Our study has several limitations. First, its observational design leaves open the possibility of selection bias. For this reason we analyzed our data in several ways, including using a grouped treatment approach, an adaptation of the instrumental variable technique, and accepted only those differences which were consistent across all models. Second, our study used claims data, and therefore we could not directly adjust for physiological measures of severity. However, the highly detailed nature of the data allowed us to adjust for numerous tests and treatments that reflected the clinician's assessment of the patient's severity, as well as the number of prior COPD admissions. Third, we cannot exclude the possibility that some patients may have had concurrent pneumonia without an ICD‐9 code. We think that the number would be small because reimbursement for pneumonia is generally higher than for COPD, so hospitals have an incentive to code pneumonia as the principal diagnosis when present. Finally, we compared initial antibiotics only. More than one‐quarter of our patients received an additional antibiotic before discharge. In particular, patients receiving macrolides were often prescribed a concomitant cephalosporin. We do not know to what extent these additional antibiotics may have affected the outcomes.
Despite the large number of patients hospitalized annually for AECOPD, there are no randomized trials comparing different antibiotics in this population. Studies comparing antibiotics in chronic bronchitis can offer little guidance, since they have primarily focused on proving equivalence between existing antibiotics and newer, more expensive formulations.35 Because many of the patients enrolled in such trials do not benefit from antibiotics at all, either because they do not have COPD or because their exacerbation is not caused by bacteria, it is relatively easy to prove equivalence. Given that AECOPD is one of the leading causes of hospitalization in the United States, large, randomized trials comparing the effectiveness of different antibiotics should be a high priority. In the meantime, macrolides (often given together with cephalosporins) and quinolones appear to be equally effective initial antibiotic choices; considering antibiotic‐associated diarrhea, macrolides appear to be the safer of the 2.
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are responsible for more than 600,000 hospitalizations annually, resulting in direct costs of over $20 billion.1 Bacterial infections appear responsible for 50% of such exacerbations,25 and current COPD guidelines recommend treatment with antibiotics for patients with severe exacerbations or a change in sputum.1,69 These recommendations are based on a number of small randomized trials, most of which were conducted more than 20 years ago using narrow spectrum antibiotics that are no longer commonly used.10 Only 4 studies, totaling 321 subjects, included hospitalized patients, and most studies excluded patients who required steroids. Because no clinical trials have compared different antibiotic regimens for AECOPD, existing guidelines offer a range of treatment options, including amoxicillin‐clavulonate, macrolides, quinolones, cephalosporins, aminopenicillins, and tetracyclines.
Among hospitalized patients, macrolides and quinolones appear to be the most frequently prescribed antibiotics.11 Both are available in oral formulations, have excellent bioavailability, and are administered once daily. In addition to their antimicrobial activity, macrolides are believed to have antiinflammatory effects, which could be especially advantageous in AECOPD.1214 In trials of chronic bronchitis, however, fluoroquinolones have been shown to reduce the risk of recurrent exacerbation when compared to macrolides.15 The wide variation that has been observed in antibiotic selection for patients hospitalized for AECOPD suggests a high degree of uncertainty among clinicians about the benefits of different treatment options.11 Given the limited evidence from randomized trials, we sought to evaluate the comparative effectiveness of macrolides and quinolones among a large, representative sample of patients hospitalized with AECOPD.
Subjects and Methods
Setting and Subjects
We conducted a retrospective cohort study of all patients hospitalized between January 1 and December 31, 2001 for AECOPD at any 1 of 375 acute care facilities in the United States that participated in Premier's Perspective, a voluntary, fee‐supported database developed for measuring quality and healthcare utilization. Participating hospitals represent all geographical regions, and are primarily small‐sized to medium‐sized nonteaching hospitals located mostly in urban areas. In addition to the information contained in the standard hospital discharge file (Uniform Billing 92) such as patient age, International Classification of Disease, 9th Edition, Clinical Modification (ICD‐9‐CM) codes, the Perspective database includes a date‐stamped log of all billed items, including diagnostic tests, medications, and other treatments, as well as costs, for individual patients. The study was approved by the Institutional Review Board of Baystate Medical Center.
Patients were included if they had a primary diagnosis consistent with AECOPD (ICD‐9 codes 491.21 and 493.22) or a primary diagnosis of respiratory failure (ICD‐9 codes 518.81 and 518.84) paired with secondary diagnosis of AECOPD; they also had to receive at least 2 consecutive days of either a macrolide or a quinolone, started within 48 hours of hospitalization. Patients receiving both antibiotics were excluded, but patients who received additional antibiotics were included. To enhance the specificity of our diagnosis codes, we limited our study to patients age 40 years.16 Because mechanical ventilation initiated after hospital day 2 was an outcome measure, we excluded patients admitted directly to the intensive care unit. We also excluded: those with other bacterial infections, such as pneumonia or cellulitis, who might have another indication for antibiotics; those with a length of stay <2 days, because we could not ascertain whether they received a full course of antibiotics; patients with a secondary diagnosis of pulmonary embolism or pneumothorax; and those whose attending physicians were not internists, family practitioners, hospitalists, pulmonologists, or intensivists. For patients with more than 1 admission during the study period, we included only the first admission.
Data Elements
For each patient, we assessed age, gender, race, marital and insurance status, principal diagnosis, comorbidities, and specialty of the attending physician. Comorbidities were identified from ICD‐9 secondary diagnosis codes and Diagnosis Related Groups using Healthcare Cost and Utilization Project Comorbidity Software (version 3.1), based on the work of Elixhauser et al.17 In addition, to assess disease severity we recorded the presence of chronic pulmonary heart disease, the number of admissions for COPD during the 12 months prior to the index admission, and arterial blood gas testing.18,19 We also identified pharmacy or diagnostic charges for interventions that were recommended in current guidelines (beta‐adrenergic and anticholinergic bronchodilators, steroids, and noninvasive positive‐pressure ventilation); those that were not recommended or were of uncertain benefit (methylxanthine bronchodilators, spirometry/pulmonary function testing, mucolytic medications, chest physiotherapy, and sputum testing); and drugs that might be associated with severe exacerbations or end‐stage COPD (loop diuretics, morphine, and nutritional supplements).1,69 Hospitals were categorized by region (Northeast, South, Midwest, or West), bed size, setting (urban vs. rural), ownership, and teaching status.
Antibiotic Class and Outcome Variables
Our primary predictor variable was the antibiotic initiated during the first 2 hospital days and continued for at least 2 days, regardless of other antibiotics the patient may have received during the course of hospitalization. Because we anticipated low in‐hospital mortality, our primary outcome was a composite measure of treatment failure, defined as initiation of mechanical ventilation after hospital day 2, in‐hospital mortality, or readmission for COPD within 30 days of discharge.20 Secondary outcomes included hospital costs and length of stay, as well as allergic reactions identified by ICD‐9 code, and antibiotic‐associated diarrhea, defined as treatment with either metronidazole or oral vancomycin begun after hospital day 3.
Statistical Analysis
Summary statistics were computed using frequencies and percents for categorical variables; and means, medians, standard deviations, and interquartile ranges for continuous variables. Associations between antibiotic selection and patient and hospital characteristics were assessed using chi‐square tests for categorical variables and z‐tests for continuous variables.
We developed a series of multivariable models to evaluate the impact of initial antibiotic selection on the risk of treatment failure, length of stay, and total cost. In order to account for the effects of within‐hospital correlation, generalized estimating equation (GEE) models with a logit link were used to assess the effect of antibiotic selection on the risk of treatment failure, and identity link models were used for analyses of length of stay and cost. Unadjusted and covariate‐adjusted models for treatment failure were evaluated with and without adjustments for propensity score. A propensity score is the probability that a given patient would receive treatment with a macrolide, derived from a nonparsimonious model in which treatment with a macrolide was considered the outcome. The propensity model included all patient characteristics, other early treatments and tests, comorbidities, hospital and physician characteristics, and selected interaction terms.21 Length of stay and cost were trimmed at 3 standard deviations above the mean, and natural log‐transformed values were modeled due to extreme positive skew. In addition, we carried out matched analyses in which we compared the outcomes of patients who were treated with a macrolide to those with similar propensity scores (ie, with similar likelihood of receiving a macrolide) who received a quinolone.22
Finally, to reduce the threat of residual confounding by indication, which can occur if sicker patients are more likely to receive a particular antibiotic, we developed a grouped treatment model, in which all patients treated at the same hospital were assigned a probability of treatment with a macrolide equal to the overall treatment rate at that hospital.23 This is an adaptation of instrumental variable analysis, a well‐accepted technique in econometrics with growing use in health care.24,25 It attempts to assess whether patients treated at a hospital at which quinolones are used more frequently have better outcomes than patients treated at hospitals at which macrolides are used more frequently, while adjusting for other patient, physician, and hospital variables. It ignores the actual treatment the patient received, and instead substitutes the hospital's rate of macrolide use. By grouping treatment at the hospital level, this method greatly reduces the possibility of residual selection bias, unless hospitals that use a lot of macrolides have patients who differ in a consistent way from hospitals which use mostly quinolones.
All analyses were performed using SAS version 9.1 (SAS Institute, Inc., Cary, NC).
Results
Of 26,248 AECOPD patients treated with antibiotics, 19,608 patients met the inclusion criteria; of these, 6139 (31%) were treated initially with a macrolide; the median age was 70 years; 60% were female; and 78% were white. A total of 86% of patients had a primary diagnosis of obstructive chronic bronchitis with acute exacerbation, and 6% had respiratory failure. The most common comorbidities were hypertension, diabetes, and congestive heart failure. Twenty‐two percent had been admitted at least once in the preceding 12 months. Treatment failure occurred in 7.7% of patients, and 1.3% died in the hospital. Mean length of stay was 4.8 days. Hospital prescribing rates for macrolides varied from 0% to 100%, with a mean of 33% and an interquartile range of 14% to 46% (Supporting Appendix Figure 1).
Compared to patients receiving macrolides, those receiving quinolones were older, more likely to have respiratory failure, to be cared for by a pulmonologist, and to have an admission in the previous year (Table 1). They were also more likely to be treated with bronchodilators, methylxanthines, steroids, diuretics, and noninvasive positive pressure ventilation, and to have an arterial blood gas, but less likely to receive concomitant treatment with a cephalosporin (11% vs. 57%). With the exception of cephalosporin treatment, these differences were small, but due to the large sample were statistically significant. Comorbidities were similar in both groups. Patients in the quinolone group were also more likely to experience treatment failure (8.1% vs. 6.8%), death (1.5% vs. 1.0%), and antibiotic‐associated diarrhea (1.1% vs. 0.5%).
Complete Cohort | Propensity‐matched Subsample | |||||
---|---|---|---|---|---|---|
Characteristic | Quinolone (n = 13469) | Macrolide (n = 6139) | P Value | Quinolone (n = 5610) | Macrolide (n = 5610) | P Value |
| ||||||
Antibiotics received during hospitalization* [n (%)] | ||||||
Macrolide | 264 (2) | 6139 (100) | 119 (2) | 5610 (100) | ||
Quinolone | 13469 (100) | 459 (8) | 5610 (100) | 424 (8) | ||
Cephalosporin | 1696 (13) | 3579 (59) | <0.001 | 726 (13) | 3305 (59) | <0.001 |
Tetracycline | 231 (2) | 75 (2) | 0.01 | 101 (2) | 73 (2) | 0.06 |
Other antibiotics | 397 (3) | 220 (4) | 0.02 | 166 (3) | 193 (3) | 0.03 |
Age (years) (mean [SD]) | 69.1 (11.4) | 68.2 (11.8) | <0.001 | 68.6 (11.7) | 68.5 (11.7) | 0.58 |
Male sex (n [%]) | 5447 (40) | 2440 (40) | 0.36 | 2207 (39) | 2196 (39) | 0.85 |
Race/ethnic group (n [%]) | <0.001 | 0.44 | ||||
White | 10454 (78) | 4758 (78) | 4359 (78) | 4368 (78) | ||
Black | 1060 (8) | 540 (9) | 470 (8) | 455 (8) | ||
Hispanic | 463 (3) | 144 (2) | 157 (3) | 134 (2) | ||
Other | 1492 (11) | 697 (11) | 624 (11) | 653 (12) | ||
Primary diagnosis (n [%]) | <0.001 | 0.78 | ||||
Obstructive chronic bronchitis with acute exacerbation | 11650 (87) | 5298 (86) | 4884 (87) | 4860 (87) | ||
Chronic obstructive asthma/asthma with COPD | 908 (7) | 569 (9) | 466 (8) | 486 (9) | ||
Respiratory failure | 911 (7) | 272 (4) | 260 (5) | 264 (5) | ||
Admissions in the prior year (n [%]) | <0.001 | 0.84 | ||||
0 | 9846 (73) | 4654 (76) | 4249 (76) | 4231 (75) | ||
1 | 1918 (14) | 816 (13) | 747 (13) | 750 (13) | ||
2+ | 1085 (8) | 445 (7) | 397 (7) | 420 (8) | ||
Missing | 620 (5) | 224 (4) | 217 (4) | 209 (4) | ||
Physician specialty (n [%]) | <0.001 | 0.84 | ||||
Internal medicine/hospitalist | 7069 (53) | 3321 (54) | 3032 (54) | 3072 (55) | ||
Family/general medicine | 3569 (27) | 2074 (34) | 1824 (33) | 1812 (32) | ||
Pulmonologist | 2776 (21) | 727 (12) | 738 (13) | 711 (13) | ||
Critical care/emntensivist | 55 (0) | 17 (0) | 16 (0) | 15 (0) | ||
Tests on hospital day 1 or 2 (n [%]) | ||||||
Arterial blood gas | 8084 (60) | 3377 (55) | <0.001 | 3195 (57) | 3129 (56) | 0.22 |
Sputum test | 1741 (13) | 766 (13) | 0.39 | 20 (0) | 16 (0) | 0.62 |
Medications/therapies on hospital day 1 or 2 (n [%]) | ||||||
Short‐acting bronchodilators | 7555 (56) | 3242 (53) | <0.001 | 2969 (53) | 2820 (50) | 0.005 |
Long‐acting beta‐2 agonists | 2068 (15) | 748 (12) | <0.001 | 704 (13) | 719 (13) | 0.69 |
Methylxanthine bronchodilators | 3051 (23) | 1149 (19) | <0.001 | 1102 (20) | 1093 (20) | 0.85 |
Steroids | 0.04 | 0.68 | ||||
Intravenous | 11148 (83) | 4989 (81) | 4547 (81) | 4581 (82) | ||
Oral | 772 (6) | 376 (6) | 334 (6) | 330 (6) | ||
Severity indicators (n [%]) | ||||||
Chronic pulmonary heart disease | 890 (7) | 401 (7) | 0.85 | 337 (6) | 368 (7) | 0.24 |
Sleep apnea | 586 (4) | 234 (4) | 0.08 | 211 (4) | 218 (4) | 0.77 |
Noninvasive positive pressure ventilation | 391 (3) | 128 (2) | <0.001 | 128 (2) | 114 (2) | 0.40 |
Loop diuretics | 4838 (36) | 1971 (32) | <0.001 | 1884 (34) | 1862 (33) | 0.67 |
Hospital characteristics (n [%]) | ||||||
Staffed beds | <0.001 | 0.71 | ||||
6200 | 3483 (26) | 1688 (28) | 1610 (29) | 1586 (28) | ||
201300 | 3132 (23) | 1198 (20) | 1174 (21) | 1154 (21) | ||
301500 | 4265 (32) | 2047 (33) | 1809 (32) | 1867 (33) | ||
500+ | 2589 (19) | 1206 (20) | 1017 (18) | 1003 (18) | ||
Hospital region (n [%]) | <0.001 | 0.65 | ||||
South | 8562 (64) | 3270 (53) | 3212 (57) | 3160 (56) | ||
Midwest | 2602 (19) | 1444 (24) | 1170 (21) | 1216 (22) | ||
Northeast | 1163 (9) | 871 (14) | 687 (12) | 704 (13) | ||
West | 1142 (9) | 554 (9) | 541 (10) | 530 (9) | ||
Teaching hospital | <0.001 | 0.63 | ||||
No | 12090 (90) | 5037 (82) | 4896 (87) | 4878 (87) | ||
Yes | 1379 (10) | 1102 (18) | 714 (13) | 732 (13) | ||
Comorbidities (n [%]) | ||||||
Congestive heart failure | 2673 (20) | 1147 (19) | 0.06 | 1081 (19) | 1060 (19) | 0.63 |
Metastatic cancer | 134 (1) | 27 (0) | <0.001 | 34 (1) | 38 (1) | 0.72 |
Depression | 1419 (11) | 669 (11) | 0.45 | 598 (11) | 603 (11) | 0.90 |
Deficiency anemias | 1155 (9) | 476 (8) | 0.05 | 426 (8) | 432 (8) | 0.86 |
Solid tumor without metastasis | 1487 (11) | 586 (10) | 0.002 | 550 (10) | 552 (10) | 0.97 |
Hypothyroidism | 1267 (9) | 527 (9) | 0.07 | 481 (9) | 482 (9) | 1.00 |
Peripheral vascular disease | 821 (6) | 312 (5) | 0.005 | 287 (5) | 288 (5) | 1.00 |
Paralysis | 165 (1) | 46 (1) | 0.003 | 49 (1) | 51 (1) | 0.92 |
Obesity | 957 (7) | 435 (7) | 0.98 | 386 (7) | 398 (7) | 0.68 |
Hypertension | 5793 (43) | 2688 (44) | 0.31 | 2474 (44) | 2468 (44) | 0.92 |
Diabetes | 0.04 | 0.45 | ||||
Without chronic complications | 2630 (20) | 1127 (18) | 1057 (19) | 1066 (19) | ||
With chronic complications | 298 (2) | 116 (2) | 115 (2) | 97 (2) |
In the unadjusted analysis, compared to patients receiving quinolones, those treated with macrolides were less likely to experience treatment failure (OR, 0.83; 95% CI, 0.740.94) (Table 2). Adjusting for all patient, hospital, and physician covariates, including the propensity for treatment with macrolides, increased the OR to 0.89 and the results were no longer significant (95% CI, 0.781.01). Propensity matching successfully balanced all measured covariates except for the use of short‐acting bronchodilators and additional antibiotics (Table 1). In the propensity‐matched sample (Figure 1), quinolone‐treated patients were more likely to experience antibiotic‐associated diarrhea (1.2% vs. 0.6%; P = 0.0003) and late mechanical ventilation (1.3% vs. 0.8%; P = 0.02). There were no differences in adjusted cost or length of stay between the 2 groups. The results of the grouped treatment analysis, substituting the hospital's specific rate of macrolide use in place of the actual treatment that each patient received suggested that the 2 antibiotics were associated with similar rates of treatment failure. The OR for a 100% hospital rate of macrolide treatment vs. a 0% rate was 1.01 (95% CI, 0.751.35).

Treatment Failure | Cost | LOS | ||||
---|---|---|---|---|---|---|
Models | OR | 95% CI | Ratio | 95% CI | Ratio | 95% CI |
| ||||||
Unadjusted | 0.83 | 0.730.93 | 0.98 | 0.971.00 | 0.96 | 0.950.98 |
Adjusted for propensity score only* | 0.89 | 0.791.01 | 1.00 | 0.981.01 | 0.98 | 0.971.00 |
Adjusted for covariates | 0.87 | 0.770.99 | 1.00 | 0.991.02 | 0.99 | 0.971.00 |
Adjusted for covariates and propensity score | 0.89 | 0.781.01 | 1.00 | 0.991.02 | 0.98 | 0.971.00 |
Matched sample, unadjusted | 0.87 | 0.751.00 | 0.99 | 0.981.01 | 0.99 | 0.971.01 |
Matched sample, adjusted for unbalanced variables | 0.87 | 0.751.01 | 1.00 | 0.981.02 | 0.99 | 0.971.01 |
Grouped treatment model, unadjusted | 0.90 | 0.681.19 | 0.97 | 0.891.06 | 0.92 | 0.870.96 |
Group treatment model, adjusted for covariates∥ | 1.01 | 0.751.35 | 0.96 | 0.881.05 | 0.96 | 0.911.00 |
Discussion
In this large observational study conducted at 375 hospitals, we took advantage of a natural experiment in which antibiotic prescribing patterns varied widely across hospitals to compare the effectiveness of 2 common antibiotic regimens for AECOPD. Treatment with macrolides and quinolones were associated with a similar risk of treatment failure, costs, and length of stay; however, patients treated with macrolides were less likely to experience late mechanical ventilation or treatment for antibiotic‐associated diarrhea.
Despite broad consensus in COPD guidelines that patients with severe acute exacerbations should receive antibiotics, there is little agreement about the preferred empiric agent. Controversy exists regarding antibiotics' comparative effectiveness, and even over which pathogens cause COPD exacerbations. Given the frequency of hospitalization for AECOPD, understanding the comparative effectiveness of treatments in this setting could have important implications for health outcomes and costs. Unfortunately, most antibiotic studies in AECOPD were conducted >20 years ago, using antibiotics that rarely appeared in our sample.26 Consequently, clinical practice guidelines offer conflicting recommendations. For example, the National Institute for Clinical Excellence recommends empirical treatment with an aminopenicillin, a macrolide, or a tetracycline,8 while the American Thoracic Society recommends amoxicillin‐clavulonate or a fluoroquinolone.9
As might be expected in light of so much uncertainty, we found wide variation in prescribing patterns across hospitals. Overall, approximately one‐third of patients received a macrolide and two‐thirds a quinolone. Both regimens provide adequate coverage of H. influenza, S. pneumoniae, and M. catarrhalis, and conform to at least 1 COPD guideline. Nevertheless, patients receiving macrolides often received a cephalosporin as well; this pattern of treatment suggests that antibiotic selection is likely to have been influenced more by guidelines for the treatment of community‐acquired pneumonia than COPD.
Previous studies comparing antibiotic effectiveness suffer from shortcomings that limit their application to patients hospitalized with AECOPD. First, they enrolled patients with chronic bronchitis, and included patients without obstructive lung disease, and most studies included patients as young as 18 years old. Second, many either did not include treatment with steroids or excluded patients receiving more than 10 mg of prednisone daily. Third, almost all enrolled only ambulatory patients.
While there are no studies comparing quinolones and macrolides in patients hospitalized for AECOPD, a meta‐analysis comparing quinolones, macrolides, and amoxicillin‐clavulonate identified 19 trials of ambulatory patients with chronic bronchitis. That study found that all 3 drugs had similar efficacy initially, but that quinolones resulted in the fewest relapses over a 26‐week period.27 Macrolides and quinolones had similar rates of adverse effects. In contrast, we did not find a difference in treatment failure, cost, or length of stay, but did find a higher rate of diarrhea associated with quinolones. Others have also documented an association between fluoroquinolones and C. difficile diarrhea.2830 This trend, first noted in 2001, is of particular concern because the fluoroquinolone‐resistant strains appear to be hypervirulent and have been associated with nosocomial epidemics.3134
Our study has several limitations. First, its observational design leaves open the possibility of selection bias. For this reason we analyzed our data in several ways, including using a grouped treatment approach, an adaptation of the instrumental variable technique, and accepted only those differences which were consistent across all models. Second, our study used claims data, and therefore we could not directly adjust for physiological measures of severity. However, the highly detailed nature of the data allowed us to adjust for numerous tests and treatments that reflected the clinician's assessment of the patient's severity, as well as the number of prior COPD admissions. Third, we cannot exclude the possibility that some patients may have had concurrent pneumonia without an ICD‐9 code. We think that the number would be small because reimbursement for pneumonia is generally higher than for COPD, so hospitals have an incentive to code pneumonia as the principal diagnosis when present. Finally, we compared initial antibiotics only. More than one‐quarter of our patients received an additional antibiotic before discharge. In particular, patients receiving macrolides were often prescribed a concomitant cephalosporin. We do not know to what extent these additional antibiotics may have affected the outcomes.
Despite the large number of patients hospitalized annually for AECOPD, there are no randomized trials comparing different antibiotics in this population. Studies comparing antibiotics in chronic bronchitis can offer little guidance, since they have primarily focused on proving equivalence between existing antibiotics and newer, more expensive formulations.35 Because many of the patients enrolled in such trials do not benefit from antibiotics at all, either because they do not have COPD or because their exacerbation is not caused by bacteria, it is relatively easy to prove equivalence. Given that AECOPD is one of the leading causes of hospitalization in the United States, large, randomized trials comparing the effectiveness of different antibiotics should be a high priority. In the meantime, macrolides (often given together with cephalosporins) and quinolones appear to be equally effective initial antibiotic choices; considering antibiotic‐associated diarrhea, macrolides appear to be the safer of the 2.
- Evidence base for management of acute exacerbations of chronic obstructive pulmonary disease.Ann Intern Med.2001;134:595–599. , , .
- Infectious etiologies in acute exacerbation of COPD.Diagn Microbiol Infect Dis.2001;40:95–102. , , , et al.
- Bacterial infections in patients requiring admission for an acute exacerbation of COPD; a 1‐year prospective study.Respir Med.2003;97:770–777. , .
- Microbiologic determinants of exacerbation in chronic obstructive pulmonary disease.Arch Intern Med.2005;165:891–897. , , , et al.
- Infection in the pathogenesis and course of chronic obstructive pulmonary disease.N Engl J Med.2008;359:2355–2365. , .
- Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary.Am J Respir Crit Care Med.2007;176:532–555. , , , et al.
- Canadian Thoracic Society recommendations for management of chronic obstructive pulmonary disease—2008 update—highlights for primary care.Can Respir J.2008;15(suppl A):1A–8A. , , , et al.
- Chronic obstructive pulmonary disease.National clinical guideline on management of chronic obstructive pulmonary disease in adults in primary and secondary care.Thorax.2004;59(suppl 1):1–232.
- Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper.Eur Respir J.2004;23:932–946. , , , et al.
- Antibiotics for exacerbations of chronic obstructive pulmonary disease.Cochrane Database Syst Rev.2006:CD004403. , , , , .
- Quality of care for patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease.Ann Intern Med.2006;144:894–903. , , , , , .
- Modulation of neutrophil and inflammation markers in chronic obstructive pulmonary disease by short‐term azithromycin treatment.Eur J Pharmacol.2005;517:132–143. , , , et al.
- Azithromycin modulates neutrophil function and circulating inflammatory mediators in healthy human subjects.Eur J Pharmacol.2002;450:277–289. , , , et al.
- The effect of clarithromycin on inflammatory markers in chronic obstructive pulmonary disease: preliminary data.Ann Pharmacother.2004;38:1400–1405. , , , , , .
- A comparison of gemifloxacin and clarithromycin in acute exacerbations of chronic bronchitis and long‐term clinical outcomes.Clin Ther.2002;24:639–652. , , , .
- In‐hospital mortality following acute exacerbations of chronic obstructive pulmonary disease.Arch Intern Med.2003;163:1180–1186. , , , .
- Comorbidity measures for use with administrative data.Med Care.1998;36:8–27. , , , .
- Outcomes following acute exacerbation of severe chronic obstructive lung disease. The SUPPORT investigators (Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments).Am J Respir Crit Care Med.1996;154:959–967. , , , et al.
- Mortality and mortality‐related factors after hospitalization for acute exacerbation of COPD.Chest.2003;124:459–467. , , .
- Effect of systemic glucocorticoids on exacerbations of chronic obstructive pulmonary disease.N Engl J Med.1999;340:1941–1947. , , , et al.
- Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group.Stat Med.1998;17:2265–2281. .
- Reducing bias in a propensity score matched‐pair sample using greedy matching techniques. Proceedings of the Twenty‐Sixth Annual SAS Users Group International Conference. Cary, NC: SAS Institute;2001,214–216. .
- Modeling treatment effects on binary outcomes with grouped‐treatment variables and individual covariates.Am J Epidemiol.2002;156:753–760. , , , .
- Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. [see Comment].JAMA.1994;272:859–866. , , .
- Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.JAMA.2007;297:278–285. , , , , , .
- Antibiotics in chronic obstructive pulmonary disease exacerbations. A meta‐analysis.JAMA.1995;273:957–960. , , , .
- Macrolides, quinolones and amoxicillin/clavulanate for chronic bronchitis: a meta‐analysis.Eur Respir J.2007;29:1127–1137. , , , , .
- Antimicrobial‐associated risk factors for Clostridium difficile infection.Clin Infect Dis.2008;46(suppl 1):S19–S31. , , , , .
- Short‐term and long‐term outcomes of moxifloxacin compared to standard antibiotic treatment in acute exacerbations of chronic bronchitis.Chest.2004;125:953–964. , , , et al.
- Oral gemifloxacin once daily for 5 days compared with sequential therapy with i.v. ceftriaxone/oral cefuroxime (maximum of 10 days) in the treatment of hospitalized patients with acute exacerbations of chronic bronchitis.Respir Med.2003;97:242–249. , , , , .
- A large outbreak of Clostridium difficile‐associated disease with an unexpected proportion of deaths and colectomies at a teaching hospital following increased fluoroquinolone use.Infect Control Hosp Epidemiol.2005;26:273–280. , , , et al.
- An epidemic, toxin gene‐variant strain of Clostridium difficile.N Engl J Med.2005;353:2433–2441. , , , et al.
- Outbreak of Clostridium difficile infection in a long‐term care facility: association with gatifloxacin use.Clin Infect Dis.2004;38:640–645. , , , et al.
- A predominantly clonal multi‐institutional outbreak of Clostridium difficile‐associated diarrhea with high morbidity and mortality.N Engl J Med.2005;353:2442–2449. , , , et al.
- No more equivalence trials for antibiotics in exacerbations of COPD, please.Chest.2004;125:811–813. , .
- Evidence base for management of acute exacerbations of chronic obstructive pulmonary disease.Ann Intern Med.2001;134:595–599. , , .
- Infectious etiologies in acute exacerbation of COPD.Diagn Microbiol Infect Dis.2001;40:95–102. , , , et al.
- Bacterial infections in patients requiring admission for an acute exacerbation of COPD; a 1‐year prospective study.Respir Med.2003;97:770–777. , .
- Microbiologic determinants of exacerbation in chronic obstructive pulmonary disease.Arch Intern Med.2005;165:891–897. , , , et al.
- Infection in the pathogenesis and course of chronic obstructive pulmonary disease.N Engl J Med.2008;359:2355–2365. , .
- Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary.Am J Respir Crit Care Med.2007;176:532–555. , , , et al.
- Canadian Thoracic Society recommendations for management of chronic obstructive pulmonary disease—2008 update—highlights for primary care.Can Respir J.2008;15(suppl A):1A–8A. , , , et al.
- Chronic obstructive pulmonary disease.National clinical guideline on management of chronic obstructive pulmonary disease in adults in primary and secondary care.Thorax.2004;59(suppl 1):1–232.
- Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper.Eur Respir J.2004;23:932–946. , , , et al.
- Antibiotics for exacerbations of chronic obstructive pulmonary disease.Cochrane Database Syst Rev.2006:CD004403. , , , , .
- Quality of care for patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease.Ann Intern Med.2006;144:894–903. , , , , , .
- Modulation of neutrophil and inflammation markers in chronic obstructive pulmonary disease by short‐term azithromycin treatment.Eur J Pharmacol.2005;517:132–143. , , , et al.
- Azithromycin modulates neutrophil function and circulating inflammatory mediators in healthy human subjects.Eur J Pharmacol.2002;450:277–289. , , , et al.
- The effect of clarithromycin on inflammatory markers in chronic obstructive pulmonary disease: preliminary data.Ann Pharmacother.2004;38:1400–1405. , , , , , .
- A comparison of gemifloxacin and clarithromycin in acute exacerbations of chronic bronchitis and long‐term clinical outcomes.Clin Ther.2002;24:639–652. , , , .
- In‐hospital mortality following acute exacerbations of chronic obstructive pulmonary disease.Arch Intern Med.2003;163:1180–1186. , , , .
- Comorbidity measures for use with administrative data.Med Care.1998;36:8–27. , , , .
- Outcomes following acute exacerbation of severe chronic obstructive lung disease. The SUPPORT investigators (Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments).Am J Respir Crit Care Med.1996;154:959–967. , , , et al.
- Mortality and mortality‐related factors after hospitalization for acute exacerbation of COPD.Chest.2003;124:459–467. , , .
- Effect of systemic glucocorticoids on exacerbations of chronic obstructive pulmonary disease.N Engl J Med.1999;340:1941–1947. , , , et al.
- Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group.Stat Med.1998;17:2265–2281. .
- Reducing bias in a propensity score matched‐pair sample using greedy matching techniques. Proceedings of the Twenty‐Sixth Annual SAS Users Group International Conference. Cary, NC: SAS Institute;2001,214–216. .
- Modeling treatment effects on binary outcomes with grouped‐treatment variables and individual covariates.Am J Epidemiol.2002;156:753–760. , , , .
- Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. [see Comment].JAMA.1994;272:859–866. , , .
- Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.JAMA.2007;297:278–285. , , , , , .
- Antibiotics in chronic obstructive pulmonary disease exacerbations. A meta‐analysis.JAMA.1995;273:957–960. , , , .
- Macrolides, quinolones and amoxicillin/clavulanate for chronic bronchitis: a meta‐analysis.Eur Respir J.2007;29:1127–1137. , , , , .
- Antimicrobial‐associated risk factors for Clostridium difficile infection.Clin Infect Dis.2008;46(suppl 1):S19–S31. , , , , .
- Short‐term and long‐term outcomes of moxifloxacin compared to standard antibiotic treatment in acute exacerbations of chronic bronchitis.Chest.2004;125:953–964. , , , et al.
- Oral gemifloxacin once daily for 5 days compared with sequential therapy with i.v. ceftriaxone/oral cefuroxime (maximum of 10 days) in the treatment of hospitalized patients with acute exacerbations of chronic bronchitis.Respir Med.2003;97:242–249. , , , , .
- A large outbreak of Clostridium difficile‐associated disease with an unexpected proportion of deaths and colectomies at a teaching hospital following increased fluoroquinolone use.Infect Control Hosp Epidemiol.2005;26:273–280. , , , et al.
- An epidemic, toxin gene‐variant strain of Clostridium difficile.N Engl J Med.2005;353:2433–2441. , , , et al.
- Outbreak of Clostridium difficile infection in a long‐term care facility: association with gatifloxacin use.Clin Infect Dis.2004;38:640–645. , , , et al.
- A predominantly clonal multi‐institutional outbreak of Clostridium difficile‐associated diarrhea with high morbidity and mortality.N Engl J Med.2005;353:2442–2449. , , , et al.
- No more equivalence trials for antibiotics in exacerbations of COPD, please.Chest.2004;125:811–813. , .
Copyright © 2010 Society of Hospital Medicine
Evaluation of Hemostasis
A previously healthy 25‐year‐old Guatemalan man presented to the emergency department with 1 day of fever, nausea, vomiting, and right lower quadrant abdominal pain. A computed tomography (CT) scan revealed acute appendicitis. The patient underwent an uncomplicated laparoscopic appendectomy and was discharged in stable condition after 48 hours.
Five days after the operation he returned to the emergency department with abdominal pain, nausea, vomiting, and lightheadedness. He was tachycardic, and his hemoglobin was 9.5 g/dL (normal, 13.3‐17.7 g/dL), decreased from 14.4 g/dL prior to his appendectomy. A CT scan showed intraperitoneal blood with active extravasation of contrast at the site of the appendectomy.
Additional laboratory testing revealed an activated partial thromboplastin time (aPTT) of 52 seconds (normal, <37 seconds) and protime (also prothrombin time [PT]) of 14 seconds (normal, <14.1 seconds). The platelet count was 449,000/L (normal, 150‐400,000/L) and the fibrinogen level was 337 mg/dL (normal, 170‐440 mg/dL). Crystalloid and packed red blood cells were administered. Since further laboratory evaluation of the prolonged aPTT was not immediately available, the patient was empirically treated with fresh frozen plasma (FFP), cryoprecipitate, and Factor VIII/von Willebrand factor concentrate. At laparotomy, bleeding was observed at the previous operative site, and 2 L of intraperitoneal blood was evacuated.
The next morning, Factor VIII and Factor IX (FIX) activities and the ristocetin cofactor study performed on specimens obtained immediately prior to the second operation were normal, but the FIX activity was 5% of normal. The diagnosis of FXI deficiency was made and 2 to 3 units of FFP (the amount necessary to maintain the patient's measured FXI activity near 20% of normal) were transfused daily. Nine days of FFP infusions were required to achieve complete wound hemostasis. The patient had no further bleeding episodes after discharge.
Upon further interviewing, the patient revealed that 2 months prior he sustained a small laceration on his arm that bled for a long time and that his brother had experienced prolonged bleeding after a dental extraction.
Commentary
Routine performance of preprocedural laboratory testing, and complete reliance on the results as a means of excluding a propensity to bleeding, may not only lead to excessive testing and delayed procedures, but also provides false reassurance because normal routine laboratory studies cannot be used to exclude some bleeding disorders (Table 1).
Von Willebrand disease |
Mild hemophilia A (Factor VIII deficiency) |
Mild hemophilia B (Factor IX deficiency) |
Mild hemophilia C (Factor XI deficiency) |
Qualitative platelet disorders (congenital or acquired) |
Factor XIII deficiency |
Disorders of fibrinolysis (eg, antiplasmin deficiency, plasminogen activator inhibitor type 1 deficiency) |
Disorders of the vasculature or integument (hereditary hemorrhagic telangiectasia, Ehlers‐Danlos syndrome) |
Most studies evaluating routine laboratory testing of hemostatic variables prior to invasive procedures come from patients undergoing elective general surgery. A 1988 study concluded that there is no benefit in the routine preoperative use of the PT, aPTT, platelet count, and bleeding time in the absence of clinical evidence of a hemostatic defect, as assessed by a patient questionnaire and a thorough physical examination.1 A subsequent European, prospective, multicenter study confirmed that abnormalities of preoperative laboratory screening in the absence of a history of bleeding or clinical abnormality were not associated with worse surgical morbidity or mortality, compared to patients with normal screening laboratory studies.2 A recent systematic review has also confirmed the poor positive predictive value of screening tests when used in isolation, and recommended a history‐based and physical exam‐based approach.3 Questionnaires have been shown to be particularly important tools for eliciting clinically significant bleeding disorders that may require revision of the surgical plan.1,4
FXI is a serine protease whose activity is crucial for robust fibrin clot formation and inhibition of fibrinolysis at sites of vascular injury.5 FXI deficiency is an autosomal recessive disorder with an incidence of 1 per 1,000,000 in the general population, with a significantly higher incidence in the Ashkenazi Jewish population. While the risk of spontaneous hemorrhage is typically low, life‐threatening bleeding may occur after surgery or trauma. The severity of the measured FXI level deficiency does not always correlate with risk of bleeding. Periprocedural prophylaxis and treatment of bleeding aim to replace FXI to the low‐normal range by administering FXI concentrate, (not available in the United States) or FFP. Antifibrinolytic agents such as tranexamic acid or ϵ‐aminocaproic acid may be used adjunctively in cases of mucosal bleeding.5
In this case, preoperative screening, either using a questionnaire or careful history‐taking, would have identified the patient's personal and family history of bleeding and prompted appropriate preoperative coagulation testing, which could have exposed the hemostatic defect, allowing for modification of the perioperative medical plan.
In summary, preoperative bleeding evaluations should be performed routinely and should begin with a careful history (use of a questionnaire may be considered) and physical examination. Excessive bleeding after prior surgery, trauma, dental extractions, parturition, or circumcision; bleeding tendency in family members; current use of medications that may increase bleeding risk (such as anticoagulants or aspirin); and physical signs associated with bleeding should be assessed. If clinical details fail to expose a potential bleeding disorder, it is safe and cost‐effective1 to proceed with surgery without performing additional laboratory testing. In contrast, any abnormality on the clinical assessment should trigger preoperative laboratory analysis of basic hemostatic parameters, which may prompt further testing or hematology consultation.
- A prospective evaluation of the efficacy of preoperative coagulation testing.Ann Surg.1988;208(5):554–557. , , .
- A prospective multicenter evaluation of preoperative hemostatic screening tests. The French Associations for Surgical Research.Am J Surg.1995;170(1):19–23. , , , , .
- Guidelines on the assessment of bleeding risk prior to surgery or invasive procedures.Br J Haematol.2008;140:496–504. , , , .
- A practical concept for preoperative identification of patients with impaired primary hemostasis.Clin Appl Thrombosis Haemost.2004;10(3):195–204. , , , et al.
- Factor XI deficiency.Haemophilia.2008;14(6):1183–1189. , .
A previously healthy 25‐year‐old Guatemalan man presented to the emergency department with 1 day of fever, nausea, vomiting, and right lower quadrant abdominal pain. A computed tomography (CT) scan revealed acute appendicitis. The patient underwent an uncomplicated laparoscopic appendectomy and was discharged in stable condition after 48 hours.
Five days after the operation he returned to the emergency department with abdominal pain, nausea, vomiting, and lightheadedness. He was tachycardic, and his hemoglobin was 9.5 g/dL (normal, 13.3‐17.7 g/dL), decreased from 14.4 g/dL prior to his appendectomy. A CT scan showed intraperitoneal blood with active extravasation of contrast at the site of the appendectomy.
Additional laboratory testing revealed an activated partial thromboplastin time (aPTT) of 52 seconds (normal, <37 seconds) and protime (also prothrombin time [PT]) of 14 seconds (normal, <14.1 seconds). The platelet count was 449,000/L (normal, 150‐400,000/L) and the fibrinogen level was 337 mg/dL (normal, 170‐440 mg/dL). Crystalloid and packed red blood cells were administered. Since further laboratory evaluation of the prolonged aPTT was not immediately available, the patient was empirically treated with fresh frozen plasma (FFP), cryoprecipitate, and Factor VIII/von Willebrand factor concentrate. At laparotomy, bleeding was observed at the previous operative site, and 2 L of intraperitoneal blood was evacuated.
The next morning, Factor VIII and Factor IX (FIX) activities and the ristocetin cofactor study performed on specimens obtained immediately prior to the second operation were normal, but the FIX activity was 5% of normal. The diagnosis of FXI deficiency was made and 2 to 3 units of FFP (the amount necessary to maintain the patient's measured FXI activity near 20% of normal) were transfused daily. Nine days of FFP infusions were required to achieve complete wound hemostasis. The patient had no further bleeding episodes after discharge.
Upon further interviewing, the patient revealed that 2 months prior he sustained a small laceration on his arm that bled for a long time and that his brother had experienced prolonged bleeding after a dental extraction.
Commentary
Routine performance of preprocedural laboratory testing, and complete reliance on the results as a means of excluding a propensity to bleeding, may not only lead to excessive testing and delayed procedures, but also provides false reassurance because normal routine laboratory studies cannot be used to exclude some bleeding disorders (Table 1).
Von Willebrand disease |
Mild hemophilia A (Factor VIII deficiency) |
Mild hemophilia B (Factor IX deficiency) |
Mild hemophilia C (Factor XI deficiency) |
Qualitative platelet disorders (congenital or acquired) |
Factor XIII deficiency |
Disorders of fibrinolysis (eg, antiplasmin deficiency, plasminogen activator inhibitor type 1 deficiency) |
Disorders of the vasculature or integument (hereditary hemorrhagic telangiectasia, Ehlers‐Danlos syndrome) |
Most studies evaluating routine laboratory testing of hemostatic variables prior to invasive procedures come from patients undergoing elective general surgery. A 1988 study concluded that there is no benefit in the routine preoperative use of the PT, aPTT, platelet count, and bleeding time in the absence of clinical evidence of a hemostatic defect, as assessed by a patient questionnaire and a thorough physical examination.1 A subsequent European, prospective, multicenter study confirmed that abnormalities of preoperative laboratory screening in the absence of a history of bleeding or clinical abnormality were not associated with worse surgical morbidity or mortality, compared to patients with normal screening laboratory studies.2 A recent systematic review has also confirmed the poor positive predictive value of screening tests when used in isolation, and recommended a history‐based and physical exam‐based approach.3 Questionnaires have been shown to be particularly important tools for eliciting clinically significant bleeding disorders that may require revision of the surgical plan.1,4
FXI is a serine protease whose activity is crucial for robust fibrin clot formation and inhibition of fibrinolysis at sites of vascular injury.5 FXI deficiency is an autosomal recessive disorder with an incidence of 1 per 1,000,000 in the general population, with a significantly higher incidence in the Ashkenazi Jewish population. While the risk of spontaneous hemorrhage is typically low, life‐threatening bleeding may occur after surgery or trauma. The severity of the measured FXI level deficiency does not always correlate with risk of bleeding. Periprocedural prophylaxis and treatment of bleeding aim to replace FXI to the low‐normal range by administering FXI concentrate, (not available in the United States) or FFP. Antifibrinolytic agents such as tranexamic acid or ϵ‐aminocaproic acid may be used adjunctively in cases of mucosal bleeding.5
In this case, preoperative screening, either using a questionnaire or careful history‐taking, would have identified the patient's personal and family history of bleeding and prompted appropriate preoperative coagulation testing, which could have exposed the hemostatic defect, allowing for modification of the perioperative medical plan.
In summary, preoperative bleeding evaluations should be performed routinely and should begin with a careful history (use of a questionnaire may be considered) and physical examination. Excessive bleeding after prior surgery, trauma, dental extractions, parturition, or circumcision; bleeding tendency in family members; current use of medications that may increase bleeding risk (such as anticoagulants or aspirin); and physical signs associated with bleeding should be assessed. If clinical details fail to expose a potential bleeding disorder, it is safe and cost‐effective1 to proceed with surgery without performing additional laboratory testing. In contrast, any abnormality on the clinical assessment should trigger preoperative laboratory analysis of basic hemostatic parameters, which may prompt further testing or hematology consultation.
A previously healthy 25‐year‐old Guatemalan man presented to the emergency department with 1 day of fever, nausea, vomiting, and right lower quadrant abdominal pain. A computed tomography (CT) scan revealed acute appendicitis. The patient underwent an uncomplicated laparoscopic appendectomy and was discharged in stable condition after 48 hours.
Five days after the operation he returned to the emergency department with abdominal pain, nausea, vomiting, and lightheadedness. He was tachycardic, and his hemoglobin was 9.5 g/dL (normal, 13.3‐17.7 g/dL), decreased from 14.4 g/dL prior to his appendectomy. A CT scan showed intraperitoneal blood with active extravasation of contrast at the site of the appendectomy.
Additional laboratory testing revealed an activated partial thromboplastin time (aPTT) of 52 seconds (normal, <37 seconds) and protime (also prothrombin time [PT]) of 14 seconds (normal, <14.1 seconds). The platelet count was 449,000/L (normal, 150‐400,000/L) and the fibrinogen level was 337 mg/dL (normal, 170‐440 mg/dL). Crystalloid and packed red blood cells were administered. Since further laboratory evaluation of the prolonged aPTT was not immediately available, the patient was empirically treated with fresh frozen plasma (FFP), cryoprecipitate, and Factor VIII/von Willebrand factor concentrate. At laparotomy, bleeding was observed at the previous operative site, and 2 L of intraperitoneal blood was evacuated.
The next morning, Factor VIII and Factor IX (FIX) activities and the ristocetin cofactor study performed on specimens obtained immediately prior to the second operation were normal, but the FIX activity was 5% of normal. The diagnosis of FXI deficiency was made and 2 to 3 units of FFP (the amount necessary to maintain the patient's measured FXI activity near 20% of normal) were transfused daily. Nine days of FFP infusions were required to achieve complete wound hemostasis. The patient had no further bleeding episodes after discharge.
Upon further interviewing, the patient revealed that 2 months prior he sustained a small laceration on his arm that bled for a long time and that his brother had experienced prolonged bleeding after a dental extraction.
Commentary
Routine performance of preprocedural laboratory testing, and complete reliance on the results as a means of excluding a propensity to bleeding, may not only lead to excessive testing and delayed procedures, but also provides false reassurance because normal routine laboratory studies cannot be used to exclude some bleeding disorders (Table 1).
Von Willebrand disease |
Mild hemophilia A (Factor VIII deficiency) |
Mild hemophilia B (Factor IX deficiency) |
Mild hemophilia C (Factor XI deficiency) |
Qualitative platelet disorders (congenital or acquired) |
Factor XIII deficiency |
Disorders of fibrinolysis (eg, antiplasmin deficiency, plasminogen activator inhibitor type 1 deficiency) |
Disorders of the vasculature or integument (hereditary hemorrhagic telangiectasia, Ehlers‐Danlos syndrome) |
Most studies evaluating routine laboratory testing of hemostatic variables prior to invasive procedures come from patients undergoing elective general surgery. A 1988 study concluded that there is no benefit in the routine preoperative use of the PT, aPTT, platelet count, and bleeding time in the absence of clinical evidence of a hemostatic defect, as assessed by a patient questionnaire and a thorough physical examination.1 A subsequent European, prospective, multicenter study confirmed that abnormalities of preoperative laboratory screening in the absence of a history of bleeding or clinical abnormality were not associated with worse surgical morbidity or mortality, compared to patients with normal screening laboratory studies.2 A recent systematic review has also confirmed the poor positive predictive value of screening tests when used in isolation, and recommended a history‐based and physical exam‐based approach.3 Questionnaires have been shown to be particularly important tools for eliciting clinically significant bleeding disorders that may require revision of the surgical plan.1,4
FXI is a serine protease whose activity is crucial for robust fibrin clot formation and inhibition of fibrinolysis at sites of vascular injury.5 FXI deficiency is an autosomal recessive disorder with an incidence of 1 per 1,000,000 in the general population, with a significantly higher incidence in the Ashkenazi Jewish population. While the risk of spontaneous hemorrhage is typically low, life‐threatening bleeding may occur after surgery or trauma. The severity of the measured FXI level deficiency does not always correlate with risk of bleeding. Periprocedural prophylaxis and treatment of bleeding aim to replace FXI to the low‐normal range by administering FXI concentrate, (not available in the United States) or FFP. Antifibrinolytic agents such as tranexamic acid or ϵ‐aminocaproic acid may be used adjunctively in cases of mucosal bleeding.5
In this case, preoperative screening, either using a questionnaire or careful history‐taking, would have identified the patient's personal and family history of bleeding and prompted appropriate preoperative coagulation testing, which could have exposed the hemostatic defect, allowing for modification of the perioperative medical plan.
In summary, preoperative bleeding evaluations should be performed routinely and should begin with a careful history (use of a questionnaire may be considered) and physical examination. Excessive bleeding after prior surgery, trauma, dental extractions, parturition, or circumcision; bleeding tendency in family members; current use of medications that may increase bleeding risk (such as anticoagulants or aspirin); and physical signs associated with bleeding should be assessed. If clinical details fail to expose a potential bleeding disorder, it is safe and cost‐effective1 to proceed with surgery without performing additional laboratory testing. In contrast, any abnormality on the clinical assessment should trigger preoperative laboratory analysis of basic hemostatic parameters, which may prompt further testing or hematology consultation.
- A prospective evaluation of the efficacy of preoperative coagulation testing.Ann Surg.1988;208(5):554–557. , , .
- A prospective multicenter evaluation of preoperative hemostatic screening tests. The French Associations for Surgical Research.Am J Surg.1995;170(1):19–23. , , , , .
- Guidelines on the assessment of bleeding risk prior to surgery or invasive procedures.Br J Haematol.2008;140:496–504. , , , .
- A practical concept for preoperative identification of patients with impaired primary hemostasis.Clin Appl Thrombosis Haemost.2004;10(3):195–204. , , , et al.
- Factor XI deficiency.Haemophilia.2008;14(6):1183–1189. , .
- A prospective evaluation of the efficacy of preoperative coagulation testing.Ann Surg.1988;208(5):554–557. , , .
- A prospective multicenter evaluation of preoperative hemostatic screening tests. The French Associations for Surgical Research.Am J Surg.1995;170(1):19–23. , , , , .
- Guidelines on the assessment of bleeding risk prior to surgery or invasive procedures.Br J Haematol.2008;140:496–504. , , , .
- A practical concept for preoperative identification of patients with impaired primary hemostasis.Clin Appl Thrombosis Haemost.2004;10(3):195–204. , , , et al.
- Factor XI deficiency.Haemophilia.2008;14(6):1183–1189. , .
Spontaneous Retroperitoneal Hematoma
A 56‐year‐old male presented to the emergency department with a 2‐week history of increasing abdominal girth, nausea, vomiting, and lower extremity edema. His girlfriend had also noted a yellow tinge to his skin and eyes. His past medical history was significant for bipolar disorder, alcohol‐related seizures, and pneumonia. He had no allergies and denied medications prior to admission. Family history was negative for liver disease and social history was notable for ongoing tobacco use and alcohol dependence. He was afebrile with stable vital signs. Physical examination demonstrated an alert gentleman whose answers to questions required occasional factual correction by his partner. His abdomen was distended and nontender with prominent vasculature and shifting dullness. Lower extremity edema was symmetric and bilateral, rated as 2+. Scattered spider angiomata and a fine bilateral hand tremor without asterixis were also noted. Initial laboratory data demonstrated a white blood cell count of 13,900/L, hematocrit 37%, and platelet count 176,000/L. His sodium was 130 mg/dL, blood urea nitrogen (BUN) 1 mg/dL, and creatinine 0.7 mg/dL. International normalized ratio (INR) was 1.8, aspartate aminotransferase (AST) was 117 U/L, alanine aminotransferase (ALT) 33 U/L, alkaline phosphatase 191 U/L, total bilirubin 9.2 mg/dL, total protein 7.0 g/dL, and albumin 1.9 g/dL. Abdominal ultrasound revealed a diffusely hyperechoic liver with a large amount of ascites.
The patient was admitted with the diagnoses of presumed alcoholic hepatitis and end‐stage liver disease. Model for End‐Stage Liver Disease (MELD) score was 21 and discriminant function 16.8. Paracentesis demonstrated a serum‐ascites albumin gradient of >1.1 and no evidence of spontaneous bacterial peritonitis. Diuresis was initiated. He was placed on unfractionated heparin at a dose of 5000 units every 8 hours for deep venous thrombosis (DVT) prophylaxis. By hospital day 3, the patient's laboratory values had improved, yet his stay was prolonged by alcohol withdrawal requiring benzodiazepines, altered mental status presumed secondary to hepatic encephalopathy, acute renal failure, aspiration pneumonia, and persistent unexplained leukocytosis. He required medical restraints during this time given confusion and propensity to ambulate without assistance, yet sustained no falls or other known trauma in care delivered during this time.
Between days 14 and 17, the patient's hematocrit fell from 36% to 30%; vital signs remained stable. He underwent an uncomplicated, ultrasound‐guided therapeutic paracentesis, which yielded 1.4 L of straw‐colored fluid on the afternoon of day 17; the procedure was attempted only on the right side. On the morning of day 18, the patient's blood pressure dropped to 78/55 mmHg with a pulse of 123 beats per minute; he became pale and unresponsive. Physical examination was notable for somnolence and a tender, warm left flank mass, contralateral to his paracentesis site. No flank or periumbilical ecchymoses were identified. Complete blood count demonstrated a white blood count (WBC) of 22,970/L, hematocrit 16%, and platelet count 104,000/L. INR was 2.0, unchanged from the last check on day 10. Partial thromboplastin time was 41 seconds and fibrinogen was 293 mg/dL (normal 150‐400 mg/dL). Peripheral blood smear was negative for red cell fragments. Blood chemistries revealed a sodium of 134 mg/dL, bicarbonate 20 mEq/L, anion gap 7, BUN 24 mg/dL, and creatinine 1.6 mg/dL (up from 1.0 mg/dL the previous day). His venous lactate level was 4.6 mmol/L and arterial blood gas sampling on room air demonstrated a pH of 7.35, partial pressure of carbon dioxide (pCO2) 29 mmHg, partial pressure of oxygen (pCO2) 54 mmHg, and bicarbonate 15 mEq/L. A femoral introducer was placed for volume resuscitation and the patient was urgently transfused with packed red blood cells (PRBCs) and fresh‐frozen plasma (FFP) to correct his coagulopathy. Computed tomography of the abdomen revealed a large left retroperitoneal hematoma measuring 15 15 22 cm3 (Figure 1). Despite transfusion, his hematocrit continued to fall. Urgent angiography was performed, upon which he was found to have active bleeding from the left L3‐L5 lumbar arteries. These were successfully embolized. He required PRBCs and FFP transfusion only once following this procedure. Given a transient decrease in his urine output, his bladder pressures were followed closely for evidence of abdominal compartment syndrome, which did not develop. He was transferred from the intensive care unit (ICU) to the floor on day 20, where his physical exam and hematocrit remained stable and his delirium slowly cleared. He was ultimately discharged to a skilled nursing facility on day 33.

