Considerable research and public attention is being paid to the quantification, risk adjustment, and reporting of inpatient mortality.15 Inpatient mortality is reported as aggregate mortality (for all hospitalized patients or those with a specific diagnosis3, 6) or intensive care unit (ICU) mortality.7, 8 While reporting aggregate hospital or aggregate ICU mortality rates is useful, it is also important to develop reporting strategies that go beyond simply using data elements found in administrative databases (eg, diagnosis and procedure codes) to quantify practice variation. Ideally, such strategies would permit delineating processes of careparticularly those potentially under the control of hospitalists, not only intensiviststo identify improvement opportunities. One such process, which can be tracked using the bed history component of a patient's electronic medical record, is the transfer of patients between different units within the same hospital.

Several studies have documented that risk of ICU death is highest among patients transferred from general medical‐surgical wards, intermediate among direct admissions from the emergency department, and lowest among surgical admissions.911 Opportunities to reduce subsequent ICU mortality have been studied among ward patients who develop sepsis and are then transferred to the ICU,12 among patients who experience cardiac arrest,13, 14 as well as among patients with any physiological deterioration (eg, through the use of rapid response teams).1517 Most of these studies have been single‐center studies and/or studies reporting only an ICU denominator. While useful in some respects, such studies are less helpful to hospitalists, who would benefit from better understanding of the types of patients transferred and the total impact that transfers to a higher level of care make on general medical‐surgical wards. In addition, entities such as the Institute for Healthcare Improvement recommend the manual review of records of patients who were transferred from the ward to the ICU18 to identify performance improvement opportunities. While laudable, such approaches do not lend themselves to automated reporting strategies.

We recently described a new risk adjustment methodology for inpatient mortality based entirely on automated data preceding hospital admission and not restricted to ICU patients. This methodology, which has been externally validated in Ottawa, Canada, after development in the Kaiser Permanente Medical Care Program (KPMCP), permits quantification of a patient's pre‐existing comorbidity burden, physiologic derangement at the time of admission, and overall inpatient mortality risk.19, 20 The primary purpose of this study was to combine this methodology with bed history analysis to quantify the in‐hospital mortality and length of stay (LOS) of patients who experienced intra‐hospital transfers in a large, multihospital system. As a secondary goal, we also wanted to assess the degree to which these transfers could be predicted based on information available prior to a patient's admission.

Materials and Methods

This project was approved by the Northern California KPMCP Institutional Review Board for the Protection of Human Subjects.

The Northern California KPMCP serves a total population of approximately 3.3 million members. Under a mutual exclusivity arrangement, physicians of The Permanente Medical Group, Inc., care for Kaiser Foundation Health Plan, Inc. members at facilities owned by Kaiser Foundation Hospitals, Inc. All Northern California KPMCP hospitals and clinics employ the same information systems with a common medical record number and can track care covered by the plan but delivered elsewhere. Databases maintained by the KPMCP capture admission and discharge times, admission and discharge diagnoses and procedures (assigned by professional coders), bed histories, inter‐hospital transfers, as well as the results of all inpatient and outpatient laboratory tests. The use of these databases for research has been described in multiple reports.2124

Our setting consisted of all 19 hospitals owned and operated by the KPMCP, whose characteristics are summarized in the Supporting Information Appendix available to interested readers. These include the 17 described in our previous report19 as well as 2 new hospitals (Antioch and Manteca) which are similar in size and type of population served. Our study population consisted of all patients admitted to these 19 hospitals who met these criteria: 1) hospitalization began from November 1st, 2006 through January 31st, 2008; 2) initial hospitalization occurred at a Northern California KPMCP hospital (ie, for inter‐hospital transfers, the first hospital stay occurred within the KPMCP); 3) age 15 years; and 4) hospitalization was not for childbirth.

We defined a linked hospitalization as the time period that began with a patient's admission to the hospital and ended with the patient's discharge (home, to a nursing home, or death). Linked hospitalizations can thus involve more than 1 hospital stay and could include a patient transfer from one hospital to another prior to definitive discharge. For linked hospitalizations, mortality was attributed to the admitting KPMCP hospital (ie, if a patient was admitted to hospital A, transferred to B, and died at hospital B, mortality was attributed to hospital A). We defined total LOS as the exact time in hours from when a patient was first admitted to the hospital until death or final discharge home or to a nursing home, while total ICU or transitional care unit (TCU, referred to as stepdown unit in some hospitals) LOS was calculated for all individual ICU or TCU stays during the hospital stay.

Results

During the study period, a total of 249,129 individual hospital stays involving 170,151 patients occurred at these 19 hospitals. After concatenation of inter‐hospital transfers, we were left with 237,208 linked hospitalizations. We excluded 26,738 linked hospitalizations that began at a non‐KPMCP hospital (ie, they were transported in), leaving a total of 210,470 linked hospitalizations involving 150,495 patients. The overall linked hospitalization mortality rate was 3.30%.

Table 1 summarizes cohort characteristics based on initial hospital location. On admission, ICU patients had the highest degree of physiologic derangement as well as the highest predicted mortality. Considerable inter‐hospital variation was present in both predictors and outcomes; details on these variations are provided in the Supporting Information Appendix.

Table 2 summarizes data from 3 groups of patients: patients initially admitted to the ward, or TCU, who did not experience a transfer to a higher level of care and patients admitted to these 2 units who did experience such a transfer. Patients who experienced a transfer constituted 5.3% (6,484/121,237) of ward patients and 6.7% (1,384/20,556) of TCU patients. Transferred patients tended to be older, have more acute physiologic derangement (higher LAPS), a greater pre‐existing illness burden (higher COPS), and a higher predicted mortality risk. Among ward patients, those with the following admission diagnoses were most likely to experience a transfer to a higher level of care: gastrointestinal bleeding (10.8% of all transfers), pneumonia (8.7%), and other infections (8.2%). The diagnoses most likely to be associated with death following transfer were cancer (death rate among transferred patients, 48%), renal disease (death rate, 36%), and liver disease (33%). Similar distributions were observed for TCU patients.

Table 3 compares outcomes among ward and TCU patients who did and did not experience a transfer to a higher level of care. The table shows that transferred patients were almost 3 times as likely to die, even after controlling for severity of illness, and that their hospital LOS was 9 days higher than expected. This increased risk was seen in all hospitals and among all transfer types (ward to ICU, ward to TCU, and TCU to ICU).

Table 3 also shows that, among decedent patients, those who never left the ward or TCU died much sooner than those who died following transfer. Among direct admits to the ICU, the median LOS at time of death was 3.9 days, with a mean of 9.4 standard deviation of 19.9 days, while the corresponding times for TCU direct admits were a median and mean LOS of 6.5 and 11.7 19.5 days.

Table 4 summarizes outcomes among different patient subgroups that did and did not experience a transfer to a higher level of care. Based on location, patients who experienced a transfer from the TCU to the ICU had the highest crude death rate, but patients transferred from the ward to the ICU had the highest OEMR. On the other hand, if one divides patients by the degree of physiologic derangement, patients with low LAPS who experienced a transfer had the highest OEMR. With respect to LOS, patients transferred from the TCU to the ICU had the highest OMELOS (13.4 extra days).

Transfers to a higher level of care at a different hospital, which in the KPMCP are usually planned, experienced lower mortality than transfers within the same hospital. For ward to TCU transfers, intra‐hospital transfers had a mortality of 12.1% while inter‐hospital transfers had a mortality of 5.7%. Corresponding rates for ward to ICU transfers were 21.7% and 11.2%, and for TCU to ICU transfers the rates were 25.9% and 12.5%, respectively.

Among patients initially admitted to the ward, a model to predict the occurrence of a transfer to a higher level of care (within 48 hours after admission) that included age, sex, admission type, primary condition, LAPS, COPS, and interaction terms had poor discrimination, with an area under the receiver operator characteristic (c statistic) of only 0.64. The c statistic for a model to predict transfer after 48 hours was 0.66. The corresponding models for TCU admits had c statistics of 0.67 and 0.68. All four models had poor calibration.

Discussion

Using automated bed history data permits characterizing a patient population with disproportionate mortality and LOS: intra‐hospital transfers to special care units (ICUs or TCUs). Indeed, the largest subset of these patients (those initially admitted to the ward or TCU) constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. These patients also had very elevated OEMRs and OMELOS. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers.

We performed multivariate analyses to explore the degree to which electronically assigned preadmission severity scores could predict these transfers. These analyses found that, compared to our ability to predict inpatient or 30‐day mortality at the time of admission, which is excellent, our ability to predict the occurrence of transfer after admission is much more limited. These results highlight the limitations of severity scores that rely on automated data, which may not have adequate discrimination when it comes to determining the risk of an adverse outcome within a narrow time frame. For example, among the 121,237 patients initially admitted to the ward who did not experience an intra‐hospital transfer, the mean LAPS was 18.9, while the mean LAPS among the 6,484 ward patients who did experience a transfer was 25.5. Differences between the mean and median LAPS, COPS, and predicted mortality risk among transferred and non‐transferred patients were significant (P < 0.0001 for all comparisons). However, examination of the distribution of LAPS, COPS, and predicted mortality risk between these two groups of patients showed considerable overlap.

Our methodology resembles Silber et al.'s27, 28 concept of failure to rescue in that it focuses on events occurring after hospitalization. Silber et al. argue that a hospital's quality can be measured by quantifying the degree to which patients who experience new problems are successfully rescued. Furthermore, quantification of those situations where rescue attempts are unsuccessful is felt to be superior to simply comparing raw or adjusted mortality rates because these are primarily determined by underlying case mix. The primary difference between Silber et al.'s approach and ours is at the level of detailthey specified a specific set of complications, whereas our measure is more generic and would include patients with many of the complications specified by Silber et al.27, 28

Most of the patients transferred to a higher level of care in our cohort survived (ie, were rescued), indicating that intensive care is beneficial. However, the fact that these patients had elevated OEMRs and OMELOS indicates that the real challenge facing hospitalists involves the timing of provision of a beneficial intervention. In theory, improved timing could result from earlier detection of problems, which is the underlying rationale for employing rapid response teams. However, the fact that our electronic tools (LAPS, COPS) cannot predict patient deteriorations within a narrow time frame suggests that early detection will remain a major challenge. Manually assigned vital signs scores designed for this purpose do not have good discrimination either.29, 30 This raises the possibility that, though patient groups may differ in terms of overall illness severity and mortality risk, differences at the individual patient level may be too subtle for clinicians to detect. Future research may thus need to focus on scores that combine laboratory data, vital signs, trends in data,31, 32 and newer proteomic markers (eg, procalcitonin).33 We also found that most transfers occurred early (within <72 hours), raising the possibility that at least some of these transfers may involve issues around triage rather than sudden deterioration.

Our study has important limitations. Due to resource constraints and limited data availability, we could not characterize the patients as well as might be desirable; in particular, we could not make full determinations of the actual reasons for patients' transfer for all patients. Broadly speaking, transfer to a higher level of care could be due to inappropriate triage, appropriate (preventive) transfer (which could include transfer to a more richly staffed unit for a specific procedure), relentless progression of disease despite maximal therapy, the occurrence of management errors, patient and family uncertainty about goals of care or inadequate understanding of treatment options and prognoses, or a combination of these factors. We could not make these distinctions with currently available electronic data. This is also true of postsurgical patients, in whom it is difficult to determine which transfers to intensive care might be planned (eg, in the case of surgical procedures where ICU care is anticipated) as opposed to the occurrence of a deterioration during or following surgery. Another major limitation of this study is our inability to identify code or no code status electronically. The elapsed LOS at time of death among patients who experienced a transfer to a higher level of care (as compared to patients who died in the ward without ever experiencing intra‐hospital transfer) suggests, but does not prove, that prolonged efforts were being made to keep them alive. We were also limited in terms of having access to other process data (eg, physician staffing levels, provision and timing of palliative care). Having ICU severity of illness scores would have permitted us to compare our cohort to those of other recent studies showing elevated mortality rates among transfer patients,911 but we have not yet developed that capability.

Consideration of our study findings suggests a possible research agenda that could be implemented by hospitalist researchers. This agenda should emphasize three areas: detection, intervention, and reflection.

With respect to detection, attention needs to be paid to better tools for quantifying patient risk at the time a decision to admit to the ward is made. It is likely that such tools will need to combine the attributes of our severity score (LAPS) with those of the manually assigned scores.30, 34 In some cases, use of these tools could lead a physician to change the locus of admission from the ward to the TCU or ICU, which could improve outcomes by ensuring more timely provision of intensive care. Since problems with initial triage could be due to factors other than the failure to suspect or anticipate impending instability, future research should also include a cognitive component (eg, quantifying what proportion of subsequent patient deteriorations could be ascribed to missed diagnoses35). Additional work also needs to be done on developing mathematical models that can inform electronic monitoring of ward (not just ICU) patients.

Research on interventions that hospitalists can use to prevent the need for intensive care or to improve the rescue rate should take two routes. The first is a disease‐specific route, which builds on the fact that a relatively small set of conditions (pneumonia, sepsis, gastrointestinal bleeding) account for most transfers to a higher level of care. Condition‐specific protocols, checklists, and bundles36 tailored to a ward environment (as opposed to the ICU or to the entire hospital) might prevent deteriorations in these patients, as has been reported for sepsis.37 The second route is to improve the overall capabilities of rapid response and code blue teams. Such research would need to include a more careful assessment of what commonalities exist among patients who were and were not successfully rescued by these teams. This approach would probably yield more insights than the current literature, which focuses on whether rapid response teams are a good thing or not.

Finally, research also needs to be performed on how hospitalists reflect on adverse outcomes among ward patients. Greater emphasis needs to be placed on moving beyond trigger tool approaches that rely on manual chart review. In an era of expanding use of electronic medical record systems, more work needs to be done on how to harness these to provide hospitalists with better quantitative and risk‐adjusted information. This information should not be limited to simply reporting rates of transfers and deaths. Rather, finer distinctions must be provided with respect of the type of patients (ie, more diagnostic detail), the clinical status of patients (ie, more physiologic detail), as well as the effects of including or excluding patients in whom therapeutic options may be limited (ie, do not resuscitate and comfort care patients) on reported rates. Ideally, researchers should develop better process and outcomes measures that could be tested in collaborative networks that include multiple nonacademic general medical‐surgical wards.