Discussion
Spontaneous retroperitoneal hematoma is a well‐recognized entity that may present with the classic triad of abdominal or groin pain, palpable abdominal or flank mass, and shock or lower extremity motor or sensory changes due to femoral nerve compression.1 Although classically described as skin findings associated with retroperitoneal hemorrhage, Cullen and Grey‐Turner signs are relatively late findings that may not develop in all patients.
Retroperitoneal hemorrhage is well‐recognized as a result of iatrogenic anticoagulation,1 but has been reported less commonly as a result of coagulopathy related to cirrhosis. Di Bisceglie and Richart2 describe 2 patients with MELD scores of 29 and 25, respectively, who developed spontaneous retroperitoneal and rectus muscle hemorrhage. Both had evidence of associated disseminated intravascular coagulation (DIC) and died. Even less common is spontaneous lumbar artery rupture, occurring rarely in the absence of trauma or instrumentation. One reported bleed developed in the context of systemic anticoagulation for a mechanical valve.3 Halak et al.4 relate a case in which the only known risk factor was chronic renal disease. Hama et al.5 describe a patient with a history of alcoholic liver cirrhosis and INR of 2.3 whose lumbar artery rupture was successfully managed with transcatheter arterial embolization.
It is difficult to ascertain the effect of prophylactic anticoagulation in development of this particular hemorrhage. Retroperitoneal bleeding is a very rare complication of pharmacologic prophylaxis for DVT reported with both low‐molecular weight and unfractionated heparins.6 There may be additive risk of prophylaxis in a cirrhotic patient with baseline elevated INR and thrombocytopenia, particularly in the context of renal failure.
Options in the management of spontaneous retroperitoneal hematoma include transarterial embolization, percutaneous decompression, and open surgery. Nonoperative management of these bleeds when possible may be preferable in cirrhotic patients, as their baseline liver disease renders them higher‐risk candidates for surgery. There are no randomized trials comparing these approaches.1
In summary, we report the case of a 56‐year‐old man with end‐stage liver disease and associated coagulopathy without evidence of DIC who survived to discharge with intravascular management of a spontaneous retroperitoneal hemorrhage from the lumbar arteries. To our knowledge, this is the second reported case of spontaneous retroperitoneal hemorrhage in a cirrhotic in which the lumbar arteries were implicated and the first in which multiple arteries were found to be bleeding simultaneously. Any hospitalized patient who develops abdominal pain, flank pain, or hemodynamic instability in the context of coagulopathy, regardless of cause, warrants evaluation for retroperitoneal bleed. Appropriate early management includes immediate resuscitation, intensive monitoring, urgent imaging, and surgical and interventional radiology consultation in order to prevent a fatal outcome.
- Management of spontaneous and iatrogenic retroperitoneal haemorrhage: conservative management, endovascular intervention or open surgery?Int J Clin Pract.2007;62:1604–1613. , , , .
- Spontaneous retroperitoneal and rectus muscle hemorrhage as a potentially lethal complication of cirrhosis.Liver Int.2006;26:1291–1293. , .
- Spontaneous rupture of a lumbar artery. A rare etiology of retroperitoneal hematoma.Urologe A.2003;42:840–844. , , .
- Spontaneous ruptured lumbar artery in a chronic renal failure patient.Eur J Vasc Endovasc Surg.2001;21:569–571. , , , , .
- Spontaneous rupture of the lumbar artery.Intern Med.2004;43:759. , , .
- The rate of bleeding complications after pharmacologic deep venous thrombosis prophylaxis.Arch Surg.2006;141:790–799. , , .
A 56‐year‐old male presented to the emergency department with a 2‐week history of increasing abdominal girth, nausea, vomiting, and lower extremity edema. His girlfriend had also noted a yellow tinge to his skin and eyes. His past medical history was significant for bipolar disorder, alcohol‐related seizures, and pneumonia. He had no allergies and denied medications prior to admission. Family history was negative for liver disease and social history was notable for ongoing tobacco use and alcohol dependence. He was afebrile with stable vital signs. Physical examination demonstrated an alert gentleman whose answers to questions required occasional factual correction by his partner. His abdomen was distended and nontender with prominent vasculature and shifting dullness. Lower extremity edema was symmetric and bilateral, rated as 2+. Scattered spider angiomata and a fine bilateral hand tremor without asterixis were also noted. Initial laboratory data demonstrated a white blood cell count of 13,900/L, hematocrit 37%, and platelet count 176,000/L. His sodium was 130 mg/dL, blood urea nitrogen (BUN) 1 mg/dL, and creatinine 0.7 mg/dL. International normalized ratio (INR) was 1.8, aspartate aminotransferase (AST) was 117 U/L, alanine aminotransferase (ALT) 33 U/L, alkaline phosphatase 191 U/L, total bilirubin 9.2 mg/dL, total protein 7.0 g/dL, and albumin 1.9 g/dL. Abdominal ultrasound revealed a diffusely hyperechoic liver with a large amount of ascites.
The patient was admitted with the diagnoses of presumed alcoholic hepatitis and end‐stage liver disease. Model for End‐Stage Liver Disease (MELD) score was 21 and discriminant function 16.8. Paracentesis demonstrated a serum‐ascites albumin gradient of >1.1 and no evidence of spontaneous bacterial peritonitis. Diuresis was initiated. He was placed on unfractionated heparin at a dose of 5000 units every 8 hours for deep venous thrombosis (DVT) prophylaxis. By hospital day 3, the patient's laboratory values had improved, yet his stay was prolonged by alcohol withdrawal requiring benzodiazepines, altered mental status presumed secondary to hepatic encephalopathy, acute renal failure, aspiration pneumonia, and persistent unexplained leukocytosis. He required medical restraints during this time given confusion and propensity to ambulate without assistance, yet sustained no falls or other known trauma in care delivered during this time.
Between days 14 and 17, the patient's hematocrit fell from 36% to 30%; vital signs remained stable. He underwent an uncomplicated, ultrasound‐guided therapeutic paracentesis, which yielded 1.4 L of straw‐colored fluid on the afternoon of day 17; the procedure was attempted only on the right side. On the morning of day 18, the patient's blood pressure dropped to 78/55 mmHg with a pulse of 123 beats per minute; he became pale and unresponsive. Physical examination was notable for somnolence and a tender, warm left flank mass, contralateral to his paracentesis site. No flank or periumbilical ecchymoses were identified. Complete blood count demonstrated a white blood count (WBC) of 22,970/L, hematocrit 16%, and platelet count 104,000/L. INR was 2.0, unchanged from the last check on day 10. Partial thromboplastin time was 41 seconds and fibrinogen was 293 mg/dL (normal 150‐400 mg/dL). Peripheral blood smear was negative for red cell fragments. Blood chemistries revealed a sodium of 134 mg/dL, bicarbonate 20 mEq/L, anion gap 7, BUN 24 mg/dL, and creatinine 1.6 mg/dL (up from 1.0 mg/dL the previous day). His venous lactate level was 4.6 mmol/L and arterial blood gas sampling on room air demonstrated a pH of 7.35, partial pressure of carbon dioxide (pCO2) 29 mmHg, partial pressure of oxygen (pCO2) 54 mmHg, and bicarbonate 15 mEq/L. A femoral introducer was placed for volume resuscitation and the patient was urgently transfused with packed red blood cells (PRBCs) and fresh‐frozen plasma (FFP) to correct his coagulopathy. Computed tomography of the abdomen revealed a large left retroperitoneal hematoma measuring 15 15 22 cm3 (Figure 1). Despite transfusion, his hematocrit continued to fall. Urgent angiography was performed, upon which he was found to have active bleeding from the left L3‐L5 lumbar arteries. These were successfully embolized. He required PRBCs and FFP transfusion only once following this procedure. Given a transient decrease in his urine output, his bladder pressures were followed closely for evidence of abdominal compartment syndrome, which did not develop. He was transferred from the intensive care unit (ICU) to the floor on day 20, where his physical exam and hematocrit remained stable and his delirium slowly cleared. He was ultimately discharged to a skilled nursing facility on day 33.

Discussion
Spontaneous retroperitoneal hematoma is a well‐recognized entity that may present with the classic triad of abdominal or groin pain, palpable abdominal or flank mass, and shock or lower extremity motor or sensory changes due to femoral nerve compression.1 Although classically described as skin findings associated with retroperitoneal hemorrhage, Cullen and Grey‐Turner signs are relatively late findings that may not develop in all patients.
Retroperitoneal hemorrhage is well‐recognized as a result of iatrogenic anticoagulation,1 but has been reported less commonly as a result of coagulopathy related to cirrhosis. Di Bisceglie and Richart2 describe 2 patients with MELD scores of 29 and 25, respectively, who developed spontaneous retroperitoneal and rectus muscle hemorrhage. Both had evidence of associated disseminated intravascular coagulation (DIC) and died. Even less common is spontaneous lumbar artery rupture, occurring rarely in the absence of trauma or instrumentation. One reported bleed developed in the context of systemic anticoagulation for a mechanical valve.3 Halak et al.4 relate a case in which the only known risk factor was chronic renal disease. Hama et al.5 describe a patient with a history of alcoholic liver cirrhosis and INR of 2.3 whose lumbar artery rupture was successfully managed with transcatheter arterial embolization.
It is difficult to ascertain the effect of prophylactic anticoagulation in development of this particular hemorrhage. Retroperitoneal bleeding is a very rare complication of pharmacologic prophylaxis for DVT reported with both low‐molecular weight and unfractionated heparins.6 There may be additive risk of prophylaxis in a cirrhotic patient with baseline elevated INR and thrombocytopenia, particularly in the context of renal failure.
Options in the management of spontaneous retroperitoneal hematoma include transarterial embolization, percutaneous decompression, and open surgery. Nonoperative management of these bleeds when possible may be preferable in cirrhotic patients, as their baseline liver disease renders them higher‐risk candidates for surgery. There are no randomized trials comparing these approaches.1
In summary, we report the case of a 56‐year‐old man with end‐stage liver disease and associated coagulopathy without evidence of DIC who survived to discharge with intravascular management of a spontaneous retroperitoneal hemorrhage from the lumbar arteries. To our knowledge, this is the second reported case of spontaneous retroperitoneal hemorrhage in a cirrhotic in which the lumbar arteries were implicated and the first in which multiple arteries were found to be bleeding simultaneously. Any hospitalized patient who develops abdominal pain, flank pain, or hemodynamic instability in the context of coagulopathy, regardless of cause, warrants evaluation for retroperitoneal bleed. Appropriate early management includes immediate resuscitation, intensive monitoring, urgent imaging, and surgical and interventional radiology consultation in order to prevent a fatal outcome.
A 56‐year‐old male presented to the emergency department with a 2‐week history of increasing abdominal girth, nausea, vomiting, and lower extremity edema. His girlfriend had also noted a yellow tinge to his skin and eyes. His past medical history was significant for bipolar disorder, alcohol‐related seizures, and pneumonia. He had no allergies and denied medications prior to admission. Family history was negative for liver disease and social history was notable for ongoing tobacco use and alcohol dependence. He was afebrile with stable vital signs. Physical examination demonstrated an alert gentleman whose answers to questions required occasional factual correction by his partner. His abdomen was distended and nontender with prominent vasculature and shifting dullness. Lower extremity edema was symmetric and bilateral, rated as 2+. Scattered spider angiomata and a fine bilateral hand tremor without asterixis were also noted. Initial laboratory data demonstrated a white blood cell count of 13,900/L, hematocrit 37%, and platelet count 176,000/L. His sodium was 130 mg/dL, blood urea nitrogen (BUN) 1 mg/dL, and creatinine 0.7 mg/dL. International normalized ratio (INR) was 1.8, aspartate aminotransferase (AST) was 117 U/L, alanine aminotransferase (ALT) 33 U/L, alkaline phosphatase 191 U/L, total bilirubin 9.2 mg/dL, total protein 7.0 g/dL, and albumin 1.9 g/dL. Abdominal ultrasound revealed a diffusely hyperechoic liver with a large amount of ascites.
The patient was admitted with the diagnoses of presumed alcoholic hepatitis and end‐stage liver disease. Model for End‐Stage Liver Disease (MELD) score was 21 and discriminant function 16.8. Paracentesis demonstrated a serum‐ascites albumin gradient of >1.1 and no evidence of spontaneous bacterial peritonitis. Diuresis was initiated. He was placed on unfractionated heparin at a dose of 5000 units every 8 hours for deep venous thrombosis (DVT) prophylaxis. By hospital day 3, the patient's laboratory values had improved, yet his stay was prolonged by alcohol withdrawal requiring benzodiazepines, altered mental status presumed secondary to hepatic encephalopathy, acute renal failure, aspiration pneumonia, and persistent unexplained leukocytosis. He required medical restraints during this time given confusion and propensity to ambulate without assistance, yet sustained no falls or other known trauma in care delivered during this time.
Between days 14 and 17, the patient's hematocrit fell from 36% to 30%; vital signs remained stable. He underwent an uncomplicated, ultrasound‐guided therapeutic paracentesis, which yielded 1.4 L of straw‐colored fluid on the afternoon of day 17; the procedure was attempted only on the right side. On the morning of day 18, the patient's blood pressure dropped to 78/55 mmHg with a pulse of 123 beats per minute; he became pale and unresponsive. Physical examination was notable for somnolence and a tender, warm left flank mass, contralateral to his paracentesis site. No flank or periumbilical ecchymoses were identified. Complete blood count demonstrated a white blood count (WBC) of 22,970/L, hematocrit 16%, and platelet count 104,000/L. INR was 2.0, unchanged from the last check on day 10. Partial thromboplastin time was 41 seconds and fibrinogen was 293 mg/dL (normal 150‐400 mg/dL). Peripheral blood smear was negative for red cell fragments. Blood chemistries revealed a sodium of 134 mg/dL, bicarbonate 20 mEq/L, anion gap 7, BUN 24 mg/dL, and creatinine 1.6 mg/dL (up from 1.0 mg/dL the previous day). His venous lactate level was 4.6 mmol/L and arterial blood gas sampling on room air demonstrated a pH of 7.35, partial pressure of carbon dioxide (pCO2) 29 mmHg, partial pressure of oxygen (pCO2) 54 mmHg, and bicarbonate 15 mEq/L. A femoral introducer was placed for volume resuscitation and the patient was urgently transfused with packed red blood cells (PRBCs) and fresh‐frozen plasma (FFP) to correct his coagulopathy. Computed tomography of the abdomen revealed a large left retroperitoneal hematoma measuring 15 15 22 cm3 (Figure 1). Despite transfusion, his hematocrit continued to fall. Urgent angiography was performed, upon which he was found to have active bleeding from the left L3‐L5 lumbar arteries. These were successfully embolized. He required PRBCs and FFP transfusion only once following this procedure. Given a transient decrease in his urine output, his bladder pressures were followed closely for evidence of abdominal compartment syndrome, which did not develop. He was transferred from the intensive care unit (ICU) to the floor on day 20, where his physical exam and hematocrit remained stable and his delirium slowly cleared. He was ultimately discharged to a skilled nursing facility on day 33.

Discussion
Spontaneous retroperitoneal hematoma is a well‐recognized entity that may present with the classic triad of abdominal or groin pain, palpable abdominal or flank mass, and shock or lower extremity motor or sensory changes due to femoral nerve compression.1 Although classically described as skin findings associated with retroperitoneal hemorrhage, Cullen and Grey‐Turner signs are relatively late findings that may not develop in all patients.
Retroperitoneal hemorrhage is well‐recognized as a result of iatrogenic anticoagulation,1 but has been reported less commonly as a result of coagulopathy related to cirrhosis. Di Bisceglie and Richart2 describe 2 patients with MELD scores of 29 and 25, respectively, who developed spontaneous retroperitoneal and rectus muscle hemorrhage. Both had evidence of associated disseminated intravascular coagulation (DIC) and died. Even less common is spontaneous lumbar artery rupture, occurring rarely in the absence of trauma or instrumentation. One reported bleed developed in the context of systemic anticoagulation for a mechanical valve.3 Halak et al.4 relate a case in which the only known risk factor was chronic renal disease. Hama et al.5 describe a patient with a history of alcoholic liver cirrhosis and INR of 2.3 whose lumbar artery rupture was successfully managed with transcatheter arterial embolization.
It is difficult to ascertain the effect of prophylactic anticoagulation in development of this particular hemorrhage. Retroperitoneal bleeding is a very rare complication of pharmacologic prophylaxis for DVT reported with both low‐molecular weight and unfractionated heparins.6 There may be additive risk of prophylaxis in a cirrhotic patient with baseline elevated INR and thrombocytopenia, particularly in the context of renal failure.
Options in the management of spontaneous retroperitoneal hematoma include transarterial embolization, percutaneous decompression, and open surgery. Nonoperative management of these bleeds when possible may be preferable in cirrhotic patients, as their baseline liver disease renders them higher‐risk candidates for surgery. There are no randomized trials comparing these approaches.1
In summary, we report the case of a 56‐year‐old man with end‐stage liver disease and associated coagulopathy without evidence of DIC who survived to discharge with intravascular management of a spontaneous retroperitoneal hemorrhage from the lumbar arteries. To our knowledge, this is the second reported case of spontaneous retroperitoneal hemorrhage in a cirrhotic in which the lumbar arteries were implicated and the first in which multiple arteries were found to be bleeding simultaneously. Any hospitalized patient who develops abdominal pain, flank pain, or hemodynamic instability in the context of coagulopathy, regardless of cause, warrants evaluation for retroperitoneal bleed. Appropriate early management includes immediate resuscitation, intensive monitoring, urgent imaging, and surgical and interventional radiology consultation in order to prevent a fatal outcome.
- Management of spontaneous and iatrogenic retroperitoneal haemorrhage: conservative management, endovascular intervention or open surgery?Int J Clin Pract.2007;62:1604–1613. , , , .
- Spontaneous retroperitoneal and rectus muscle hemorrhage as a potentially lethal complication of cirrhosis.Liver Int.2006;26:1291–1293. , .
- Spontaneous rupture of a lumbar artery. A rare etiology of retroperitoneal hematoma.Urologe A.2003;42:840–844. , , .
- Spontaneous ruptured lumbar artery in a chronic renal failure patient.Eur J Vasc Endovasc Surg.2001;21:569–571. , , , , .
- Spontaneous rupture of the lumbar artery.Intern Med.2004;43:759. , , .
- The rate of bleeding complications after pharmacologic deep venous thrombosis prophylaxis.Arch Surg.2006;141:790–799. , , .
- Management of spontaneous and iatrogenic retroperitoneal haemorrhage: conservative management, endovascular intervention or open surgery?Int J Clin Pract.2007;62:1604–1613. , , , .
- Spontaneous retroperitoneal and rectus muscle hemorrhage as a potentially lethal complication of cirrhosis.Liver Int.2006;26:1291–1293. , .
- Spontaneous rupture of a lumbar artery. A rare etiology of retroperitoneal hematoma.Urologe A.2003;42:840–844. , , .
- Spontaneous ruptured lumbar artery in a chronic renal failure patient.Eur J Vasc Endovasc Surg.2001;21:569–571. , , , , .
- Spontaneous rupture of the lumbar artery.Intern Med.2004;43:759. , , .
- The rate of bleeding complications after pharmacologic deep venous thrombosis prophylaxis.Arch Surg.2006;141:790–799. , , .
“Patchy” pneumonia
A 49‐year‐old retired air‐force officer presented with productive cough lasting 1 week. He was in good health except for back pain after a motor vehicle accident, 2 years ago, for which he took unspecified pain medications. His condition rapidly deteriorated necessitating mechanical ventilation. Chest x‐ray showed multilobar pneumonia (Figure 1). Blood and sputum cultures grew Serratia marcescens. Chest computed tomography (CT)‐scan showed multilobar involvement with debris in his airways that was thought to be respiratory secretions (Figure 2, arrows). He remained intubated for 2 weeks before his clinical status improved enough to permit extubation. Immediately following extubation, he coughed up a ball of crumpled, plastic material. His condition subsequently improved dramatically with complete radiological resolution of his pneumonia. On analyzing the foreign body, it turned out to be a fenestrated fentanyl patch (Figure 3). The patient then reported that he often cut‐up used fentanyl patches and sucked on them for an extra high. In retrospect, the endobronchial debris was actually the patch straddling the carina and had prevented rapid recovery. Aspiration of unusual foreign bodies have been reported in the literature. This foreign body masqueraded as respiratory secretions on imaging preventing early detection. This clinical encounter brings to light yet another innovative, but potentially deadly, form of drug abuse. It also emphasizes the importance of early bronchoscopy in nonresolving pneumonias.



A 49‐year‐old retired air‐force officer presented with productive cough lasting 1 week. He was in good health except for back pain after a motor vehicle accident, 2 years ago, for which he took unspecified pain medications. His condition rapidly deteriorated necessitating mechanical ventilation. Chest x‐ray showed multilobar pneumonia (Figure 1). Blood and sputum cultures grew Serratia marcescens. Chest computed tomography (CT)‐scan showed multilobar involvement with debris in his airways that was thought to be respiratory secretions (Figure 2, arrows). He remained intubated for 2 weeks before his clinical status improved enough to permit extubation. Immediately following extubation, he coughed up a ball of crumpled, plastic material. His condition subsequently improved dramatically with complete radiological resolution of his pneumonia. On analyzing the foreign body, it turned out to be a fenestrated fentanyl patch (Figure 3). The patient then reported that he often cut‐up used fentanyl patches and sucked on them for an extra high. In retrospect, the endobronchial debris was actually the patch straddling the carina and had prevented rapid recovery. Aspiration of unusual foreign bodies have been reported in the literature. This foreign body masqueraded as respiratory secretions on imaging preventing early detection. This clinical encounter brings to light yet another innovative, but potentially deadly, form of drug abuse. It also emphasizes the importance of early bronchoscopy in nonresolving pneumonias.



A 49‐year‐old retired air‐force officer presented with productive cough lasting 1 week. He was in good health except for back pain after a motor vehicle accident, 2 years ago, for which he took unspecified pain medications. His condition rapidly deteriorated necessitating mechanical ventilation. Chest x‐ray showed multilobar pneumonia (Figure 1). Blood and sputum cultures grew Serratia marcescens. Chest computed tomography (CT)‐scan showed multilobar involvement with debris in his airways that was thought to be respiratory secretions (Figure 2, arrows). He remained intubated for 2 weeks before his clinical status improved enough to permit extubation. Immediately following extubation, he coughed up a ball of crumpled, plastic material. His condition subsequently improved dramatically with complete radiological resolution of his pneumonia. On analyzing the foreign body, it turned out to be a fenestrated fentanyl patch (Figure 3). The patient then reported that he often cut‐up used fentanyl patches and sucked on them for an extra high. In retrospect, the endobronchial debris was actually the patch straddling the carina and had prevented rapid recovery. Aspiration of unusual foreign bodies have been reported in the literature. This foreign body masqueraded as respiratory secretions on imaging preventing early detection. This clinical encounter brings to light yet another innovative, but potentially deadly, form of drug abuse. It also emphasizes the importance of early bronchoscopy in nonresolving pneumonias.