Considerable research and public attention is being paid to the quantification, risk adjustment, and reporting of inpatient mortality.15 Inpatient mortality is reported as aggregate mortality (for all hospitalized patients or those with a specific diagnosis3, 6) or intensive care unit (ICU) mortality.7, 8 While reporting aggregate hospital or aggregate ICU mortality rates is useful, it is also important to develop reporting strategies that go beyond simply using data elements found in administrative databases (eg, diagnosis and procedure codes) to quantify practice variation. Ideally, such strategies would permit delineating processes of careparticularly those potentially under the control of hospitalists, not only intensiviststo identify improvement opportunities. One such process, which can be tracked using the bed history component of a patient's electronic medical record, is the transfer of patients between different units within the same hospital.

Several studies have documented that risk of ICU death is highest among patients transferred from general medical‐surgical wards, intermediate among direct admissions from the emergency department, and lowest among surgical admissions.911 Opportunities to reduce subsequent ICU mortality have been studied among ward patients who develop sepsis and are then transferred to the ICU,12 among patients who experience cardiac arrest,13, 14 as well as among patients with any physiological deterioration (eg, through the use of rapid response teams).1517 Most of these studies have been single‐center studies and/or studies reporting only an ICU denominator. While useful in some respects, such studies are less helpful to hospitalists, who would benefit from better understanding of the types of patients transferred and the total impact that transfers to a higher level of care make on general medical‐surgical wards. In addition, entities such as the Institute for Healthcare Improvement recommend the manual review of records of patients who were transferred from the ward to the ICU18 to identify performance improvement opportunities. While laudable, such approaches do not lend themselves to automated reporting strategies.

We recently described a new risk adjustment methodology for inpatient mortality based entirely on automated data preceding hospital admission and not restricted to ICU patients. This methodology, which has been externally validated in Ottawa, Canada, after development in the Kaiser Permanente Medical Care Program (KPMCP), permits quantification of a patient's pre‐existing comorbidity burden, physiologic derangement at the time of admission, and overall inpatient mortality risk.19, 20 The primary purpose of this study was to combine this methodology with bed history analysis to quantify the in‐hospital mortality and length of stay (LOS) of patients who experienced intra‐hospital transfers in a large, multihospital system. As a secondary goal, we also wanted to assess the degree to which these transfers could be predicted based on information available prior to a patient's admission.

Materials and Methods

This project was approved by the Northern California KPMCP Institutional Review Board for the Protection of Human Subjects.

The Northern California KPMCP serves a total population of approximately 3.3 million members. Under a mutual exclusivity arrangement, physicians of The Permanente Medical Group, Inc., care for Kaiser Foundation Health Plan, Inc. members at facilities owned by Kaiser Foundation Hospitals, Inc. All Northern California KPMCP hospitals and clinics employ the same information systems with a common medical record number and can track care covered by the plan but delivered elsewhere. Databases maintained by the KPMCP capture admission and discharge times, admission and discharge diagnoses and procedures (assigned by professional coders), bed histories, inter‐hospital transfers, as well as the results of all inpatient and outpatient laboratory tests. The use of these databases for research has been described in multiple reports.2124

Our setting consisted of all 19 hospitals owned and operated by the KPMCP, whose characteristics are summarized in the Supporting Information Appendix available to interested readers. These include the 17 described in our previous report19 as well as 2 new hospitals (Antioch and Manteca) which are similar in size and type of population served. Our study population consisted of all patients admitted to these 19 hospitals who met these criteria: 1) hospitalization began from November 1st, 2006 through January 31st, 2008; 2) initial hospitalization occurred at a Northern California KPMCP hospital (ie, for inter‐hospital transfers, the first hospital stay occurred within the KPMCP); 3) age 15 years; and 4) hospitalization was not for childbirth.

We defined a linked hospitalization as the time period that began with a patient's admission to the hospital and ended with the patient's discharge (home, to a nursing home, or death). Linked hospitalizations can thus involve more than 1 hospital stay and could include a patient transfer from one hospital to another prior to definitive discharge. For linked hospitalizations, mortality was attributed to the admitting KPMCP hospital (ie, if a patient was admitted to hospital A, transferred to B, and died at hospital B, mortality was attributed to hospital A). We defined total LOS as the exact time in hours from when a patient was first admitted to the hospital until death or final discharge home or to a nursing home, while total ICU or transitional care unit (TCU, referred to as stepdown unit in some hospitals) LOS was calculated for all individual ICU or TCU stays during the hospital stay.

Results

During the study period, a total of 249,129 individual hospital stays involving 170,151 patients occurred at these 19 hospitals. After concatenation of inter‐hospital transfers, we were left with 237,208 linked hospitalizations. We excluded 26,738 linked hospitalizations that began at a non‐KPMCP hospital (ie, they were transported in), leaving a total of 210,470 linked hospitalizations involving 150,495 patients. The overall linked hospitalization mortality rate was 3.30%.

Table 1 summarizes cohort characteristics based on initial hospital location. On admission, ICU patients had the highest degree of physiologic derangement as well as the highest predicted mortality. Considerable inter‐hospital variation was present in both predictors and outcomes; details on these variations are provided in the Supporting Information Appendix.

Table 2 summarizes data from 3 groups of patients: patients initially admitted to the ward, or TCU, who did not experience a transfer to a higher level of care and patients admitted to these 2 units who did experience such a transfer. Patients who experienced a transfer constituted 5.3% (6,484/121,237) of ward patients and 6.7% (1,384/20,556) of TCU patients. Transferred patients tended to be older, have more acute physiologic derangement (higher LAPS), a greater pre‐existing illness burden (higher COPS), and a higher predicted mortality risk. Among ward patients, those with the following admission diagnoses were most likely to experience a transfer to a higher level of care: gastrointestinal bleeding (10.8% of all transfers), pneumonia (8.7%), and other infections (8.2%). The diagnoses most likely to be associated with death following transfer were cancer (death rate among transferred patients, 48%), renal disease (death rate, 36%), and liver disease (33%). Similar distributions were observed for TCU patients.

Table 3 compares outcomes among ward and TCU patients who did and did not experience a transfer to a higher level of care. The table shows that transferred patients were almost 3 times as likely to die, even after controlling for severity of illness, and that their hospital LOS was 9 days higher than expected. This increased risk was seen in all hospitals and among all transfer types (ward to ICU, ward to TCU, and TCU to ICU).

Table 3 also shows that, among decedent patients, those who never left the ward or TCU died much sooner than those who died following transfer. Among direct admits to the ICU, the median LOS at time of death was 3.9 days, with a mean of 9.4 standard deviation of 19.9 days, while the corresponding times for TCU direct admits were a median and mean LOS of 6.5 and 11.7 19.5 days.

Table 4 summarizes outcomes among different patient subgroups that did and did not experience a transfer to a higher level of care. Based on location, patients who experienced a transfer from the TCU to the ICU had the highest crude death rate, but patients transferred from the ward to the ICU had the highest OEMR. On the other hand, if one divides patients by the degree of physiologic derangement, patients with low LAPS who experienced a transfer had the highest OEMR. With respect to LOS, patients transferred from the TCU to the ICU had the highest OMELOS (13.4 extra days).