Language Barriers and Hospital Care
Forty‐five‐million Americans speak a language other than English and more than 19 million of these speak English less than very wellor are limited English proficient (LEP).1 The number of non‐English‐speaking and LEP people in the US has risen in recent decades, presenting a challenge to healthcare systems to provide high‐quality, patient‐centered care for these patients.2
For outpatients, language barriers are a fundamental contributor to gaps in health care. In the clinic setting, patients who do not speak English well have less access to a usual source of care and lower rates of physician visits and preventive services.36 Even when patients with language barriers do have access to care, they have poorer adherence, decreased comprehension of their diagnoses, decreased satisfaction with care, and increased medication complications.710
Few studies, however, have examined how language influences outcomes of hospital care. Compared to English‐speakers, patients who do not speak English well may experience longer lengths of stay,11 and have more adverse events while in the hospital.12 However, these previous studies have not investigated outcomes immediately post‐hospitalization, such as readmission rates and mortality, nor have they directly addressed the interaction between ethnicity and language.
To understand these questions, we analyzed data collected from a university‐based teaching hospital which cares for patients of diverse cultural and language backgrounds. Using these data, we examined how patients' primary language influenced hospital costs, length of stay (LOS), 30‐day readmission, and 30‐day mortality risk.
Patients and Methods
Patient Population and Setting
Our study examined patients admitted to the General Medicine Service at the University of California, San Francisco Medical Center (UCSF) between July 1, 2001 and June 30th, 2003, the time period during which UCSF participated in the Multicenter Hospitalist Trial (MHT) a prospective quasi‐randomized trial of hospitalist care for general medicine patients.13, 14
UCSF Moffitt‐Long Hospital is a 400‐bed urban academic medical center which provides services to the City and County of San Francisco, an ethnically and linguistically diverse area. UCSF employs staff language interpreters in Spanish, Chinese and Russian who travel to its many outpatient clinics, Comprehensive Cancer Center, Children's Hospital, as well as to Moffitt‐Long Hospital upon request; phone interpretation is also available when in‐person interpreters are not available, for off hours needs and for less common languages. During the period of this study there were no specific inpatient guidelines in place for use of interpretation services at UCSF, nor were there any specific interventions targeting LEP or non‐English speaking inpatients.
Patients were eligible for the MHT if they were 18 years of age or older and admitted at random to a hospitalist or non‐hospitalist physician (eg, outpatient general internist attending on average 1‐month/year); a minority of patients were cared for directly by their primary care physician while in the hospital, and were excluded. For purposes of our study, which merged MHT data with hospital administrative data on primary language, we further excluded all admissions for patients for whom primary language was missing (n = 5), whose listing was unknown or other language (n = 78), sign language (n = 3) or whose language was listed but was not one of the included languages (n = 258). Included languages were English, Chinese, Russian and Spanish. Because LOS and cost data were skewed, we excluded those admissions with the top 1% longest stays and the top 1% highest cost (n = 176); these exclusions did not alter the proportion of admissions across language and ethnicity. In addition, we excluded 102 admissions that were missing data on cost and 11 with costs <$500 and which were likely to be erroneous. Our research was approved by the UCSF Institutional Review Board.
Data Sources
We collected administrative data from Transition Systems Inc (TSI, Boston, MA) billing databases at UCSF as part of the MHT. These data include patient demographics, insurance, costs, ICD‐9CM diagnostic codes, admission and discharge dates in Uniform Bill 92 format. Patient mortality information was collected as part of the MHT using the National Death Index.14
Language data were collected from a separate patient‐registration database (STOR) at UCSF. Information on a patient's primary language is entered at the time each patient first registers at UCSF, whether for the index hospitalization or for prior clinic visits, and is based generally on patient self report. As part of our validation step, we cross‐checked 829 STOR language entries against patient reports and found 91% agreement with the majority of the errors classifying non‐English speakers as English‐speakers.
Measures
Predictor
Our primary language variable was derived using language designations collected from patient registration databases described above. Using these data we specified our key language groups as English, Chinese (Cantonese or Mandarin), Russian, or Spanish.
Outcomes
LOS and total cost of hospital stay for each hospitalization derived from administrative data sources. Readmissions were identified at the time patients were readmitted to UCSF (eg, flagged in administrative data). Mortality was determined by whether an individual patient with an admission in the database was recorded in the National Death Index as dead within 30‐days of admission.
Covariates
Additional covariates included age at admission, gender, ethnicity as recorded in registration databases (White, African American, Asian, Latino, Other), insurance, principal billing diagnosis, whether or not a patient received intensive care unit (ICU) care, type of admitting attending physician (Hospitalist/non‐Hospitalist), and an administrative Charlson comorbidity score.15 To collapse the principal diagnoses into categories, we used the Healthcare Cost and Utilization Project (HCUP)'s Clinical Classification System, which allowed us to classify each diagnosis in 1 of 14 generally accepted categories.16
Analysis
Statistical analyses were performed using STATA statistical software (STATACorp, Version 9, College Station, TX). We examined descriptive means and proportions for all variables, including sociodemographic, hospitalization, comorbidity and outcome variables. We compared English and non‐English speakers on all covariate and outcome variables using t‐tests for comparison of means and chi‐square for comparison of categorical variables.
It was not possible to fully test the language‐by‐ethnicity interactionwhether or not the impact of language varied by ethnic groupbecause many cells of the joint distribution were very sparse (eg, the sample contained very few non‐English‐speaking African Americans). Therefore, to better understand the influence of English vs. non‐English language usage across different ethnic groups, we created a combined language‐ethnicity predictor variable which categorized each subject first by language and then for the English‐speakers by ethnicity. For example, a Chinese, Spanish or Russian speaker would be categorized as such, and an English‐speaker could fall into the English‐White, English‐African American, English‐Asian or English‐Latino group. This allowed us to test whether there were any differences in language effects across the White, Asian, and Latino ethnicities, and any difference in ethnicity effects among English‐speakers.
Because cost and LOS were skewed, we used negative binomial models for LOS and log transformed costs. We performed a sensitivity analysis testing whether our results were robust to the exclusion of the admissions with the top 1% LOS and top 1% cost. We used logistic regression for the 30‐day readmission and mortality outcomes.
Our primary predictor was the language‐ethnicity variable described above. To determine the independent association between this predictor and our key outcomes, we then built models which included additional potential confounders selected either for face validity or because of observed confounding with other covariates. Our inclusion of potential confounders was limited by the variables available in the administrative database; thus, we were not able to pursue detailed analyses of communication and literacy factors and their interaction with our predictor or their independent impact on outcomes. Models also included a linear spline with a single knot at age 65 years as a further adjustment for age in Medicare recipients.1719 For the 30‐day readmission outcome model, we excluded those admissions for which the patient either died in the hospital or was discharged to hospice care. Within each model we tested the impact of a language barrier using custom contrasts. This allowed us to examine the language‐ethnicity effect aggregating all non‐English speakers compared to all English‐speakers, comparing each non‐English speaking group to all English‐speakers, comparing Chinese speakers to English‐speaking Asians and Spanish speakers to English‐speaking‐Latinos, as well as to test whether the effect of English language is the same across ethnicities.
Results
Admission Characteristics of the Sample
A total of 7023 patients were admitted to the General Medicine service, 5877 (84%) of whom were English‐speakers and 1146 (16%) non‐English‐speakers (Table 1). Overall, half of the admitted patients were women (50%), and the vast majority was insured (93%). The most common principal diagnoses were respiratory and gastrointestinal disorders. Only a small number of non‐English speakers 164 (14%) were recorded in the UCSF Interpreter Services database as having had any interaction with a professional staff interpreter during their hospitalization.
English (n = 5877) n (%) | Non‐English (n = 1146) n (%) | |
---|---|---|
| ||
Socio‐economic variables | ||
Language‐ethnicity | ||
English | ||
White | 3066 (52.2) | |
African American | 1351 (23.0) | |
Asian | 544 (9.3) | |
Latino | 298 (5.1) | |
Other | 618 (10.5) | |
Chinese speakers | 584 (51.0) | |
Spanish speakers | 272 (25.3) | |
Russian speakers | 290 (23.7) | |
Age mean (SD) (range 18‐105) | 58.8 (20.3) | 72.3 (15.5) |
Gender | ||
Male | 2967 (50.5) | 514 (44.8) |
Female | 2910 (49.5) | 632 (55.2) |
Insurance | ||
Medicare | 2878 (49.0) | 800 (69.8) |
Medicaid | 1201 (20.4) | 193 (16.8) |
Commercial | 1358 (23.1) | 106 (9.3) |
Charity/other | 440 (7.5) | 47 (4.1) |
Hospitalization variables | ||
Admitted to ICU | ||
Yes | 721 (12.3) | 149 (13.0) |
Attending physician | ||
Hospitalist | 3950 (67.2) | 781 (68.2) |
Comorbidity variables | ||
Principal Diagnosis | ||
Respiratory disorder | 1061 (18.1) | 225 (19.6) |
Gastrointestinal disorder | 963 (16.4) | 205 (17.9) |
Circulatory disorder | 613 (10.4) | 140 (12.2) |
Endocrine/metabolism | 671 (11.4) | 80 (7.0) |
Injury/poisoning | 475 (8.1) | 64 (5.6) |
Malignancy | 395 (6.7) | 107 (9.3) |
Renal/urinary disorder | 383 (6.5) | 108 (9.4) |
Skin disorder | 278 (4.7) | 28 (2.9) |
Infection/fatigue NOS | 206 (3.5) | 45 (3.4) |
Blood disorder (non‐malignant) | 189 (3.2) | 38 (3.3) |
Musculoskeletal/connective tissue disorder | 164 (2.8) | 33 (2.9) |
Mental disorder/substance abuse | 171 (2.9) | 7 (0.6) |
Nervous system/brain infection | 137 (2.3) | 26 (2.3) |
Unclassified | 171 (2.9) | 40 (3.5) |
Charlson Index score mean (SD) | 0.97 1.33 | 1.10 1.42 |
Among English speakers, Whites and African Americans were the most common ethnicities; however, more than 500 admissions were categorized as Asian ethnicity, and more than 600 as patients of other ethnicity. Close to 300 admissions were for Latinos. Among non‐English speakers, Chinese speakers had the largest number of admissions (n = 584), while Spanish and Russian speakers had similar numbers (n = 272 and 290 respectively).
Non‐English speakers were older, more likely to be female, more likely to be insured by Medicare, and more likely to have a higher comorbidity index score. While comorbidity scores were similar among non‐English speakers (Chinese 1.13 1.50; Russian 1.09 1.37; Spanish 1.06 1.30), they differed considerably among English speakers (White 0.94 1.29; African American 1.05 1.40; Asian 1.04 1.45; Latino 0.89 1.23; Other 0.91 1.29).
Hospital Outcome by Language‐Ethnicity Group (Table 2)
When aggregated together, non‐English speakers were somewhat more likely to be dead at 30‐days and have lower cost admissions; however, they did not differ from English speakers on LOS or readmission rates. While differences among disaggregated language‐ethnicity groups were not all statistically significant, English‐speaking Whites had the longest LOS (mean = 4.9 days) and highest costs (mean = $10,530). English‐speaking African Americans, Chinese and Spanish speakers had the highest 30‐day readmission rates; whereas, English‐speaking Latinos and Russian speakers had markedly lower 30‐day readmission rates (2.5% and 6.4%, respectively). Chinese speakers had the highest 30‐day mortality, followed by English speaking Whites and Asians.
Language‐Ethnicity Groups | LOS* Mean #Days (SD) | Cost Mean Cost $ (SD) | 30‐Day Readmission, n (%) | 30‐Day Mortality, n (%) |
---|---|---|---|---|
| ||||
English speakers (all) | 4.7 (4.5) | 10,035 (15,041) | 648 (11.9) | 613 (10.4) |
White | 4.9 (5.1) | 10,530 (15,894) | 322 (11.4) | 377 (12.3) |
African American | 4.5 (4.8) | 9107 (13,314) | 227 (17.5) | 91 (6.7) |
Asian | 4.3 (4.5) | 9933 (15,607) | 43 (8.8) | 67 (12.3) |
Latino | 4.6 (4.8) | 9823 (14,113) | 7 (2.5) | 18 (6.0) |
Other | 4.5 (4.8) | 9662 (14,016) | 49 (8.5) | 60 (9.7) |
Non‐English speakers (all) | 4.5 (4.5) | 9515 (13,213) | 117 (11.0) | 147 (12.8) |
Chinese speakers | 4.5 (4.6) | 9505 (12,841) | 69 (12.8) | 85 (14.6) |
Spanish speakers | 4.5 (4.5) | 9115 (13,846) | 31 (12.0) | 28 (10.3) |
Russian speakers | 4.7 (4.2) | 9846 (13,360) | 17 (6.4) | 34 (11.7) |
We further investigated differences among English speakers to better understand the very high rate of readmission for African Americans and the very low rate for English‐speaking Latinos. African Americans were on average younger than other English speakers (55 19 years vs. 60 21 years; P < 0.001); but, they had higher comorbidity scores than other English speakers (1.05 1.40 vs. 0.94 1.31; P = 0.008), and were more likely to be admitted for non‐malignant blood disorders (eg, sickle cell disease), endocrine disorders (eg, diabetes mellitus), and circulatory disorders (eg, stroke). In contrast, English‐speaking Latinos were also younger than other English speakers (53 21 years vs. 59 20 years; P < 0.001), but they trended toward lower comorbidity scores (0.87 1.23 vs. 0.97 1.33; P = 0.2), and were more likely to be admitted for gastrointestinal and musculoskeletal disorders, and less likely to be admitted for malignancy and endocrine disorders.
Multivariate Analyses: Association of Aggregated and Disaggregated Language‐Ethnicity Groups With Hospital Outcomes (Table 3)
In multivariate models examining aggregated language‐ethnicity groups, non‐English speakers had a trend toward higher odds of readmission at 30‐days post‐discharge than the English‐speaking group (odds ratio [OR], 1.3; 95% confidence interval [CI], 1.0‐1.7). There were no significant differences for LOS, cost, or 30‐day mortality. Compared to English speakers, Chinese and Spanish speakers had 70% and 50% higher adjusted odds of readmission at 30‐days post‐discharge respectively, while Russian speakers' odds of readmission was not increased. Additionally, Chinese speakers had 7% shorter LOS than English‐speakers. There were no significant differences among any of the language‐ethnicity groups for 30‐day mortality. The increased odds of readmission for Chinese and Spanish speakers compared to English speakers was robust to reinclusion of the admissions with the top 1% LOS and top 1% cost.
Language Categorization | LOS, % Difference (95% CI) | Total Cost, % Difference (95% CI) | 30‐Day Readmission,* OR (95% CI) | Mortality, OR (95% CI) |
---|---|---|---|---|
| ||||
All English speakers | Reference | Reference | Reference | Reference |
Non‐English speakers | 3.1 (8.7 to 3.1) | 2.5 (8.3 to 2.1) | 1.3 (1.0 to 1.7) | 0.9 (0.7 to 1.2) |
All English speakers | Reference | Reference | Reference | Reference |
Chinese speakers | 7.2 (13.9 to 0) | 5.3 (12.2 to 2.1) | 1.7 (1.2 to 2.3) | 1.0 (0.8 to 1.4) |
Spanish speakers | 3.0 (12.6 to 7.6) | 3.0 (12.7 to 7.7) | 1.5 (1.0 to 2.3) | 0.9 (0.6 to 1.5) |
Russian speakers | 1.5 (8.3 to 12.2) | 0.9 (8.9 to 11.8) | 0.8 (0.5 to 1.4) | 0.8 (0.5 to 1.2) |
Multivariate Analyses: Association of Language for Asians and Latinos, and of Ethnicity for English speakers, With Hospital Outcomes (Table 4)
Both Chinese and Spanish speakers had significantly higher odds of 30‐day readmission than their English speaking Asian and Latino counterparts. There were no significant differences in LOS, cost, or 30‐day mortality in this within‐ethnicity analysis. Among English speakers, admissions for patients with Asian ethnicity were 15% shorter and resulted in 9% lower costs than for Whites. While LOS and cost were similar for English‐speaking Latino and White admissions, English‐speaking Latinos had markedly lower odds of 30‐day readmission than their White counterparts. Whereas African‐Americans had 6% shorter LOS, 40% higher odds of readmission and 30% lower odds of mortality at 30‐days than English speaking Whites.
Language‐Ethnicity Comparisons | LOS, % Difference (95% CI) | Total Cost, % Difference (95% CI) | 30‐Day Readmission,* OR (95% CI) | Mortality, OR (95% CI) |
---|---|---|---|---|
| ||||
English speaking Asians | Reference | Reference | Reference | Reference |
Chinese speakers | 2.2 (7.4 to 12.7) | 0.3 (9.2 to 10.7) | 1.5 (1.0 to 2.3) | 0.8 (0.6 to 1.2) |
English speaking Latinos | Reference | Reference | Reference | Reference |
Spanish speakers | 4.5 (16.8 to 9.5) | 1.2 (14.0 to 13.5) | 5.7 (2.4 to 13.2) | 1.2 (0.6 to 2.4) |
English‐White | Reference | Reference | Reference | Reference |
English‐African American | 6.2 (11.3 to 0.9) | 4.4 (9.6 to 1.1) | 1.4 (1.1 to 1.7) | 0.6 (0.5 to 0.8) |
English‐Asian | 14.6 (20.9 to 7.9) | 8.6 (15.4 to 1.4) | 0.8 (0.5 to 1.0) | 1.0 (0.7 to 1.4) |
English‐Latino | 4.5 (13.5 to 5.4) | 5.0 (14.0 to 5.0) | 0.2 (0.1 to 0.4) | 0.6 (0.4 to 1.0) |
Conclusion/Discussion
Our results indicate that language barriers may contribute to higher readmission rates for non‐English speakers, but that they have less impact on care efficiency or mortality. This finding of an association between language and readmission, without a similar association with efficiency, suggests a potentially communication‐critical step in care.20, 21 Patients with language barriers are more likely to experience adverse events, and those events are often caused by errors in communication.12 It is conceivable that higher readmission risk for Chinese and Spanish speakers in our study was, at least in part, due to gaps in communication that are present in all patient groups, and exacerbated by the presence of a language barrier. This barrier is likely present during hospitalization but magnified at discharge, limiting caregivers' ability to understand patients' needs for home care, while simultaneously limiting patients' understanding of the discharge plan. After discharge, it is also possible that non‐English speakers are less able to communicate their needs as they arise, ormore subtlyfeel less supported by a primarily English speaking healthcare system. As in other clinical arenas,22 it is quite possible that increased access to professional interpreters in the hospital setting, and particularly at the time of discharge, would enhance communication and outcomes for LEP patients. Our interpreter services data showed that the patients in our study had quite limited access to staff professional interpreters.
Our findings differ somewhat from those of John‐Baptiste et al.,11 who found that language barriers contributed to increased LOS for patients with cardiac and major surgical diagnoses. Our study's findings are akin to recent research suggesting that being a monolingual Spanish speaker or receiving interpreter services may not significantly impact LOS or cost of hospitalization,23 and that LOS and in‐hospital mortality do not differ for non‐English speakers and English speakers after acute myocardial infarction.24 These studies, along with our results, suggest that that care efficiency in the hospital may be driven much more by clinical acuity (eg, the need to respond rapidly to urgent clinical signs such as hypotension, fever and respiratory distress) than by adequacy of communication. For example, elderly LEP patients may be even more likely than English speakers to have vigilant family members at the bedside throughout their hospitalization due to their need for communication assistance; these family members can quickly alert hospital staff to concerning changes in the patient's condition.
Our results also suggest the possibility that language and ethnicity are not monolithic concepts, and that even within language and ethnic groups there are potential differences in care pattern. For example, not speaking English may be a surrogate marker for unmeasured factors such as social supports and access to care. Language is intimately associated with culture; it remains plausible that cultural differences between highly acculturated and less acculturated members of a given ethno‐cultural group may have contributed to our observed differences in readmission rates. Differences in culture and associated factors, such as social support or use of multiple hospital systems, may account for lack of higher readmission risk in Russian speakers, while Chinese and Spanish speakers had higher readmission risk.
In addition, our finding that English‐speaking Latinos had lower readmission risk than any other group may be more consistent with their clinical characteristicseg, younger age, fewer comorbiditiesthan with cultural factors. Our finding that African American patients had the highest readmission risk in our hospital was both surprising and concerning. Some of this increased risk may be explained by clinical characteristics, such as higher comorbidities and higher rates of diagnoses leading to frequent admissions (eg, sickle cell disease); however, the reasons for this disparity deserves further investigation.
Our study has limitations. First, our data are administrative, and lack information about patients' educational attainment, social support, acculturation, utilization of other hospital systems, and usual source of care. Despite this, we were able to account for many significant covariates that might contribute to readmission rates, including age, insurance status, gender, comorbidities, and admission to the intensive care unit.2528 Second, our information about patients' English language proficiency is limited. While direct assessments of English proficiency are more accurate ways to determine a patient's ability to communicate with health care providers in English,29 our language validation work conducted in preparation for this study suggests that most of our patients recorded as having a non‐English primary language (87%) also have a low score on a language acculturation scale.
Third, only 14% of our non‐English speaking subjects utilized professional staff interpreters, and we had no information on the use of professional telephonic interpreters, or ad hoc interpretersfamily members, non‐interpreter staff membersand their impact on our results. It is well‐documented that ad hoc interpreters are used frequently in healthcare, particularly in the hospital setting, and thus we can assume this to be true in our study.30, 31 As noted above, it is likely that the advocacy of family members and friends at the bedside helped to minimize potential differences in care efficiency for patients with language barriers. Finally, our study was performed at a single university based hospital and may not produce results which are applicable to other care settings.
Our findings point to several avenues for future research on language barriers and hospitalized patients. First, the field would benefit from an examination of the impact of easy access to professional interpreters during hospitalization on outcomes of hospital care, in particular on readmission rates. Second, there is need for development and assessment of best practices for creating a culture of professional interpreter utilization in the hospital among physicians and nursing staff. Third, investigation of the role of caregiver presence in the hospital room and how this might differ by patient culture, age and language ability may further elucidate some of the differences across language groups observed in our study. Lastly, a more granular investigation of clinician‐patient communication and the importance of interpersonal processes of care on both patient satisfaction and understanding of and adherence to discharge instructions could lead to the development of detailed interventions to enhance this communication and these outcomes as it has done for communication‐sensitive outcomes in the outpatient arena.3234
In summary, our study suggests that higher risk for readmission can be added to the unfortunate list of outcomes which are worsened due to language barriers, pointing to transition from the hospital as a potentially communication‐critical step in care which may be amenable to intervention. Our findings also suggest that this risk can vary even between groups of patients who do not speak English primarily. Whether and to what degree language and communication barriers aloneincluding access to professional interpreters and patient‐centered communicationduring hospitalization, or differences in caregiver social support both during and after hospitalization as well as access to care post‐hospitalization contribute to these findings is a worthy subject of future research.
Acknowledgements
The authors acknowledge Dr. Eliseo J. Prez‐Stable for his mentorship on this project.
- 2000. Available at: http://www.census.gov/prod/2003pubs/c2kbr‐29.pdf. Accessed January 2010. , . Language Use and English‐Speaking Ability:
- U.S. Department of Health and Human Services.2006National Healthcare Disparities Report. AHRQ Publication No. 070012; 2006.
- Disparities in health care by race, ethnicity, and language among the insured: findings from a national sample.Med Care.2002;40(1):52–59. , , , .
- The effect of physician‐patient communication on mammography utilization by different ethnic groups.Med Care.1991;29(11):1065–1082. , .
- Language of interview:relevance for research of Southwest Hispanics.Am J Pub Health.1991;81(11):1399–1404. , .
- Is language a barrier to the use of preventive services?J Gen Intern Med.1997;12(8):472–477. , , , .
- Impact of language barriers on patient satisfaction in an emergency department.JGIM.1999;14:82–87. , , , .
- Patient comprehension of doctor‐patient communication on discharge from the emergency department.J Emerg Med.1997;15(1):1–7. .
- Drug complications in outpatients.J Gen Intern Med.2000;15:149–154. , , , et al.
- Language concordance as a determinant of patient compliance and emergency room use in patients with asthma.Med Care.1988;26(12):1119–1128. .
- The effect of English language proficiency on length of stay and in‐hospital mortality.J Gen Intern Med.2004;19:221–228. , , ,et al.
- Language proficiency and adverse events in US hospitals: a pilot study.Int J Qual Health Care.2007;19(2):60–67. , , , .
- Factors associated with discussion of care plans and code status at the time of hospital admission: results from the Multicenter Hospitalist Study.J Hosp Med.2008;3(6):437–445. , , , et al.
- Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non‐hospitalists.J Gen Intern Med.2008;23(9):1399–1406. , , , et al.
- A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40(5):373–383. , , , .
- AHRQ. Healthcare Cost and Utilizlation Project: Tools 132(3):191–200.
- Free knot splines for logistic models and threshold selection.Comput Methods Programs Biomed.2005;77(1):1–9. , , .
- Statistical methods in epidemiology: a comparison of statistical methods to analyze dose‐response and trend analysis in epidemiologic studies.J Clin Epidemiol.1998;51(12):1223–1233. , , , , .
- The Care Transitions Project. Health Care Policy and Research, Practitioner Tools. Available at: http://www.caretransitions.org/practitioner_tools.asp.
- AHRQ. Improving safety at the point of care. Available at: http://www.ahrq.gov/qual/pips. Accessed January2010.
- Do professional interpreters improve clinical care for patients with limited english proficiency? A systematic review of the literature.Health Serv Res.2007;42(2):727–754. , , , .
- The impact of an enhanced interpreter service intervention on hospital costs and patient satisfaction.J Gen Intern Med.2007;22 Suppl 2:306–311. , , .
- Acute myocardial infarction length of stay and hospital mortality are not associated with language preference.J Gen Intern Med.2008;23(2):190–194. , , , , .
- A systematic literature review of factors affecting outcome in older medical patients admitted to hospital.Age Ageing.2004;33(2):110–115. , , .
- Risk factors and prognostic predictors of unexpected intensive care unit admission within 3 days after ED discharge.Am J Emerg Med.2007;25(9):1009–1014. , , , , , .
- Effect of gender, ethnicity, pulmonary disease, and symptom stability on rehospitalization in patients with heart failure.Am J Cardiol.2007;100(7):1139–1144. , .
- Bouncing back: patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke.J Am Geriatr Soc.2007;55(3):365–373. , , , .
- Identification of limited English proficient patients in clinical care.J Gen Intern Med.2008;23(10):1555–1560. , , , , .
- Hospital langague services for patients with limited English proficiency: results from a national survey.Health Research October2006. , , , , .
- Hospitals, Language, and Culture: a Snapshot of the Nation. The Joint Commission and The California Endowment;2007. , .
- A randomized controlled trial of interventions to enhance patient‐physician partnership, patient adherence and high blood pressure control among ethnic minorities and poor persons: study protocol NCT00123045.Implement Sci.2009;4:7. , , , et al.
- Interpersonal processes of care and patient satisfaction: do associations differ by race, ethnicity, and language?Health Serv Res.2009;44(4):1326–1344. , , , , .
- Understanding concordance in patient‐physician relationships: personal and ethnic dimensions of shared identity.Ann Fam Med.2008;6(3):198–205. , , , .
Forty‐five‐million Americans speak a language other than English and more than 19 million of these speak English less than very wellor are limited English proficient (LEP).1 The number of non‐English‐speaking and LEP people in the US has risen in recent decades, presenting a challenge to healthcare systems to provide high‐quality, patient‐centered care for these patients.2
For outpatients, language barriers are a fundamental contributor to gaps in health care. In the clinic setting, patients who do not speak English well have less access to a usual source of care and lower rates of physician visits and preventive services.36 Even when patients with language barriers do have access to care, they have poorer adherence, decreased comprehension of their diagnoses, decreased satisfaction with care, and increased medication complications.710
Few studies, however, have examined how language influences outcomes of hospital care. Compared to English‐speakers, patients who do not speak English well may experience longer lengths of stay,11 and have more adverse events while in the hospital.12 However, these previous studies have not investigated outcomes immediately post‐hospitalization, such as readmission rates and mortality, nor have they directly addressed the interaction between ethnicity and language.
To understand these questions, we analyzed data collected from a university‐based teaching hospital which cares for patients of diverse cultural and language backgrounds. Using these data, we examined how patients' primary language influenced hospital costs, length of stay (LOS), 30‐day readmission, and 30‐day mortality risk.
Patients and Methods
Patient Population and Setting
Our study examined patients admitted to the General Medicine Service at the University of California, San Francisco Medical Center (UCSF) between July 1, 2001 and June 30th, 2003, the time period during which UCSF participated in the Multicenter Hospitalist Trial (MHT) a prospective quasi‐randomized trial of hospitalist care for general medicine patients.13, 14
UCSF Moffitt‐Long Hospital is a 400‐bed urban academic medical center which provides services to the City and County of San Francisco, an ethnically and linguistically diverse area. UCSF employs staff language interpreters in Spanish, Chinese and Russian who travel to its many outpatient clinics, Comprehensive Cancer Center, Children's Hospital, as well as to Moffitt‐Long Hospital upon request; phone interpretation is also available when in‐person interpreters are not available, for off hours needs and for less common languages. During the period of this study there were no specific inpatient guidelines in place for use of interpretation services at UCSF, nor were there any specific interventions targeting LEP or non‐English speaking inpatients.
Patients were eligible for the MHT if they were 18 years of age or older and admitted at random to a hospitalist or non‐hospitalist physician (eg, outpatient general internist attending on average 1‐month/year); a minority of patients were cared for directly by their primary care physician while in the hospital, and were excluded. For purposes of our study, which merged MHT data with hospital administrative data on primary language, we further excluded all admissions for patients for whom primary language was missing (n = 5), whose listing was unknown or other language (n = 78), sign language (n = 3) or whose language was listed but was not one of the included languages (n = 258). Included languages were English, Chinese, Russian and Spanish. Because LOS and cost data were skewed, we excluded those admissions with the top 1% longest stays and the top 1% highest cost (n = 176); these exclusions did not alter the proportion of admissions across language and ethnicity. In addition, we excluded 102 admissions that were missing data on cost and 11 with costs <$500 and which were likely to be erroneous. Our research was approved by the UCSF Institutional Review Board.
Data Sources
We collected administrative data from Transition Systems Inc (TSI, Boston, MA) billing databases at UCSF as part of the MHT. These data include patient demographics, insurance, costs, ICD‐9CM diagnostic codes, admission and discharge dates in Uniform Bill 92 format. Patient mortality information was collected as part of the MHT using the National Death Index.14
Language data were collected from a separate patient‐registration database (STOR) at UCSF. Information on a patient's primary language is entered at the time each patient first registers at UCSF, whether for the index hospitalization or for prior clinic visits, and is based generally on patient self report. As part of our validation step, we cross‐checked 829 STOR language entries against patient reports and found 91% agreement with the majority of the errors classifying non‐English speakers as English‐speakers.
Measures
Predictor
Our primary language variable was derived using language designations collected from patient registration databases described above. Using these data we specified our key language groups as English, Chinese (Cantonese or Mandarin), Russian, or Spanish.
Outcomes
LOS and total cost of hospital stay for each hospitalization derived from administrative data sources. Readmissions were identified at the time patients were readmitted to UCSF (eg, flagged in administrative data). Mortality was determined by whether an individual patient with an admission in the database was recorded in the National Death Index as dead within 30‐days of admission.
Covariates
Additional covariates included age at admission, gender, ethnicity as recorded in registration databases (White, African American, Asian, Latino, Other), insurance, principal billing diagnosis, whether or not a patient received intensive care unit (ICU) care, type of admitting attending physician (Hospitalist/non‐Hospitalist), and an administrative Charlson comorbidity score.15 To collapse the principal diagnoses into categories, we used the Healthcare Cost and Utilization Project (HCUP)'s Clinical Classification System, which allowed us to classify each diagnosis in 1 of 14 generally accepted categories.16
Analysis
Statistical analyses were performed using STATA statistical software (STATACorp, Version 9, College Station, TX). We examined descriptive means and proportions for all variables, including sociodemographic, hospitalization, comorbidity and outcome variables. We compared English and non‐English speakers on all covariate and outcome variables using t‐tests for comparison of means and chi‐square for comparison of categorical variables.
It was not possible to fully test the language‐by‐ethnicity interactionwhether or not the impact of language varied by ethnic groupbecause many cells of the joint distribution were very sparse (eg, the sample contained very few non‐English‐speaking African Americans). Therefore, to better understand the influence of English vs. non‐English language usage across different ethnic groups, we created a combined language‐ethnicity predictor variable which categorized each subject first by language and then for the English‐speakers by ethnicity. For example, a Chinese, Spanish or Russian speaker would be categorized as such, and an English‐speaker could fall into the English‐White, English‐African American, English‐Asian or English‐Latino group. This allowed us to test whether there were any differences in language effects across the White, Asian, and Latino ethnicities, and any difference in ethnicity effects among English‐speakers.
Because cost and LOS were skewed, we used negative binomial models for LOS and log transformed costs. We performed a sensitivity analysis testing whether our results were robust to the exclusion of the admissions with the top 1% LOS and top 1% cost. We used logistic regression for the 30‐day readmission and mortality outcomes.
Our primary predictor was the language‐ethnicity variable described above. To determine the independent association between this predictor and our key outcomes, we then built models which included additional potential confounders selected either for face validity or because of observed confounding with other covariates. Our inclusion of potential confounders was limited by the variables available in the administrative database; thus, we were not able to pursue detailed analyses of communication and literacy factors and their interaction with our predictor or their independent impact on outcomes. Models also included a linear spline with a single knot at age 65 years as a further adjustment for age in Medicare recipients.1719 For the 30‐day readmission outcome model, we excluded those admissions for which the patient either died in the hospital or was discharged to hospice care. Within each model we tested the impact of a language barrier using custom contrasts. This allowed us to examine the language‐ethnicity effect aggregating all non‐English speakers compared to all English‐speakers, comparing each non‐English speaking group to all English‐speakers, comparing Chinese speakers to English‐speaking Asians and Spanish speakers to English‐speaking‐Latinos, as well as to test whether the effect of English language is the same across ethnicities.
Results
Admission Characteristics of the Sample
A total of 7023 patients were admitted to the General Medicine service, 5877 (84%) of whom were English‐speakers and 1146 (16%) non‐English‐speakers (Table 1). Overall, half of the admitted patients were women (50%), and the vast majority was insured (93%). The most common principal diagnoses were respiratory and gastrointestinal disorders. Only a small number of non‐English speakers 164 (14%) were recorded in the UCSF Interpreter Services database as having had any interaction with a professional staff interpreter during their hospitalization.
English (n = 5877) n (%) | Non‐English (n = 1146) n (%) | |
---|---|---|
| ||
Socio‐economic variables | ||
Language‐ethnicity | ||
English | ||
White | 3066 (52.2) | |
African American | 1351 (23.0) | |
Asian | 544 (9.3) | |
Latino | 298 (5.1) | |
Other | 618 (10.5) | |
Chinese speakers | 584 (51.0) | |
Spanish speakers | 272 (25.3) | |
Russian speakers | 290 (23.7) | |
Age mean (SD) (range 18‐105) | 58.8 (20.3) | 72.3 (15.5) |
Gender | ||
Male | 2967 (50.5) | 514 (44.8) |
Female | 2910 (49.5) | 632 (55.2) |
Insurance | ||
Medicare | 2878 (49.0) | 800 (69.8) |
Medicaid | 1201 (20.4) | 193 (16.8) |
Commercial | 1358 (23.1) | 106 (9.3) |
Charity/other | 440 (7.5) | 47 (4.1) |
Hospitalization variables | ||
Admitted to ICU | ||
Yes | 721 (12.3) | 149 (13.0) |
Attending physician | ||
Hospitalist | 3950 (67.2) | 781 (68.2) |
Comorbidity variables | ||
Principal Diagnosis | ||
Respiratory disorder | 1061 (18.1) | 225 (19.6) |
Gastrointestinal disorder | 963 (16.4) | 205 (17.9) |
Circulatory disorder | 613 (10.4) | 140 (12.2) |
Endocrine/metabolism | 671 (11.4) | 80 (7.0) |
Injury/poisoning | 475 (8.1) | 64 (5.6) |
Malignancy | 395 (6.7) | 107 (9.3) |
Renal/urinary disorder | 383 (6.5) | 108 (9.4) |
Skin disorder | 278 (4.7) | 28 (2.9) |
Infection/fatigue NOS | 206 (3.5) | 45 (3.4) |
Blood disorder (non‐malignant) | 189 (3.2) | 38 (3.3) |
Musculoskeletal/connective tissue disorder | 164 (2.8) | 33 (2.9) |
Mental disorder/substance abuse | 171 (2.9) | 7 (0.6) |
Nervous system/brain infection | 137 (2.3) | 26 (2.3) |
Unclassified | 171 (2.9) | 40 (3.5) |
Charlson Index score mean (SD) | 0.97 1.33 | 1.10 1.42 |
Among English speakers, Whites and African Americans were the most common ethnicities; however, more than 500 admissions were categorized as Asian ethnicity, and more than 600 as patients of other ethnicity. Close to 300 admissions were for Latinos. Among non‐English speakers, Chinese speakers had the largest number of admissions (n = 584), while Spanish and Russian speakers had similar numbers (n = 272 and 290 respectively).
Non‐English speakers were older, more likely to be female, more likely to be insured by Medicare, and more likely to have a higher comorbidity index score. While comorbidity scores were similar among non‐English speakers (Chinese 1.13 1.50; Russian 1.09 1.37; Spanish 1.06 1.30), they differed considerably among English speakers (White 0.94 1.29; African American 1.05 1.40; Asian 1.04 1.45; Latino 0.89 1.23; Other 0.91 1.29).
Hospital Outcome by Language‐Ethnicity Group (Table 2)
When aggregated together, non‐English speakers were somewhat more likely to be dead at 30‐days and have lower cost admissions; however, they did not differ from English speakers on LOS or readmission rates. While differences among disaggregated language‐ethnicity groups were not all statistically significant, English‐speaking Whites had the longest LOS (mean = 4.9 days) and highest costs (mean = $10,530). English‐speaking African Americans, Chinese and Spanish speakers had the highest 30‐day readmission rates; whereas, English‐speaking Latinos and Russian speakers had markedly lower 30‐day readmission rates (2.5% and 6.4%, respectively). Chinese speakers had the highest 30‐day mortality, followed by English speaking Whites and Asians.
Language‐Ethnicity Groups | LOS* Mean #Days (SD) | Cost Mean Cost $ (SD) | 30‐Day Readmission, n (%) | 30‐Day Mortality, n (%) |
---|---|---|---|---|
| ||||
English speakers (all) | 4.7 (4.5) | 10,035 (15,041) | 648 (11.9) | 613 (10.4) |
White | 4.9 (5.1) | 10,530 (15,894) | 322 (11.4) | 377 (12.3) |
African American | 4.5 (4.8) | 9107 (13,314) | 227 (17.5) | 91 (6.7) |
Asian | 4.3 (4.5) | 9933 (15,607) | 43 (8.8) | 67 (12.3) |
Latino | 4.6 (4.8) | 9823 (14,113) | 7 (2.5) | 18 (6.0) |
Other | 4.5 (4.8) | 9662 (14,016) | 49 (8.5) | 60 (9.7) |
Non‐English speakers (all) | 4.5 (4.5) | 9515 (13,213) | 117 (11.0) | 147 (12.8) |
Chinese speakers | 4.5 (4.6) | 9505 (12,841) | 69 (12.8) | 85 (14.6) |
Spanish speakers | 4.5 (4.5) | 9115 (13,846) | 31 (12.0) | 28 (10.3) |
Russian speakers | 4.7 (4.2) | 9846 (13,360) | 17 (6.4) | 34 (11.7) |
We further investigated differences among English speakers to better understand the very high rate of readmission for African Americans and the very low rate for English‐speaking Latinos. African Americans were on average younger than other English speakers (55 19 years vs. 60 21 years; P < 0.001); but, they had higher comorbidity scores than other English speakers (1.05 1.40 vs. 0.94 1.31; P = 0.008), and were more likely to be admitted for non‐malignant blood disorders (eg, sickle cell disease), endocrine disorders (eg, diabetes mellitus), and circulatory disorders (eg, stroke). In contrast, English‐speaking Latinos were also younger than other English speakers (53 21 years vs. 59 20 years; P < 0.001), but they trended toward lower comorbidity scores (0.87 1.23 vs. 0.97 1.33; P = 0.2), and were more likely to be admitted for gastrointestinal and musculoskeletal disorders, and less likely to be admitted for malignancy and endocrine disorders.
Multivariate Analyses: Association of Aggregated and Disaggregated Language‐Ethnicity Groups With Hospital Outcomes (Table 3)
In multivariate models examining aggregated language‐ethnicity groups, non‐English speakers had a trend toward higher odds of readmission at 30‐days post‐discharge than the English‐speaking group (odds ratio [OR], 1.3; 95% confidence interval [CI], 1.0‐1.7). There were no significant differences for LOS, cost, or 30‐day mortality. Compared to English speakers, Chinese and Spanish speakers had 70% and 50% higher adjusted odds of readmission at 30‐days post‐discharge respectively, while Russian speakers' odds of readmission was not increased. Additionally, Chinese speakers had 7% shorter LOS than English‐speakers. There were no significant differences among any of the language‐ethnicity groups for 30‐day mortality. The increased odds of readmission for Chinese and Spanish speakers compared to English speakers was robust to reinclusion of the admissions with the top 1% LOS and top 1% cost.
Language Categorization | LOS, % Difference (95% CI) | Total Cost, % Difference (95% CI) | 30‐Day Readmission,* OR (95% CI) | Mortality, OR (95% CI) |
---|---|---|---|---|
| ||||
All English speakers | Reference | Reference | Reference | Reference |
Non‐English speakers | 3.1 (8.7 to 3.1) | 2.5 (8.3 to 2.1) | 1.3 (1.0 to 1.7) | 0.9 (0.7 to 1.2) |
All English speakers | Reference | Reference | Reference | Reference |
Chinese speakers | 7.2 (13.9 to 0) | 5.3 (12.2 to 2.1) | 1.7 (1.2 to 2.3) | 1.0 (0.8 to 1.4) |
Spanish speakers | 3.0 (12.6 to 7.6) | 3.0 (12.7 to 7.7) | 1.5 (1.0 to 2.3) | 0.9 (0.6 to 1.5) |
Russian speakers | 1.5 (8.3 to 12.2) | 0.9 (8.9 to 11.8) | 0.8 (0.5 to 1.4) | 0.8 (0.5 to 1.2) |
Multivariate Analyses: Association of Language for Asians and Latinos, and of Ethnicity for English speakers, With Hospital Outcomes (Table 4)
Both Chinese and Spanish speakers had significantly higher odds of 30‐day readmission than their English speaking Asian and Latino counterparts. There were no significant differences in LOS, cost, or 30‐day mortality in this within‐ethnicity analysis. Among English speakers, admissions for patients with Asian ethnicity were 15% shorter and resulted in 9% lower costs than for Whites. While LOS and cost were similar for English‐speaking Latino and White admissions, English‐speaking Latinos had markedly lower odds of 30‐day readmission than their White counterparts. Whereas African‐Americans had 6% shorter LOS, 40% higher odds of readmission and 30% lower odds of mortality at 30‐days than English speaking Whites.
Language‐Ethnicity Comparisons | LOS, % Difference (95% CI) | Total Cost, % Difference (95% CI) | 30‐Day Readmission,* OR (95% CI) | Mortality, OR (95% CI) |
---|---|---|---|---|
| ||||
English speaking Asians | Reference | Reference | Reference | Reference |
Chinese speakers | 2.2 (7.4 to 12.7) | 0.3 (9.2 to 10.7) | 1.5 (1.0 to 2.3) | 0.8 (0.6 to 1.2) |
English speaking Latinos | Reference | Reference | Reference | Reference |
Spanish speakers | 4.5 (16.8 to 9.5) | 1.2 (14.0 to 13.5) | 5.7 (2.4 to 13.2) | 1.2 (0.6 to 2.4) |
English‐White | Reference | Reference | Reference | Reference |
English‐African American | 6.2 (11.3 to 0.9) | 4.4 (9.6 to 1.1) | 1.4 (1.1 to 1.7) | 0.6 (0.5 to 0.8) |
English‐Asian | 14.6 (20.9 to 7.9) | 8.6 (15.4 to 1.4) | 0.8 (0.5 to 1.0) | 1.0 (0.7 to 1.4) |
English‐Latino | 4.5 (13.5 to 5.4) | 5.0 (14.0 to 5.0) | 0.2 (0.1 to 0.4) | 0.6 (0.4 to 1.0) |
Conclusion/Discussion