Transfers to a higher level of care at a different hospital, which in the KPMCP are usually planned, experienced lower mortality than transfers within the same hospital. For ward to TCU transfers, intra‐hospital transfers had a mortality of 12.1% while inter‐hospital transfers had a mortality of 5.7%. Corresponding rates for ward to ICU transfers were 21.7% and 11.2%, and for TCU to ICU transfers the rates were 25.9% and 12.5%, respectively.

Among patients initially admitted to the ward, a model to predict the occurrence of a transfer to a higher level of care (within 48 hours after admission) that included age, sex, admission type, primary condition, LAPS, COPS, and interaction terms had poor discrimination, with an area under the receiver operator characteristic (c statistic) of only 0.64. The c statistic for a model to predict transfer after 48 hours was 0.66. The corresponding models for TCU admits had c statistics of 0.67 and 0.68. All four models had poor calibration.

Discussion

Using automated bed history data permits characterizing a patient population with disproportionate mortality and LOS: intra‐hospital transfers to special care units (ICUs or TCUs). Indeed, the largest subset of these patients (those initially admitted to the ward or TCU) constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. These patients also had very elevated OEMRs and OMELOS. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers.

We performed multivariate analyses to explore the degree to which electronically assigned preadmission severity scores could predict these transfers. These analyses found that, compared to our ability to predict inpatient or 30‐day mortality at the time of admission, which is excellent, our ability to predict the occurrence of transfer after admission is much more limited. These results highlight the limitations of severity scores that rely on automated data, which may not have adequate discrimination when it comes to determining the risk of an adverse outcome within a narrow time frame. For example, among the 121,237 patients initially admitted to the ward who did not experience an intra‐hospital transfer, the mean LAPS was 18.9, while the mean LAPS among the 6,484 ward patients who did experience a transfer was 25.5. Differences between the mean and median LAPS, COPS, and predicted mortality risk among transferred and non‐transferred patients were significant (P < 0.0001 for all comparisons). However, examination of the distribution of LAPS, COPS, and predicted mortality risk between these two groups of patients showed considerable overlap.

Our methodology resembles Silber et al.'s27, 28 concept of failure to rescue in that it focuses on events occurring after hospitalization. Silber et al. argue that a hospital's quality can be measured by quantifying the degree to which patients who experience new problems are successfully rescued. Furthermore, quantification of those situations where rescue attempts are unsuccessful is felt to be superior to simply comparing raw or adjusted mortality rates because these are primarily determined by underlying case mix. The primary difference between Silber et al.'s approach and ours is at the level of detailthey specified a specific set of complications, whereas our measure is more generic and would include patients with many of the complications specified by Silber et al.27, 28

Most of the patients transferred to a higher level of care in our cohort survived (ie, were rescued), indicating that intensive care is beneficial. However, the fact that these patients had elevated OEMRs and OMELOS indicates that the real challenge facing hospitalists involves the timing of provision of a beneficial intervention. In theory, improved timing could result from earlier detection of problems, which is the underlying rationale for employing rapid response teams. However, the fact that our electronic tools (LAPS, COPS) cannot predict patient deteriorations within a narrow time frame suggests that early detection will remain a major challenge. Manually assigned vital signs scores designed for this purpose do not have good discrimination either.29, 30 This raises the possibility that, though patient groups may differ in terms of overall illness severity and mortality risk, differences at the individual patient level may be too subtle for clinicians to detect. Future research may thus need to focus on scores that combine laboratory data, vital signs, trends in data,31, 32 and newer proteomic markers (eg, procalcitonin).33 We also found that most transfers occurred early (within <72 hours), raising the possibility that at least some of these transfers may involve issues around triage rather than sudden deterioration.

Our study has important limitations. Due to resource constraints and limited data availability, we could not characterize the patients as well as might be desirable; in particular, we could not make full determinations of the actual reasons for patients' transfer for all patients. Broadly speaking, transfer to a higher level of care could be due to inappropriate triage, appropriate (preventive) transfer (which could include transfer to a more richly staffed unit for a specific procedure), relentless progression of disease despite maximal therapy, the occurrence of management errors, patient and family uncertainty about goals of care or inadequate understanding of treatment options and prognoses, or a combination of these factors. We could not make these distinctions with currently available electronic data. This is also true of postsurgical patients, in whom it is difficult to determine which transfers to intensive care might be planned (eg, in the case of surgical procedures where ICU care is anticipated) as opposed to the occurrence of a deterioration during or following surgery. Another major limitation of this study is our inability to identify code or no code status electronically. The elapsed LOS at time of death among patients who experienced a transfer to a higher level of care (as compared to patients who died in the ward without ever experiencing intra‐hospital transfer) suggests, but does not prove, that prolonged efforts were being made to keep them alive. We were also limited in terms of having access to other process data (eg, physician staffing levels, provision and timing of palliative care). Having ICU severity of illness scores would have permitted us to compare our cohort to those of other recent studies showing elevated mortality rates among transfer patients,911 but we have not yet developed that capability.

Consideration of our study findings suggests a possible research agenda that could be implemented by hospitalist researchers. This agenda should emphasize three areas: detection, intervention, and reflection.

With respect to detection, attention needs to be paid to better tools for quantifying patient risk at the time a decision to admit to the ward is made. It is likely that such tools will need to combine the attributes of our severity score (LAPS) with those of the manually assigned scores.30, 34 In some cases, use of these tools could lead a physician to change the locus of admission from the ward to the TCU or ICU, which could improve outcomes by ensuring more timely provision of intensive care. Since problems with initial triage could be due to factors other than the failure to suspect or anticipate impending instability, future research should also include a cognitive component (eg, quantifying what proportion of subsequent patient deteriorations could be ascribed to missed diagnoses35). Additional work also needs to be done on developing mathematical models that can inform electronic monitoring of ward (not just ICU) patients.

Research on interventions that hospitalists can use to prevent the need for intensive care or to improve the rescue rate should take two routes. The first is a disease‐specific route, which builds on the fact that a relatively small set of conditions (pneumonia, sepsis, gastrointestinal bleeding) account for most transfers to a higher level of care. Condition‐specific protocols, checklists, and bundles36 tailored to a ward environment (as opposed to the ICU or to the entire hospital) might prevent deteriorations in these patients, as has been reported for sepsis.37 The second route is to improve the overall capabilities of rapid response and code blue teams. Such research would need to include a more careful assessment of what commonalities exist among patients who were and were not successfully rescued by these teams. This approach would probably yield more insights than the current literature, which focuses on whether rapid response teams are a good thing or not.

Finally, research also needs to be performed on how hospitalists reflect on adverse outcomes among ward patients. Greater emphasis needs to be placed on moving beyond trigger tool approaches that rely on manual chart review. In an era of expanding use of electronic medical record systems, more work needs to be done on how to harness these to provide hospitalists with better quantitative and risk‐adjusted information. This information should not be limited to simply reporting rates of transfers and deaths. Rather, finer distinctions must be provided with respect of the type of patients (ie, more diagnostic detail), the clinical status of patients (ie, more physiologic detail), as well as the effects of including or excluding patients in whom therapeutic options may be limited (ie, do not resuscitate and comfort care patients) on reported rates. Ideally, researchers should develop better process and outcomes measures that could be tested in collaborative networks that include multiple nonacademic general medical‐surgical wards.

Considerable research and public attention is being paid to the quantification, risk adjustment, and reporting of inpatient mortality.15 Inpatient mortality is reported as aggregate mortality (for all hospitalized patients or those with a specific diagnosis3, 6) or intensive care unit (ICU) mortality.7, 8 While reporting aggregate hospital or aggregate ICU mortality rates is useful, it is also important to develop reporting strategies that go beyond simply using data elements found in administrative databases (eg, diagnosis and procedure codes) to quantify practice variation. Ideally, such strategies would permit delineating processes of careparticularly those potentially under the control of hospitalists, not only intensiviststo identify improvement opportunities. One such process, which can be tracked using the bed history component of a patient's electronic medical record, is the transfer of patients between different units within the same hospital.

Several studies have documented that risk of ICU death is highest among patients transferred from general medical‐surgical wards, intermediate among direct admissions from the emergency department, and lowest among surgical admissions.911 Opportunities to reduce subsequent ICU mortality have been studied among ward patients who develop sepsis and are then transferred to the ICU,12 among patients who experience cardiac arrest,13, 14 as well as among patients with any physiological deterioration (eg, through the use of rapid response teams).1517 Most of these studies have been single‐center studies and/or studies reporting only an ICU denominator. While useful in some respects, such studies are less helpful to hospitalists, who would benefit from better understanding of the types of patients transferred and the total impact that transfers to a higher level of care make on general medical‐surgical wards. In addition, entities such as the Institute for Healthcare Improvement recommend the manual review of records of patients who were transferred from the ward to the ICU18 to identify performance improvement opportunities. While laudable, such approaches do not lend themselves to automated reporting strategies.

We recently described a new risk adjustment methodology for inpatient mortality based entirely on automated data preceding hospital admission and not restricted to ICU patients. This methodology, which has been externally validated in Ottawa, Canada, after development in the Kaiser Permanente Medical Care Program (KPMCP), permits quantification of a patient's pre‐existing comorbidity burden, physiologic derangement at the time of admission, and overall inpatient mortality risk.19, 20 The primary purpose of this study was to combine this methodology with bed history analysis to quantify the in‐hospital mortality and length of stay (LOS) of patients who experienced intra‐hospital transfers in a large, multihospital system. As a secondary goal, we also wanted to assess the degree to which these transfers could be predicted based on information available prior to a patient's admission.

Materials and Methods

This project was approved by the Northern California KPMCP Institutional Review Board for the Protection of Human Subjects.

The Northern California KPMCP serves a total population of approximately 3.3 million members. Under a mutual exclusivity arrangement, physicians of The Permanente Medical Group, Inc., care for Kaiser Foundation Health Plan, Inc. members at facilities owned by Kaiser Foundation Hospitals, Inc. All Northern California KPMCP hospitals and clinics employ the same information systems with a common medical record number and can track care covered by the plan but delivered elsewhere. Databases maintained by the KPMCP capture admission and discharge times, admission and discharge diagnoses and procedures (assigned by professional coders), bed histories, inter‐hospital transfers, as well as the results of all inpatient and outpatient laboratory tests. The use of these databases for research has been described in multiple reports.2124

Our setting consisted of all 19 hospitals owned and operated by the KPMCP, whose characteristics are summarized in the Supporting Information Appendix available to interested readers. These include the 17 described in our previous report19 as well as 2 new hospitals (Antioch and Manteca) which are similar in size and type of population served. Our study population consisted of all patients admitted to these 19 hospitals who met these criteria: 1) hospitalization began from November 1st, 2006 through January 31st, 2008; 2) initial hospitalization occurred at a Northern California KPMCP hospital (ie, for inter‐hospital transfers, the first hospital stay occurred within the KPMCP); 3) age 15 years; and 4) hospitalization was not for childbirth.

We defined a linked hospitalization as the time period that began with a patient's admission to the hospital and ended with the patient's discharge (home, to a nursing home, or death). Linked hospitalizations can thus involve more than 1 hospital stay and could include a patient transfer from one hospital to another prior to definitive discharge. For linked hospitalizations, mortality was attributed to the admitting KPMCP hospital (ie, if a patient was admitted to hospital A, transferred to B, and died at hospital B, mortality was attributed to hospital A). We defined total LOS as the exact time in hours from when a patient was first admitted to the hospital until death or final discharge home or to a nursing home, while total ICU or transitional care unit (TCU, referred to as stepdown unit in some hospitals) LOS was calculated for all individual ICU or TCU stays during the hospital stay.

Results

During the study period, a total of 249,129 individual hospital stays involving 170,151 patients occurred at these 19 hospitals. After concatenation of inter‐hospital transfers, we were left with 237,208 linked hospitalizations. We excluded 26,738 linked hospitalizations that began at a non‐KPMCP hospital (ie, they were transported in), leaving a total of 210,470 linked hospitalizations involving 150,495 patients. The overall linked hospitalization mortality rate was 3.30%.

Table 1 summarizes cohort characteristics based on initial hospital location. On admission, ICU patients had the highest degree of physiologic derangement as well as the highest predicted mortality. Considerable inter‐hospital variation was present in both predictors and outcomes; details on these variations are provided in the Supporting Information Appendix.

Table 2 summarizes data from 3 groups of patients: patients initially admitted to the ward, or TCU, who did not experience a transfer to a higher level of care and patients admitted to these 2 units who did experience such a transfer. Patients who experienced a transfer constituted 5.3% (6,484/121,237) of ward patients and 6.7% (1,384/20,556) of TCU patients. Transferred patients tended to be older, have more acute physiologic derangement (higher LAPS), a greater pre‐existing illness burden (higher COPS), and a higher predicted mortality risk. Among ward patients, those with the following admission diagnoses were most likely to experience a transfer to a higher level of care: gastrointestinal bleeding (10.8% of all transfers), pneumonia (8.7%), and other infections (8.2%). The diagnoses most likely to be associated with death following transfer were cancer (death rate among transferred patients, 48%), renal disease (death rate, 36%), and liver disease (33%). Similar distributions were observed for TCU patients.

Table 3 compares outcomes among ward and TCU patients who did and did not experience a transfer to a higher level of care. The table shows that transferred patients were almost 3 times as likely to die, even after controlling for severity of illness, and that their hospital LOS was 9 days higher than expected. This increased risk was seen in all hospitals and among all transfer types (ward to ICU, ward to TCU, and TCU to ICU).

Table 3 also shows that, among decedent patients, those who never left the ward or TCU died much sooner than those who died following transfer. Among direct admits to the ICU, the median LOS at time of death was 3.9 days, with a mean of 9.4 standard deviation of 19.9 days, while the corresponding times for TCU direct admits were a median and mean LOS of 6.5 and 11.7 19.5 days.

Table 4 summarizes outcomes among different patient subgroups that did and did not experience a transfer to a higher level of care. Based on location, patients who experienced a transfer from the TCU to the ICU had the highest crude death rate, but patients transferred from the ward to the ICU had the highest OEMR. On the other hand, if one divides patients by the degree of physiologic derangement, patients with low LAPS who experienced a transfer had the highest OEMR. With respect to LOS, patients transferred from the TCU to the ICU had the highest OMELOS (13.4 extra days).

Transfers to a higher level of care at a different hospital, which in the KPMCP are usually planned, experienced lower mortality than transfers within the same hospital. For ward to TCU transfers, intra‐hospital transfers had a mortality of 12.1% while inter‐hospital transfers had a mortality of 5.7%. Corresponding rates for ward to ICU transfers were 21.7% and 11.2%, and for TCU to ICU transfers the rates were 25.9% and 12.5%, respectively.