Our results indicate that language barriers may contribute to higher readmission rates for non‐English speakers, but that they have less impact on care efficiency or mortality. This finding of an association between language and readmission, without a similar association with efficiency, suggests a potentially communication‐critical step in care.20, 21 Patients with language barriers are more likely to experience adverse events, and those events are often caused by errors in communication.12 It is conceivable that higher readmission risk for Chinese and Spanish speakers in our study was, at least in part, due to gaps in communication that are present in all patient groups, and exacerbated by the presence of a language barrier. This barrier is likely present during hospitalization but magnified at discharge, limiting caregivers' ability to understand patients' needs for home care, while simultaneously limiting patients' understanding of the discharge plan. After discharge, it is also possible that non‐English speakers are less able to communicate their needs as they arise, ormore subtlyfeel less supported by a primarily English speaking healthcare system. As in other clinical arenas,22 it is quite possible that increased access to professional interpreters in the hospital setting, and particularly at the time of discharge, would enhance communication and outcomes for LEP patients. Our interpreter services data showed that the patients in our study had quite limited access to staff professional interpreters.
Our findings differ somewhat from those of John‐Baptiste et al.,11 who found that language barriers contributed to increased LOS for patients with cardiac and major surgical diagnoses. Our study's findings are akin to recent research suggesting that being a monolingual Spanish speaker or receiving interpreter services may not significantly impact LOS or cost of hospitalization,23 and that LOS and in‐hospital mortality do not differ for non‐English speakers and English speakers after acute myocardial infarction.24 These studies, along with our results, suggest that that care efficiency in the hospital may be driven much more by clinical acuity (eg, the need to respond rapidly to urgent clinical signs such as hypotension, fever and respiratory distress) than by adequacy of communication. For example, elderly LEP patients may be even more likely than English speakers to have vigilant family members at the bedside throughout their hospitalization due to their need for communication assistance; these family members can quickly alert hospital staff to concerning changes in the patient's condition.
Our results also suggest the possibility that language and ethnicity are not monolithic concepts, and that even within language and ethnic groups there are potential differences in care pattern. For example, not speaking English may be a surrogate marker for unmeasured factors such as social supports and access to care. Language is intimately associated with culture; it remains plausible that cultural differences between highly acculturated and less acculturated members of a given ethno‐cultural group may have contributed to our observed differences in readmission rates. Differences in culture and associated factors, such as social support or use of multiple hospital systems, may account for lack of higher readmission risk in Russian speakers, while Chinese and Spanish speakers had higher readmission risk.
In addition, our finding that English‐speaking Latinos had lower readmission risk than any other group may be more consistent with their clinical characteristicseg, younger age, fewer comorbiditiesthan with cultural factors. Our finding that African American patients had the highest readmission risk in our hospital was both surprising and concerning. Some of this increased risk may be explained by clinical characteristics, such as higher comorbidities and higher rates of diagnoses leading to frequent admissions (eg, sickle cell disease); however, the reasons for this disparity deserves further investigation.
Our study has limitations. First, our data are administrative, and lack information about patients' educational attainment, social support, acculturation, utilization of other hospital systems, and usual source of care. Despite this, we were able to account for many significant covariates that might contribute to readmission rates, including age, insurance status, gender, comorbidities, and admission to the intensive care unit.2528 Second, our information about patients' English language proficiency is limited. While direct assessments of English proficiency are more accurate ways to determine a patient's ability to communicate with health care providers in English,29 our language validation work conducted in preparation for this study suggests that most of our patients recorded as having a non‐English primary language (87%) also have a low score on a language acculturation scale.
Third, only 14% of our non‐English speaking subjects utilized professional staff interpreters, and we had no information on the use of professional telephonic interpreters, or ad hoc interpretersfamily members, non‐interpreter staff membersand their impact on our results. It is well‐documented that ad hoc interpreters are used frequently in healthcare, particularly in the hospital setting, and thus we can assume this to be true in our study.30, 31 As noted above, it is likely that the advocacy of family members and friends at the bedside helped to minimize potential differences in care efficiency for patients with language barriers. Finally, our study was performed at a single university based hospital and may not produce results which are applicable to other care settings.
Our findings point to several avenues for future research on language barriers and hospitalized patients. First, the field would benefit from an examination of the impact of easy access to professional interpreters during hospitalization on outcomes of hospital care, in particular on readmission rates. Second, there is need for development and assessment of best practices for creating a culture of professional interpreter utilization in the hospital among physicians and nursing staff. Third, investigation of the role of caregiver presence in the hospital room and how this might differ by patient culture, age and language ability may further elucidate some of the differences across language groups observed in our study. Lastly, a more granular investigation of clinician‐patient communication and the importance of interpersonal processes of care on both patient satisfaction and understanding of and adherence to discharge instructions could lead to the development of detailed interventions to enhance this communication and these outcomes as it has done for communication‐sensitive outcomes in the outpatient arena.3234
In summary, our study suggests that higher risk for readmission can be added to the unfortunate list of outcomes which are worsened due to language barriers, pointing to transition from the hospital as a potentially communication‐critical step in care which may be amenable to intervention. Our findings also suggest that this risk can vary even between groups of patients who do not speak English primarily. Whether and to what degree language and communication barriers aloneincluding access to professional interpreters and patient‐centered communicationduring hospitalization, or differences in caregiver social support both during and after hospitalization as well as access to care post‐hospitalization contribute to these findings is a worthy subject of future research.
Acknowledgements
The authors acknowledge Dr. Eliseo J. Prez‐Stable for his mentorship on this project.
Forty‐five‐million Americans speak a language other than English and more than 19 million of these speak English less than very wellor are limited English proficient (LEP).1 The number of non‐English‐speaking and LEP people in the US has risen in recent decades, presenting a challenge to healthcare systems to provide high‐quality, patient‐centered care for these patients.2
For outpatients, language barriers are a fundamental contributor to gaps in health care. In the clinic setting, patients who do not speak English well have less access to a usual source of care and lower rates of physician visits and preventive services.36 Even when patients with language barriers do have access to care, they have poorer adherence, decreased comprehension of their diagnoses, decreased satisfaction with care, and increased medication complications.710
Few studies, however, have examined how language influences outcomes of hospital care. Compared to English‐speakers, patients who do not speak English well may experience longer lengths of stay,11 and have more adverse events while in the hospital.12 However, these previous studies have not investigated outcomes immediately post‐hospitalization, such as readmission rates and mortality, nor have they directly addressed the interaction between ethnicity and language.
To understand these questions, we analyzed data collected from a university‐based teaching hospital which cares for patients of diverse cultural and language backgrounds. Using these data, we examined how patients' primary language influenced hospital costs, length of stay (LOS), 30‐day readmission, and 30‐day mortality risk.
Patients and Methods
Patient Population and Setting
Our study examined patients admitted to the General Medicine Service at the University of California, San Francisco Medical Center (UCSF) between July 1, 2001 and June 30th, 2003, the time period during which UCSF participated in the Multicenter Hospitalist Trial (MHT) a prospective quasi‐randomized trial of hospitalist care for general medicine patients.13, 14
UCSF Moffitt‐Long Hospital is a 400‐bed urban academic medical center which provides services to the City and County of San Francisco, an ethnically and linguistically diverse area. UCSF employs staff language interpreters in Spanish, Chinese and Russian who travel to its many outpatient clinics, Comprehensive Cancer Center, Children's Hospital, as well as to Moffitt‐Long Hospital upon request; phone interpretation is also available when in‐person interpreters are not available, for off hours needs and for less common languages. During the period of this study there were no specific inpatient guidelines in place for use of interpretation services at UCSF, nor were there any specific interventions targeting LEP or non‐English speaking inpatients.
Patients were eligible for the MHT if they were 18 years of age or older and admitted at random to a hospitalist or non‐hospitalist physician (eg, outpatient general internist attending on average 1‐month/year); a minority of patients were cared for directly by their primary care physician while in the hospital, and were excluded. For purposes of our study, which merged MHT data with hospital administrative data on primary language, we further excluded all admissions for patients for whom primary language was missing (n = 5), whose listing was unknown or other language (n = 78), sign language (n = 3) or whose language was listed but was not one of the included languages (n = 258). Included languages were English, Chinese, Russian and Spanish. Because LOS and cost data were skewed, we excluded those admissions with the top 1% longest stays and the top 1% highest cost (n = 176); these exclusions did not alter the proportion of admissions across language and ethnicity. In addition, we excluded 102 admissions that were missing data on cost and 11 with costs <$500 and which were likely to be erroneous. Our research was approved by the UCSF Institutional Review Board.
Data Sources
We collected administrative data from Transition Systems Inc (TSI, Boston, MA) billing databases at UCSF as part of the MHT. These data include patient demographics, insurance, costs, ICD‐9CM diagnostic codes, admission and discharge dates in Uniform Bill 92 format. Patient mortality information was collected as part of the MHT using the National Death Index.14
Language data were collected from a separate patient‐registration database (STOR) at UCSF. Information on a patient's primary language is entered at the time each patient first registers at UCSF, whether for the index hospitalization or for prior clinic visits, and is based generally on patient self report. As part of our validation step, we cross‐checked 829 STOR language entries against patient reports and found 91% agreement with the majority of the errors classifying non‐English speakers as English‐speakers.
Measures
Predictor
Our primary language variable was derived using language designations collected from patient registration databases described above. Using these data we specified our key language groups as English, Chinese (Cantonese or Mandarin), Russian, or Spanish.
Outcomes
LOS and total cost of hospital stay for each hospitalization derived from administrative data sources. Readmissions were identified at the time patients were readmitted to UCSF (eg, flagged in administrative data). Mortality was determined by whether an individual patient with an admission in the database was recorded in the National Death Index as dead within 30‐days of admission.
Covariates
Additional covariates included age at admission, gender, ethnicity as recorded in registration databases (White, African American, Asian, Latino, Other), insurance, principal billing diagnosis, whether or not a patient received intensive care unit (ICU) care, type of admitting attending physician (Hospitalist/non‐Hospitalist), and an administrative Charlson comorbidity score.15 To collapse the principal diagnoses into categories, we used the Healthcare Cost and Utilization Project (HCUP)'s Clinical Classification System, which allowed us to classify each diagnosis in 1 of 14 generally accepted categories.16
Analysis
Statistical analyses were performed using STATA statistical software (STATACorp, Version 9, College Station, TX). We examined descriptive means and proportions for all variables, including sociodemographic, hospitalization, comorbidity and outcome variables. We compared English and non‐English speakers on all covariate and outcome variables using t‐tests for comparison of means and chi‐square for comparison of categorical variables.
It was not possible to fully test the language‐by‐ethnicity interactionwhether or not the impact of language varied by ethnic groupbecause many cells of the joint distribution were very sparse (eg, the sample contained very few non‐English‐speaking African Americans). Therefore, to better understand the influence of English vs. non‐English language usage across different ethnic groups, we created a combined language‐ethnicity predictor variable which categorized each subject first by language and then for the English‐speakers by ethnicity. For example, a Chinese, Spanish or Russian speaker would be categorized as such, and an English‐speaker could fall into the English‐White, English‐African American, English‐Asian or English‐Latino group. This allowed us to test whether there were any differences in language effects across the White, Asian, and Latino ethnicities, and any difference in ethnicity effects among English‐speakers.
Because cost and LOS were skewed, we used negative binomial models for LOS and log transformed costs. We performed a sensitivity analysis testing whether our results were robust to the exclusion of the admissions with the top 1% LOS and top 1% cost. We used logistic regression for the 30‐day readmission and mortality outcomes.
Our primary predictor was the language‐ethnicity variable described above. To determine the independent association between this predictor and our key outcomes, we then built models which included additional potential confounders selected either for face validity or because of observed confounding with other covariates. Our inclusion of potential confounders was limited by the variables available in the administrative database; thus, we were not able to pursue detailed analyses of communication and literacy factors and their interaction with our predictor or their independent impact on outcomes. Models also included a linear spline with a single knot at age 65 years as a further adjustment for age in Medicare recipients.1719 For the 30‐day readmission outcome model, we excluded those admissions for which the patient either died in the hospital or was discharged to hospice care. Within each model we tested the impact of a language barrier using custom contrasts. This allowed us to examine the language‐ethnicity effect aggregating all non‐English speakers compared to all English‐speakers, comparing each non‐English speaking group to all English‐speakers, comparing Chinese speakers to English‐speaking Asians and Spanish speakers to English‐speaking‐Latinos, as well as to test whether the effect of English language is the same across ethnicities.
Results
Admission Characteristics of the Sample
A total of 7023 patients were admitted to the General Medicine service, 5877 (84%) of whom were English‐speakers and 1146 (16%) non‐English‐speakers (Table 1). Overall, half of the admitted patients were women (50%), and the vast majority was insured (93%). The most common principal diagnoses were respiratory and gastrointestinal disorders. Only a small number of non‐English speakers 164 (14%) were recorded in the UCSF Interpreter Services database as having had any interaction with a professional staff interpreter during their hospitalization.
English (n = 5877) n (%) | Non‐English (n = 1146) n (%) | |
---|---|---|
| ||
Socio‐economic variables | ||
Language‐ethnicity | ||
English | ||
White | 3066 (52.2) | |
African American | 1351 (23.0) | |
Asian | 544 (9.3) | |
Latino | 298 (5.1) | |
Other | 618 (10.5) | |
Chinese speakers | 584 (51.0) | |
Spanish speakers | 272 (25.3) | |
Russian speakers | 290 (23.7) | |
Age mean (SD) (range 18‐105) | 58.8 (20.3) | 72.3 (15.5) |
Gender | ||
Male | 2967 (50.5) | 514 (44.8) |
Female | 2910 (49.5) | 632 (55.2) |
Insurance | ||
Medicare | 2878 (49.0) | 800 (69.8) |
Medicaid | 1201 (20.4) | 193 (16.8) |
Commercial | 1358 (23.1) | 106 (9.3) |
Charity/other | 440 (7.5) | 47 (4.1) |
Hospitalization variables | ||
Admitted to ICU | ||
Yes | 721 (12.3) | 149 (13.0) |
Attending physician | ||
Hospitalist | 3950 (67.2) | 781 (68.2) |
Comorbidity variables | ||
Principal Diagnosis | ||
Respiratory disorder | 1061 (18.1) | 225 (19.6) |
Gastrointestinal disorder | 963 (16.4) | 205 (17.9) |
Circulatory disorder | 613 (10.4) | 140 (12.2) |
Endocrine/metabolism | 671 (11.4) | 80 (7.0) |
Injury/poisoning | 475 (8.1) | 64 (5.6) |
Malignancy | 395 (6.7) | 107 (9.3) |
Renal/urinary disorder | 383 (6.5) | 108 (9.4) |
Skin disorder | 278 (4.7) | 28 (2.9) |
Infection/fatigue NOS | 206 (3.5) | 45 (3.4) |
Blood disorder (non‐malignant) | 189 (3.2) | 38 (3.3) |
Musculoskeletal/connective tissue disorder | 164 (2.8) | 33 (2.9) |
Mental disorder/substance abuse | 171 (2.9) | 7 (0.6) |
Nervous system/brain infection | 137 (2.3) | 26 (2.3) |
Unclassified | 171 (2.9) | 40 (3.5) |
Charlson Index score mean (SD) | 0.97 1.33 | 1.10 1.42 |
Among English speakers, Whites and African Americans were the most common ethnicities; however, more than 500 admissions were categorized as Asian ethnicity, and more than 600 as patients of other ethnicity. Close to 300 admissions were for Latinos. Among non‐English speakers, Chinese speakers had the largest number of admissions (n = 584), while Spanish and Russian speakers had similar numbers (n = 272 and 290 respectively).
Non‐English speakers were older, more likely to be female, more likely to be insured by Medicare, and more likely to have a higher comorbidity index score. While comorbidity scores were similar among non‐English speakers (Chinese 1.13 1.50; Russian 1.09 1.37; Spanish 1.06 1.30), they differed considerably among English speakers (White 0.94 1.29; African American 1.05 1.40; Asian 1.04 1.45; Latino 0.89 1.23; Other 0.91 1.29).
Hospital Outcome by Language‐Ethnicity Group (Table 2)
When aggregated together, non‐English speakers were somewhat more likely to be dead at 30‐days and have lower cost admissions; however, they did not differ from English speakers on LOS or readmission rates. While differences among disaggregated language‐ethnicity groups were not all statistically significant, English‐speaking Whites had the longest LOS (mean = 4.9 days) and highest costs (mean = $10,530). English‐speaking African Americans, Chinese and Spanish speakers had the highest 30‐day readmission rates; whereas, English‐speaking Latinos and Russian speakers had markedly lower 30‐day readmission rates (2.5% and 6.4%, respectively). Chinese speakers had the highest 30‐day mortality, followed by English speaking Whites and Asians.
Language‐Ethnicity Groups | LOS* Mean #Days (SD) | Cost Mean Cost $ (SD) | 30‐Day Readmission, n (%) | 30‐Day Mortality, n (%) |
---|---|---|---|---|
| ||||
English speakers (all) | 4.7 (4.5) | 10,035 (15,041) | 648 (11.9) | 613 (10.4) |
White | 4.9 (5.1) | 10,530 (15,894) | 322 (11.4) | 377 (12.3) |
African American | 4.5 (4.8) | 9107 (13,314) | 227 (17.5) | 91 (6.7) |
Asian | 4.3 (4.5) | 9933 (15,607) | 43 (8.8) | 67 (12.3) |
Latino | 4.6 (4.8) | 9823 (14,113) | 7 (2.5) | 18 (6.0) |
Other | 4.5 (4.8) | 9662 (14,016) | 49 (8.5) | 60 (9.7) |
Non‐English speakers (all) | 4.5 (4.5) | 9515 (13,213) | 117 (11.0) | 147 (12.8) |
Chinese speakers | 4.5 (4.6) | 9505 (12,841) | 69 (12.8) | 85 (14.6) |
Spanish speakers | 4.5 (4.5) | 9115 (13,846) | 31 (12.0) | 28 (10.3) |
Russian speakers | 4.7 (4.2) | 9846 (13,360) | 17 (6.4) | 34 (11.7) |
We further investigated differences among English speakers to better understand the very high rate of readmission for African Americans and the very low rate for English‐speaking Latinos. African Americans were on average younger than other English speakers (55 19 years vs. 60 21 years; P < 0.001); but, they had higher comorbidity scores than other English speakers (1.05 1.40 vs. 0.94 1.31; P = 0.008), and were more likely to be admitted for non‐malignant blood disorders (eg, sickle cell disease), endocrine disorders (eg, diabetes mellitus), and circulatory disorders (eg, stroke). In contrast, English‐speaking Latinos were also younger than other English speakers (53 21 years vs. 59 20 years; P < 0.001), but they trended toward lower comorbidity scores (0.87 1.23 vs. 0.97 1.33; P = 0.2), and were more likely to be admitted for gastrointestinal and musculoskeletal disorders, and less likely to be admitted for malignancy and endocrine disorders.
Multivariate Analyses: Association of Aggregated and Disaggregated Language‐Ethnicity Groups With Hospital Outcomes (Table 3)
In multivariate models examining aggregated language‐ethnicity groups, non‐English speakers had a trend toward higher odds of readmission at 30‐days post‐discharge than the English‐speaking group (odds ratio [OR], 1.3; 95% confidence interval [CI], 1.0‐1.7). There were no significant differences for LOS, cost, or 30‐day mortality. Compared to English speakers, Chinese and Spanish speakers had 70% and 50% higher adjusted odds of readmission at 30‐days post‐discharge respectively, while Russian speakers' odds of readmission was not increased. Additionally, Chinese speakers had 7% shorter LOS than English‐speakers. There were no significant differences among any of the language‐ethnicity groups for 30‐day mortality. The increased odds of readmission for Chinese and Spanish speakers compared to English speakers was robust to reinclusion of the admissions with the top 1% LOS and top 1% cost.
Language Categorization | LOS, % Difference (95% CI) | Total Cost, % Difference (95% CI) | 30‐Day Readmission,* OR (95% CI) | Mortality, OR (95% CI) |
---|---|---|---|---|
| ||||
All English speakers | Reference | Reference | Reference | Reference |
Non‐English speakers | 3.1 (8.7 to 3.1) | 2.5 (8.3 to 2.1) | 1.3 (1.0 to 1.7) | 0.9 (0.7 to 1.2) |
All English speakers | Reference | Reference | Reference | Reference |
Chinese speakers | 7.2 (13.9 to 0) | 5.3 (12.2 to 2.1) | 1.7 (1.2 to 2.3) | 1.0 (0.8 to 1.4) |
Spanish speakers | 3.0 (12.6 to 7.6) | 3.0 (12.7 to 7.7) | 1.5 (1.0 to 2.3) | 0.9 (0.6 to 1.5) |
Russian speakers | 1.5 (8.3 to 12.2) | 0.9 (8.9 to 11.8) | 0.8 (0.5 to 1.4) | 0.8 (0.5 to 1.2) |
Multivariate Analyses: Association of Language for Asians and Latinos, and of Ethnicity for English speakers, With Hospital Outcomes (Table 4)
Both Chinese and Spanish speakers had significantly higher odds of 30‐day readmission than their English speaking Asian and Latino counterparts. There were no significant differences in LOS, cost, or 30‐day mortality in this within‐ethnicity analysis. Among English speakers, admissions for patients with Asian ethnicity were 15% shorter and resulted in 9% lower costs than for Whites. While LOS and cost were similar for English‐speaking Latino and White admissions, English‐speaking Latinos had markedly lower odds of 30‐day readmission than their White counterparts. Whereas African‐Americans had 6% shorter LOS, 40% higher odds of readmission and 30% lower odds of mortality at 30‐days than English speaking Whites.
Language‐Ethnicity Comparisons | LOS, % Difference (95% CI) | Total Cost, % Difference (95% CI) | 30‐Day Readmission,* OR (95% CI) | Mortality, OR (95% CI) |
---|---|---|---|---|
| ||||
English speaking Asians | Reference | Reference | Reference | Reference |
Chinese speakers | 2.2 (7.4 to 12.7) | 0.3 (9.2 to 10.7) | 1.5 (1.0 to 2.3) | 0.8 (0.6 to 1.2) |
English speaking Latinos | Reference | Reference | Reference | Reference |
Spanish speakers | 4.5 (16.8 to 9.5) | 1.2 (14.0 to 13.5) | 5.7 (2.4 to 13.2) | 1.2 (0.6 to 2.4) |
English‐White | Reference | Reference | Reference | Reference |
English‐African American | 6.2 (11.3 to 0.9) | 4.4 (9.6 to 1.1) | 1.4 (1.1 to 1.7) | 0.6 (0.5 to 0.8) |
English‐Asian | 14.6 (20.9 to 7.9) | 8.6 (15.4 to 1.4) | 0.8 (0.5 to 1.0) | 1.0 (0.7 to 1.4) |
English‐Latino | 4.5 (13.5 to 5.4) | 5.0 (14.0 to 5.0) | 0.2 (0.1 to 0.4) | 0.6 (0.4 to 1.0) |
Conclusion/Discussion
Our results indicate that language barriers may contribute to higher readmission rates for non‐English speakers, but that they have less impact on care efficiency or mortality. This finding of an association between language and readmission, without a similar association with efficiency, suggests a potentially communication‐critical step in care.20, 21 Patients with language barriers are more likely to experience adverse events, and those events are often caused by errors in communication.12 It is conceivable that higher readmission risk for Chinese and Spanish speakers in our study was, at least in part, due to gaps in communication that are present in all patient groups, and exacerbated by the presence of a language barrier. This barrier is likely present during hospitalization but magnified at discharge, limiting caregivers' ability to understand patients' needs for home care, while simultaneously limiting patients' understanding of the discharge plan. After discharge, it is also possible that non‐English speakers are less able to communicate their needs as they arise, ormore subtlyfeel less supported by a primarily English speaking healthcare system. As in other clinical arenas,22 it is quite possible that increased access to professional interpreters in the hospital setting, and particularly at the time of discharge, would enhance communication and outcomes for LEP patients. Our interpreter services data showed that the patients in our study had quite limited access to staff professional interpreters.
Our findings differ somewhat from those of John‐Baptiste et al.,11 who found that language barriers contributed to increased LOS for patients with cardiac and major surgical diagnoses. Our study's findings are akin to recent research suggesting that being a monolingual Spanish speaker or receiving interpreter services may not significantly impact LOS or cost of hospitalization,23 and that LOS and in‐hospital mortality do not differ for non‐English speakers and English speakers after acute myocardial infarction.24 These studies, along with our results, suggest that that care efficiency in the hospital may be driven much more by clinical acuity (eg, the need to respond rapidly to urgent clinical signs such as hypotension, fever and respiratory distress) than by adequacy of communication. For example, elderly LEP patients may be even more likely than English speakers to have vigilant family members at the bedside throughout their hospitalization due to their need for communication assistance; these family members can quickly alert hospital staff to concerning changes in the patient's condition.
Our results also suggest the possibility that language and ethnicity are not monolithic concepts, and that even within language and ethnic groups there are potential differences in care pattern. For example, not speaking English may be a surrogate marker for unmeasured factors such as social supports and access to care. Language is intimately associated with culture; it remains plausible that cultural differences between highly acculturated and less acculturated members of a given ethno‐cultural group may have contributed to our observed differences in readmission rates. Differences in culture and associated factors, such as social support or use of multiple hospital systems, may account for lack of higher readmission risk in Russian speakers, while Chinese and Spanish speakers had higher readmission risk.
In addition, our finding that English‐speaking Latinos had lower readmission risk than any other group may be more consistent with their clinical characteristicseg, younger age, fewer comorbiditiesthan with cultural factors. Our finding that African American patients had the highest readmission risk in our hospital was both surprising and concerning. Some of this increased risk may be explained by clinical characteristics, such as higher comorbidities and higher rates of diagnoses leading to frequent admissions (eg, sickle cell disease); however, the reasons for this disparity deserves further investigation.
Our study has limitations. First, our data are administrative, and lack information about patients' educational attainment, social support, acculturation, utilization of other hospital systems, and usual source of care. Despite this, we were able to account for many significant covariates that might contribute to readmission rates, including age, insurance status, gender, comorbidities, and admission to the intensive care unit.2528 Second, our information about patients' English language proficiency is limited. While direct assessments of English proficiency are more accurate ways to determine a patient's ability to communicate with health care providers in English,29 our language validation work conducted in preparation for this study suggests that most of our patients recorded as having a non‐English primary language (87%) also have a low score on a language acculturation scale.
Third, only 14% of our non‐English speaking subjects utilized professional staff interpreters, and we had no information on the use of professional telephonic interpreters, or ad hoc interpretersfamily members, non‐interpreter staff membersand their impact on our results. It is well‐documented that ad hoc interpreters are used frequently in healthcare, particularly in the hospital setting, and thus we can assume this to be true in our study.30, 31 As noted above, it is likely that the advocacy of family members and friends at the bedside helped to minimize potential differences in care efficiency for patients with language barriers. Finally, our study was performed at a single university based hospital and may not produce results which are applicable to other care settings.
Our findings point to several avenues for future research on language barriers and hospitalized patients. First, the field would benefit from an examination of the impact of easy access to professional interpreters during hospitalization on outcomes of hospital care, in particular on readmission rates. Second, there is need for development and assessment of best practices for creating a culture of professional interpreter utilization in the hospital among physicians and nursing staff. Third, investigation of the role of caregiver presence in the hospital room and how this might differ by patient culture, age and language ability may further elucidate some of the differences across language groups observed in our study. Lastly, a more granular investigation of clinician‐patient communication and the importance of interpersonal processes of care on both patient satisfaction and understanding of and adherence to discharge instructions could lead to the development of detailed interventions to enhance this communication and these outcomes as it has done for communication‐sensitive outcomes in the outpatient arena.3234
In summary, our study suggests that higher risk for readmission can be added to the unfortunate list of outcomes which are worsened due to language barriers, pointing to transition from the hospital as a potentially communication‐critical step in care which may be amenable to intervention. Our findings also suggest that this risk can vary even between groups of patients who do not speak English primarily. Whether and to what degree language and communication barriers aloneincluding access to professional interpreters and patient‐centered communicationduring hospitalization, or differences in caregiver social support both during and after hospitalization as well as access to care post‐hospitalization contribute to these findings is a worthy subject of future research.
Acknowledgements
The authors acknowledge Dr. Eliseo J. Prez‐Stable for his mentorship on this project.
- 2000. Available at: http://www.census.gov/prod/2003pubs/c2kbr‐29.pdf. Accessed January 2010. , . Language Use and English‐Speaking Ability:
- U.S. Department of Health and Human Services.2006National Healthcare Disparities Report. AHRQ Publication No. 070012; 2006.
- Disparities in health care by race, ethnicity, and language among the insured: findings from a national sample.Med Care.2002;40(1):52–59. , , , .
- The effect of physician‐patient communication on mammography utilization by different ethnic groups.Med Care.1991;29(11):1065–1082. , .
- Language of interview:relevance for research of Southwest Hispanics.Am J Pub Health.1991;81(11):1399–1404. , .
- Is language a barrier to the use of preventive services?J Gen Intern Med.1997;12(8):472–477. , , , .
- Impact of language barriers on patient satisfaction in an emergency department.JGIM.1999;14:82–87. , , , .
- Patient comprehension of doctor‐patient communication on discharge from the emergency department.J Emerg Med.1997;15(1):1–7. .
- Drug complications in outpatients.J Gen Intern Med.2000;15:149–154. , , , et al.
- Language concordance as a determinant of patient compliance and emergency room use in patients with asthma.Med Care.1988;26(12):1119–1128. .
- The effect of English language proficiency on length of stay and in‐hospital mortality.J Gen Intern Med.2004;19:221–228. , , ,et al.
- Language proficiency and adverse events in US hospitals: a pilot study.Int J Qual Health Care.2007;19(2):60–67. , , , .
- Factors associated with discussion of care plans and code status at the time of hospital admission: results from the Multicenter Hospitalist Study.J Hosp Med.2008;3(6):437–445. , , , et al.
- Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non‐hospitalists.J Gen Intern Med.2008;23(9):1399–1406. , , , et al.
- A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40(5):373–383. , , , .
- AHRQ. Healthcare Cost and Utilizlation Project: Tools 132(3):191–200.
- Free knot splines for logistic models and threshold selection.Comput Methods Programs Biomed.2005;77(1):1–9. , , .
- Statistical methods in epidemiology: a comparison of statistical methods to analyze dose‐response and trend analysis in epidemiologic studies.J Clin Epidemiol.1998;51(12):1223–1233. , , , , .
- The Care Transitions Project. Health Care Policy and Research, Practitioner Tools. Available at: http://www.caretransitions.org/practitioner_tools.asp.
- AHRQ. Improving safety at the point of care. Available at: http://www.ahrq.gov/qual/pips. Accessed January2010.
- Do professional interpreters improve clinical care for patients with limited english proficiency? A systematic review of the literature.Health Serv Res.2007;42(2):727–754. , , , .
- The impact of an enhanced interpreter service intervention on hospital costs and patient satisfaction.J Gen Intern Med.2007;22 Suppl 2:306–311. , , .
- Acute myocardial infarction length of stay and hospital mortality are not associated with language preference.J Gen Intern Med.2008;23(2):190–194. , , , , .
- A systematic literature review of factors affecting outcome in older medical patients admitted to hospital.Age Ageing.2004;33(2):110–115. , , .
- Risk factors and prognostic predictors of unexpected intensive care unit admission within 3 days after ED discharge.Am J Emerg Med.2007;25(9):1009–1014. , , , , , .
- Effect of gender, ethnicity, pulmonary disease, and symptom stability on rehospitalization in patients with heart failure.Am J Cardiol.2007;100(7):1139–1144. , .
- Bouncing back: patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke.J Am Geriatr Soc.2007;55(3):365–373. , , , .
- Identification of limited English proficient patients in clinical care.J Gen Intern Med.2008;23(10):1555–1560. , , , , .
- Hospital langague services for patients with limited English proficiency: results from a national survey.Health Research October2006. , , , , .
- Hospitals, Language, and Culture: a Snapshot of the Nation. The Joint Commission and The California Endowment;2007. , .
- A randomized controlled trial of interventions to enhance patient‐physician partnership, patient adherence and high blood pressure control among ethnic minorities and poor persons: study protocol NCT00123045.Implement Sci.2009;4:7. , , , et al.
- Interpersonal processes of care and patient satisfaction: do associations differ by race, ethnicity, and language?Health Serv Res.2009;44(4):1326–1344. , , , , .
- Understanding concordance in patient‐physician relationships: personal and ethnic dimensions of shared identity.Ann Fam Med.2008;6(3):198–205. , , , .
- 2000. Available at: http://www.census.gov/prod/2003pubs/c2kbr‐29.pdf. Accessed January 2010. , . Language Use and English‐Speaking Ability:
- U.S. Department of Health and Human Services.2006National Healthcare Disparities Report. AHRQ Publication No. 070012; 2006.
- Disparities in health care by race, ethnicity, and language among the insured: findings from a national sample.Med Care.2002;40(1):52–59. , , , .
- The effect of physician‐patient communication on mammography utilization by different ethnic groups.Med Care.1991;29(11):1065–1082. , .
- Language of interview:relevance for research of Southwest Hispanics.Am J Pub Health.1991;81(11):1399–1404. , .
- Is language a barrier to the use of preventive services?J Gen Intern Med.1997;12(8):472–477. , , , .
- Impact of language barriers on patient satisfaction in an emergency department.JGIM.1999;14:82–87. , , , .
- Patient comprehension of doctor‐patient communication on discharge from the emergency department.J Emerg Med.1997;15(1):1–7. .
- Drug complications in outpatients.J Gen Intern Med.2000;15:149–154. , , , et al.
- Language concordance as a determinant of patient compliance and emergency room use in patients with asthma.Med Care.1988;26(12):1119–1128. .
- The effect of English language proficiency on length of stay and in‐hospital mortality.J Gen Intern Med.2004;19:221–228. , , ,et al.
- Language proficiency and adverse events in US hospitals: a pilot study.Int J Qual Health Care.2007;19(2):60–67. , , , .
- Factors associated with discussion of care plans and code status at the time of hospital admission: results from the Multicenter Hospitalist Study.J Hosp Med.2008;3(6):437–445. , , , et al.
- Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non‐hospitalists.J Gen Intern Med.2008;23(9):1399–1406. , , , et al.
- A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40(5):373–383. , , , .
- AHRQ. Healthcare Cost and Utilizlation Project: Tools 132(3):191–200.
- Free knot splines for logistic models and threshold selection.Comput Methods Programs Biomed.2005;77(1):1–9. , , .
- Statistical methods in epidemiology: a comparison of statistical methods to analyze dose‐response and trend analysis in epidemiologic studies.J Clin Epidemiol.1998;51(12):1223–1233. , , , , .
- The Care Transitions Project. Health Care Policy and Research, Practitioner Tools. Available at: http://www.caretransitions.org/practitioner_tools.asp.
- AHRQ. Improving safety at the point of care. Available at: http://www.ahrq.gov/qual/pips. Accessed January2010.
- Do professional interpreters improve clinical care for patients with limited english proficiency? A systematic review of the literature.Health Serv Res.2007;42(2):727–754. , , , .
- The impact of an enhanced interpreter service intervention on hospital costs and patient satisfaction.J Gen Intern Med.2007;22 Suppl 2:306–311. , , .
- Acute myocardial infarction length of stay and hospital mortality are not associated with language preference.J Gen Intern Med.2008;23(2):190–194. , , , , .
- A systematic literature review of factors affecting outcome in older medical patients admitted to hospital.Age Ageing.2004;33(2):110–115. , , .
- Risk factors and prognostic predictors of unexpected intensive care unit admission within 3 days after ED discharge.Am J Emerg Med.2007;25(9):1009–1014. , , , , , .
- Effect of gender, ethnicity, pulmonary disease, and symptom stability on rehospitalization in patients with heart failure.Am J Cardiol.2007;100(7):1139–1144. , .
- Bouncing back: patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke.J Am Geriatr Soc.2007;55(3):365–373. , , , .
- Identification of limited English proficient patients in clinical care.J Gen Intern Med.2008;23(10):1555–1560. , , , , .
- Hospital langague services for patients with limited English proficiency: results from a national survey.Health Research October2006. , , , , .
- Hospitals, Language, and Culture: a Snapshot of the Nation. The Joint Commission and The California Endowment;2007. , .
- A randomized controlled trial of interventions to enhance patient‐physician partnership, patient adherence and high blood pressure control among ethnic minorities and poor persons: study protocol NCT00123045.Implement Sci.2009;4:7. , , , et al.
- Interpersonal processes of care and patient satisfaction: do associations differ by race, ethnicity, and language?Health Serv Res.2009;44(4):1326–1344. , , , , .
- Understanding concordance in patient‐physician relationships: personal and ethnic dimensions of shared identity.Ann Fam Med.2008;6(3):198–205. , , , .
Copyright © 2010 Society of Hospital Medicine
Hemoglobin Levels in Hospitalized Patients
Studies of the red blood cell mass performed in hospitalized patients were first done in the early 1970s.1 Thereafter, a decrease in the hemoglobin concentration ([Hb]) and its potential causes have been further reported, especially in critically ill patients.25 The decrease in [Hb] can occur as a result of blood draws for diagnostic testing; blood loss associated with invasive procedures and bleeding; occult gastrointestinal bleeding; hemolysis; shortening of red cell survival; iron, folic acid or cobalamine deficiencies; renal, liver or endocrine failure; hemodilution associated with fluid therapy; and the so‐called anemia of inflammation (AI).68 The latter would be a consequence of a blunted response of the bone marrow due to several factors such as inadequate secretion of erythropoietin, inhibition of the proliferation and differentiation of the erythroid precursors of the bone marrow and an hepcidin‐mediated functional iron deficiency.810
As mentioned, most studies were done in critically ill patients and scarce information is available about general ward admitted patients (GWAP) with less severe illnesses. In this scenario, some of the proposed mechanisms may have a less clear role. It is widely accepted that [Hb] may decrease without overt bleeding in GWAP. However, given the lack of information on this matter, laboratory controls and invasive procedures are undertaken to determine its potential causes.
The purpose of the present study is to describe [Hb] variation over time in nonbleeding GWAP, to estimate the proportion of patients with [Hb] decreases 1.5 g/dL, and to evaluate possible related variables.
Materials and Methods
A 16‐week (September 2004‐January 2005) prospective observational study was conducted in Internal Medicine GWAP at 2 Buenos Aires teaching hospitals.
All consecutive patients older than 16 years were evaluated. Patients admitted for the following reasons were excluded: bleeding, trauma, surgery, invasive procedures associated with blood loss (biopsies, biliary drainage, endovascular therapeutic procedures, and chest tubes), blood transfusions, anemia, chemotherapy, and acute renal failure.
Patients with a bleeding history, chemotherapy or radiation therapy within two months prior to admission, patients on dialysis and patients with current oncologic or hematologic disease were excluded, as well as those with length of stay less than 3 days, or with less than 2 [Hb] or hematocrit (HCT) measurements.
Patients were followed until discharge, death or transfer to a critical care unit. Patients were withdrawn from the study if they presented with bleeding or hemolysis, underwent red blood cell transfusion or therapy affecting hemoglobin levels (iron, chemotherapy or erythropoietin) or if either a surgical or invasive procedure associated with blood loss was performed.
Data were collected from patients' medical records and additional information was obtained from treating physicians and patients by the authors (AL, NC, SM, AN, MH). Standardized case report forms were completed during the hospitalization including: age, sex, admissions in the previous 3 months (readmissions) and whether or not the patient lived in a nursing home. Patients were categorized according to discharge diagnosis as reported by Nguyen et al.4 with modifications related to our general ward population. Upon admission Katz daily activity index (ADL),11, 12 APACHE II acute physiology score (APS),13, 14 and Charlson comorbidity score (Charlson)15, 16 were assessed. In all cases the [Hb] and HCT values were registered on admission and on days 3, 6, 10 and prior to discharge as well as any other determination required by the treating physician. Anemia was defined as [Hb] 13 g/dL for men and 12 g/dL for women.17 Based on previous reports a 1.5 g/dL decrease in [Hb] and of 4.5 points in HCT compared to admission values were considered a significant fall.1, 4 Acute renal failure was defined as an increase in creatinine level 0.5 mg/dL or a 50% increase from baseline.18, 19
Every procedure without significant blood loss (PWSBL) such as venous catheter placement, thoracentesis, lumbar punction, paracentesis, skin biopsy, arthrocentesis, and diagnostic angiogram was also recorded. The study was approved by the Hospital's ethical committee.
Data analysis and processing were performed with Excel 2000 and Stata version 8.0 (Stata Corp; USA). For continuous variables, results were expressed by the mean value its standard deviation (SD), and compared with Student's t‐test. The chi‐square test was used to compare categorical variables. A survival curve was developed with the Kaplan‐Meier method to analyze the time to a significant fall in [Hb]. Finally, Cox proportional hazard modeling was performed to assess the association between a significant fall in [Hb] and other variables. We accepted P < 0,05 as significant.
Results
A total of 338 patients were admitted to the Internal Medicine Inpatient Services in the study period. Thirty‐nine percent (131) of these patients were included. Exclusion criteria were: diagnosis at admission (n = 95, 45.9%), past medical history (n = 56, 27%) and length of stay less than 3 days, or less than 2 determinations of [Hb] or HCT (n = 56, 27%). Data collection was discontinued for the following reasons: discharge (81.7%), death (6.9%), surgery or procedures associated with blood loss (5.3%), transfer to a critical care unit (3%), transfusions (2.3%), and chemotherapy (0.8%). The baseline characteristics of the study patients are shown in Table 1.
n | % | Mean (SD) | Median | Min/Max | |
---|---|---|---|---|---|
| |||||
Age, years | 71.9 (17.4) | 77 | 18/97 | ||
18‐40 | 11 | 8.4 | |||
41‐60 | 16 | 12.2 | |||
61‐80 | 52 | 39.7 | |||
>80 | 52 | 39.7 | |||
Gender | |||||
Female | 75 | 57.2 | |||
Lenght of stay (days) | 7 (4.8) | 6 | 3/28 | ||
APS | 4.9 (4.2) | 4 | 0/22 | ||
0‐4 | 71 | 54.2 | |||
5‐8 | 36 | 27.5 | |||
>8 | 24 | 18.3 | |||
ADL | 4.5 (2.3) | 6 | 0/6 | ||
0‐2 | 33 | 25.2 | |||
3‐5 | 11 | 8.4 | |||
6 | 87 | 66.4 | |||
CHARLSON | 2.2 (2.3) | 2 | 0/11 | ||
0 | 32 | 24.4 | |||
1 | 32 | 24.4 | |||
2 | 22 | 16.8 | |||
3 | 18 | 13.7 | |||
>3 | 27 | 20.6 | |||
Readmissions | 28 | 21.4 | |||
PWSBL | 14 | 10.7 | |||
Anemia at admission | 63 | 48.1 | |||
[Hb] at admission | 12.5 (1.7) | 12.5 | 8.6/17 | ||
[Hb] at admission males | 12.8 (1.9) | 12.6 | 8.7/17 | ||
[Hb] at admission females | 12.3(1.5) | 12.3 | 8.6/15.5 |
Diagnoses at discharge were divided into the following categories: infections (25.2%), electrolyte disturbances (7.6%), cardiac diseases (9.9%), neurologic (19.1%), respiratory (16.8%), gastrointestinal (10.7%), and other diagnosis (10.7%).
No evidence of bleeding was found in the included patients. Bleeding was only observed in 4 of the initially evaluated patients who were excluded since they failed to meet the required number of [Hb] determinations before bleeding.
A mean decrease in [Hb] of 0.71 g/dL was found between admission and discharge day (P < 0.001; 95% CI, 0.47‐0.97). Mean nadir [Hb] was 1.45 g/dL lower than admission [Hb] (P < 0.001; 95% CI, 1.24‐1.67). Mean nadir day occurred between hospitalization days 3 and 4. Mean [Hb] at discharge was 11.8 1.8 g/dL. This value is higher than the mean concentration at nadir (0.74 g/dL P < 0.001; 95% CI, 0.60‐0.97) (Figure 1).

Table 2 shows the rate of patients with a decrease in the [Hb] for different cutoff levels. Forty‐five percent of the study population (59 patients) had a significant fall in [Hb] during hospitalization. This was observed on day 2 in 50% of the patients. Likewise, a significant fall in HCT value was found in 42.7% of patients. If [Hb] decrease during hospitalization is analyzed as a proportion of [Hb] at admission, 52.7% of patients had a 10% or greater [Hb] decrease during their hospital stay.
[Hb] fall (g/dL) | 0.5 | 1 | 1.5 | 2 | 2.5 | 3 | 3.5 | 4 | 4.5 |
---|---|---|---|---|---|---|---|---|---|
% of patients | 80.9 | 60.3 | 45.0 | 28.2 | 17.6 | 9.9 | 5.3 | 3.8 | 2.3 |
Using Kaplan‐Meier analysis, the estimated proportion showing no significant fall of [Hb] was 0.63 (95% CI, 0.55‐0.71) on day 3 and 0.48 (95% CI, 0.29‐0.67) on day 10. By day 15, only 3.8% of the initially included patients were still hospitalized and showed no decrease in [Hb] 1.5 g/dl. These patients maintained their [Hb] stable for the rest of the follow up period (Figure 2).

In the univariate analysis, comparing patients that experienced a decrease in [Hb] 1.5 g/dL with those who did not, significant differences were only found in the following variables: length of stay, APS, anemia at admission, [Hb] at admission, and infectious, gastrointestinal or cardiac diseases at discharge diagnosis (Table 3).
Patients with a significant fall | Patients without a significant fall | P Value | |
---|---|---|---|
| |||
n | 59 (45%) | 72 (55%) | |
Age, years | 73.15 (18.7) | 70.83 (16.2) | 0.448 |
Gender, female | 32 (54.2%) | 43 (59.7%) | 0.527 |
Length of stay (days) | 8.30 (5.6) | 5.91 (3.7) | <0.007 |
APS | 6.13 (4.5) | 3.97 (3.7) | <0.004 |
ADL | 4.33 (2.5) | 4.68 (2.1) | 0.410 |
CHARLSON | 2.03 (1.8) | 2.37 (2.5) | 0.382 |
Nurse home residents | 4 (6.8%) | 3 (4.2%) | 0.700 |
Readmissions | 11 (18.6%) | 17 (23.6%) | 0.490 |
PWSBL | 6 (10.2%) | 8 (11.1%) | 0.862 |
Anemia at admission | 20 (33.9%) | 43 (59.7%) | <0.004 |
[Hb] at admission | 13.09 (1.7) | 12.01 (1.5) | <0.001 |
Diagnosis at discharge | |||
Infectious | 20 (33.9%) | 13 (18.1%) | <0.05 |
Respiratory | 8 (13.6%) | 14 (19.4%) | 0.370 |
Neurologic | 9 (15.2%) | 16 (22.2%) | 0.312 |
Gastrointestinal | 11 (18.6%) | 3 (4.2%) | <0.01 |
Cardiac | 2 (3.4%) | 11(15.3%) | <0.05 |
Electrolyte disturbances | 6 (10.2%) | 4 (5.6%) | 0.512 |
Others | 3 (5.1%) | 11 (15.3%) | 0.087 |
In the Cox proportional hazard model adjusting for the variables shown in Table 4, a significant independent direct association was found between a significant fall in [Hb] during hospital stay and APS, [Hb] at admission, and either diagnosis of infectious or gastrointestinal disease at discharge. Similar results were found if a significant fall was redefined as a 12% decrease in [Hb] or as a 4.5 point decrease in HCT from baseline.
Variable | HRR | P Value | 95% CI |
---|---|---|---|
| |||
APS | 1.07 | 0.007 | 1.02‐1.12 |
ADL | 1.11 | 0.132 | 0.97‐1.27 |
Charlson | 0.88 | 0.121 | 0.75‐1.03 |
Nurse home resident | 1.52 | 0.361 | 0.62‐3.72 |
PWSBL | 0.67 | 0.390 | 0.27‐1.66 |
Readmission | 1.14 | 0.710 | 0.57‐2.29 |
Female sex | 0.98 | 0.944 | 0.57‐1.69 |
Age | 1.39 | 0.098 | 0.94‐2.07 |
[Hb] at admission | 1.27 | 0.005 | 1.07‐1.51 |
Diagnosis at discharge | |||
Infectious | 2.70 | 0.015 | 1.21‐6.05 |
Neurologic | 1.42 | 0.457 | 0.57‐3.55 |
Gastrointestinal | 3.74 | 0.002 | 1.62‐8.64 |
Cardiac | 0.41 | 0.289 | 0.08‐2.12 |
Electrolyte dist. | 2.08 | 0.176 | 0.72‐6.05 |
Others | 0.95 | 0.946 | 0.24‐3.81 |
Discussion
This study describes the variation of [Hb] and HCT values in GWAP without bleeding or other obvious medical conditions associated with a decrease in the red blood cell mass. As previously described,4, 20, 21 we found that [Hb] decreases during hospital stay are frequently observed. The mean [Hb] fall from admission was 1.45 g/dL, and was mainly recorded between hospitalization days 3 and 4. In approximately half of the study population, a 1.5 g/dL decrease in [Hb] was observed. In the survival analysis, 40% and 55% of patients are expected to present such a fall on day 4 and 12 respectively. In accordance with prior reports4, 21 a greater decrease in [Hb] occurred in the first days of hospitalization, and a high proportion of patients were already anemic at the time of admission.
The following variables were associated with a decrease in the [Hb] during hospitalization: higher APS score, higher [Hb] at admission, and diagnosis of infectious or gastrointestinal disease at discharge. Even though several mechanisms seem to contribute to the decrease in [Hb] during hospitalization our data suggest that 1 of these factors seems to be the severity of the disease, as previously proposed by Nguyen et al.4 This observation is supported by the association found in our study between [Hb] decrease and APS. No association was found with Charlson, ADL, and being a nursing home resident. These variables, which have not been previously analyzed, seem to indicate that patients with chronic illnesses are not more likely to have decreases in [Hb] during hospitalization.
Patients with higher [Hb] at admission had a greater [Hb] decrease during their hospital stay. This finding could suggest that the mechanism related to this decrease had less effect on patients with lower [Hb]. This is similar to that observed in the anemia of chronic diseases where [Hb] does not usually fall to extreme values. Our analysis reveals a greater decrease during the first days of hospitalization. Given the high rate of patients anemic at admission, it is possible that the decrease in [Hb] had begun prior to admission.
Previous papers describe a low prevalence of cyanocobalamine (2%), folic acid (2%), and iron (9%) deficiency in patients admitted to critical care units.3 In these patients an inadequate bone marrow response has been proposed as a mechanism for [Hb] decrease, a phenomenon that has been called AI or anemia of critical illness.8, 2228 Inflammatory response associated with acute disease causes this hypoproliferative anemia through 3 different pathways: relative erythropoietin deficit, a direct inhibition of the erythropoiesis in the bone marrow through different mediators (ie, interleukin [IL]‐1, IL‐6, tumor necrosis factor [TNF]) and a functional iron deficit. This relative iron deficiency is produced mainly by the IL‐6 induced overexpression of the hepcidin gene in hepatocytes. Hepcidin causes impaired intestinal iron absorption and an inadequate delivery of iron from the iron‐recycling macrophages to the erythroid precursors in the bone marrow.8, 9
AI could explain some of our findings, such as the greater decrease observed in the first hospital days when inflamatory mediators levels are expected to be higher. The association between APS and infectious disease diagnosis and a greater [Hb] decrease may also be explained by this mechanism. Nonetheless, the daily bone marrow production of red blood cells is small compared with the circulating red mass cell and therefore it is necessary to have a better explanation on how AI could be associated with this rapid decrease in the [Hb]. Rice et al.30, 31 have explored some mechanisms leading to a rapid decrease in the red mass cell related to acute variations in erytropoietin level. A role of this mechanism and other could be hypothesized.29
Overt bleeding was not found in our study population and procedures without significant blood loss (PWSBL) were infrequent, therefore it is unlikely that they could have had an impact on the decrease of [Hb]. The influence of other variables, such as blood volume drawn for diagnostic testing and occult blood losses (especially from the gastrointestinal tract) was not investigated. Parenteral hydration and hemodilution neither were evaluated in our study nor were extensively described in the literature. We think this last mentioned mechanism plays a role in the large [Hb] decrease during the first days of hospitalization. However a lack of association was mentioned in the only study that evaluated this issue in critically ill patients. Intravascular hemolysis has been cited as an infrequent event in these patients.2, 4
Only patients without a clear explanation for their [Hb] decrease were included because they represent a matter of concern for hospitalists and other treating physicians. Therefore 60% of the initially evaluated patients were excluded, since they were admitted for conditions likely to cause a decrease in [Hb]. Nevertheless, it seems likely that the mechanisms involved in the included patients may play a role in those with an obvious cause for [Hb] variation as well. Accordingly, the decrease of [Hb] expected in patients admitted with disorders known to cause a decrease in the red blood cell mass would be greater than the 1 observed in our study population.
This [Hb] drop during hospitalization may be clinically relevant in a number of ways: it could cause the attending physicians to order costly and invasive studies, it could have prognostic value as it occurs in patients admitted with myocardial infarction, and it could trigger an acute coronary syndrome.20 We observed an association between [Hb] drop and a higher APS in our patients. This score has been validated as a prognostic factor in previous studies. However, it is not possible to conclude that this [Hb] drop is associated with a worse prognosis.
Our study had several limitations. The sample's heterogeneity inherent to our general ward population could have altered our results and their generalization. To overcome this bias we used a categorization system based on discharge diagnosis. However, this particular system has several limitations. The small sample size and the absence of follow up data after discharge limited our capacity to detect any prognostic significance of [Hb] decrease. In the present study, the total decrease of [Hb] may have been underestimated because the onset of the acute illness precedes hospital admission by a variable time. Finally, a relatively small sample size limited our ability to detect other possible significant predictors of [Hb] decrease. However, this study was not designed to assess the mechanisms associated with the decrease of [Hb], but rather to establish its occurrence, measure it and explore related variables.
These results may be useful for further studies to evaluate [Hb] variation in admitted patients and its relation to other variables, such as bone marrow production, oxygen use, erythrocyte survival, nutritional deficiencies, and erythropoietin and inflammatory mediators levels.
In conclusion, our general medical inpatients had a mean 1.45 g/dL [Hb] decrease during hospitalization, which was greater in the first days of hospitalization, even though an evident cause was not present. These findings would help attending physicians in general wards make a rational and efficient approach when dealing with patients' decrease in [Hb].
Acknowledgements
The authors thank Jorge Lopez Camelo and Hugo Krupitzki for statistical advice and Valeria Melia for helping in the preparation of this manuscript.
- Nosocomial anemia.JAMA.1973;223(1):73–74. , .
- Important role of nondiagnostic blood loss and blunted erythropoietic response in the anemia of medical intensive care patients.Crit Care Med.1999;27(12):2630–2639. , , , , .
- Nutritional deficiencies and blunted erythropoietin response as causes of the anemia of critical illness.J Crit Care.2001;16(1):36–41. , , , , , .
- Time course of hemoglobin concentrations in nonbleeding intensive care unit patients.Crit Care Med.2003;31(2):406–410. , , , .
- Erithropoietin response in critically ill patients.Crit Care Med.1997;25(Suppl1):a82. , , , et al.
- Acute event‐related anaemia.Br J Haematol.2001;115(4):739–743. , , .
- Response of erythropoiesis and iron metabolism to recombinant human erythropoietin in intensive care unit patients.Crit Care Med.2000;28(8):2773–2778. , , , , , .
- The anemia of inflammation/malignancy: mechanisms and management.Hematology Am Soc Hematol Educ Program.2008:159–165. .
- The regulation of hepcidin and its effects on systemic and cellular iron metabolism.Hematology Am Soc Hematol Educ Program.2008:151–158. .
- Erythropoietin response is blunted in critically ill patients.Intensive Care Med.1997;23:159–162. , , , et al.,
- Studies of illness in the aged. The index of ADL: standardized measure of biological and psychosocial function.JAMA.1963;185:914–919. , , , , .
- Progress in the development of the index of ADL.Gerontologist.1970;10:20–30. , , ,
- Measuring prognosis and case mix in hospitalized elders. The importance of functional status.J Gen Intern Med.1997;12:203–208. , , , , .
- APACHE II: A severity of disease classification system.Crit Care Med.1985;12(10):818–829. , , , .
- A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chron Dis.1997;40:373–383. , , , .
- How to measure comorbidity: a critical review of available methods.J Clin Epidemiol.2003;56:221–229. , , , .
- Nutritional anaemias. Report of a WHO Scientific Group.World Health Organ Tech Rep Ser.1968;405:1–40. , , , et al.
- Relationship between hematocrit and renal function in men and women.Kidney Int.2001;59(2):725–731. , , , .
- Epidemiology of anemia associated with chronic renal insufficiency among adults in the United States: results from the Third National Health and Nutrition Examination Survey.J Am Soc Nephrol.2002;13(2):504–510. , , .
- Changes in haemoglobin levels during hospital course and long‐term outcome after myocardial infarction.Eur Heart J.2007;28(11):1289–1296. , , , et al.,
- Do blood test cause anemia in hospitalized patients?J Gen Intern Med.2005;20:520–524. , , , , .
- Red blood cell physiology in critical illness.Crit Care Med.2003;31(12 Suppl)( ):S651–S657. , .
- Anemia in the critically ill.Crit Care Clin.2004;20:159–178. .
- Anemia of the critically ill: acute anemia of chronic disease.Crit Care Med.2000;28(8):3098–3099. , .
- Anemia and blood transfusion in the critically ill patient: role of erythropoietin.Crit Care.2004;8(Suppl 2):S42–S44. .
- Anemia of critical illness – implications for understanding and treating rHuEPO resistance.Nephrol Dial Transplant.2002;17(Suppl 5):S48–S55. .
- Transfusion practice in the critically ill.Crit Care Med.2003;31(Suppl):S668–S671. , , .
- Anemia in critically ill patients.Eur J Inter Med.2004;15(8):481–486. .
- Neocytolysis: a physiologic down‐regulator of red blood cell mass.Lancet.1997;349:1389–1390. , , , .
- Neocytolysis contributes to the anemia of renal disease.Am J Kidney Dis.1999;33:59–62. , , , , , .
- Neocytolysis on descent from altitude: a newly recognized mechanism for the control of red cell mass.Ann Intern Med.2001;134:652–656. , , , et al.
- Phlebotomy for diagnostic laboratory test in adults. Pattern of use and effect on transfusion requirements.N Engl J Med.1986;314:1233–1235. , .
Studies of the red blood cell mass performed in hospitalized patients were first done in the early 1970s.1 Thereafter, a decrease in the hemoglobin concentration ([Hb]) and its potential causes have been further reported, especially in critically ill patients.25 The decrease in [Hb] can occur as a result of blood draws for diagnostic testing; blood loss associated with invasive procedures and bleeding; occult gastrointestinal bleeding; hemolysis; shortening of red cell survival; iron, folic acid or cobalamine deficiencies; renal, liver or endocrine failure; hemodilution associated with fluid therapy; and the so‐called anemia of inflammation (AI).68 The latter would be a consequence of a blunted response of the bone marrow due to several factors such as inadequate secretion of erythropoietin, inhibition of the proliferation and differentiation of the erythroid precursors of the bone marrow and an hepcidin‐mediated functional iron deficiency.810
As mentioned, most studies were done in critically ill patients and scarce information is available about general ward admitted patients (GWAP) with less severe illnesses. In this scenario, some of the proposed mechanisms may have a less clear role. It is widely accepted that [Hb] may decrease without overt bleeding in GWAP. However, given the lack of information on this matter, laboratory controls and invasive procedures are undertaken to determine its potential causes.
The purpose of the present study is to describe [Hb] variation over time in nonbleeding GWAP, to estimate the proportion of patients with [Hb] decreases 1.5 g/dL, and to evaluate possible related variables.
Materials and Methods
A 16‐week (September 2004‐January 2005) prospective observational study was conducted in Internal Medicine GWAP at 2 Buenos Aires teaching hospitals.
All consecutive patients older than 16 years were evaluated. Patients admitted for the following reasons were excluded: bleeding, trauma, surgery, invasive procedures associated with blood loss (biopsies, biliary drainage, endovascular therapeutic procedures, and chest tubes), blood transfusions, anemia, chemotherapy, and acute renal failure.
Patients with a bleeding history, chemotherapy or radiation therapy within two months prior to admission, patients on dialysis and patients with current oncologic or hematologic disease were excluded, as well as those with length of stay less than 3 days, or with less than 2 [Hb] or hematocrit (HCT) measurements.
Patients were followed until discharge, death or transfer to a critical care unit. Patients were withdrawn from the study if they presented with bleeding or hemolysis, underwent red blood cell transfusion or therapy affecting hemoglobin levels (iron, chemotherapy or erythropoietin) or if either a surgical or invasive procedure associated with blood loss was performed.
Data were collected from patients' medical records and additional information was obtained from treating physicians and patients by the authors (AL, NC, SM, AN, MH). Standardized case report forms were completed during the hospitalization including: age, sex, admissions in the previous 3 months (readmissions) and whether or not the patient lived in a nursing home. Patients were categorized according to discharge diagnosis as reported by Nguyen et al.4 with modifications related to our general ward population. Upon admission Katz daily activity index (ADL),11, 12 APACHE II acute physiology score (APS),13, 14 and Charlson comorbidity score (Charlson)15, 16 were assessed. In all cases the [Hb] and HCT values were registered on admission and on days 3, 6, 10 and prior to discharge as well as any other determination required by the treating physician. Anemia was defined as [Hb] 13 g/dL for men and 12 g/dL for women.17 Based on previous reports a 1.5 g/dL decrease in [Hb] and of 4.5 points in HCT compared to admission values were considered a significant fall.1, 4 Acute renal failure was defined as an increase in creatinine level 0.5 mg/dL or a 50% increase from baseline.18, 19
Every procedure without significant blood loss (PWSBL) such as venous catheter placement, thoracentesis, lumbar punction, paracentesis, skin biopsy, arthrocentesis, and diagnostic angiogram was also recorded. The study was approved by the Hospital's ethical committee.
Data analysis and processing were performed with Excel 2000 and Stata version 8.0 (Stata Corp; USA). For continuous variables, results were expressed by the mean value its standard deviation (SD), and compared with Student's t‐test. The chi‐square test was used to compare categorical variables. A survival curve was developed with the Kaplan‐Meier method to analyze the time to a significant fall in [Hb]. Finally, Cox proportional hazard modeling was performed to assess the association between a significant fall in [Hb] and other variables. We accepted P < 0,05 as significant.
Results
A total of 338 patients were admitted to the Internal Medicine Inpatient Services in the study period. Thirty‐nine percent (131) of these patients were included. Exclusion criteria were: diagnosis at admission (n = 95, 45.9%), past medical history (n = 56, 27%) and length of stay less than 3 days, or less than 2 determinations of [Hb] or HCT (n = 56, 27%). Data collection was discontinued for the following reasons: discharge (81.7%), death (6.9%), surgery or procedures associated with blood loss (5.3%), transfer to a critical care unit (3%), transfusions (2.3%), and chemotherapy (0.8%). The baseline characteristics of the study patients are shown in Table 1.
n | % | Mean (SD) | Median | Min/Max | |
---|---|---|---|---|---|
| |||||
Age, years | 71.9 (17.4) | 77 | 18/97 | ||
18‐40 | 11 | 8.4 | |||
41‐60 | 16 | 12.2 | |||
61‐80 | 52 | 39.7 | |||
>80 | 52 | 39.7 | |||
Gender | |||||
Female | 75 | 57.2 | |||
Lenght of stay (days) | 7 (4.8) | 6 | 3/28 | ||
APS | 4.9 (4.2) | 4 | 0/22 | ||
0‐4 | 71 | 54.2 | |||
5‐8 | 36 | 27.5 | |||
>8 | 24 | 18.3 | |||
ADL | 4.5 (2.3) | 6 | 0/6 | ||
0‐2 | 33 | 25.2 | |||
3‐5 | 11 | 8.4 | |||
6 | 87 | 66.4 | |||
CHARLSON | 2.2 (2.3) | 2 | 0/11 | ||
0 | 32 | 24.4 | |||
1 | 32 | 24.4 | |||
2 | 22 | 16.8 | |||
3 | 18 | 13.7 | |||
>3 | 27 | 20.6 | |||
Readmissions | 28 | 21.4 | |||
PWSBL | 14 | 10.7 | |||
Anemia at admission | 63 | 48.1 | |||
[Hb] at admission | 12.5 (1.7) | 12.5 | 8.6/17 | ||
[Hb] at admission males | 12.8 (1.9) | 12.6 | 8.7/17 | ||
[Hb] at admission females | 12.3(1.5) | 12.3 | 8.6/15.5 |
Diagnoses at discharge were divided into the following categories: infections (25.2%), electrolyte disturbances (7.6%), cardiac diseases (9.9%), neurologic (19.1%), respiratory (16.8%), gastrointestinal (10.7%), and other diagnosis (10.7%).
No evidence of bleeding was found in the included patients. Bleeding was only observed in 4 of the initially evaluated patients who were excluded since they failed to meet the required number of [Hb] determinations before bleeding.
A mean decrease in [Hb] of 0.71 g/dL was found between admission and discharge day (P < 0.001; 95% CI, 0.47‐0.97). Mean nadir [Hb] was 1.45 g/dL lower than admission [Hb] (P < 0.001; 95% CI, 1.24‐1.67). Mean nadir day occurred between hospitalization days 3 and 4. Mean [Hb] at discharge was 11.8 1.8 g/dL. This value is higher than the mean concentration at nadir (0.74 g/dL P < 0.001; 95% CI, 0.60‐0.97) (Figure 1).

Table 2 shows the rate of patients with a decrease in the [Hb] for different cutoff levels. Forty‐five percent of the study population (59 patients) had a significant fall in [Hb] during hospitalization. This was observed on day 2 in 50% of the patients. Likewise, a significant fall in HCT value was found in 42.7% of patients. If [Hb] decrease during hospitalization is analyzed as a proportion of [Hb] at admission, 52.7% of patients had a 10% or greater [Hb] decrease during their hospital stay.
[Hb] fall (g/dL) | 0.5 | 1 | 1.5 | 2 | 2.5 | 3 | 3.5 | 4 | 4.5 |
---|---|---|---|---|---|---|---|---|---|
% of patients | 80.9 | 60.3 | 45.0 | 28.2 | 17.6 | 9.9 | 5.3 | 3.8 | 2.3 |
Using Kaplan‐Meier analysis, the estimated proportion showing no significant fall of [Hb] was 0.63 (95% CI, 0.55‐0.71) on day 3 and 0.48 (95% CI, 0.29‐0.67) on day 10. By day 15, only 3.8% of the initially included patients were still hospitalized and showed no decrease in [Hb] 1.5 g/dl. These patients maintained their [Hb] stable for the rest of the follow up period (Figure 2).

In the univariate analysis, comparing patients that experienced a decrease in [Hb] 1.5 g/dL with those who did not, significant differences were only found in the following variables: length of stay, APS, anemia at admission, [Hb] at admission, and infectious, gastrointestinal or cardiac diseases at discharge diagnosis (Table 3).
Patients with a significant fall | Patients without a significant fall | P Value | |
---|---|---|---|
| |||
n | 59 (45%) | 72 (55%) | |
Age, years | 73.15 (18.7) | 70.83 (16.2) | 0.448 |
Gender, female | 32 (54.2%) | 43 (59.7%) | 0.527 |
Length of stay (days) | 8.30 (5.6) | 5.91 (3.7) | <0.007 |
APS | 6.13 (4.5) | 3.97 (3.7) | <0.004 |
ADL | 4.33 (2.5) | 4.68 (2.1) | 0.410 |
CHARLSON | 2.03 (1.8) | 2.37 (2.5) | 0.382 |
Nurse home residents | 4 (6.8%) | 3 (4.2%) | 0.700 |
Readmissions | 11 (18.6%) | 17 (23.6%) | 0.490 |
PWSBL | 6 (10.2%) | 8 (11.1%) | 0.862 |
Anemia at admission | 20 (33.9%) | 43 (59.7%) | <0.004 |
[Hb] at admission | 13.09 (1.7) | 12.01 (1.5) | <0.001 |
Diagnosis at discharge | |||
Infectious | 20 (33.9%) | 13 (18.1%) | <0.05 |
Respiratory | 8 (13.6%) | 14 (19.4%) | 0.370 |
Neurologic | 9 (15.2%) | 16 (22.2%) | 0.312 |
Gastrointestinal | 11 (18.6%) | 3 (4.2%) | <0.01 |
Cardiac | 2 (3.4%) | 11(15.3%) | <0.05 |
Electrolyte disturbances | 6 (10.2%) | 4 (5.6%) | 0.512 |
Others | 3 (5.1%) | 11 (15.3%) | 0.087 |
In the Cox proportional hazard model adjusting for the variables shown in Table 4, a significant independent direct association was found between a significant fall in [Hb] during hospital stay and APS, [Hb] at admission, and either diagnosis of infectious or gastrointestinal disease at discharge. Similar results were found if a significant fall was redefined as a 12% decrease in [Hb] or as a 4.5 point decrease in HCT from baseline.
Variable | HRR | P Value | 95% CI |
---|---|---|---|
| |||
APS | 1.07 | 0.007 | 1.02‐1.12 |
ADL | 1.11 | 0.132 | 0.97‐1.27 |
Charlson | 0.88 | 0.121 | 0.75‐1.03 |
Nurse home resident | 1.52 | 0.361 | 0.62‐3.72 |
PWSBL | 0.67 | 0.390 | 0.27‐1.66 |
Readmission | 1.14 | 0.710 | 0.57‐2.29 |
Female sex | 0.98 | 0.944 | 0.57‐1.69 |
Age | 1.39 | 0.098 | 0.94‐2.07 |
[Hb] at admission | 1.27 | 0.005 | 1.07‐1.51 |
Diagnosis at discharge | |||
Infectious | 2.70 | 0.015 | 1.21‐6.05 |
Neurologic | 1.42 | 0.457 | 0.57‐3.55 |
Gastrointestinal | 3.74 | 0.002 | 1.62‐8.64 |
Cardiac | 0.41 | 0.289 | 0.08‐2.12 |
Electrolyte dist. | 2.08 | 0.176 | 0.72‐6.05 |
Others | 0.95 | 0.946 | 0.24‐3.81 |
Discussion
This study describes the variation of [Hb] and HCT values in GWAP without bleeding or other obvious medical conditions associated with a decrease in the red blood cell mass. As previously described,4, 20, 21 we found that [Hb] decreases during hospital stay are frequently observed. The mean [Hb] fall from admission was 1.45 g/dL, and was mainly recorded between hospitalization days 3 and 4. In approximately half of the study population, a 1.5 g/dL decrease in [Hb] was observed. In the survival analysis, 40% and 55% of patients are expected to present such a fall on day 4 and 12 respectively. In accordance with prior reports4, 21 a greater decrease in [Hb] occurred in the first days of hospitalization, and a high proportion of patients were already anemic at the time of admission.
The following variables were associated with a decrease in the [Hb] during hospitalization: higher APS score, higher [Hb] at admission, and diagnosis of infectious or gastrointestinal disease at discharge. Even though several mechanisms seem to contribute to the decrease in [Hb] during hospitalization our data suggest that 1 of these factors seems to be the severity of the disease, as previously proposed by Nguyen et al.4 This observation is supported by the association found in our study between [Hb] decrease and APS. No association was found with Charlson, ADL, and being a nursing home resident. These variables, which have not been previously analyzed, seem to indicate that patients with chronic illnesses are not more likely to have decreases in [Hb] during hospitalization.
Patients with higher [Hb] at admission had a greater [Hb] decrease during their hospital stay. This finding could suggest that the mechanism related to this decrease had less effect on patients with lower [Hb]. This is similar to that observed in the anemia of chronic diseases where [Hb] does not usually fall to extreme values. Our analysis reveals a greater decrease during the first days of hospitalization. Given the high rate of patients anemic at admission, it is possible that the decrease in [Hb] had begun prior to admission.
Previous papers describe a low prevalence of cyanocobalamine (2%), folic acid (2%), and iron (9%) deficiency in patients admitted to critical care units.3 In these patients an inadequate bone marrow response has been proposed as a mechanism for [Hb] decrease, a phenomenon that has been called AI or anemia of critical illness.8, 2228 Inflammatory response associated with acute disease causes this hypoproliferative anemia through 3 different pathways: relative erythropoietin deficit, a direct inhibition of the erythropoiesis in the bone marrow through different mediators (ie, interleukin [IL]‐1, IL‐6, tumor necrosis factor [TNF]) and a functional iron deficit. This relative iron deficiency is produced mainly by the IL‐6 induced overexpression of the hepcidin gene in hepatocytes. Hepcidin causes impaired intestinal iron absorption and an inadequate delivery of iron from the iron‐recycling macrophages to the erythroid precursors in the bone marrow.8, 9
AI could explain some of our findings, such as the greater decrease observed in the first hospital days when inflamatory mediators levels are expected to be higher. The association between APS and infectious disease diagnosis and a greater [Hb] decrease may also be explained by this mechanism. Nonetheless, the daily bone marrow production of red blood cells is small compared with the circulating red mass cell and therefore it is necessary to have a better explanation on how AI could be associated with this rapid decrease in the [Hb]. Rice et al.30, 31 have explored some mechanisms leading to a rapid decrease in the red mass cell related to acute variations in erytropoietin level. A role of this mechanism and other could be hypothesized.29
Overt bleeding was not found in our study population and procedures without significant blood loss (PWSBL) were infrequent, therefore it is unlikely that they could have had an impact on the decrease of [Hb]. The influence of other variables, such as blood volume drawn for diagnostic testing and occult blood losses (especially from the gastrointestinal tract) was not investigated. Parenteral hydration and hemodilution neither were evaluated in our study nor were extensively described in the literature. We think this last mentioned mechanism plays a role in the large [Hb] decrease during the first days of hospitalization. However a lack of association was mentioned in the only study that evaluated this issue in critically ill patients. Intravascular hemolysis has been cited as an infrequent event in these patients.2, 4
Only patients without a clear explanation for their [Hb] decrease were included because they represent a matter of concern for hospitalists and other treating physicians. Therefore 60% of the initially evaluated patients were excluded, since they were admitted for conditions likely to cause a decrease in [Hb]. Nevertheless, it seems likely that the mechanisms involved in the included patients may play a role in those with an obvious cause for [Hb] variation as well. Accordingly, the decrease of [Hb] expected in patients admitted with disorders known to cause a decrease in the red blood cell mass would be greater than the 1 observed in our study population.
This [Hb] drop during hospitalization may be clinically relevant in a number of ways: it could cause the attending physicians to order costly and invasive studies, it could have prognostic value as it occurs in patients admitted with myocardial infarction, and it could trigger an acute coronary syndrome.20 We observed an association between [Hb] drop and a higher APS in our patients. This score has been validated as a prognostic factor in previous studies. However, it is not possible to conclude that this [Hb] drop is associated with a worse prognosis.
Our study had several limitations. The sample's heterogeneity inherent to our general ward population could have altered our results and their generalization. To overcome this bias we used a categorization system based on discharge diagnosis. However, this particular system has several limitations. The small sample size and the absence of follow up data after discharge limited our capacity to detect any prognostic significance of [Hb] decrease. In the present study, the total decrease of [Hb] may have been underestimated because the onset of the acute illness precedes hospital admission by a variable time. Finally, a relatively small sample size limited our ability to detect other possible significant predictors of [Hb] decrease. However, this study was not designed to assess the mechanisms associated with the decrease of [Hb], but rather to establish its occurrence, measure it and explore related variables.
These results may be useful for further studies to evaluate [Hb] variation in admitted patients and its relation to other variables, such as bone marrow production, oxygen use, erythrocyte survival, nutritional deficiencies, and erythropoietin and inflammatory mediators levels.
In conclusion, our general medical inpatients had a mean 1.45 g/dL [Hb] decrease during hospitalization, which was greater in the first days of hospitalization, even though an evident cause was not present. These findings would help attending physicians in general wards make a rational and efficient approach when dealing with patients' decrease in [Hb].
Acknowledgements
The authors thank Jorge Lopez Camelo and Hugo Krupitzki for statistical advice and Valeria Melia for helping in the preparation of this manuscript.
Studies of the red blood cell mass performed in hospitalized patients were first done in the early 1970s.1 Thereafter, a decrease in the hemoglobin concentration ([Hb]) and its potential causes have been further reported, especially in critically ill patients.25 The decrease in [Hb] can occur as a result of blood draws for diagnostic testing; blood loss associated with invasive procedures and bleeding; occult gastrointestinal bleeding; hemolysis; shortening of red cell survival; iron, folic acid or cobalamine deficiencies; renal, liver or endocrine failure; hemodilution associated with fluid therapy; and the so‐called anemia of inflammation (AI).68 The latter would be a consequence of a blunted response of the bone marrow due to several factors such as inadequate secretion of erythropoietin, inhibition of the proliferation and differentiation of the erythroid precursors of the bone marrow and an hepcidin‐mediated functional iron deficiency.810
As mentioned, most studies were done in critically ill patients and scarce information is available about general ward admitted patients (GWAP) with less severe illnesses. In this scenario, some of the proposed mechanisms may have a less clear role. It is widely accepted that [Hb] may decrease without overt bleeding in GWAP. However, given the lack of information on this matter, laboratory controls and invasive procedures are undertaken to determine its potential causes.
The purpose of the present study is to describe [Hb] variation over time in nonbleeding GWAP, to estimate the proportion of patients with [Hb] decreases 1.5 g/dL, and to evaluate possible related variables.
Materials and Methods
A 16‐week (September 2004‐January 2005) prospective observational study was conducted in Internal Medicine GWAP at 2 Buenos Aires teaching hospitals.
All consecutive patients older than 16 years were evaluated. Patients admitted for the following reasons were excluded: bleeding, trauma, surgery, invasive procedures associated with blood loss (biopsies, biliary drainage, endovascular therapeutic procedures, and chest tubes), blood transfusions, anemia, chemotherapy, and acute renal failure.
Patients with a bleeding history, chemotherapy or radiation therapy within two months prior to admission, patients on dialysis and patients with current oncologic or hematologic disease were excluded, as well as those with length of stay less than 3 days, or with less than 2 [Hb] or hematocrit (HCT) measurements.
Patients were followed until discharge, death or transfer to a critical care unit. Patients were withdrawn from the study if they presented with bleeding or hemolysis, underwent red blood cell transfusion or therapy affecting hemoglobin levels (iron, chemotherapy or erythropoietin) or if either a surgical or invasive procedure associated with blood loss was performed.
Data were collected from patients' medical records and additional information was obtained from treating physicians and patients by the authors (AL, NC, SM, AN, MH). Standardized case report forms were completed during the hospitalization including: age, sex, admissions in the previous 3 months (readmissions) and whether or not the patient lived in a nursing home. Patients were categorized according to discharge diagnosis as reported by Nguyen et al.4 with modifications related to our general ward population. Upon admission Katz daily activity index (ADL),11, 12 APACHE II acute physiology score (APS),13, 14 and Charlson comorbidity score (Charlson)15, 16 were assessed. In all cases the [Hb] and HCT values were registered on admission and on days 3, 6, 10 and prior to discharge as well as any other determination required by the treating physician. Anemia was defined as [Hb] 13 g/dL for men and 12 g/dL for women.17 Based on previous reports a 1.5 g/dL decrease in [Hb] and of 4.5 points in HCT compared to admission values were considered a significant fall.1, 4 Acute renal failure was defined as an increase in creatinine level 0.5 mg/dL or a 50% increase from baseline.18, 19
Every procedure without significant blood loss (PWSBL) such as venous catheter placement, thoracentesis, lumbar punction, paracentesis, skin biopsy, arthrocentesis, and diagnostic angiogram was also recorded. The study was approved by the Hospital's ethical committee.
Data analysis and processing were performed with Excel 2000 and Stata version 8.0 (Stata Corp; USA). For continuous variables, results were expressed by the mean value its standard deviation (SD), and compared with Student's t‐test. The chi‐square test was used to compare categorical variables. A survival curve was developed with the Kaplan‐Meier method to analyze the time to a significant fall in [Hb]. Finally, Cox proportional hazard modeling was performed to assess the association between a significant fall in [Hb] and other variables. We accepted P < 0,05 as significant.
Results
A total of 338 patients were admitted to the Internal Medicine Inpatient Services in the study period. Thirty‐nine percent (131) of these patients were included. Exclusion criteria were: diagnosis at admission (n = 95, 45.9%), past medical history (n = 56, 27%) and length of stay less than 3 days, or less than 2 determinations of [Hb] or HCT (n = 56, 27%). Data collection was discontinued for the following reasons: discharge (81.7%), death (6.9%), surgery or procedures associated with blood loss (5.3%), transfer to a critical care unit (3%), transfusions (2.3%), and chemotherapy (0.8%). The baseline characteristics of the study patients are shown in Table 1.
n | % | Mean (SD) | Median | Min/Max | |
---|---|---|---|---|---|
| |||||
Age, years | 71.9 (17.4) | 77 | 18/97 | ||
18‐40 | 11 | 8.4 | |||
41‐60 | 16 | 12.2 | |||
61‐80 | 52 | 39.7 | |||
>80 | 52 | 39.7 | |||
Gender | |||||
Female | 75 | 57.2 | |||
Lenght of stay (days) | 7 (4.8) | 6 | 3/28 | ||
APS | 4.9 (4.2) | 4 | 0/22 | ||
0‐4 | 71 | 54.2 | |||
5‐8 | 36 | 27.5 | |||
>8 | 24 | 18.3 | |||
ADL | 4.5 (2.3) | 6 | 0/6 | ||
0‐2 | 33 | 25.2 | |||
3‐5 | 11 | 8.4 | |||
6 | 87 | 66.4 | |||
CHARLSON | 2.2 (2.3) | 2 | 0/11 | ||
0 | 32 | 24.4 | |||
1 | 32 | 24.4 | |||
2 | 22 | 16.8 | |||
3 | 18 | 13.7 | |||
>3 | 27 | 20.6 | |||
Readmissions | 28 | 21.4 | |||
PWSBL | 14 | 10.7 | |||
Anemia at admission | 63 | 48.1 | |||
[Hb] at admission | 12.5 (1.7) | 12.5 | 8.6/17 | ||
[Hb] at admission males | 12.8 (1.9) | 12.6 | 8.7/17 | ||
[Hb] at admission females | 12.3(1.5) | 12.3 | 8.6/15.5 |
Diagnoses at discharge were divided into the following categories: infections (25.2%), electrolyte disturbances (7.6%), cardiac diseases (9.9%), neurologic (19.1%), respiratory (16.8%), gastrointestinal (10.7%), and other diagnosis (10.7%).
No evidence of bleeding was found in the included patients. Bleeding was only observed in 4 of the initially evaluated patients who were excluded since they failed to meet the required number of [Hb] determinations before bleeding.
A mean decrease in [Hb] of 0.71 g/dL was found between admission and discharge day (P < 0.001; 95% CI, 0.47‐0.97). Mean nadir [Hb] was 1.45 g/dL lower than admission [Hb] (P < 0.001; 95% CI, 1.24‐1.67). Mean nadir day occurred between hospitalization days 3 and 4. Mean [Hb] at discharge was 11.8 1.8 g/dL. This value is higher than the mean concentration at nadir (0.74 g/dL P < 0.001; 95% CI, 0.60‐0.97) (Figure 1).