Among patients initially admitted to the ward, a model to predict the occurrence of a transfer to a higher level of care (within 48 hours after admission) that included age, sex, admission type, primary condition, LAPS, COPS, and interaction terms had poor discrimination, with an area under the receiver operator characteristic (c statistic) of only 0.64. The c statistic for a model to predict transfer after 48 hours was 0.66. The corresponding models for TCU admits had c statistics of 0.67 and 0.68. All four models had poor calibration.

Discussion

Using automated bed history data permits characterizing a patient population with disproportionate mortality and LOS: intra‐hospital transfers to special care units (ICUs or TCUs). Indeed, the largest subset of these patients (those initially admitted to the ward or TCU) constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. These patients also had very elevated OEMRs and OMELOS. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers.

We performed multivariate analyses to explore the degree to which electronically assigned preadmission severity scores could predict these transfers. These analyses found that, compared to our ability to predict inpatient or 30‐day mortality at the time of admission, which is excellent, our ability to predict the occurrence of transfer after admission is much more limited. These results highlight the limitations of severity scores that rely on automated data, which may not have adequate discrimination when it comes to determining the risk of an adverse outcome within a narrow time frame. For example, among the 121,237 patients initially admitted to the ward who did not experience an intra‐hospital transfer, the mean LAPS was 18.9, while the mean LAPS among the 6,484 ward patients who did experience a transfer was 25.5. Differences between the mean and median LAPS, COPS, and predicted mortality risk among transferred and non‐transferred patients were significant (P < 0.0001 for all comparisons). However, examination of the distribution of LAPS, COPS, and predicted mortality risk between these two groups of patients showed considerable overlap.

Our methodology resembles Silber et al.'s27, 28 concept of failure to rescue in that it focuses on events occurring after hospitalization. Silber et al. argue that a hospital's quality can be measured by quantifying the degree to which patients who experience new problems are successfully rescued. Furthermore, quantification of those situations where rescue attempts are unsuccessful is felt to be superior to simply comparing raw or adjusted mortality rates because these are primarily determined by underlying case mix. The primary difference between Silber et al.'s approach and ours is at the level of detailthey specified a specific set of complications, whereas our measure is more generic and would include patients with many of the complications specified by Silber et al.27, 28

Most of the patients transferred to a higher level of care in our cohort survived (ie, were rescued), indicating that intensive care is beneficial. However, the fact that these patients had elevated OEMRs and OMELOS indicates that the real challenge facing hospitalists involves the timing of provision of a beneficial intervention. In theory, improved timing could result from earlier detection of problems, which is the underlying rationale for employing rapid response teams. However, the fact that our electronic tools (LAPS, COPS) cannot predict patient deteriorations within a narrow time frame suggests that early detection will remain a major challenge. Manually assigned vital signs scores designed for this purpose do not have good discrimination either.29, 30 This raises the possibility that, though patient groups may differ in terms of overall illness severity and mortality risk, differences at the individual patient level may be too subtle for clinicians to detect. Future research may thus need to focus on scores that combine laboratory data, vital signs, trends in data,31, 32 and newer proteomic markers (eg, procalcitonin).33 We also found that most transfers occurred early (within <72 hours), raising the possibility that at least some of these transfers may involve issues around triage rather than sudden deterioration.

Our study has important limitations. Due to resource constraints and limited data availability, we could not characterize the patients as well as might be desirable; in particular, we could not make full determinations of the actual reasons for patients' transfer for all patients. Broadly speaking, transfer to a higher level of care could be due to inappropriate triage, appropriate (preventive) transfer (which could include transfer to a more richly staffed unit for a specific procedure), relentless progression of disease despite maximal therapy, the occurrence of management errors, patient and family uncertainty about goals of care or inadequate understanding of treatment options and prognoses, or a combination of these factors. We could not make these distinctions with currently available electronic data. This is also true of postsurgical patients, in whom it is difficult to determine which transfers to intensive care might be planned (eg, in the case of surgical procedures where ICU care is anticipated) as opposed to the occurrence of a deterioration during or following surgery. Another major limitation of this study is our inability to identify code or no code status electronically. The elapsed LOS at time of death among patients who experienced a transfer to a higher level of care (as compared to patients who died in the ward without ever experiencing intra‐hospital transfer) suggests, but does not prove, that prolonged efforts were being made to keep them alive. We were also limited in terms of having access to other process data (eg, physician staffing levels, provision and timing of palliative care). Having ICU severity of illness scores would have permitted us to compare our cohort to those of other recent studies showing elevated mortality rates among transfer patients,911 but we have not yet developed that capability.

Consideration of our study findings suggests a possible research agenda that could be implemented by hospitalist researchers. This agenda should emphasize three areas: detection, intervention, and reflection.

With respect to detection, attention needs to be paid to better tools for quantifying patient risk at the time a decision to admit to the ward is made. It is likely that such tools will need to combine the attributes of our severity score (LAPS) with those of the manually assigned scores.30, 34 In some cases, use of these tools could lead a physician to change the locus of admission from the ward to the TCU or ICU, which could improve outcomes by ensuring more timely provision of intensive care. Since problems with initial triage could be due to factors other than the failure to suspect or anticipate impending instability, future research should also include a cognitive component (eg, quantifying what proportion of subsequent patient deteriorations could be ascribed to missed diagnoses35). Additional work also needs to be done on developing mathematical models that can inform electronic monitoring of ward (not just ICU) patients.

Research on interventions that hospitalists can use to prevent the need for intensive care or to improve the rescue rate should take two routes. The first is a disease‐specific route, which builds on the fact that a relatively small set of conditions (pneumonia, sepsis, gastrointestinal bleeding) account for most transfers to a higher level of care. Condition‐specific protocols, checklists, and bundles36 tailored to a ward environment (as opposed to the ICU or to the entire hospital) might prevent deteriorations in these patients, as has been reported for sepsis.37 The second route is to improve the overall capabilities of rapid response and code blue teams. Such research would need to include a more careful assessment of what commonalities exist among patients who were and were not successfully rescued by these teams. This approach would probably yield more insights than the current literature, which focuses on whether rapid response teams are a good thing or not.

Finally, research also needs to be performed on how hospitalists reflect on adverse outcomes among ward patients. Greater emphasis needs to be placed on moving beyond trigger tool approaches that rely on manual chart review. In an era of expanding use of electronic medical record systems, more work needs to be done on how to harness these to provide hospitalists with better quantitative and risk‐adjusted information. This information should not be limited to simply reporting rates of transfers and deaths. Rather, finer distinctions must be provided with respect of the type of patients (ie, more diagnostic detail), the clinical status of patients (ie, more physiologic detail), as well as the effects of including or excluding patients in whom therapeutic options may be limited (ie, do not resuscitate and comfort care patients) on reported rates. Ideally, researchers should develop better process and outcomes measures that could be tested in collaborative networks that include multiple nonacademic general medical‐surgical wards.

There are many controversial topics relating to renal disease in hospitalized patients. The aim of this review is to shed light on some important and often debated issues. We will first discuss topics related to electrolytes disorder commonly seen in hospitalized patients (hyponatremia, hyperkalemia, metabolic acidosis) then the use of diuretics in patients with allergy to sulfa containing antibiotics.