Table 2 shows the rate of patients with a decrease in the [Hb] for different cutoff levels. Forty‐five percent of the study population (59 patients) had a significant fall in [Hb] during hospitalization. This was observed on day 2 in 50% of the patients. Likewise, a significant fall in HCT value was found in 42.7% of patients. If [Hb] decrease during hospitalization is analyzed as a proportion of [Hb] at admission, 52.7% of patients had a 10% or greater [Hb] decrease during their hospital stay.
[Hb] fall (g/dL) | 0.5 | 1 | 1.5 | 2 | 2.5 | 3 | 3.5 | 4 | 4.5 |
---|---|---|---|---|---|---|---|---|---|
% of patients | 80.9 | 60.3 | 45.0 | 28.2 | 17.6 | 9.9 | 5.3 | 3.8 | 2.3 |
Using Kaplan‐Meier analysis, the estimated proportion showing no significant fall of [Hb] was 0.63 (95% CI, 0.55‐0.71) on day 3 and 0.48 (95% CI, 0.29‐0.67) on day 10. By day 15, only 3.8% of the initially included patients were still hospitalized and showed no decrease in [Hb] 1.5 g/dl. These patients maintained their [Hb] stable for the rest of the follow up period (Figure 2).

In the univariate analysis, comparing patients that experienced a decrease in [Hb] 1.5 g/dL with those who did not, significant differences were only found in the following variables: length of stay, APS, anemia at admission, [Hb] at admission, and infectious, gastrointestinal or cardiac diseases at discharge diagnosis (Table 3).
Patients with a significant fall | Patients without a significant fall | P Value | |
---|---|---|---|
| |||
n | 59 (45%) | 72 (55%) | |
Age, years | 73.15 (18.7) | 70.83 (16.2) | 0.448 |
Gender, female | 32 (54.2%) | 43 (59.7%) | 0.527 |
Length of stay (days) | 8.30 (5.6) | 5.91 (3.7) | <0.007 |
APS | 6.13 (4.5) | 3.97 (3.7) | <0.004 |
ADL | 4.33 (2.5) | 4.68 (2.1) | 0.410 |
CHARLSON | 2.03 (1.8) | 2.37 (2.5) | 0.382 |
Nurse home residents | 4 (6.8%) | 3 (4.2%) | 0.700 |
Readmissions | 11 (18.6%) | 17 (23.6%) | 0.490 |
PWSBL | 6 (10.2%) | 8 (11.1%) | 0.862 |
Anemia at admission | 20 (33.9%) | 43 (59.7%) | <0.004 |
[Hb] at admission | 13.09 (1.7) | 12.01 (1.5) | <0.001 |
Diagnosis at discharge | |||
Infectious | 20 (33.9%) | 13 (18.1%) | <0.05 |
Respiratory | 8 (13.6%) | 14 (19.4%) | 0.370 |
Neurologic | 9 (15.2%) | 16 (22.2%) | 0.312 |
Gastrointestinal | 11 (18.6%) | 3 (4.2%) | <0.01 |
Cardiac | 2 (3.4%) | 11(15.3%) | <0.05 |
Electrolyte disturbances | 6 (10.2%) | 4 (5.6%) | 0.512 |
Others | 3 (5.1%) | 11 (15.3%) | 0.087 |
In the Cox proportional hazard model adjusting for the variables shown in Table 4, a significant independent direct association was found between a significant fall in [Hb] during hospital stay and APS, [Hb] at admission, and either diagnosis of infectious or gastrointestinal disease at discharge. Similar results were found if a significant fall was redefined as a 12% decrease in [Hb] or as a 4.5 point decrease in HCT from baseline.
Variable | HRR | P Value | 95% CI |
---|---|---|---|
| |||
APS | 1.07 | 0.007 | 1.02‐1.12 |
ADL | 1.11 | 0.132 | 0.97‐1.27 |
Charlson | 0.88 | 0.121 | 0.75‐1.03 |
Nurse home resident | 1.52 | 0.361 | 0.62‐3.72 |
PWSBL | 0.67 | 0.390 | 0.27‐1.66 |
Readmission | 1.14 | 0.710 | 0.57‐2.29 |
Female sex | 0.98 | 0.944 | 0.57‐1.69 |
Age | 1.39 | 0.098 | 0.94‐2.07 |
[Hb] at admission | 1.27 | 0.005 | 1.07‐1.51 |
Diagnosis at discharge | |||
Infectious | 2.70 | 0.015 | 1.21‐6.05 |
Neurologic | 1.42 | 0.457 | 0.57‐3.55 |
Gastrointestinal | 3.74 | 0.002 | 1.62‐8.64 |
Cardiac | 0.41 | 0.289 | 0.08‐2.12 |
Electrolyte dist. | 2.08 | 0.176 | 0.72‐6.05 |
Others | 0.95 | 0.946 | 0.24‐3.81 |
Discussion
This study describes the variation of [Hb] and HCT values in GWAP without bleeding or other obvious medical conditions associated with a decrease in the red blood cell mass. As previously described,4, 20, 21 we found that [Hb] decreases during hospital stay are frequently observed. The mean [Hb] fall from admission was 1.45 g/dL, and was mainly recorded between hospitalization days 3 and 4. In approximately half of the study population, a 1.5 g/dL decrease in [Hb] was observed. In the survival analysis, 40% and 55% of patients are expected to present such a fall on day 4 and 12 respectively. In accordance with prior reports4, 21 a greater decrease in [Hb] occurred in the first days of hospitalization, and a high proportion of patients were already anemic at the time of admission.
The following variables were associated with a decrease in the [Hb] during hospitalization: higher APS score, higher [Hb] at admission, and diagnosis of infectious or gastrointestinal disease at discharge. Even though several mechanisms seem to contribute to the decrease in [Hb] during hospitalization our data suggest that 1 of these factors seems to be the severity of the disease, as previously proposed by Nguyen et al.4 This observation is supported by the association found in our study between [Hb] decrease and APS. No association was found with Charlson, ADL, and being a nursing home resident. These variables, which have not been previously analyzed, seem to indicate that patients with chronic illnesses are not more likely to have decreases in [Hb] during hospitalization.
Patients with higher [Hb] at admission had a greater [Hb] decrease during their hospital stay. This finding could suggest that the mechanism related to this decrease had less effect on patients with lower [Hb]. This is similar to that observed in the anemia of chronic diseases where [Hb] does not usually fall to extreme values. Our analysis reveals a greater decrease during the first days of hospitalization. Given the high rate of patients anemic at admission, it is possible that the decrease in [Hb] had begun prior to admission.
Previous papers describe a low prevalence of cyanocobalamine (2%), folic acid (2%), and iron (9%) deficiency in patients admitted to critical care units.3 In these patients an inadequate bone marrow response has been proposed as a mechanism for [Hb] decrease, a phenomenon that has been called AI or anemia of critical illness.8, 2228 Inflammatory response associated with acute disease causes this hypoproliferative anemia through 3 different pathways: relative erythropoietin deficit, a direct inhibition of the erythropoiesis in the bone marrow through different mediators (ie, interleukin [IL]‐1, IL‐6, tumor necrosis factor [TNF]) and a functional iron deficit. This relative iron deficiency is produced mainly by the IL‐6 induced overexpression of the hepcidin gene in hepatocytes. Hepcidin causes impaired intestinal iron absorption and an inadequate delivery of iron from the iron‐recycling macrophages to the erythroid precursors in the bone marrow.8, 9
AI could explain some of our findings, such as the greater decrease observed in the first hospital days when inflamatory mediators levels are expected to be higher. The association between APS and infectious disease diagnosis and a greater [Hb] decrease may also be explained by this mechanism. Nonetheless, the daily bone marrow production of red blood cells is small compared with the circulating red mass cell and therefore it is necessary to have a better explanation on how AI could be associated with this rapid decrease in the [Hb]. Rice et al.30, 31 have explored some mechanisms leading to a rapid decrease in the red mass cell related to acute variations in erytropoietin level. A role of this mechanism and other could be hypothesized.29
Overt bleeding was not found in our study population and procedures without significant blood loss (PWSBL) were infrequent, therefore it is unlikely that they could have had an impact on the decrease of [Hb]. The influence of other variables, such as blood volume drawn for diagnostic testing and occult blood losses (especially from the gastrointestinal tract) was not investigated. Parenteral hydration and hemodilution neither were evaluated in our study nor were extensively described in the literature. We think this last mentioned mechanism plays a role in the large [Hb] decrease during the first days of hospitalization. However a lack of association was mentioned in the only study that evaluated this issue in critically ill patients. Intravascular hemolysis has been cited as an infrequent event in these patients.2, 4
Only patients without a clear explanation for their [Hb] decrease were included because they represent a matter of concern for hospitalists and other treating physicians. Therefore 60% of the initially evaluated patients were excluded, since they were admitted for conditions likely to cause a decrease in [Hb]. Nevertheless, it seems likely that the mechanisms involved in the included patients may play a role in those with an obvious cause for [Hb] variation as well. Accordingly, the decrease of [Hb] expected in patients admitted with disorders known to cause a decrease in the red blood cell mass would be greater than the 1 observed in our study population.
This [Hb] drop during hospitalization may be clinically relevant in a number of ways: it could cause the attending physicians to order costly and invasive studies, it could have prognostic value as it occurs in patients admitted with myocardial infarction, and it could trigger an acute coronary syndrome.20 We observed an association between [Hb] drop and a higher APS in our patients. This score has been validated as a prognostic factor in previous studies. However, it is not possible to conclude that this [Hb] drop is associated with a worse prognosis.
Our study had several limitations. The sample's heterogeneity inherent to our general ward population could have altered our results and their generalization. To overcome this bias we used a categorization system based on discharge diagnosis. However, this particular system has several limitations. The small sample size and the absence of follow up data after discharge limited our capacity to detect any prognostic significance of [Hb] decrease. In the present study, the total decrease of [Hb] may have been underestimated because the onset of the acute illness precedes hospital admission by a variable time. Finally, a relatively small sample size limited our ability to detect other possible significant predictors of [Hb] decrease. However, this study was not designed to assess the mechanisms associated with the decrease of [Hb], but rather to establish its occurrence, measure it and explore related variables.
These results may be useful for further studies to evaluate [Hb] variation in admitted patients and its relation to other variables, such as bone marrow production, oxygen use, erythrocyte survival, nutritional deficiencies, and erythropoietin and inflammatory mediators levels.
In conclusion, our general medical inpatients had a mean 1.45 g/dL [Hb] decrease during hospitalization, which was greater in the first days of hospitalization, even though an evident cause was not present. These findings would help attending physicians in general wards make a rational and efficient approach when dealing with patients' decrease in [Hb].
Acknowledgements
The authors thank Jorge Lopez Camelo and Hugo Krupitzki for statistical advice and Valeria Melia for helping in the preparation of this manuscript.
- Nosocomial anemia.JAMA.1973;223(1):73–74. , .
- Important role of nondiagnostic blood loss and blunted erythropoietic response in the anemia of medical intensive care patients.Crit Care Med.1999;27(12):2630–2639. , , , , .
- Nutritional deficiencies and blunted erythropoietin response as causes of the anemia of critical illness.J Crit Care.2001;16(1):36–41. , , , , , .
- Time course of hemoglobin concentrations in nonbleeding intensive care unit patients.Crit Care Med.2003;31(2):406–410. , , , .
- Erithropoietin response in critically ill patients.Crit Care Med.1997;25(Suppl1):a82. , , , et al.
- Acute event‐related anaemia.Br J Haematol.2001;115(4):739–743. , , .
- Response of erythropoiesis and iron metabolism to recombinant human erythropoietin in intensive care unit patients.Crit Care Med.2000;28(8):2773–2778. , , , , , .
- The anemia of inflammation/malignancy: mechanisms and management.Hematology Am Soc Hematol Educ Program.2008:159–165. .
- The regulation of hepcidin and its effects on systemic and cellular iron metabolism.Hematology Am Soc Hematol Educ Program.2008:151–158. .
- Erythropoietin response is blunted in critically ill patients.Intensive Care Med.1997;23:159–162. , , , et al.,
- Studies of illness in the aged. The index of ADL: standardized measure of biological and psychosocial function.JAMA.1963;185:914–919. , , , , .
- Progress in the development of the index of ADL.Gerontologist.1970;10:20–30. , , ,
- Measuring prognosis and case mix in hospitalized elders. The importance of functional status.J Gen Intern Med.1997;12:203–208. , , , , .
- APACHE II: A severity of disease classification system.Crit Care Med.1985;12(10):818–829. , , , .
- A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chron Dis.1997;40:373–383. , , , .
- How to measure comorbidity: a critical review of available methods.J Clin Epidemiol.2003;56:221–229. , , , .
- Nutritional anaemias. Report of a WHO Scientific Group.World Health Organ Tech Rep Ser.1968;405:1–40. , , , et al.
- Relationship between hematocrit and renal function in men and women.Kidney Int.2001;59(2):725–731. , , , .
- Epidemiology of anemia associated with chronic renal insufficiency among adults in the United States: results from the Third National Health and Nutrition Examination Survey.J Am Soc Nephrol.2002;13(2):504–510. , , .
- Changes in haemoglobin levels during hospital course and long‐term outcome after myocardial infarction.Eur Heart J.2007;28(11):1289–1296. , , , et al.,
- Do blood test cause anemia in hospitalized patients?J Gen Intern Med.2005;20:520–524. , , , , .
- Red blood cell physiology in critical illness.Crit Care Med.2003;31(12 Suppl)( ):S651–S657. , .
- Anemia in the critically ill.Crit Care Clin.2004;20:159–178. .
- Anemia of the critically ill: acute anemia of chronic disease.Crit Care Med.2000;28(8):3098–3099. , .
- Anemia and blood transfusion in the critically ill patient: role of erythropoietin.Crit Care.2004;8(Suppl 2):S42–S44. .
- Anemia of critical illness – implications for understanding and treating rHuEPO resistance.Nephrol Dial Transplant.2002;17(Suppl 5):S48–S55. .
- Transfusion practice in the critically ill.Crit Care Med.2003;31(Suppl):S668–S671. , , .
- Anemia in critically ill patients.Eur J Inter Med.2004;15(8):481–486. .
- Neocytolysis: a physiologic down‐regulator of red blood cell mass.Lancet.1997;349:1389–1390. , , , .
- Neocytolysis contributes to the anemia of renal disease.Am J Kidney Dis.1999;33:59–62. , , , , , .
- Neocytolysis on descent from altitude: a newly recognized mechanism for the control of red cell mass.Ann Intern Med.2001;134:652–656. , , , et al.
- Phlebotomy for diagnostic laboratory test in adults. Pattern of use and effect on transfusion requirements.N Engl J Med.1986;314:1233–1235. , .
- Nosocomial anemia.JAMA.1973;223(1):73–74. , .
- Important role of nondiagnostic blood loss and blunted erythropoietic response in the anemia of medical intensive care patients.Crit Care Med.1999;27(12):2630–2639. , , , , .
- Nutritional deficiencies and blunted erythropoietin response as causes of the anemia of critical illness.J Crit Care.2001;16(1):36–41. , , , , , .
- Time course of hemoglobin concentrations in nonbleeding intensive care unit patients.Crit Care Med.2003;31(2):406–410. , , , .
- Erithropoietin response in critically ill patients.Crit Care Med.1997;25(Suppl1):a82. , , , et al.
- Acute event‐related anaemia.Br J Haematol.2001;115(4):739–743. , , .
- Response of erythropoiesis and iron metabolism to recombinant human erythropoietin in intensive care unit patients.Crit Care Med.2000;28(8):2773–2778. , , , , , .
- The anemia of inflammation/malignancy: mechanisms and management.Hematology Am Soc Hematol Educ Program.2008:159–165. .
- The regulation of hepcidin and its effects on systemic and cellular iron metabolism.Hematology Am Soc Hematol Educ Program.2008:151–158. .
- Erythropoietin response is blunted in critically ill patients.Intensive Care Med.1997;23:159–162. , , , et al.,
- Studies of illness in the aged. The index of ADL: standardized measure of biological and psychosocial function.JAMA.1963;185:914–919. , , , , .
- Progress in the development of the index of ADL.Gerontologist.1970;10:20–30. , , ,
- Measuring prognosis and case mix in hospitalized elders. The importance of functional status.J Gen Intern Med.1997;12:203–208. , , , , .
- APACHE II: A severity of disease classification system.Crit Care Med.1985;12(10):818–829. , , , .
- A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chron Dis.1997;40:373–383. , , , .
- How to measure comorbidity: a critical review of available methods.J Clin Epidemiol.2003;56:221–229. , , , .
- Nutritional anaemias. Report of a WHO Scientific Group.World Health Organ Tech Rep Ser.1968;405:1–40. , , , et al.
- Relationship between hematocrit and renal function in men and women.Kidney Int.2001;59(2):725–731. , , , .
- Epidemiology of anemia associated with chronic renal insufficiency among adults in the United States: results from the Third National Health and Nutrition Examination Survey.J Am Soc Nephrol.2002;13(2):504–510. , , .
- Changes in haemoglobin levels during hospital course and long‐term outcome after myocardial infarction.Eur Heart J.2007;28(11):1289–1296. , , , et al.,
- Do blood test cause anemia in hospitalized patients?J Gen Intern Med.2005;20:520–524. , , , , .
- Red blood cell physiology in critical illness.Crit Care Med.2003;31(12 Suppl)( ):S651–S657. , .
- Anemia in the critically ill.Crit Care Clin.2004;20:159–178. .
- Anemia of the critically ill: acute anemia of chronic disease.Crit Care Med.2000;28(8):3098–3099. , .
- Anemia and blood transfusion in the critically ill patient: role of erythropoietin.Crit Care.2004;8(Suppl 2):S42–S44. .
- Anemia of critical illness – implications for understanding and treating rHuEPO resistance.Nephrol Dial Transplant.2002;17(Suppl 5):S48–S55. .
- Transfusion practice in the critically ill.Crit Care Med.2003;31(Suppl):S668–S671. , , .
- Anemia in critically ill patients.Eur J Inter Med.2004;15(8):481–486. .
- Neocytolysis: a physiologic down‐regulator of red blood cell mass.Lancet.1997;349:1389–1390. , , , .
- Neocytolysis contributes to the anemia of renal disease.Am J Kidney Dis.1999;33:59–62. , , , , , .
- Neocytolysis on descent from altitude: a newly recognized mechanism for the control of red cell mass.Ann Intern Med.2001;134:652–656. , , , et al.
- Phlebotomy for diagnostic laboratory test in adults. Pattern of use and effect on transfusion requirements.N Engl J Med.1986;314:1233–1235. , .
Copyright © 2010 Society of Hospital Medicine
Health Care‐Associated Candidemia
In the United States, candida now accounts for between 8% and 12% of all catheter‐associated blood stream infections (BSIs).1 Additionally, crude mortality rates in candidemia exceed 40%, and a recent systematic review demonstrated that the attributable mortality of candidemia ranges from 5% to 71%.2 Candidal BSIs also affect resource utilization. These infections independently increase length of stay and result in substantial excess costs.3 Most cases of candidemia arise in noncritically ill patients and thus may be managed by hospitalists.
Historically, the majority of candidal BSIs were caused by C. albicans. Presently, C. albicans accounts for only half of all yeast BSIs, and approximately 20% of these infections are caused by organisms such as C. glabrata and C. krusei.4 These 2 organisms have either variable or no susceptibility to agents, such as fluconazole, empirically employed against yeast. Parallel with the evolution in microbiology of candidemia has been recognition that inappropriate treatment of these infections independently increases mortality.5 These factors underscore the need for the clinician to treat suspected candidal BSIs aggressively in order to avoid the risks associated with inappropriate treatment.
Efforts to enhance rates of initial appropriate therapy for bacterial infections have encompassed the realization that health care‐associated infections (HAIs) represent a distinct syndrome.6, 7 Traditionally, infections were considered either community‐acquired or nosocomial in origin. However, with the spread of health care delivery beyond the hospital, multiple studies indicate that patients may now present to the emergency department with infections caused by pathogens such as Methicillin‐resistant Staphylococcus aureus (MRSA) and P. aeruginosaorganisms that were previously thought limited to hospital‐acquired processes.69 Furthermore, hospitalists often encounter subjects presenting to the hospital with suspected BSIs who have an active and ongoing interaction with the healthcare system.
The importance of candida as a health care‐associated pathogen in BSI remains unclear. We hypothesized that health care‐associated candidemia (HCAC) represented a distinct clinical entity. In order to confirm our theory, we conducted a retrospective analysis of all cases of candidal BSI at our institution over a 3‐year period.
Methods
We reviewed the records of all patients diagnosed with candidemia at our hospital between January 1, 2004 and December 31, 2006. Our institutional review board approved this study. We included adult patients diagnosed with candidemia. The diagnosis of candidemia was based on the isolation of yeast from the blood in at least one blood culture. We employ the BACTEC 9240 blood Culture System (Becton Dickinson Microbiology Systems, Sparks, MD). We excluded subjects who were admitted to the hospital within one month of a known diagnosis of candidemia.
We defined a nosocomial candidal BSI as the diagnosis of candidemia based on cultures drawn after the patient had been hospitalized for >48 hours. We considered HCAC to be present based on previously employed criteria for identifying HAI.69 Specifically, for patients with candidemia based on blood cultures obtained within 48 hours of hospitalization, a patient had to meet at least 1 of the following criteria: (1) receipt of intravenous therapy outside the hospital, (2) end stage renal disease necessitating hemodialysis (ESRD requiring HD), (3) hospitalization within previous 30 days, (4) residence in a nursing home or long term care facility, or (5) underwent an invasive procedure as an outpatient within 30 days of presentation. Community‐acquired candidemia was restricted to patients whose index culture was drawn within 48 hours of admission but who failed to meet the definition for HCAC.
The prevalence of the various forms of candidemia served as our primary endpoint. In addition, we compared patients with respect to demographic factors, comorbidities, and severity of illness. Severity of illness was calculated based on the Acute Physiology and Chronic Health Evaluation (APACHE) II score. We further noted rates of immune suppression in the cohort and defined this as treatment with corticosteroids (10 mg of prednisone or equivalent daily for more than 30 consecutive days), other immunosuppressants (eg, methotrexate), or chemotherapy. Those with acquired immune deficiency syndrome (AIDS) or another immunodeficiency syndrome were defined as immunosuppressed as well. We examined the distribution of yeast species across the 3 forms of candidemia. Finally, we assessed the prevalence of fluconazole resistance. Fluconazole susceptibilities were determined based on Etest (AB BIODISK, Solna, Sweden). An isolate was considered resistant to fluconazole if the minimum inhibitory concentration was >64 g/mL.
We compared categorical variables with the Fisher's exact test. Continuous variables were analyzed with either the Student's t‐test or a Mann‐Whitney test, as appropriate. All tests were 2 tailed and a P value of <0.05 was assumed to represent statistical significance. Analyses were performed with Stata 9.1 (Stata Corp., College Station, TX).
Results
The final cohort included 223 subjects. The mean age of the patients was 59.6 15.7 years and 49% were male. Nearly one quarter (n = 55) fulfilled our criteria for HCAC. The remainder met the definition for nosocomial candidemia. We observed no cases of community‐acquired candidemia. Most (n = 33) patients with HCAC had exposure to more than 1 health care‐related source and many were initially admitted to the medicine/hospitalist service as opposed to the intensive care unit (ICU). The most common criteria leading to categorization as HCAC was recent hospitalization (n = 30, 54.5% of all HCAC). The median time from recent hospitalization to admission was 17 days (Range: 5‐28 days). Other common reasons for classification as HCAC included ESRD requiring HD (30.9%), residence in a nursing home (25.5%), and undergoing an invasive outpatient procedure (16.4%). More than 75% of subjects with HCAC (n = 42) had central venous catheters in place at presentation. Between 2004 and 2006, the proportion of all candidemia due to HCAC increased from 20.9% to 26.9%, but this difference was not statistically significant.
Patients with HCAC were similar to those with nosocomial candidemia (Table 1). There was no difference in either severity of illness or the frequency of neutropenia. The prevalence of most comorbidities did not differ between those with nosocomial candidemia and persons with HCAC. However, immunosuppression was more prevalent among patients with HCAC (prevalence ratio, 1.67; 95% CI, 1.13‐3.08; P = 0.004). In part this finding is expected given that our definition of HCAC includes exposure to agents which may lead to immunosuppression, such as chemotherapy. Of patients with HCAC, the majority (n = 38, 69.1%) were initially admitted to the general medicine service and not to the ICU.
Characteristic | Healthcare‐Associated Candidemia (n = 55) | Nosocomial Candidemia (n = 168) | P |
---|---|---|---|
| |||
Demographics | |||
Age, mean SD | 61.0 12.9 | 59.1 16.6 | 0.45 |
Male, % | 60.0 | 45.8 | 0.08 |
Severity of illness | |||
APACHE II score, mean SD | 15.9 6.8 | 14.6 6.3 | 0.21 |
Co‐morbid illnesses | |||
Diabetes mellitus, % | 36.4 | 32.7 | 0.87 |
Malignancy, % | 36.4 | 22.6 | 0.04 |
ESRD on HD, % | 30.9 | 23.2 | 0.25 |
AIDS, % | 7.2 | 6.0 | 0.73 |
Immunosupressed, % | 54.5 | 32.7 | 0.004 |
White cell status | |||
ANC, 1000/mm3, mean SD | 10.7 7.2 | 12.3 8.0 | 0.20 |
Neutropenic, % | 2.0 | 2.2 | 0.91 |
A multitude of various yeast species were recovered (Figure 1). Overall, nonalbicans candida were responsible for nearly 60% of all infections. Nonalbicans yeast were as likely to be recovered in HCAC as in nosocomial yeast infection. Among both types of Candidemia, C. krusei was a rare culprit accounting for fewer than 2% of infections. C. glabrata, however, occurred more often in HCAC. Specifically, C. glabrata represented 1 in 5 cases of HCAC as opposed to approximately 10% of all nosocomial yeast BSIs (P = 0.05). In part reflecting this, fluconazole resistance was noted more often in HCAC (18.2% of patients vs. 7.7% among nosocomial candidemia, P = 0.036). There was no difference in the eventual diagnosis of deep‐seeded yeast infections (ie, endocarditis, endopthlamitis, or osteomyelitis) between those with HCAC and persons with nosocomial candidemia (3 cases in each group).

Discussion
This analysis demonstrates that HCAC accounts for approximately a quarter of all candidemia. Our findings underscore that candidemia can present to the emergency department as an HAI and may potentially be initially cared for by a hospitalist. In addition, patients with HCAC and nosocomial candidemia share many attributes. Furthermore, nonalbicans yeast are as prevalent in HCAC as in nosocomial candidal infection. Nonetheless, there appear to be important differences in these syndromes. Immunosuppression appears to be more common in HCAC as does infection due to C. glabrata.
Others have explored the concept of HCAC. Kung et al.10 described community‐onset candidemia at a single center over a 10‐year period. They described 56 patients and noted that the majority had been recently hospitalized or had ongoing interaction with the healthcare system. Sofair et al.11 followed subjects presenting to emergency departments with candidemia. Overall, more than one‐third met criteria for community‐onset infection. In this analysis, though, Sofair et al.11 did not distinguish between community‐acquired processes and HCAC. From a population perspective, Chen et al.12 explored candidemia in Australia. Among over 1000 patients, the noted that 11.6% represented HCAC and, as we note, that select nonalbicans yeast occurred more often in HCAC than in nosocomial candidemia. Our project builds on and adds to these earlier efforts. First, we confirm the general observation that candidemia is no longer solely a nosocomial pathogen. Second, unlike several of these earlier reports we examined a larger cohort of candidemia. Third, beyond the observations of Chen et al.,12 we note that currently, the proportion of Candidal BSI classified as HACA relative to nosocomial candidemia seems larger than reported in the past. Finally, a unique aspect of our report is that we employed express criteria to define HAI.
Our findings have several implications. First, hospitalists and emergency department physicians, along with others, must remain vigilant when approaching patients presenting to the hospital with signs and symptoms of BSI and multiple risk factors for candidal BSI. The fact that the patient has not been hospitalized should not preclude consideration of and treatment for candidemia. The current evidence does not support broad, empiric use of antifungal agents, as this would lead to excessive costs and potentially expose many patients to unnecessary antifungal coverage. On the other hand, given the association between delayed antifungal therapy and the risk for death in candidemia, failure to consider this infection in at‐risk subjects may have adverse consequences. Second, our observations emphasize the need for clinical risk stratification schemes and rapid diagnostic modalities. Such tools are urgently needed if physicians hope to target antifungal therapies more appropriately. Third, if the clinician opts to initiate therapy for possible HCAC, reliance on fluconazole alone may prove inadequate. As the generalizability of our conclusions is necessarily limited, we recommend that infection control practitioners review local epidemiologic data regarding HCAC so that physicians can have the best available guidance.
Our study has several important limitations. Its retrospective nature exposes it to several forms of bias. The single center design limits the generalizability of our findings. Prospective, multicenter studies are needed to validate our results. Additionally, no universally accepted criteria exist to define HAI syndromes. Nonetheless, the criteria we employed have been used by others. We also lacked data on exposure to recent broad spectrum antimicrobials. Selection pressure via exposure to such agents is a risk factor for candidemia and without this data we cannot gauge the impact of this on our findings. Finally, we cannot control for the possibility that some patients were miscategorized. This could have arisen because of: (1) either limitations inherent in the definition of HCAC or (2) because the clinician delayed the decision to obtain blood cultures. Some patients classified as nosocomial may actually have had HCAC or community‐acquired diseasebut for some reason blood cultures were not drawn at time of admission but were deferred until later. Although a difficult issue to address in any study of the epidemiology of infection, the significance of this misclassification bias must be considered a significant concern.
In summary, Candidemia can be the cause of BSI presenting to the hospital. Moreover, HCAC represents a significant proportion of all Candidemia. Although patients with HCAC and nosocomial candidemia share select characteristics, there appear to be some differences in the microbiology of these syndromes.
- CDC.National Nosocomial Infections Surveillance (NNIS) System report, data summary from January 1990‐‐May 1999, issued June 1999.Am J Infect Control.1999;27:520–532.
- Attributable mortality of candidemia: a systematic review of matched cohort and case‐control studies.Eur J Clin Microbiol Infect Dis.2006;25:419–425. , , .
- Excess mortality, hospital stay, and cost due to candidemia: a case‐control study using data from population‐based candidemia surveillance.Infect Control Hosp Epidemiol.2005;26:540–547. , , , et al.
- Shifting patterns in the epidemiology of nosocomial Candida infections.Chest.2003;123:500S–503S. .
- Delaying the empiric treatment of candida bloodstream infection until positive blood culture results are obtained: a potential risk factor for hospital mortality.Antimicrob Agents Chemother.2005;49:3640–3645. , , .
- Healthcare‐associated bloodstream infection: A distinct entity? Insights from a large U.S. database.Crit Care Med.2006;34:2588–2595. , , , et al.
- Health care‐‐associated bloodstream infections in adults: a reason to change the accepted definition of community‐acquired infections.Ann Intern Med.2002;137:791–797. , , , et al.
- Epidemiology of healthcare‐associated pneumonia (HCAP).Semin Respir Crit Care Med.2009;30:10–15. , .
- Health care associated pneumonia and community‐acquired pneumonia: a single‐center experience.Antimicrob Agents Chemother.2007;51:3568–3573. , , , et al.
- Communtiy‐onset candidemia at a university hospital, 1995‐2005.J Microbiol Immunol Infect.2007;40:355–363. , , , et al.
- Epidemiology of community‐onset candidemia in Connecticut and Maryland.Clin Infect Dis.2006;43:32–39. , , , et al.
- Active surveillance for candidemia, Australia.Emerg Infect Dis.2006;12:1508–1516. , , , et al.
In the United States, candida now accounts for between 8% and 12% of all catheter‐associated blood stream infections (BSIs).1 Additionally, crude mortality rates in candidemia exceed 40%, and a recent systematic review demonstrated that the attributable mortality of candidemia ranges from 5% to 71%.2 Candidal BSIs also affect resource utilization. These infections independently increase length of stay and result in substantial excess costs.3 Most cases of candidemia arise in noncritically ill patients and thus may be managed by hospitalists.
Historically, the majority of candidal BSIs were caused by C. albicans. Presently, C. albicans accounts for only half of all yeast BSIs, and approximately 20% of these infections are caused by organisms such as C. glabrata and C. krusei.4 These 2 organisms have either variable or no susceptibility to agents, such as fluconazole, empirically employed against yeast. Parallel with the evolution in microbiology of candidemia has been recognition that inappropriate treatment of these infections independently increases mortality.5 These factors underscore the need for the clinician to treat suspected candidal BSIs aggressively in order to avoid the risks associated with inappropriate treatment.
Efforts to enhance rates of initial appropriate therapy for bacterial infections have encompassed the realization that health care‐associated infections (HAIs) represent a distinct syndrome.6, 7 Traditionally, infections were considered either community‐acquired or nosocomial in origin. However, with the spread of health care delivery beyond the hospital, multiple studies indicate that patients may now present to the emergency department with infections caused by pathogens such as Methicillin‐resistant Staphylococcus aureus (MRSA) and P. aeruginosaorganisms that were previously thought limited to hospital‐acquired processes.69 Furthermore, hospitalists often encounter subjects presenting to the hospital with suspected BSIs who have an active and ongoing interaction with the healthcare system.
The importance of candida as a health care‐associated pathogen in BSI remains unclear. We hypothesized that health care‐associated candidemia (HCAC) represented a distinct clinical entity. In order to confirm our theory, we conducted a retrospective analysis of all cases of candidal BSI at our institution over a 3‐year period.
Methods
We reviewed the records of all patients diagnosed with candidemia at our hospital between January 1, 2004 and December 31, 2006. Our institutional review board approved this study. We included adult patients diagnosed with candidemia. The diagnosis of candidemia was based on the isolation of yeast from the blood in at least one blood culture. We employ the BACTEC 9240 blood Culture System (Becton Dickinson Microbiology Systems, Sparks, MD). We excluded subjects who were admitted to the hospital within one month of a known diagnosis of candidemia.
We defined a nosocomial candidal BSI as the diagnosis of candidemia based on cultures drawn after the patient had been hospitalized for >48 hours. We considered HCAC to be present based on previously employed criteria for identifying HAI.69 Specifically, for patients with candidemia based on blood cultures obtained within 48 hours of hospitalization, a patient had to meet at least 1 of the following criteria: (1) receipt of intravenous therapy outside the hospital, (2) end stage renal disease necessitating hemodialysis (ESRD requiring HD), (3) hospitalization within previous 30 days, (4) residence in a nursing home or long term care facility, or (5) underwent an invasive procedure as an outpatient within 30 days of presentation. Community‐acquired candidemia was restricted to patients whose index culture was drawn within 48 hours of admission but who failed to meet the definition for HCAC.
The prevalence of the various forms of candidemia served as our primary endpoint. In addition, we compared patients with respect to demographic factors, comorbidities, and severity of illness. Severity of illness was calculated based on the Acute Physiology and Chronic Health Evaluation (APACHE) II score. We further noted rates of immune suppression in the cohort and defined this as treatment with corticosteroids (10 mg of prednisone or equivalent daily for more than 30 consecutive days), other immunosuppressants (eg, methotrexate), or chemotherapy. Those with acquired immune deficiency syndrome (AIDS) or another immunodeficiency syndrome were defined as immunosuppressed as well. We examined the distribution of yeast species across the 3 forms of candidemia. Finally, we assessed the prevalence of fluconazole resistance. Fluconazole susceptibilities were determined based on Etest (AB BIODISK, Solna, Sweden). An isolate was considered resistant to fluconazole if the minimum inhibitory concentration was >64 g/mL.
We compared categorical variables with the Fisher's exact test. Continuous variables were analyzed with either the Student's t‐test or a Mann‐Whitney test, as appropriate. All tests were 2 tailed and a P value of <0.05 was assumed to represent statistical significance. Analyses were performed with Stata 9.1 (Stata Corp., College Station, TX).
Results
The final cohort included 223 subjects. The mean age of the patients was 59.6 15.7 years and 49% were male. Nearly one quarter (n = 55) fulfilled our criteria for HCAC. The remainder met the definition for nosocomial candidemia. We observed no cases of community‐acquired candidemia. Most (n = 33) patients with HCAC had exposure to more than 1 health care‐related source and many were initially admitted to the medicine/hospitalist service as opposed to the intensive care unit (ICU). The most common criteria leading to categorization as HCAC was recent hospitalization (n = 30, 54.5% of all HCAC). The median time from recent hospitalization to admission was 17 days (Range: 5‐28 days). Other common reasons for classification as HCAC included ESRD requiring HD (30.9%), residence in a nursing home (25.5%), and undergoing an invasive outpatient procedure (16.4%). More than 75% of subjects with HCAC (n = 42) had central venous catheters in place at presentation. Between 2004 and 2006, the proportion of all candidemia due to HCAC increased from 20.9% to 26.9%, but this difference was not statistically significant.
Patients with HCAC were similar to those with nosocomial candidemia (Table 1). There was no difference in either severity of illness or the frequency of neutropenia. The prevalence of most comorbidities did not differ between those with nosocomial candidemia and persons with HCAC. However, immunosuppression was more prevalent among patients with HCAC (prevalence ratio, 1.67; 95% CI, 1.13‐3.08; P = 0.004). In part this finding is expected given that our definition of HCAC includes exposure to agents which may lead to immunosuppression, such as chemotherapy. Of patients with HCAC, the majority (n = 38, 69.1%) were initially admitted to the general medicine service and not to the ICU.
Characteristic | Healthcare‐Associated Candidemia (n = 55) | Nosocomial Candidemia (n = 168) | P |
---|---|---|---|
| |||
Demographics | |||
Age, mean SD | 61.0 12.9 | 59.1 16.6 | 0.45 |
Male, % | 60.0 | 45.8 | 0.08 |
Severity of illness | |||
APACHE II score, mean SD | 15.9 6.8 | 14.6 6.3 | 0.21 |
Co‐morbid illnesses | |||
Diabetes mellitus, % | 36.4 | 32.7 | 0.87 |
Malignancy, % | 36.4 | 22.6 | 0.04 |
ESRD on HD, % | 30.9 | 23.2 | 0.25 |
AIDS, % | 7.2 | 6.0 | 0.73 |
Immunosupressed, % | 54.5 | 32.7 | 0.004 |
White cell status | |||
ANC, 1000/mm3, mean SD | 10.7 7.2 | 12.3 8.0 | 0.20 |
Neutropenic, % | 2.0 | 2.2 | 0.91 |
A multitude of various yeast species were recovered (Figure 1). Overall, nonalbicans candida were responsible for nearly 60% of all infections. Nonalbicans yeast were as likely to be recovered in HCAC as in nosocomial yeast infection. Among both types of Candidemia, C. krusei was a rare culprit accounting for fewer than 2% of infections. C. glabrata, however, occurred more often in HCAC. Specifically, C. glabrata represented 1 in 5 cases of HCAC as opposed to approximately 10% of all nosocomial yeast BSIs (P = 0.05). In part reflecting this, fluconazole resistance was noted more often in HCAC (18.2% of patients vs. 7.7% among nosocomial candidemia, P = 0.036). There was no difference in the eventual diagnosis of deep‐seeded yeast infections (ie, endocarditis, endopthlamitis, or osteomyelitis) between those with HCAC and persons with nosocomial candidemia (3 cases in each group).

Discussion
This analysis demonstrates that HCAC accounts for approximately a quarter of all candidemia. Our findings underscore that candidemia can present to the emergency department as an HAI and may potentially be initially cared for by a hospitalist. In addition, patients with HCAC and nosocomial candidemia share many attributes. Furthermore, nonalbicans yeast are as prevalent in HCAC as in nosocomial candidal infection. Nonetheless, there appear to be important differences in these syndromes. Immunosuppression appears to be more common in HCAC as does infection due to C. glabrata.
Others have explored the concept of HCAC. Kung et al.10 described community‐onset candidemia at a single center over a 10‐year period. They described 56 patients and noted that the majority had been recently hospitalized or had ongoing interaction with the healthcare system. Sofair et al.11 followed subjects presenting to emergency departments with candidemia. Overall, more than one‐third met criteria for community‐onset infection. In this analysis, though, Sofair et al.11 did not distinguish between community‐acquired processes and HCAC. From a population perspective, Chen et al.12 explored candidemia in Australia. Among over 1000 patients, the noted that 11.6% represented HCAC and, as we note, that select nonalbicans yeast occurred more often in HCAC than in nosocomial candidemia. Our project builds on and adds to these earlier efforts. First, we confirm the general observation that candidemia is no longer solely a nosocomial pathogen. Second, unlike several of these earlier reports we examined a larger cohort of candidemia. Third, beyond the observations of Chen et al.,12 we note that currently, the proportion of Candidal BSI classified as HACA relative to nosocomial candidemia seems larger than reported in the past. Finally, a unique aspect of our report is that we employed express criteria to define HAI.
Our findings have several implications. First, hospitalists and emergency department physicians, along with others, must remain vigilant when approaching patients presenting to the hospital with signs and symptoms of BSI and multiple risk factors for candidal BSI. The fact that the patient has not been hospitalized should not preclude consideration of and treatment for candidemia. The current evidence does not support broad, empiric use of antifungal agents, as this would lead to excessive costs and potentially expose many patients to unnecessary antifungal coverage. On the other hand, given the association between delayed antifungal therapy and the risk for death in candidemia, failure to consider this infection in at‐risk subjects may have adverse consequences. Second, our observations emphasize the need for clinical risk stratification schemes and rapid diagnostic modalities. Such tools are urgently needed if physicians hope to target antifungal therapies more appropriately. Third, if the clinician opts to initiate therapy for possible HCAC, reliance on fluconazole alone may prove inadequate. As the generalizability of our conclusions is necessarily limited, we recommend that infection control practitioners review local epidemiologic data regarding HCAC so that physicians can have the best available guidance.
Our study has several important limitations. Its retrospective nature exposes it to several forms of bias. The single center design limits the generalizability of our findings. Prospective, multicenter studies are needed to validate our results. Additionally, no universally accepted criteria exist to define HAI syndromes. Nonetheless, the criteria we employed have been used by others. We also lacked data on exposure to recent broad spectrum antimicrobials. Selection pressure via exposure to such agents is a risk factor for candidemia and without this data we cannot gauge the impact of this on our findings. Finally, we cannot control for the possibility that some patients were miscategorized. This could have arisen because of: (1) either limitations inherent in the definition of HCAC or (2) because the clinician delayed the decision to obtain blood cultures. Some patients classified as nosocomial may actually have had HCAC or community‐acquired diseasebut for some reason blood cultures were not drawn at time of admission but were deferred until later. Although a difficult issue to address in any study of the epidemiology of infection, the significance of this misclassification bias must be considered a significant concern.
In summary, Candidemia can be the cause of BSI presenting to the hospital. Moreover, HCAC represents a significant proportion of all Candidemia. Although patients with HCAC and nosocomial candidemia share select characteristics, there appear to be some differences in the microbiology of these syndromes.
In the United States, candida now accounts for between 8% and 12% of all catheter‐associated blood stream infections (BSIs).1 Additionally, crude mortality rates in candidemia exceed 40%, and a recent systematic review demonstrated that the attributable mortality of candidemia ranges from 5% to 71%.2 Candidal BSIs also affect resource utilization. These infections independently increase length of stay and result in substantial excess costs.3 Most cases of candidemia arise in noncritically ill patients and thus may be managed by hospitalists.
Historically, the majority of candidal BSIs were caused by C. albicans. Presently, C. albicans accounts for only half of all yeast BSIs, and approximately 20% of these infections are caused by organisms such as C. glabrata and C. krusei.4 These 2 organisms have either variable or no susceptibility to agents, such as fluconazole, empirically employed against yeast. Parallel with the evolution in microbiology of candidemia has been recognition that inappropriate treatment of these infections independently increases mortality.5 These factors underscore the need for the clinician to treat suspected candidal BSIs aggressively in order to avoid the risks associated with inappropriate treatment.
Efforts to enhance rates of initial appropriate therapy for bacterial infections have encompassed the realization that health care‐associated infections (HAIs) represent a distinct syndrome.6, 7 Traditionally, infections were considered either community‐acquired or nosocomial in origin. However, with the spread of health care delivery beyond the hospital, multiple studies indicate that patients may now present to the emergency department with infections caused by pathogens such as Methicillin‐resistant Staphylococcus aureus (MRSA) and P. aeruginosaorganisms that were previously thought limited to hospital‐acquired processes.69 Furthermore, hospitalists often encounter subjects presenting to the hospital with suspected BSIs who have an active and ongoing interaction with the healthcare system.
The importance of candida as a health care‐associated pathogen in BSI remains unclear. We hypothesized that health care‐associated candidemia (HCAC) represented a distinct clinical entity. In order to confirm our theory, we conducted a retrospective analysis of all cases of candidal BSI at our institution over a 3‐year period.
Methods
We reviewed the records of all patients diagnosed with candidemia at our hospital between January 1, 2004 and December 31, 2006. Our institutional review board approved this study. We included adult patients diagnosed with candidemia. The diagnosis of candidemia was based on the isolation of yeast from the blood in at least one blood culture. We employ the BACTEC 9240 blood Culture System (Becton Dickinson Microbiology Systems, Sparks, MD). We excluded subjects who were admitted to the hospital within one month of a known diagnosis of candidemia.
We defined a nosocomial candidal BSI as the diagnosis of candidemia based on cultures drawn after the patient had been hospitalized for >48 hours. We considered HCAC to be present based on previously employed criteria for identifying HAI.69 Specifically, for patients with candidemia based on blood cultures obtained within 48 hours of hospitalization, a patient had to meet at least 1 of the following criteria: (1) receipt of intravenous therapy outside the hospital, (2) end stage renal disease necessitating hemodialysis (ESRD requiring HD), (3) hospitalization within previous 30 days, (4) residence in a nursing home or long term care facility, or (5) underwent an invasive procedure as an outpatient within 30 days of presentation. Community‐acquired candidemia was restricted to patients whose index culture was drawn within 48 hours of admission but who failed to meet the definition for HCAC.
The prevalence of the various forms of candidemia served as our primary endpoint. In addition, we compared patients with respect to demographic factors, comorbidities, and severity of illness. Severity of illness was calculated based on the Acute Physiology and Chronic Health Evaluation (APACHE) II score. We further noted rates of immune suppression in the cohort and defined this as treatment with corticosteroids (10 mg of prednisone or equivalent daily for more than 30 consecutive days), other immunosuppressants (eg, methotrexate), or chemotherapy. Those with acquired immune deficiency syndrome (AIDS) or another immunodeficiency syndrome were defined as immunosuppressed as well. We examined the distribution of yeast species across the 3 forms of candidemia. Finally, we assessed the prevalence of fluconazole resistance. Fluconazole susceptibilities were determined based on Etest (AB BIODISK, Solna, Sweden). An isolate was considered resistant to fluconazole if the minimum inhibitory concentration was >64 g/mL.
We compared categorical variables with the Fisher's exact test. Continuous variables were analyzed with either the Student's t‐test or a Mann‐Whitney test, as appropriate. All tests were 2 tailed and a P value of <0.05 was assumed to represent statistical significance. Analyses were performed with Stata 9.1 (Stata Corp., College Station, TX).
Results
The final cohort included 223 subjects. The mean age of the patients was 59.6 15.7 years and 49% were male. Nearly one quarter (n = 55) fulfilled our criteria for HCAC. The remainder met the definition for nosocomial candidemia. We observed no cases of community‐acquired candidemia. Most (n = 33) patients with HCAC had exposure to more than 1 health care‐related source and many were initially admitted to the medicine/hospitalist service as opposed to the intensive care unit (ICU). The most common criteria leading to categorization as HCAC was recent hospitalization (n = 30, 54.5% of all HCAC). The median time from recent hospitalization to admission was 17 days (Range: 5‐28 days). Other common reasons for classification as HCAC included ESRD requiring HD (30.9%), residence in a nursing home (25.5%), and undergoing an invasive outpatient procedure (16.4%). More than 75% of subjects with HCAC (n = 42) had central venous catheters in place at presentation. Between 2004 and 2006, the proportion of all candidemia due to HCAC increased from 20.9% to 26.9%, but this difference was not statistically significant.
Patients with HCAC were similar to those with nosocomial candidemia (Table 1). There was no difference in either severity of illness or the frequency of neutropenia. The prevalence of most comorbidities did not differ between those with nosocomial candidemia and persons with HCAC. However, immunosuppression was more prevalent among patients with HCAC (prevalence ratio, 1.67; 95% CI, 1.13‐3.08; P = 0.004). In part this finding is expected given that our definition of HCAC includes exposure to agents which may lead to immunosuppression, such as chemotherapy. Of patients with HCAC, the majority (n = 38, 69.1%) were initially admitted to the general medicine service and not to the ICU.
Characteristic | Healthcare‐Associated Candidemia (n = 55) | Nosocomial Candidemia (n = 168) | P |
---|---|---|---|
| |||
Demographics | |||
Age, mean SD | 61.0 12.9 | 59.1 16.6 | 0.45 |
Male, % | 60.0 | 45.8 | 0.08 |
Severity of illness | |||
APACHE II score, mean SD | 15.9 6.8 | 14.6 6.3 | 0.21 |
Co‐morbid illnesses | |||
Diabetes mellitus, % | 36.4 | 32.7 | 0.87 |
Malignancy, % | 36.4 | 22.6 | 0.04 |
ESRD on HD, % | 30.9 | 23.2 | 0.25 |
AIDS, % | 7.2 | 6.0 | 0.73 |
Immunosupressed, % | 54.5 | 32.7 | 0.004 |
White cell status | |||
ANC, 1000/mm3, mean SD | 10.7 7.2 | 12.3 8.0 | 0.20 |
Neutropenic, % | 2.0 | 2.2 | 0.91 |
A multitude of various yeast species were recovered (Figure 1). Overall, nonalbicans candida were responsible for nearly 60% of all infections. Nonalbicans yeast were as likely to be recovered in HCAC as in nosocomial yeast infection. Among both types of Candidemia, C. krusei was a rare culprit accounting for fewer than 2% of infections. C. glabrata, however, occurred more often in HCAC. Specifically, C. glabrata represented 1 in 5 cases of HCAC as opposed to approximately 10% of all nosocomial yeast BSIs (P = 0.05). In part reflecting this, fluconazole resistance was noted more often in HCAC (18.2% of patients vs. 7.7% among nosocomial candidemia, P = 0.036). There was no difference in the eventual diagnosis of deep‐seeded yeast infections (ie, endocarditis, endopthlamitis, or osteomyelitis) between those with HCAC and persons with nosocomial candidemia (3 cases in each group).