Hypothyroidism and Hyponatremia

Hyponatremia is common in hospitalized patients and is associated with worse outcomes.1 It can be seen with a variety of conditions ranging from congestive heart failure to volume depletion. Careful history and physical examination are paramount and the initial work‐up usually includes serum and urine osmolality and urine sodium concentration.

For euvolemic hyponatremia, the differential diagnosis includes the syndrome of inappropriate adenine dinucleotide (ADH) secretion (SIADH), hypoadrenalism, and beer potomania. Additionally, many authorities also include hypothyroidism.

Although the simultaneous finding of hypothyroidism and hyponatremia can occur in patients as both diseases are widely prevalent in the general population, causation has yet to be convincingly demonstrated.

ADH is released in response to effective volume depletion; consequently when hypothyroidism is encountered in the setting of complete pituitary failure there is often hyponatremia.2, 3 Alternatively, with myxedema, the ability of the kidney to handle a water load and concentrate urine can be impaired.4

However, the observation that thyroid hormone administration did not raise sodium values in newborns with congenital hypothyroidism or in adults supports the absence of causal effect.5, 6And in addition, large studies done in the hospital and outpatient setting showed no differences between the serum sodium values of hypothyroid patients and that of controls.7, 8 In the study of outpatients, among those with hypothyroidism, for every increase of 10 mU/L of thyroid‐stimulating hormone (TSH), there was a drop of only 0.14 mmol/L of Na concentration.8 Thus, the elevation of TSH required for a clinically meaningful drop in sodium to occur was considerable.

Hence in patients with hyponatremia, the hospitalist should look for etiologies other than hypothyroidism and should only consider thyroid hypofunction as a culprit in cases of myxedema, or panhypopituitarism.

Sodium Bicarbonate for Hyperkalemia

Hyperkalemia is one of the most feared electrolyte disorders encountered in hospitalized patients and can lead to dire outcomes.9, 10

Potassium (K+) homeostasis is maintained in the body by 2 complimentary systems: a short‐term system that regulates K+ variation by modifying translocation across the cellular membrane and a long‐term system that adjusts overall K+ balance. The translocation system is regulated primarily by insulin and ‐2 stimulation. Overall K+ balance is mainly controlled by the kidney (90‐95%) although the gastrointestinal (GI) tract can have a more preponderant role in anephric patients.

Hyperkalemia can ensue by either a dysregulation of the translocation system (as in diabetic Ketoacidosis secondary to insulin deficiency) or impairment of K+ elimination.

Acid‐base status was previously thought to have a prominent influence on K+ concentration, based on studies that demonstrated that. However, studies looking at metabolic acidosis revealed that contrary to the effect of mineral acidosis (excess of nonmetabolizable anions)11 where there is an inverse correlation between potassium concentration and pH; organic acidosis (excess of metabolizable anions)11 was not associated with hyperkalemia.1214 However, organic acidosis can be seen simultaneously and induced by a same underlying disease (such as organ ischemia with lactic acidosis or insulin deficiency complicated by ketoacidosis). Also, when changes in pH are induced by respiratory variations or with alkalosis, the impact on serum K+ concentration is less remarkable.15 Hence, it seems that it is the nature of the acid‐base disturbance that impacts K+ concentration more than the change in pH itself.

In the kidney, the main site for regulation of K+ balance is the collecting duct. Factors that affect elimination include urinary sodium delivery, urine flow, and aldosterone.16 In order to adequately eliminate K+ these factors must be optimized in conjunction.

Treatment of hyperkalemia includes the sequential administration of agents that stabilize the cardiac membrane (calcium gluconate), shift the potassium intracellularly (insulin, ‐2 agonists), and remove the potassium (diuretics, sodium polystyrene, or dialysis).

The use of sodium bicarbonate for treatment of hyperkalemia has been long advocated.17 It was thought to act by translocation of potassium hence could be used to quickly lower K+ concentration. However, this dogma has been challenged recently.

To assess the true impact of sodium bicarbonate on potassium translocation, studies have been conducted on anephric patients with hyperkalemia. Bicarbonate infusion failed to elicit a significant rapid change in serum K+ concentration despite increase in bicarbonate concentration, arguing against a translocation mechanism.1821 After 60 minutes of treatment, neither isotonic nor hypertonic bicarbonate infusion affected Serum K+ levels in end‐stage renal disease (ESRD) patients.19, 20 On the contrary, hypertonic sodium bicarbonate increased the K+ concentration after 180 minutes of treatment,20 and it took a prolonged infusion of 4 hours to see a significant decrease in K+ concentration (0.6 mmol/L); half of which could be accounted for solely by volume administration. Moreover, this reduction was highly variable.

Rather, sodium bicarbonate seems to enhance potassium elimination by increasing sodium delivery to the distal tubule, increasing urinary pH and negative luminal charge and potentiating the action of diuretics.23 In an elegant study on normovolemic patients, the induction of bicarbonaturia practically doubled potassium excretion.23 However, such an effect is heterogeneous and usually takes place over 4 hours to 6 hours.17

At the cellular level, 2 ion exchange pumps cooperate to handle Na/K/H movement across the cellular membrane: an Na⁺/H⁺ exchanger (NHE) and the Na⁺/K⁺ ATP‐ase pump (Figure 1). The NHE is normally inactive and is only upregulated in cases of severe intracellular acidosis.24 The infusion of sodium bicarbonate to patients with severe metabolic acidosis could possibly decrease the serum potassium concentration by translocation if the NHE was significantly upregulated. However, this treatment can be associated with a drop in the ionized calcium level, a worsening of the intracellular acidosis, and a decreased peripheral oxygen delivery.25 Thus, the benefits should be balanced with the potential adverse effects and, even in cases of severe metabolic acidosis with hyperkalemia, we would advise the clinician to restrictively administer sodium bicarbonate.

Figure 1

In addition, in ESRD patients, the administration of sodium bicarbonate can be problematic owing to the osmotic and volume burden it carries. It should also be avoided in patients who are volume overload or in those with decreased ability to eliminate potassium.

When treating hyperkalemic patients, hospitalists should use sodium bicarbonate to potentiate urinary elimination of potassium and should consider administering it either with acetazolamide or a loop diuretic, anticipating a lowering effect after a few hours.26 It should be avoided in patients with volume overload and anuria. Immediate translocation of potassium into cells is best achieved by insulin and ‐2 agonists.

5‐Oxoprolinuria: A Newly Recognized Cause of High Anion Gap Metabolic Acidosis

There are several causes of metabolic high anion gap acidosis in hospitalized patients. However, despite careful investigations, the cause of that disorder is not always apparent.27 Recently, 5‐oxoprolinuria (also called pyroglutamic acidosis) has become increasingly recognized as a potential etiology.2832

The metabolism of glutamate (though the ‐Glutamyl cycle) generates glutathione which provides negative feedback to the ‐Glutamyl‐cysteine synthetase enzyme. The depletion of glutathione increases 5‐oxoproline production owing to the loss of that inhibition (Figures 2 and 3). Low glutathione levels can be seen with liver disease,33 chronic alcohol intake,34 acetaminophen use,35 malnutrition,36 renal dysfunction,29 use of vigabatrim (an antiepileptic that received Food and Drug Administration [FDA] approval for use in April 2009)37 and sepsis.38 Most of the reported cases were female and had more than one risk factor.39

Figure 2

Figure 3

Typically, patients present with high anion gap acidosis (often more than 20)28 with normal acetaminophen levels and all usual tests being negative. A history of chronic acetaminophen use with or without other risk factors can frequently be found. The true independent impact of this type of acidosis on outcomes is difficult to determine as all of the reported cases had many confounding factors.