Discussion
This analysis demonstrates that HCAC accounts for approximately a quarter of all candidemia. Our findings underscore that candidemia can present to the emergency department as an HAI and may potentially be initially cared for by a hospitalist. In addition, patients with HCAC and nosocomial candidemia share many attributes. Furthermore, nonalbicans yeast are as prevalent in HCAC as in nosocomial candidal infection. Nonetheless, there appear to be important differences in these syndromes. Immunosuppression appears to be more common in HCAC as does infection due to C. glabrata.
Others have explored the concept of HCAC. Kung et al.10 described community‐onset candidemia at a single center over a 10‐year period. They described 56 patients and noted that the majority had been recently hospitalized or had ongoing interaction with the healthcare system. Sofair et al.11 followed subjects presenting to emergency departments with candidemia. Overall, more than one‐third met criteria for community‐onset infection. In this analysis, though, Sofair et al.11 did not distinguish between community‐acquired processes and HCAC. From a population perspective, Chen et al.12 explored candidemia in Australia. Among over 1000 patients, the noted that 11.6% represented HCAC and, as we note, that select nonalbicans yeast occurred more often in HCAC than in nosocomial candidemia. Our project builds on and adds to these earlier efforts. First, we confirm the general observation that candidemia is no longer solely a nosocomial pathogen. Second, unlike several of these earlier reports we examined a larger cohort of candidemia. Third, beyond the observations of Chen et al.,12 we note that currently, the proportion of Candidal BSI classified as HACA relative to nosocomial candidemia seems larger than reported in the past. Finally, a unique aspect of our report is that we employed express criteria to define HAI.
Our findings have several implications. First, hospitalists and emergency department physicians, along with others, must remain vigilant when approaching patients presenting to the hospital with signs and symptoms of BSI and multiple risk factors for candidal BSI. The fact that the patient has not been hospitalized should not preclude consideration of and treatment for candidemia. The current evidence does not support broad, empiric use of antifungal agents, as this would lead to excessive costs and potentially expose many patients to unnecessary antifungal coverage. On the other hand, given the association between delayed antifungal therapy and the risk for death in candidemia, failure to consider this infection in at‐risk subjects may have adverse consequences. Second, our observations emphasize the need for clinical risk stratification schemes and rapid diagnostic modalities. Such tools are urgently needed if physicians hope to target antifungal therapies more appropriately. Third, if the clinician opts to initiate therapy for possible HCAC, reliance on fluconazole alone may prove inadequate. As the generalizability of our conclusions is necessarily limited, we recommend that infection control practitioners review local epidemiologic data regarding HCAC so that physicians can have the best available guidance.
Our study has several important limitations. Its retrospective nature exposes it to several forms of bias. The single center design limits the generalizability of our findings. Prospective, multicenter studies are needed to validate our results. Additionally, no universally accepted criteria exist to define HAI syndromes. Nonetheless, the criteria we employed have been used by others. We also lacked data on exposure to recent broad spectrum antimicrobials. Selection pressure via exposure to such agents is a risk factor for candidemia and without this data we cannot gauge the impact of this on our findings. Finally, we cannot control for the possibility that some patients were miscategorized. This could have arisen because of: (1) either limitations inherent in the definition of HCAC or (2) because the clinician delayed the decision to obtain blood cultures. Some patients classified as nosocomial may actually have had HCAC or community‐acquired diseasebut for some reason blood cultures were not drawn at time of admission but were deferred until later. Although a difficult issue to address in any study of the epidemiology of infection, the significance of this misclassification bias must be considered a significant concern.
In summary, Candidemia can be the cause of BSI presenting to the hospital. Moreover, HCAC represents a significant proportion of all Candidemia. Although patients with HCAC and nosocomial candidemia share select characteristics, there appear to be some differences in the microbiology of these syndromes.
- CDC.National Nosocomial Infections Surveillance (NNIS) System report, data summary from January 1990‐‐May 1999, issued June 1999.Am J Infect Control.1999;27:520–532.
- Attributable mortality of candidemia: a systematic review of matched cohort and case‐control studies.Eur J Clin Microbiol Infect Dis.2006;25:419–425. , , .
- Excess mortality, hospital stay, and cost due to candidemia: a case‐control study using data from population‐based candidemia surveillance.Infect Control Hosp Epidemiol.2005;26:540–547. , , , et al.
- Shifting patterns in the epidemiology of nosocomial Candida infections.Chest.2003;123:500S–503S. .
- Delaying the empiric treatment of candida bloodstream infection until positive blood culture results are obtained: a potential risk factor for hospital mortality.Antimicrob Agents Chemother.2005;49:3640–3645. , , .
- Healthcare‐associated bloodstream infection: A distinct entity? Insights from a large U.S. database.Crit Care Med.2006;34:2588–2595. , , , et al.
- Health care‐‐associated bloodstream infections in adults: a reason to change the accepted definition of community‐acquired infections.Ann Intern Med.2002;137:791–797. , , , et al.
- Epidemiology of healthcare‐associated pneumonia (HCAP).Semin Respir Crit Care Med.2009;30:10–15. , .
- Health care associated pneumonia and community‐acquired pneumonia: a single‐center experience.Antimicrob Agents Chemother.2007;51:3568–3573. , , , et al.
- Communtiy‐onset candidemia at a university hospital, 1995‐2005.J Microbiol Immunol Infect.2007;40:355–363. , , , et al.
- Epidemiology of community‐onset candidemia in Connecticut and Maryland.Clin Infect Dis.2006;43:32–39. , , , et al.
- Active surveillance for candidemia, Australia.Emerg Infect Dis.2006;12:1508–1516. , , , et al.
- CDC.National Nosocomial Infections Surveillance (NNIS) System report, data summary from January 1990‐‐May 1999, issued June 1999.Am J Infect Control.1999;27:520–532.
- Attributable mortality of candidemia: a systematic review of matched cohort and case‐control studies.Eur J Clin Microbiol Infect Dis.2006;25:419–425. , , .
- Excess mortality, hospital stay, and cost due to candidemia: a case‐control study using data from population‐based candidemia surveillance.Infect Control Hosp Epidemiol.2005;26:540–547. , , , et al.
- Shifting patterns in the epidemiology of nosocomial Candida infections.Chest.2003;123:500S–503S. .
- Delaying the empiric treatment of candida bloodstream infection until positive blood culture results are obtained: a potential risk factor for hospital mortality.Antimicrob Agents Chemother.2005;49:3640–3645. , , .
- Healthcare‐associated bloodstream infection: A distinct entity? Insights from a large U.S. database.Crit Care Med.2006;34:2588–2595. , , , et al.
- Health care‐‐associated bloodstream infections in adults: a reason to change the accepted definition of community‐acquired infections.Ann Intern Med.2002;137:791–797. , , , et al.
- Epidemiology of healthcare‐associated pneumonia (HCAP).Semin Respir Crit Care Med.2009;30:10–15. , .
- Health care associated pneumonia and community‐acquired pneumonia: a single‐center experience.Antimicrob Agents Chemother.2007;51:3568–3573. , , , et al.
- Communtiy‐onset candidemia at a university hospital, 1995‐2005.J Microbiol Immunol Infect.2007;40:355–363. , , , et al.
- Epidemiology of community‐onset candidemia in Connecticut and Maryland.Clin Infect Dis.2006;43:32–39. , , , et al.
- Active surveillance for candidemia, Australia.Emerg Infect Dis.2006;12:1508–1516. , , , et al.
Hospital Self‐Discharge and Patient Trust
Patients who leave a hospital against medical advice (AMA) have increased risks of negative clinical outcomes. Leaving a hospital AMA has been associated with increased risks of emergency department and hospital readmission, longer lengths of stay upon hospital readmission, increased risk of morbidities, and increased mortality.13 Ibrahim et al.4 estimated that 1.44% of all hospitalizations for adults in the United States end with the patient leaving AMA. Prior research, however, has shown that specific patient populations may experience greater AMA discharge rates, as up to 4.9% of asthma‐related hospitalizations,2 13% of human immunodeficiency virus (HIV)‐related hospitalizations,1 and 30% of psychiatric‐related hospitalizations have been shown to end with the patient leaving AMA.5
A small body of research examines the reasons that patients leave AMA. In prior studies, dissatisfaction with care and conflicts with medical staff were among the most commonly cited reasons given by patients.2, 6 In a study of healthcare provider reflections on recent patients who left AMA, providers identified patient mistrust, suboptimal physician‐patient communication, and physician‐patient conflict as important contributors to the patient leaving AMA.7
Sickle‐cell disease (SCD) is a painful genetic condition which in the United States affects mostly African Americans and leads to frequent hospital utilization. Patients with SCD frequently report having poor‐quality interpersonal relations with health care providers when the patient seeks treatment for his or her pain.8 Patients often report that their health care providers do not believe the patient's reports of pain, providers do not involve the patient to his or her satisfaction in setting the course of care, and providers typically stigmatize the patient as having a substance abuse problem.817 Indeed, a recent systematic review has shown that there is a high level of evidence that negative health care provider attitudes serve as a barrier to appropriate pain management in SCD.18
To our knowledge, the only study to date of hospital self‐discharge history among adults with SCD was conducted in the United Kingdom by Elander et al.19 These investigators found that 14% of their sample of patients reported ever having discharged themselves from a hospital. The most common reasons for this behavior given by these patients were that they grew tired of waiting for relief of their pain, there were other conflicts that occurred on the medical ward, and because they just simply wanted to go home.
Given the interpersonal conflicts and poor‐quality pain management often found in SCD care, additional examinations of hospital self‐discharge in this population are warranted. We hypothesized that SCD patient reports of interpersonal conflict during previous health care encounters would have an independent association with the likelihood of the patient having ever self‐discharged from a hospital. We also hypothesized that there would be an independent association between a patient's level of trust in the medical profession and their self‐discharge history. The aim of this study was to test these hypotheses.
Methods
Study Design, Setting, and Sample
We conducted a cross‐sectional study of patients with SCD seeking care at a mid‐Atlantic, urban academic medical center, from September 2006 to June 2007. Eligible patients were adults age 18 years or older with any sickle‐cell hemoglobinopathy (HbSS, HbSC, HbS/‐thalassemia, or HbS/‐thalassemia) who were seen at the medical center during the study period.
Data Collection Procedures
Eligible patients were recruited from the adult sickle‐cell and hematology outpatient clinics, the Emergency Department, the inpatient units, or within 5 days after discharge from an acute hospital visit. We collected data by patient interview and medical record abstraction. The interview assessed demographic characteristics (eg, age, sex, educational attainment), patient‐reported annual hospital utilization for pain, previous interpersonal healthcare experiences, and trust toward the medical profession. A trained interviewer conducted the patient interview, which lasted approximately 15 minutes. Patients received $10 for completion of the interview. We abstracted from the patient's medical record their hemoglobinopathy type, their previous complications from SCD, and the presence of other comorbidities. The academic medical center's institutional review board reviewed and approved the study procedures. All participating patients gave informed consent.
Measures
Patient History of Hospital Self‐Discharge
A patient's history of hospital self‐discharge, the dependent variable, was assessed using a single, dichotomous (yes/no) item developed by Elander et al.,19 which asks Have you ever discharged yourself from a hospital, or left suddenly or unexpectedly?
Previous Interpersonal Health Care Experiences
Previous interpersonal health care experiences were assessed using a single, dichotomous (yes/no) item developed by Elander et al.,19 which asks the patient to report whether or not they have ever had difficulty persuading medical staff about their sickle‐cell pain.
Trust in the Medical Profession
A patient's level of trust in the medical profession was assessed using the previously validated 5‐item Wake Forest Trust in the Medical Profession scale.20 There is evidence for the construct validity of this unidimensional scale through its positive associations with trust in a specific physician, general satisfaction with care, and following a doctor's recommendation, and its negative association with having had a prior dispute with a physician, having sought a second opinion, or having changed physicians. This measure uses 5‐point Likert scaling (strongly disagree to strongly agree) to assess patient agreement with the following statements: (1) sometimes doctors care more about what is convenient for them than about their patient's medical needs (reverse coded); (2) doctors are extremely thorough and careful; (3) you completely trust doctors' decisions about which treatments are best; (4) a doctor would never mislead you about anything; and (5) all in all, you trust doctors completely. Scores on each of the items were summed to form a composite score, and then transformed onto a 0 to 100 scale. Higher scores indicated greater levels of trust toward the medical profession. The factorial validity of this measure in the current study was assessed using confirmatory factor analyses (data not shown) which supported the unidimensionality of the measure in our sample. This measure demonstrated good internal consistency in the current sample with a Cronbach's alpha of 0.80.
Covariates
We assessed a number of additional characteristics that could confound the relationship between previous interpersonal health care experiences, trust, and patient history of hospital self‐discharge. We assessed demographic variables for patient age (continuous), sex, education (high‐school education or less vs. greater than a high‐school education), and annual household income (<$10,000, $10,000‐$35,000, and $35,000+). We used a categorical variable that examined the patient's self‐report of their annual hospital utilization for treatment of vasoocclusive crises (VOC) (3 per year vs. 3+ per year). We assessed the patient's clinical characteristics (hemoglobinopathy type, and histories of acute chest syndrome, pulmonary hypertension, avascular necrosis, renal complications, hypertension, and hepatitis C). We also used an indicator variable to identify patients who possessed a positive urine toxicology screen for cocaine or marijuana use upon a hospital admission at any time within the previous 5 years of the patient interview. We restricted the toxicology screen results to these 2 substances alone as the standard therapeutic regimen for pain relief for many patients in this population could lead to a positive toxicology screen for opioids. Finally, we included a categorical variable to represent whether or not the patient's interview occurred in the outpatient or inpatient setting to assess the potential that interview setting might be associated with hospital reported self‐discharge history.
Analytic Methods
We restricted all analyses to those patient records that had complete data on all of the variables of interest. Bivariate relationships between the primary independent variables, the covariates, and the dependent variable were examined using t tests and chi‐square tests as appropriate. Due to sample size considerations, only variables related to the dependent variable at a P value of 0.20 were retained for inclusion in subsequent regression models. We used exact logistic regression modeling to examine adjusted relationships between the primary independent variables of interest and the patient's history of hospital self‐discharge, while controlling for any covariates retained from the bivariate analyses. Exact logistic regression modeling is preferred over the maximum likelihood estimation found in traditional logistic regression models for data with sample sizes of less than 100.21, 22
Results
Patient Characteristics
Ninety‐five patients were enrolled into the study. Of these, 86 had complete data on all variables of interest and are thus the subjects of this analysis. Overall, 40 patients (46.5%) had a history of self‐discharge. Table 1 summarizes the patient characteristics and provides the bivariate comparisons between patients with and without a history of hospital self‐discharge. Patients with a history of hospital self‐discharge were more likely to report experiencing 3 or more hospitalizations each year for treatment of their sickle‐cell pain (62.5% vs. 34.8%; P = 0.01). Patients with a history of hospital self‐discharge were about twice as likely to have a positive toxicology screen in the past 5 years (27.5% vs. 13.0%; P = 0.09).
History of Sudden Hospital Self‐Discharge | |||
---|---|---|---|
No (n = 46) | Yes (n = 40) | P Value | |
| |||
Patient characteristics | |||
Age (years) [mean (SD)] | 33.9 (12.1) | 31.8 (8.4) | 0.32 |
Female (%) | 56.5 | 62.5 | 0.57 |
With high school education or less (%) | 43.5 | 55.0 | 0.29 |
Household income (%) | 0.51 | ||
<$10,000 | 28.3 | 37.5 | |
$10,000‐$35,000 | 30.4 | 32.5 | |
$35,000+ | 41.3 | 30.0 | |
Hospital visits per year for vasoocclusive crises (% with 3+ visits) | 34.8 | 62.5 | 0.01 |
Positive toxicology screen in past 5 years (%) | 13.0 | 27.5 | 0.09 |
Clinical characteristics (%) | |||
Hemoglobin SS disease | 65.2 | 61.5 | 0.73 |
History of acute chest syndrome | 56.5 | 60.0 | 0.74 |
Avascular necrosis | 24.4 | 32.5 | 0.41 |
Pulmonary hypertension | 36.9 | 30.0 | 0.49 |
Renal complications | 13.3 | 20.5 | 0.38 |
History of hypertension | 21.7 | 17.5 | 0.62 |
History of hepatitis C | 10.9 | 15.0 | 0.58 |
Interview location, inpatient (%) | 43.5 | 55.0 | 0.29 |
Previous interpersonal experiences (% reporting previous difficulty persuading medical staff about pain) | 47.8 | 85.0 | <0.001 |
Trust (interpersonal trust) [mean (SD)] | 62.3(19.8) | 44.8(19.2) | 0.0001 |
Associations Among Interpersonal Experiences, Trust, and Hospital Self‐Discharge
In unadjusted analyses, having a history of hospital self‐discharge was associated with a greater likelihood of reporting difficulty persuading medical staff about sickle‐cell pain (85% vs. 47.8%; P < 0.001) and with lower levels of trust in the medical profession (44.8 vs. 62.3; P = 0.0001).
Table 2 reports the results of a multivariate exact logistic regression analysis. Persons reporting difficulty persuading medical staff about sickle‐cell pain were more likely to report having ever self‐discharged from a hospital, even after controlling for patient trust, hospital utilization, and 5‐year toxicology screen history (adjusted odds ratio [AOR], 3.89; P = 0.04; 95% confidence interval [CI], 1.05‐16.26). Patients with greater levels of trust in the medical profession were less likely to have ever self‐discharged from a hospital, controlling for difficulty persuading medical staff about pain, hospital utilization, and 5‐year positive toxicology screen history (AOR, 0.96; P = 0.003; 95% CI, 0.93‐0.99). Independent associations between hospital utilization and self‐discharge history, or between 5‐year toxicology screen history and self‐discharge history, were not observed in this study.
Adjusted Odds Ratio (95% confidence interval) (n = 86) | |
---|---|
| |
Difficulty persuading medical staff about pain | 3.89 (1.0516.26)* |
Trust in the medical profession | 0.96 (0.930.99) |
3+ hospital visits/year due to VOC | 0.96 (0.263.36) |
Positive toxicology screen in the past 5 years | 4.29 (0.8924.64) |
Discussion
In this study, we found that a high proportion of patients with SCD had a history of hospital self‐discharge. Patients with lower trust, and those who reported difficulty in persuading medical staff about sickle‐cell pain, were more likely to report having ever self‐discharged from a hospital, even after controlling for self‐reported hospital utilization for sickle‐cell pain, and the patient's 5‐year toxicology screen history. Because hospital self‐discharge is potentially dangerous,13 our study reveals an understudied aspect of how low trust and poor health care experiences may put patients with SCD at risk for poor outcomes.
In our study, 46.5% of our sample reported ever having self‐discharged from a hospital, which is much higher than the 14% found by Elander et al.19 in their United Kingdom (London‐based) sample. Other differences in our 2 patient populations may account for this discrepancy. Compared to the Elander sample, a much greater percentage of our patients reported ever having difficulty persuading medical staff about their pain (65% vs. 39%). As difficulty persuading medical staff about pain was independently associated with an increased hospital self‐discharge history in our study, one might expect that our sample, which had a higher percentage of patients reporting difficulty, would also be found to have a higher percentage of patients reporting a history of hospital self‐discharge. A second difference between the two patient samples is that our sample of patients experienced a greater number of hospital visits in the 12 months preceding the study compared to the Elander et al.19 sample. If this difference reflects an underlying difference in the overall hospital utilization experiences of the 2 groups, then our sample of adults would have greater opportunities, on average, than the Elander et al.19 sample to experience hospital self‐discharge. Other factors, such as patient behavioral or cultural differences between patients in the United Kingdom (with a national health system) and the United States (without a national health system), might be explored in future studies.
It is important to note that the wording of the hospital self‐discharge item as used both in our study and by Elander et al.19 would not only capture AMA discharges, but additionally may capture other sudden decisions about hospital discharge made by patients. A national‐level study of AMA discharges among adults with SCD in the United States that uses hospital records and/or chart review is needed in order to provide a more generalizable estimate of the prevalence of AMA discharge among this patient population in the United States.
Elander et al.19 suggest that while hospital self‐discharge among SCD patients may be interpreted by many as a sign that the patient engages in problematic use of opioids or other substances, it may be more appropriate to view this behavior as a sign that the patient has received inadequate management of their pain.19 In our study, hospital self‐discharge tended to be associated with having a history of substance abuse as operationalized by a positive toxicology screen for cocaine or marijuana use during any admission in the previous 5 years. Elander et al.19 found that hospital self‐discharge and other so‐called concern raising behaviors such as use of illicit substances were found to be significantly associated with patient attempts to obtain relief from their pain, but were not significantly associated with symptoms of actual substance dependence or addiction. For example, each instance of illicit substance use reported by the patients in the Elander et al.19 study described patient attempts to use marijuana in efforts to manage pain, to relax, or as alternatives to prescribed analgesics. Clinicians in the United States who observe positive toxicology screen results for SCD patients may see these results as casting doubt upon the legitimacy of the patient's pain reports, thus causing a reduction in the amount of pain medicine provided to the patient, when in fact, a substantial percentage of these results may reflect SCD patients attempts to manage their pain outside of a hospital setting. This potential discrepancy between clinician interpretations of the meaning of positive toxicology screen results for SCD patients, and the actual significance of these results for many patients as reflecting attempts to manage pain, could contribute to interpersonal conflicts between the clinicians and patients, and ultimately, patient self‐discharge and decreases in patient trust in clinicians. Further, to the extent that SCD patient positive toxicology screen results reflect use of illicit substances for reasons other than attempts to manage pain, this should signal for clinicians a need to refer the patient for substance abuse treatment and counseling in addition to (and not instead of) efforts to manage the patient's pain.
Our study is among the first to show empirically that persons with a history of hospital self‐discharge have lower levels of trust in the medical profession. Discharging himself or herself from a hospital could cause a patient to view future health care experiences in a more negative light, and cause the patient to have lower trust in the medical profession. Healthcare providers often label patients with a history of leaving AMA as challenging patients. Seeing in the medical record that a patient has left AMA before may bias the provider to view the patient in a more negative light, and consequently affect the quality of their communication with the patient, leading to lower patient trust. Alternatively, a patient could already possess lower trust in the medical profession due to poor‐quality interpersonal experiences, and thus be more likely to self‐discharge from a hospital during a future acute care visit due to a heightened wariness or greater level of anxiety.
The most consistent and robust predictors of trust found across studies in the literature are the quality of previous interactions with medical care.23 Poor physician communication, and experiences of conflict with staff have been associated with lower ratings of trust among a wide variety of patient populations.2427 Interestingly, we found a relationship between trust and hospital self‐discharge even after controlling for the quality of previous interpersonal experiences as measured by prior difficulty persuading staff about pain. Future studies should examine the relationship between trust and hospital self‐discharge history while controlling for other measures of previous interpersonal healthcare experiences among this patient population.
There are limitations to the current study that must be considered. First, as a single‐institution study, these results may not be generalizable to patients with SCD seeking care at other institutions. Also, we did not assess the actual reasons why patients chose to self‐discharge. Thus, while our data suggests that patient perceptions of poor‐quality care contributed to this behavior, we cannot state this definitively. The validity of a self‐reported annual hospital utilization measure as used in this study may be limited by inaccurate patient recall. However, we compared our patient's self‐report of annual hospital utilization with chart documented hospital utilization in the previous 12 months and found that the 2 measures were correlated in the appropriate direction, thereby giving us greater confidence in the validity of our self‐reported measure. Finally, the cross‐sectional nature of the data in this study makes it impossible to specify with certainty the causal directionality of the associations found here. Prospective research must be conducted to help tease apart the potentially complex relationships among trust, interpersonal experiences with care, and hospital self‐discharge.
Adults with SCD who have ever self‐discharged from a hospital have lower trust in the medical profession, and are more likely to report having had prior difficulty persuading medical staff about their sickle‐cell pain. The clinical consequences of hospital self‐discharge in this patient population must be examined, and the specific reasons behind this behavior must be further elucidated, so that clinicians and researchers may be able to design interventions to mitigate the occurrence of this potentially dangerous phenomenon.
- Leaving hospital against medical advice among HIV‐positive patients.CMAJ.2002;167(6):633–637. , , , , , .
- Hospitalized patients with asthma who leave against medical advice: characteristics, reasons, and outcomes.J Allergy Clin Immunol.2007;119(4):924–929. , , , , .
- Hospital discharge against advice after myocardial infarction: deaths and readmissions.Am J Med.2007;120(12):1047–1053. , , .
- Factors associated with patients who leave acute‐care hospitals against medical advice.Am J Public Health.2007;97(12):2204–2208. , , .
- Discharge against medical advice: ethical considerations and professional obligations.J Hosp Med.2008;3(5):403–408. .
- Why patients sign out against medical advice (AMA): factors motivating patients to sign out AMA.Am J Drug Alcohol Abuse.2004;30(2):489–493. , , , .
- Providers' perceptions of relationships and professional roles when caring for patients who leave the hospital against medical advice.J Gen Intern Med.2008;23(10):1698–1707. , .
- Experiences of hospital care and treatment seeking for pain from sickle cell disease: qualitative study.BMJ.1999;318(7198):1585–1590. , , .
- Painful crises in sickle cell disease—patients' perspectives.BMJ.1988;297(6646):452–454. , .
- Health perceptions and medical care opinions of inner‐city adults with sickle cell disease or asthma compared with those of their siblings.South Med J.1989;82(1):9–12. , , , , , .
- Sickle cell mutual assistance groups and the health services delivery system.J Health Soc Policy.1994;5(3–4):243–259. , , .
- Functions of an adult sickle cell group: education, task orientation, and support.Health Soc Work.1993;18(1):49–56. , .
- The management of sickle cell crisis pain as experienced by patients and their carers.J Adv Nurs.1994;19(4):725–732. , .
- Adults with sickle cell disease: psychological impact and experience of hospital services.Psychol Health Med.1998;3(2):171–179. , , .
- Use of focus groups for pain and quality of life assessment in adults with sickle cell disease.J Natl Black Nurses Assoc.2001;12(2):36–43. , , , , .
- The psychosocial experience of people with sickle cell disease and its impact on quality of life: qualitative findings from focus groups.Br J Health Psychol.2002;7(part 3):345–363. , .
- Pain management in sickle cell disease.Chronic Illn.2006;2(1):39–50. , , , .
- A systematic review of barriers and interventions to improve appropriate use of therapies for sickle cell disease.J Natl Med Assoc.2009;101(10):1022–1033. , , , et al.
- Understanding the causes of problematic pain management in sickle cell disease: evidence that pseudoaddiction plays a more important role than genuine analgesic dependence.J Pain Symptom Manage.2004;27(2):156–169. , , , , .
- Development of abbreviated measures to assess patient trust in a physician, a health insurer, and the medical profession.BMC Health Services Research.2005;5(1):64. , , .
- Applied Logistic Regression.2nd ed.New York:Wiley;2000. , .
- Regression Models for Categorical Dependent Variables Using Stata.2nd ed.College Station, TX:StataCorp LP;2006. , .
- Trust in physicians and medical institutions: what is it, can it be measured, and does it matter?Milbank Q.2001;79(4):613–639. , , , .
- How are patients' specific ambulatory care experiences related to trust, satisfaction, and considering changing physicians?J Gen Intern Med.2002;17(1):29–39. , , , , , .
- Racial differences in trust and lung cancer patients' perceptions of physician communication.J Clin Oncol.2006;24(6):904–909. , , , , .
- How patient‐physician encounters in critical medical situations affect trust: results of a national survey.BMC Health Serv Res.2004;4(1):24. , , .
- Trust in the medical profession: conceptual and measurement issues.Health Serv Res.2002;37(5):1419–1439. , , , .
Patients who leave a hospital against medical advice (AMA) have increased risks of negative clinical outcomes. Leaving a hospital AMA has been associated with increased risks of emergency department and hospital readmission, longer lengths of stay upon hospital readmission, increased risk of morbidities, and increased mortality.13 Ibrahim et al.4 estimated that 1.44% of all hospitalizations for adults in the United States end with the patient leaving AMA. Prior research, however, has shown that specific patient populations may experience greater AMA discharge rates, as up to 4.9% of asthma‐related hospitalizations,2 13% of human immunodeficiency virus (HIV)‐related hospitalizations,1 and 30% of psychiatric‐related hospitalizations have been shown to end with the patient leaving AMA.5
A small body of research examines the reasons that patients leave AMA. In prior studies, dissatisfaction with care and conflicts with medical staff were among the most commonly cited reasons given by patients.2, 6 In a study of healthcare provider reflections on recent patients who left AMA, providers identified patient mistrust, suboptimal physician‐patient communication, and physician‐patient conflict as important contributors to the patient leaving AMA.7
Sickle‐cell disease (SCD) is a painful genetic condition which in the United States affects mostly African Americans and leads to frequent hospital utilization. Patients with SCD frequently report having poor‐quality interpersonal relations with health care providers when the patient seeks treatment for his or her pain.8 Patients often report that their health care providers do not believe the patient's reports of pain, providers do not involve the patient to his or her satisfaction in setting the course of care, and providers typically stigmatize the patient as having a substance abuse problem.817 Indeed, a recent systematic review has shown that there is a high level of evidence that negative health care provider attitudes serve as a barrier to appropriate pain management in SCD.18
To our knowledge, the only study to date of hospital self‐discharge history among adults with SCD was conducted in the United Kingdom by Elander et al.19 These investigators found that 14% of their sample of patients reported ever having discharged themselves from a hospital. The most common reasons for this behavior given by these patients were that they grew tired of waiting for relief of their pain, there were other conflicts that occurred on the medical ward, and because they just simply wanted to go home.
Given the interpersonal conflicts and poor‐quality pain management often found in SCD care, additional examinations of hospital self‐discharge in this population are warranted. We hypothesized that SCD patient reports of interpersonal conflict during previous health care encounters would have an independent association with the likelihood of the patient having ever self‐discharged from a hospital. We also hypothesized that there would be an independent association between a patient's level of trust in the medical profession and their self‐discharge history. The aim of this study was to test these hypotheses.
Methods
Study Design, Setting, and Sample
We conducted a cross‐sectional study of patients with SCD seeking care at a mid‐Atlantic, urban academic medical center, from September 2006 to June 2007. Eligible patients were adults age 18 years or older with any sickle‐cell hemoglobinopathy (HbSS, HbSC, HbS/‐thalassemia, or HbS/‐thalassemia) who were seen at the medical center during the study period.
Data Collection Procedures
Eligible patients were recruited from the adult sickle‐cell and hematology outpatient clinics, the Emergency Department, the inpatient units, or within 5 days after discharge from an acute hospital visit. We collected data by patient interview and medical record abstraction. The interview assessed demographic characteristics (eg, age, sex, educational attainment), patient‐reported annual hospital utilization for pain, previous interpersonal healthcare experiences, and trust toward the medical profession. A trained interviewer conducted the patient interview, which lasted approximately 15 minutes. Patients received $10 for completion of the interview. We abstracted from the patient's medical record their hemoglobinopathy type, their previous complications from SCD, and the presence of other comorbidities. The academic medical center's institutional review board reviewed and approved the study procedures. All participating patients gave informed consent.
Measures
Patient History of Hospital Self‐Discharge
A patient's history of hospital self‐discharge, the dependent variable, was assessed using a single, dichotomous (yes/no) item developed by Elander et al.,19 which asks Have you ever discharged yourself from a hospital, or left suddenly or unexpectedly?
Previous Interpersonal Health Care Experiences
Previous interpersonal health care experiences were assessed using a single, dichotomous (yes/no) item developed by Elander et al.,19 which asks the patient to report whether or not they have ever had difficulty persuading medical staff about their sickle‐cell pain.
Trust in the Medical Profession
A patient's level of trust in the medical profession was assessed using the previously validated 5‐item Wake Forest Trust in the Medical Profession scale.20 There is evidence for the construct validity of this unidimensional scale through its positive associations with trust in a specific physician, general satisfaction with care, and following a doctor's recommendation, and its negative association with having had a prior dispute with a physician, having sought a second opinion, or having changed physicians. This measure uses 5‐point Likert scaling (strongly disagree to strongly agree) to assess patient agreement with the following statements: (1) sometimes doctors care more about what is convenient for them than about their patient's medical needs (reverse coded); (2) doctors are extremely thorough and careful; (3) you completely trust doctors' decisions about which treatments are best; (4) a doctor would never mislead you about anything; and (5) all in all, you trust doctors completely. Scores on each of the items were summed to form a composite score, and then transformed onto a 0 to 100 scale. Higher scores indicated greater levels of trust toward the medical profession. The factorial validity of this measure in the current study was assessed using confirmatory factor analyses (data not shown) which supported the unidimensionality of the measure in our sample. This measure demonstrated good internal consistency in the current sample with a Cronbach's alpha of 0.80.
Covariates
We assessed a number of additional characteristics that could confound the relationship between previous interpersonal health care experiences, trust, and patient history of hospital self‐discharge. We assessed demographic variables for patient age (continuous), sex, education (high‐school education or less vs. greater than a high‐school education), and annual household income (<$10,000, $10,000‐$35,000, and $35,000+). We used a categorical variable that examined the patient's self‐report of their annual hospital utilization for treatment of vasoocclusive crises (VOC) (3 per year vs. 3+ per year). We assessed the patient's clinical characteristics (hemoglobinopathy type, and histories of acute chest syndrome, pulmonary hypertension, avascular necrosis, renal complications, hypertension, and hepatitis C). We also used an indicator variable to identify patients who possessed a positive urine toxicology screen for cocaine or marijuana use upon a hospital admission at any time within the previous 5 years of the patient interview. We restricted the toxicology screen results to these 2 substances alone as the standard therapeutic regimen for pain relief for many patients in this population could lead to a positive toxicology screen for opioids. Finally, we included a categorical variable to represent whether or not the patient's interview occurred in the outpatient or inpatient setting to assess the potential that interview setting might be associated with hospital reported self‐discharge history.
Analytic Methods
We restricted all analyses to those patient records that had complete data on all of the variables of interest. Bivariate relationships between the primary independent variables, the covariates, and the dependent variable were examined using t tests and chi‐square tests as appropriate. Due to sample size considerations, only variables related to the dependent variable at a P value of 0.20 were retained for inclusion in subsequent regression models. We used exact logistic regression modeling to examine adjusted relationships between the primary independent variables of interest and the patient's history of hospital self‐discharge, while controlling for any covariates retained from the bivariate analyses. Exact logistic regression modeling is preferred over the maximum likelihood estimation found in traditional logistic regression models for data with sample sizes of less than 100.21, 22
Results
Patient Characteristics
Ninety‐five patients were enrolled into the study. Of these, 86 had complete data on all variables of interest and are thus the subjects of this analysis. Overall, 40 patients (46.5%) had a history of self‐discharge. Table 1 summarizes the patient characteristics and provides the bivariate comparisons between patients with and without a history of hospital self‐discharge. Patients with a history of hospital self‐discharge were more likely to report experiencing 3 or more hospitalizations each year for treatment of their sickle‐cell pain (62.5% vs. 34.8%; P = 0.01). Patients with a history of hospital self‐discharge were about twice as likely to have a positive toxicology screen in the past 5 years (27.5% vs. 13.0%; P = 0.09).
History of Sudden Hospital Self‐Discharge | |||
---|---|---|---|
No (n = 46) | Yes (n = 40) | P Value | |
| |||
Patient characteristics | |||
Age (years) [mean (SD)] | 33.9 (12.1) | 31.8 (8.4) | 0.32 |
Female (%) | 56.5 | 62.5 | 0.57 |
With high school education or less (%) | 43.5 | 55.0 | 0.29 |
Household income (%) | 0.51 | ||
<$10,000 | 28.3 | 37.5 | |
$10,000‐$35,000 | 30.4 | 32.5 | |
$35,000+ | 41.3 | 30.0 | |
Hospital visits per year for vasoocclusive crises (% with 3+ visits) | 34.8 | 62.5 | 0.01 |
Positive toxicology screen in past 5 years (%) | 13.0 | 27.5 | 0.09 |
Clinical characteristics (%) | |||
Hemoglobin SS disease | 65.2 | 61.5 | 0.73 |
History of acute chest syndrome | 56.5 | 60.0 | 0.74 |
Avascular necrosis | 24.4 | 32.5 | 0.41 |
Pulmonary hypertension | 36.9 | 30.0 | 0.49 |
Renal complications | 13.3 | 20.5 | 0.38 |
History of hypertension | 21.7 | 17.5 | 0.62 |
History of hepatitis C | 10.9 | 15.0 | 0.58 |
Interview location, inpatient (%) | 43.5 | 55.0 | 0.29 |
Previous interpersonal experiences (% reporting previous difficulty persuading medical staff about pain) | 47.8 | 85.0 | <0.001 |
Trust (interpersonal trust) [mean (SD)] | 62.3(19.8) | 44.8(19.2) | 0.0001 |
Associations Among Interpersonal Experiences, Trust, and Hospital Self‐Discharge
In unadjusted analyses, having a history of hospital self‐discharge was associated with a greater likelihood of reporting difficulty persuading medical staff about sickle‐cell pain (85% vs. 47.8%; P < 0.001) and with lower levels of trust in the medical profession (44.8 vs. 62.3; P = 0.0001).
Table 2 reports the results of a multivariate exact logistic regression analysis. Persons reporting difficulty persuading medical staff about sickle‐cell pain were more likely to report having ever self‐discharged from a hospital, even after controlling for patient trust, hospital utilization, and 5‐year toxicology screen history (adjusted odds ratio [AOR], 3.89; P = 0.04; 95% confidence interval [CI], 1.05‐16.26). Patients with greater levels of trust in the medical profession were less likely to have ever self‐discharged from a hospital, controlling for difficulty persuading medical staff about pain, hospital utilization, and 5‐year positive toxicology screen history (AOR, 0.96; P = 0.003; 95% CI, 0.93‐0.99). Independent associations between hospital utilization and self‐discharge history, or between 5‐year toxicology screen history and self‐discharge history, were not observed in this study.
Adjusted Odds Ratio (95% confidence interval) (n = 86) | |
---|---|
| |
Difficulty persuading medical staff about pain | 3.89 (1.0516.26)* |
Trust in the medical profession | 0.96 (0.930.99) |
3+ hospital visits/year due to VOC | 0.96 (0.263.36) |
Positive toxicology screen in the past 5 years | 4.29 (0.8924.64) |
Discussion
In this study, we found that a high proportion of patients with SCD had a history of hospital self‐discharge. Patients with lower trust, and those who reported difficulty in persuading medical staff about sickle‐cell pain, were more likely to report having ever self‐discharged from a hospital, even after controlling for self‐reported hospital utilization for sickle‐cell pain, and the patient's 5‐year toxicology screen history. Because hospital self‐discharge is potentially dangerous,13 our study reveals an understudied aspect of how low trust and poor health care experiences may put patients with SCD at risk for poor outcomes.
In our study, 46.5% of our sample reported ever having self‐discharged from a hospital, which is much higher than the 14% found by Elander et al.19 in their United Kingdom (London‐based) sample. Other differences in our 2 patient populations may account for this discrepancy. Compared to the Elander sample, a much greater percentage of our patients reported ever having difficulty persuading medical staff about their pain (65% vs. 39%). As difficulty persuading medical staff about pain was independently associated with an increased hospital self‐discharge history in our study, one might expect that our sample, which had a higher percentage of patients reporting difficulty, would also be found to have a higher percentage of patients reporting a history of hospital self‐discharge. A second difference between the two patient samples is that our sample of patients experienced a greater number of hospital visits in the 12 months preceding the study compared to the Elander et al.19 sample. If this difference reflects an underlying difference in the overall hospital utilization experiences of the 2 groups, then our sample of adults would have greater opportunities, on average, than the Elander et al.19 sample to experience hospital self‐discharge. Other factors, such as patient behavioral or cultural differences between patients in the United Kingdom (with a national health system) and the United States (without a national health system), might be explored in future studies.
It is important to note that the wording of the hospital self‐discharge item as used both in our study and by Elander et al.19 would not only capture AMA discharges, but additionally may capture other sudden decisions about hospital discharge made by patients. A national‐level study of AMA discharges among adults with SCD in the United States that uses hospital records and/or chart review is needed in order to provide a more generalizable estimate of the prevalence of AMA discharge among this patient population in the United States.
Elander et al.19 suggest that while hospital self‐discharge among SCD patients may be interpreted by many as a sign that the patient engages in problematic use of opioids or other substances, it may be more appropriate to view this behavior as a sign that the patient has received inadequate management of their pain.19 In our study, hospital self‐discharge tended to be associated with having a history of substance abuse as operationalized by a positive toxicology screen for cocaine or marijuana use during any admission in the previous 5 years. Elander et al.19 found that hospital self‐discharge and other so‐called concern raising behaviors such as use of illicit substances were found to be significantly associated with patient attempts to obtain relief from their pain, but were not significantly associated with symptoms of actual substance dependence or addiction. For example, each instance of illicit substance use reported by the patients in the Elander et al.19 study described patient attempts to use marijuana in efforts to manage pain, to relax, or as alternatives to prescribed analgesics. Clinicians in the United States who observe positive toxicology screen results for SCD patients may see these results as casting doubt upon the legitimacy of the patient's pain reports, thus causing a reduction in the amount of pain medicine provided to the patient, when in fact, a substantial percentage of these results may reflect SCD patients attempts to manage their pain outside of a hospital setting. This potential discrepancy between clinician interpretations of the meaning of positive toxicology screen results for SCD patients, and the actual significance of these results for many patients as reflecting attempts to manage pain, could contribute to interpersonal conflicts between the clinicians and patients, and ultimately, patient self‐discharge and decreases in patient trust in clinicians. Further, to the extent that SCD patient positive toxicology screen results reflect use of illicit substances for reasons other than attempts to manage pain, this should signal for clinicians a need to refer the patient for substance abuse treatment and counseling in addition to (and not instead of) efforts to manage the patient's pain.
Our study is among the first to show empirically that persons with a history of hospital self‐discharge have lower levels of trust in the medical profession. Discharging himself or herself from a hospital could cause a patient to view future health care experiences in a more negative light, and cause the patient to have lower trust in the medical profession. Healthcare providers often label patients with a history of leaving AMA as challenging patients. Seeing in the medical record that a patient has left AMA before may bias the provider to view the patient in a more negative light, and consequently affect the quality of their communication with the patient, leading to lower patient trust. Alternatively, a patient could already possess lower trust in the medical profession due to poor‐quality interpersonal experiences, and thus be more likely to self‐discharge from a hospital during a future acute care visit due to a heightened wariness or greater level of anxiety.
The most consistent and robust predictors of trust found across studies in the literature are the quality of previous interactions with medical care.23 Poor physician communication, and experiences of conflict with staff have been associated with lower ratings of trust among a wide variety of patient populations.2427 Interestingly, we found a relationship between trust and hospital self‐discharge even after controlling for the quality of previous interpersonal experiences as measured by prior difficulty persuading staff about pain. Future studies should examine the relationship between trust and hospital self‐discharge history while controlling for other measures of previous interpersonal healthcare experiences among this patient population.
There are limitations to the current study that must be considered. First, as a single‐institution study, these results may not be generalizable to patients with SCD seeking care at other institutions. Also, we did not assess the actual reasons why patients chose to self‐discharge. Thus, while our data suggests that patient perceptions of poor‐quality care contributed to this behavior, we cannot state this definitively. The validity of a self‐reported annual hospital utilization measure as used in this study may be limited by inaccurate patient recall. However, we compared our patient's self‐report of annual hospital utilization with chart documented hospital utilization in the previous 12 months and found that the 2 measures were correlated in the appropriate direction, thereby giving us greater confidence in the validity of our self‐reported measure. Finally, the cross‐sectional nature of the data in this study makes it impossible to specify with certainty the causal directionality of the associations found here. Prospective research must be conducted to help tease apart the potentially complex relationships among trust, interpersonal experiences with care, and hospital self‐discharge.
Adults with SCD who have ever self‐discharged from a hospital have lower trust in the medical profession, and are more likely to report having had prior difficulty persuading medical staff about their sickle‐cell pain. The clinical consequences of hospital self‐discharge in this patient population must be examined, and the specific reasons behind this behavior must be further elucidated, so that clinicians and researchers may be able to design interventions to mitigate the occurrence of this potentially dangerous phenomenon.
Patients who leave a hospital against medical advice (AMA) have increased risks of negative clinical outcomes. Leaving a hospital AMA has been associated with increased risks of emergency department and hospital readmission, longer lengths of stay upon hospital readmission, increased risk of morbidities, and increased mortality.13 Ibrahim et al.4 estimated that 1.44% of all hospitalizations for adults in the United States end with the patient leaving AMA. Prior research, however, has shown that specific patient populations may experience greater AMA discharge rates, as up to 4.9% of asthma‐related hospitalizations,2 13% of human immunodeficiency virus (HIV)‐related hospitalizations,1 and 30% of psychiatric‐related hospitalizations have been shown to end with the patient leaving AMA.5
A small body of research examines the reasons that patients leave AMA. In prior studies, dissatisfaction with care and conflicts with medical staff were among the most commonly cited reasons given by patients.2, 6 In a study of healthcare provider reflections on recent patients who left AMA, providers identified patient mistrust, suboptimal physician‐patient communication, and physician‐patient conflict as important contributors to the patient leaving AMA.7
Sickle‐cell disease (SCD) is a painful genetic condition which in the United States affects mostly African Americans and leads to frequent hospital utilization. Patients with SCD frequently report having poor‐quality interpersonal relations with health care providers when the patient seeks treatment for his or her pain.8 Patients often report that their health care providers do not believe the patient's reports of pain, providers do not involve the patient to his or her satisfaction in setting the course of care, and providers typically stigmatize the patient as having a substance abuse problem.817 Indeed, a recent systematic review has shown that there is a high level of evidence that negative health care provider attitudes serve as a barrier to appropriate pain management in SCD.18
To our knowledge, the only study to date of hospital self‐discharge history among adults with SCD was conducted in the United Kingdom by Elander et al.19 These investigators found that 14% of their sample of patients reported ever having discharged themselves from a hospital. The most common reasons for this behavior given by these patients were that they grew tired of waiting for relief of their pain, there were other conflicts that occurred on the medical ward, and because they just simply wanted to go home.
Given the interpersonal conflicts and poor‐quality pain management often found in SCD care, additional examinations of hospital self‐discharge in this population are warranted. We hypothesized that SCD patient reports of interpersonal conflict during previous health care encounters would have an independent association with the likelihood of the patient having ever self‐discharged from a hospital. We also hypothesized that there would be an independent association between a patient's level of trust in the medical profession and their self‐discharge history. The aim of this study was to test these hypotheses.
Methods
Study Design, Setting, and Sample
We conducted a cross‐sectional study of patients with SCD seeking care at a mid‐Atlantic, urban academic medical center, from September 2006 to June 2007. Eligible patients were adults age 18 years or older with any sickle‐cell hemoglobinopathy (HbSS, HbSC, HbS/‐thalassemia, or HbS/‐thalassemia) who were seen at the medical center during the study period.
Data Collection Procedures
Eligible patients were recruited from the adult sickle‐cell and hematology outpatient clinics, the Emergency Department, the inpatient units, or within 5 days after discharge from an acute hospital visit. We collected data by patient interview and medical record abstraction. The interview assessed demographic characteristics (eg, age, sex, educational attainment), patient‐reported annual hospital utilization for pain, previous interpersonal healthcare experiences, and trust toward the medical profession. A trained interviewer conducted the patient interview, which lasted approximately 15 minutes. Patients received $10 for completion of the interview. We abstracted from the patient's medical record their hemoglobinopathy type, their previous complications from SCD, and the presence of other comorbidities. The academic medical center's institutional review board reviewed and approved the study procedures. All participating patients gave informed consent.
Measures
Patient History of Hospital Self‐Discharge
A patient's history of hospital self‐discharge, the dependent variable, was assessed using a single, dichotomous (yes/no) item developed by Elander et al.,19 which asks Have you ever discharged yourself from a hospital, or left suddenly or unexpectedly?
Previous Interpersonal Health Care Experiences
Previous interpersonal health care experiences were assessed using a single, dichotomous (yes/no) item developed by Elander et al.,19 which asks the patient to report whether or not they have ever had difficulty persuading medical staff about their sickle‐cell pain.
Trust in the Medical Profession
A patient's level of trust in the medical profession was assessed using the previously validated 5‐item Wake Forest Trust in the Medical Profession scale.20 There is evidence for the construct validity of this unidimensional scale through its positive associations with trust in a specific physician, general satisfaction with care, and following a doctor's recommendation, and its negative association with having had a prior dispute with a physician, having sought a second opinion, or having changed physicians. This measure uses 5‐point Likert scaling (strongly disagree to strongly agree) to assess patient agreement with the following statements: (1) sometimes doctors care more about what is convenient for them than about their patient's medical needs (reverse coded); (2) doctors are extremely thorough and careful; (3) you completely trust doctors' decisions about which treatments are best; (4) a doctor would never mislead you about anything; and (5) all in all, you trust doctors completely. Scores on each of the items were summed to form a composite score, and then transformed onto a 0 to 100 scale. Higher scores indicated greater levels of trust toward the medical profession. The factorial validity of this measure in the current study was assessed using confirmatory factor analyses (data not shown) which supported the unidimensionality of the measure in our sample. This measure demonstrated good internal consistency in the current sample with a Cronbach's alpha of 0.80.
Covariates
We assessed a number of additional characteristics that could confound the relationship between previous interpersonal health care experiences, trust, and patient history of hospital self‐discharge. We assessed demographic variables for patient age (continuous), sex, education (high‐school education or less vs. greater than a high‐school education), and annual household income (<$10,000, $10,000‐$35,000, and $35,000+). We used a categorical variable that examined the patient's self‐report of their annual hospital utilization for treatment of vasoocclusive crises (VOC) (3 per year vs. 3+ per year). We assessed the patient's clinical characteristics (hemoglobinopathy type, and histories of acute chest syndrome, pulmonary hypertension, avascular necrosis, renal complications, hypertension, and hepatitis C). We also used an indicator variable to identify patients who possessed a positive urine toxicology screen for cocaine or marijuana use upon a hospital admission at any time within the previous 5 years of the patient interview. We restricted the toxicology screen results to these 2 substances alone as the standard therapeutic regimen for pain relief for many patients in this population could lead to a positive toxicology screen for opioids. Finally, we included a categorical variable to represent whether or not the patient's interview occurred in the outpatient or inpatient setting to assess the potential that interview setting might be associated with hospital reported self‐discharge history.
Analytic Methods
We restricted all analyses to those patient records that had complete data on all of the variables of interest. Bivariate relationships between the primary independent variables, the covariates, and the dependent variable were examined using t tests and chi‐square tests as appropriate. Due to sample size considerations, only variables related to the dependent variable at a P value of 0.20 were retained for inclusion in subsequent regression models. We used exact logistic regression modeling to examine adjusted relationships between the primary independent variables of interest and the patient's history of hospital self‐discharge, while controlling for any covariates retained from the bivariate analyses. Exact logistic regression modeling is preferred over the maximum likelihood estimation found in traditional logistic regression models for data with sample sizes of less than 100.21, 22
Results
Patient Characteristics
Ninety‐five patients were enrolled into the study. Of these, 86 had complete data on all variables of interest and are thus the subjects of this analysis. Overall, 40 patients (46.5%) had a history of self‐discharge. Table 1 summarizes the patient characteristics and provides the bivariate comparisons between patients with and without a history of hospital self‐discharge. Patients with a history of hospital self‐discharge were more likely to report experiencing 3 or more hospitalizations each year for treatment of their sickle‐cell pain (62.5% vs. 34.8%; P = 0.01). Patients with a history of hospital self‐discharge were about twice as likely to have a positive toxicology screen in the past 5 years (27.5% vs. 13.0%; P = 0.09).
History of Sudden Hospital Self‐Discharge | |||
---|---|---|---|
No (n = 46) | Yes (n = 40) | P Value | |
| |||
Patient characteristics | |||
Age (years) [mean (SD)] | 33.9 (12.1) | 31.8 (8.4) | 0.32 |
Female (%) | 56.5 | 62.5 | 0.57 |
With high school education or less (%) | 43.5 | 55.0 | 0.29 |
Household income (%) | 0.51 | ||
<$10,000 | 28.3 | 37.5 | |
$10,000‐$35,000 | 30.4 | 32.5 | |
$35,000+ | 41.3 | 30.0 | |
Hospital visits per year for vasoocclusive crises (% with 3+ visits) | 34.8 | 62.5 | 0.01 |
Positive toxicology screen in past 5 years (%) | 13.0 | 27.5 | 0.09 |
Clinical characteristics (%) | |||
Hemoglobin SS disease | 65.2 | 61.5 | 0.73 |
History of acute chest syndrome | 56.5 | 60.0 | 0.74 |
Avascular necrosis | 24.4 | 32.5 | 0.41 |
Pulmonary hypertension | 36.9 | 30.0 | 0.49 |
Renal complications | 13.3 | 20.5 | 0.38 |
History of hypertension | 21.7 | 17.5 | 0.62 |
History of hepatitis C | 10.9 | 15.0 | 0.58 |
Interview location, inpatient (%) | 43.5 | 55.0 | 0.29 |
Previous interpersonal experiences (% reporting previous difficulty persuading medical staff about pain) | 47.8 | 85.0 | <0.001 |
Trust (interpersonal trust) [mean (SD)] | 62.3(19.8) | 44.8(19.2) | 0.0001 |
Associations Among Interpersonal Experiences, Trust, and Hospital Self‐Discharge
In unadjusted analyses, having a history of hospital self‐discharge was associated with a greater likelihood of reporting difficulty persuading medical staff about sickle‐cell pain (85% vs. 47.8%; P < 0.001) and with lower levels of trust in the medical profession (44.8 vs. 62.3; P = 0.0001).
Table 2 reports the results of a multivariate exact logistic regression analysis. Persons reporting difficulty persuading medical staff about sickle‐cell pain were more likely to report having ever self‐discharged from a hospital, even after controlling for patient trust, hospital utilization, and 5‐year toxicology screen history (adjusted odds ratio [AOR], 3.89; P = 0.04; 95% confidence interval [CI], 1.05‐16.26). Patients with greater levels of trust in the medical profession were less likely to have ever self‐discharged from a hospital, controlling for difficulty persuading medical staff about pain, hospital utilization, and 5‐year positive toxicology screen history (AOR, 0.96; P = 0.003; 95% CI, 0.93‐0.99). Independent associations between hospital utilization and self‐discharge history, or between 5‐year toxicology screen history and self‐discharge history, were not observed in this study.
Adjusted Odds Ratio (95% confidence interval) (n = 86) | |
---|---|
| |
Difficulty persuading medical staff about pain | 3.89 (1.0516.26)* |
Trust in the medical profession | 0.96 (0.930.99) |
3+ hospital visits/year due to VOC | 0.96 (0.263.36) |
Positive toxicology screen in the past 5 years | 4.29 (0.8924.64) |
Discussion
In this study, we found that a high proportion of patients with SCD had a history of hospital self‐discharge. Patients with lower trust, and those who reported difficulty in persuading medical staff about sickle‐cell pain, were more likely to report having ever self‐discharged from a hospital, even after controlling for self‐reported hospital utilization for sickle‐cell pain, and the patient's 5‐year toxicology screen history. Because hospital self‐discharge is potentially dangerous,13 our study reveals an understudied aspect of how low trust and poor health care experiences may put patients with SCD at risk for poor outcomes.
In our study, 46.5% of our sample reported ever having self‐discharged from a hospital, which is much higher than the 14% found by Elander et al.19 in their United Kingdom (London‐based) sample. Other differences in our 2 patient populations may account for this discrepancy. Compared to the Elander sample, a much greater percentage of our patients reported ever having difficulty persuading medical staff about their pain (65% vs. 39%). As difficulty persuading medical staff about pain was independently associated with an increased hospital self‐discharge history in our study, one might expect that our sample, which had a higher percentage of patients reporting difficulty, would also be found to have a higher percentage of patients reporting a history of hospital self‐discharge. A second difference between the two patient samples is that our sample of patients experienced a greater number of hospital visits in the 12 months preceding the study compared to the Elander et al.19 sample. If this difference reflects an underlying difference in the overall hospital utilization experiences of the 2 groups, then our sample of adults would have greater opportunities, on average, than the Elander et al.19 sample to experience hospital self‐discharge. Other factors, such as patient behavioral or cultural differences between patients in the United Kingdom (with a national health system) and the United States (without a national health system), might be explored in future studies.
It is important to note that the wording of the hospital self‐discharge item as used both in our study and by Elander et al.19 would not only capture AMA discharges, but additionally may capture other sudden decisions about hospital discharge made by patients. A national‐level study of AMA discharges among adults with SCD in the United States that uses hospital records and/or chart review is needed in order to provide a more generalizable estimate of the prevalence of AMA discharge among this patient population in the United States.
Elander et al.19 suggest that while hospital self‐discharge among SCD patients may be interpreted by many as a sign that the patient engages in problematic use of opioids or other substances, it may be more appropriate to view this behavior as a sign that the patient has received inadequate management of their pain.19 In our study, hospital self‐discharge tended to be associated with having a history of substance abuse as operationalized by a positive toxicology screen for cocaine or marijuana use during any admission in the previous 5 years. Elander et al.19 found that hospital self‐discharge and other so‐called concern raising behaviors such as use of illicit substances were found to be significantly associated with patient attempts to obtain relief from their pain, but were not significantly associated with symptoms of actual substance dependence or addiction. For example, each instance of illicit substance use reported by the patients in the Elander et al.19 study described patient attempts to use marijuana in efforts to manage pain, to relax, or as alternatives to prescribed analgesics. Clinicians in the United States who observe positive toxicology screen results for SCD patients may see these results as casting doubt upon the legitimacy of the patient's pain reports, thus causing a reduction in the amount of pain medicine provided to the patient, when in fact, a substantial percentage of these results may reflect SCD patients attempts to manage their pain outside of a hospital setting. This potential discrepancy between clinician interpretations of the meaning of positive toxicology screen results for SCD patients, and the actual significance of these results for many patients as reflecting attempts to manage pain, could contribute to interpersonal conflicts between the clinicians and patients, and ultimately, patient self‐discharge and decreases in patient trust in clinicians. Further, to the extent that SCD patient positive toxicology screen results reflect use of illicit substances for reasons other than attempts to manage pain, this should signal for clinicians a need to refer the patient for substance abuse treatment and counseling in addition to (and not instead of) efforts to manage the patient's pain.
Our study is among the first to show empirically that persons with a history of hospital self‐discharge have lower levels of trust in the medical profession. Discharging himself or herself from a hospital could cause a patient to view future health care experiences in a more negative light, and cause the patient to have lower trust in the medical profession. Healthcare providers often label patients with a history of leaving AMA as challenging patients. Seeing in the medical record that a patient has left AMA before may bias the provider to view the patient in a more negative light, and consequently affect the quality of their communication with the patient, leading to lower patient trust. Alternatively, a patient could already possess lower trust in the medical profession due to poor‐quality interpersonal experiences, and thus be more likely to self‐discharge from a hospital during a future acute care visit due to a heightened wariness or greater level of anxiety.
The most consistent and robust predictors of trust found across studies in the literature are the quality of previous interactions with medical care.23 Poor physician communication, and experiences of conflict with staff have been associated with lower ratings of trust among a wide variety of patient populations.2427 Interestingly, we found a relationship between trust and hospital self‐discharge even after controlling for the quality of previous interpersonal experiences as measured by prior difficulty persuading staff about pain. Future studies should examine the relationship between trust and hospital self‐discharge history while controlling for other measures of previous interpersonal healthcare experiences among this patient population.
There are limitations to the current study that must be considered. First, as a single‐institution study, these results may not be generalizable to patients with SCD seeking care at other institutions. Also, we did not assess the actual reasons why patients chose to self‐discharge. Thus, while our data suggests that patient perceptions of poor‐quality care contributed to this behavior, we cannot state this definitively. The validity of a self‐reported annual hospital utilization measure as used in this study may be limited by inaccurate patient recall. However, we compared our patient's self‐report of annual hospital utilization with chart documented hospital utilization in the previous 12 months and found that the 2 measures were correlated in the appropriate direction, thereby giving us greater confidence in the validity of our self‐reported measure. Finally, the cross‐sectional nature of the data in this study makes it impossible to specify with certainty the causal directionality of the associations found here. Prospective research must be conducted to help tease apart the potentially complex relationships among trust, interpersonal experiences with care, and hospital self‐discharge.
Adults with SCD who have ever self‐discharged from a hospital have lower trust in the medical profession, and are more likely to report having had prior difficulty persuading medical staff about their sickle‐cell pain. The clinical consequences of hospital self‐discharge in this patient population must be examined, and the specific reasons behind this behavior must be further elucidated, so that clinicians and researchers may be able to design interventions to mitigate the occurrence of this potentially dangerous phenomenon.
- Leaving hospital against medical advice among HIV‐positive patients.CMAJ.2002;167(6):633–637. , , , , , .
- Hospitalized patients with asthma who leave against medical advice: characteristics, reasons, and outcomes.J Allergy Clin Immunol.2007;119(4):924–929. , , , , .
- Hospital discharge against advice after myocardial infarction: deaths and readmissions.Am J Med.2007;120(12):1047–1053. , , .
- Factors associated with patients who leave acute‐care hospitals against medical advice.Am J Public Health.2007;97(12):2204–2208. , , .
- Discharge against medical advice: ethical considerations and professional obligations.J Hosp Med.2008;3(5):403–408. .
- Why patients sign out against medical advice (AMA): factors motivating patients to sign out AMA.Am J Drug Alcohol Abuse.2004;30(2):489–493. , , , .
- Providers' perceptions of relationships and professional roles when caring for patients who leave the hospital against medical advice.J Gen Intern Med.2008;23(10):1698–1707. , .
- Experiences of hospital care and treatment seeking for pain from sickle cell disease: qualitative study.BMJ.1999;318(7198):1585–1590. , , .
- Painful crises in sickle cell disease—patients' perspectives.BMJ.1988;297(6646):452–454. , .
- Health perceptions and medical care opinions of inner‐city adults with sickle cell disease or asthma compared with those of their siblings.South Med J.1989;82(1):9–12. , , , , , .
- Sickle cell mutual assistance groups and the health services delivery system.J Health Soc Policy.1994;5(3–4):243–259. , , .
- Functions of an adult sickle cell group: education, task orientation, and support.Health Soc Work.1993;18(1):49–56. , .
- The management of sickle cell crisis pain as experienced by patients and their carers.J Adv Nurs.1994;19(4):725–732. , .
- Adults with sickle cell disease: psychological impact and experience of hospital services.Psychol Health Med.1998;3(2):171–179. , , .
- Use of focus groups for pain and quality of life assessment in adults with sickle cell disease.J Natl Black Nurses Assoc.2001;12(2):36–43. , , , , .
- The psychosocial experience of people with sickle cell disease and its impact on quality of life: qualitative findings from focus groups.Br J Health Psychol.2002;7(part 3):345–363. , .
- Pain management in sickle cell disease.Chronic Illn.2006;2(1):39–50. , , , .
- A systematic review of barriers and interventions to improve appropriate use of therapies for sickle cell disease.J Natl Med Assoc.2009;101(10):1022–1033. , , , et al.
- Understanding the causes of problematic pain management in sickle cell disease: evidence that pseudoaddiction plays a more important role than genuine analgesic dependence.J Pain Symptom Manage.2004;27(2):156–169. , , , , .
- Development of abbreviated measures to assess patient trust in a physician, a health insurer, and the medical profession.BMC Health Services Research.2005;5(1):64. , , .
- Applied Logistic Regression.2nd ed.New York:Wiley;2000. , .
- Regression Models for Categorical Dependent Variables Using Stata.2nd ed.College Station, TX:StataCorp LP;2006. , .
- Trust in physicians and medical institutions: what is it, can it be measured, and does it matter?Milbank Q.2001;79(4):613–639. , , , .
- How are patients' specific ambulatory care experiences related to trust, satisfaction, and considering changing physicians?J Gen Intern Med.2002;17(1):29–39. , , , , , .
- Racial differences in trust and lung cancer patients' perceptions of physician communication.J Clin Oncol.2006;24(6):904–909. , , , , .
- How patient‐physician encounters in critical medical situations affect trust: results of a national survey.BMC Health Serv Res.2004;4(1):24. , , .
- Trust in the medical profession: conceptual and measurement issues.Health Serv Res.2002;37(5):1419–1439. , , , .
- Leaving hospital against medical advice among HIV‐positive patients.CMAJ.2002;167(6):633–637. , , , , , .
- Hospitalized patients with asthma who leave against medical advice: characteristics, reasons, and outcomes.J Allergy Clin Immunol.2007;119(4):924–929. , , , , .
- Hospital discharge against advice after myocardial infarction: deaths and readmissions.Am J Med.2007;120(12):1047–1053. , , .
- Factors associated with patients who leave acute‐care hospitals against medical advice.Am J Public Health.2007;97(12):2204–2208. , , .
- Discharge against medical advice: ethical considerations and professional obligations.J Hosp Med.2008;3(5):403–408. .
- Why patients sign out against medical advice (AMA): factors motivating patients to sign out AMA.Am J Drug Alcohol Abuse.2004;30(2):489–493. , , , .
- Providers' perceptions of relationships and professional roles when caring for patients who leave the hospital against medical advice.J Gen Intern Med.2008;23(10):1698–1707. , .
- Experiences of hospital care and treatment seeking for pain from sickle cell disease: qualitative study.BMJ.1999;318(7198):1585–1590. , , .
- Painful crises in sickle cell disease—patients' perspectives.BMJ.1988;297(6646):452–454. , .
- Health perceptions and medical care opinions of inner‐city adults with sickle cell disease or asthma compared with those of their siblings.South Med J.1989;82(1):9–12. , , , , , .
- Sickle cell mutual assistance groups and the health services delivery system.J Health Soc Policy.1994;5(3–4):243–259. , , .
- Functions of an adult sickle cell group: education, task orientation, and support.Health Soc Work.1993;18(1):49–56. , .
- The management of sickle cell crisis pain as experienced by patients and their carers.J Adv Nurs.1994;19(4):725–732. , .
- Adults with sickle cell disease: psychological impact and experience of hospital services.Psychol Health Med.1998;3(2):171–179. , , .
- Use of focus groups for pain and quality of life assessment in adults with sickle cell disease.J Natl Black Nurses Assoc.2001;12(2):36–43. , , , , .
- The psychosocial experience of people with sickle cell disease and its impact on quality of life: qualitative findings from focus groups.Br J Health Psychol.2002;7(part 3):345–363. , .
- Pain management in sickle cell disease.Chronic Illn.2006;2(1):39–50. , , , .
- A systematic review of barriers and interventions to improve appropriate use of therapies for sickle cell disease.J Natl Med Assoc.2009;101(10):1022–1033. , , , et al.
- Understanding the causes of problematic pain management in sickle cell disease: evidence that pseudoaddiction plays a more important role than genuine analgesic dependence.J Pain Symptom Manage.2004;27(2):156–169. , , , , .
- Development of abbreviated measures to assess patient trust in a physician, a health insurer, and the medical profession.BMC Health Services Research.2005;5(1):64. , , .
- Applied Logistic Regression.2nd ed.New York:Wiley;2000. , .
- Regression Models for Categorical Dependent Variables Using Stata.2nd ed.College Station, TX:StataCorp LP;2006. , .
- Trust in physicians and medical institutions: what is it, can it be measured, and does it matter?Milbank Q.2001;79(4):613–639. , , , .
- How are patients' specific ambulatory care experiences related to trust, satisfaction, and considering changing physicians?J Gen Intern Med.2002;17(1):29–39. , , , , , .
- Racial differences in trust and lung cancer patients' perceptions of physician communication.J Clin Oncol.2006;24(6):904–909. , , , , .
- How patient‐physician encounters in critical medical situations affect trust: results of a national survey.BMC Health Serv Res.2004;4(1):24. , , .
- Trust in the medical profession: conceptual and measurement issues.Health Serv Res.2002;37(5):1419–1439. , , , .
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