A urinary organic acid level is diagnostic and will reveal increased levels of pyroglutamic acid. Alternatively, the finding of a positive urinary anion gap (U_Na +U_K U_Cl) with a positive urinary osmolar gap (Uosm_measured‐Uosm_calculated) in the appropriate clinical setting (unexplained high anion gap acidosis with negative workup and presence of risk factors for 5‐oxoproliniuria) can point towards the diagnosis.40

A study of patients with unexplained metabolic acidosis did not find any cases of 5‐oxoprolinuria.41 Although this might suggest that the incidence of this disease is low, very few of those patients were actually taking acetaminophen (therefore had a reduced propensity for developing pyroglutamic acidosis).41 Thus, the actual incidence of 5‐oxoprolinuria is hard to determine.

Once recognized, acetaminophen should be withheld and N‐acetylcysteine (NAC) can be used to replete glutathione levels although there is no convincing evidence for this use.42 It is important for hospitalists to be aware of this disorder as it can pose a diagnostic challenge (negative usual work‐up), is easy to treat by stopping acetaminophen, and can (possibly) negatively affect outcomes.

Furosemide and Patients With Sulfa Allergy

Allergic reactions are a common occurrence with sulfa‐containing antibiotics (SCA) and reports estimates the incidence to be approximately 3% to 5%.43, 44

One misbelief is that patients who are allergic to SCAs should not receive sulfa containing diuretics or other sulfa‐containing medications.45 This leads some physicians to substitute commonly used diuretics (such as furosemide or thiazides) for ethacrynic acid. The use of ethacrynic acid has several challenges: the limited supply of the intravenous form, the discontinuation of the oral form, the increased cost, and the risk of permanent ototoxicity.

The evidence for potential allergic cross‐reactivity among medications containing the sulfa moiety has been primarily derived from Case Reports.4649

The molecular structures of sulfamethoxazole and furosemide are shown below. The allergic antigen is most often the N1 component,45 and sometimes N4 but not the sulfa moiety. Both of the incriminated antigens are not present in the furosemide structure (as well as all other sulfa containing diuretics) (Figures 4 and 5).

Figure 4

Figure 5

Experimental data showed that serum from patients allergic to SCAs did not bind to diuretics.50 In addition, clinical reports failed to demonstrate cross‐reactivity.5153 In a large clinical trial, Strom et al.53 showed that although there was a higher risk for allergic reaction to sulfa containing medications (SCM) in patients allergic to SCA (compared to those who were not), it was lower among patients with an allergy to sulfa antibiotic than among patients with a history of hypersensitivity to penicillins, suggesting this was due to a predisposition to allergic reactions in general rather than true cross‐reactivity. In another report, patients who were receiving ethacrynic acid for many years were successfully and uneventfully switched to furosemide.54

Taken together, these findings suggest that there is no evidence for withholding sulfa nonantibiotics in patients allergic to sulfa containing antibiotics.

Conclusion

Hypothyroidism, unlike myxedema, is not a cause of hyponatremia (although it can be sometimes seen in conjunction with the latter) and additional investigations should be done to determine its etiology. Sodium bicarbonate is effective for treatment of hyperkalemia by enhancing renal potassium elimination, rather than from shifting potassium into cells. The 5‐oxoprolinuria is a newly recognized cause of high anion‐gap metabolic acidosis and should be considered in patients who have taken acetaminophen. Furosemide (and sulfa containing diuretics) can be used safely in patients with an allergy to SCA.

There are many controversial topics relating to renal disease in hospitalized patients. The aim of this review is to shed light on some important and often debated issues. We will first discuss topics related to electrolytes disorder commonly seen in hospitalized patients (hyponatremia, hyperkalemia, metabolic acidosis) then the use of diuretics in patients with allergy to sulfa containing antibiotics.

Hypothyroidism and Hyponatremia

Hyponatremia is common in hospitalized patients and is associated with worse outcomes.1 It can be seen with a variety of conditions ranging from congestive heart failure to volume depletion. Careful history and physical examination are paramount and the initial work‐up usually includes serum and urine osmolality and urine sodium concentration.

For euvolemic hyponatremia, the differential diagnosis includes the syndrome of inappropriate adenine dinucleotide (ADH) secretion (SIADH), hypoadrenalism, and beer potomania. Additionally, many authorities also include hypothyroidism.

Although the simultaneous finding of hypothyroidism and hyponatremia can occur in patients as both diseases are widely prevalent in the general population, causation has yet to be convincingly demonstrated.

ADH is released in response to effective volume depletion; consequently when hypothyroidism is encountered in the setting of complete pituitary failure there is often hyponatremia.2, 3 Alternatively, with myxedema, the ability of the kidney to handle a water load and concentrate urine can be impaired.4

However, the observation that thyroid hormone administration did not raise sodium values in newborns with congenital hypothyroidism or in adults supports the absence of causal effect.5, 6And in addition, large studies done in the hospital and outpatient setting showed no differences between the serum sodium values of hypothyroid patients and that of controls.7, 8 In the study of outpatients, among those with hypothyroidism, for every increase of 10 mU/L of thyroid‐stimulating hormone (TSH), there was a drop of only 0.14 mmol/L of Na concentration.8 Thus, the elevation of TSH required for a clinically meaningful drop in sodium to occur was considerable.

Hence in patients with hyponatremia, the hospitalist should look for etiologies other than hypothyroidism and should only consider thyroid hypofunction as a culprit in cases of myxedema, or panhypopituitarism.

Sodium Bicarbonate for Hyperkalemia

Hyperkalemia is one of the most feared electrolyte disorders encountered in hospitalized patients and can lead to dire outcomes.9, 10

Potassium (K+) homeostasis is maintained in the body by 2 complimentary systems: a short‐term system that regulates K+ variation by modifying translocation across the cellular membrane and a long‐term system that adjusts overall K+ balance. The translocation system is regulated primarily by insulin and ‐2 stimulation. Overall K+ balance is mainly controlled by the kidney (90‐95%) although the gastrointestinal (GI) tract can have a more preponderant role in anephric patients.

Hyperkalemia can ensue by either a dysregulation of the translocation system (as in diabetic Ketoacidosis secondary to insulin deficiency) or impairment of K+ elimination.

Acid‐base status was previously thought to have a prominent influence on K+ concentration, based on studies that demonstrated that. However, studies looking at metabolic acidosis revealed that contrary to the effect of mineral acidosis (excess of nonmetabolizable anions)11 where there is an inverse correlation between potassium concentration and pH; organic acidosis (excess of metabolizable anions)11 was not associated with hyperkalemia.1214 However, organic acidosis can be seen simultaneously and induced by a same underlying disease (such as organ ischemia with lactic acidosis or insulin deficiency complicated by ketoacidosis). Also, when changes in pH are induced by respiratory variations or with alkalosis, the impact on serum K+ concentration is less remarkable.15 Hence, it seems that it is the nature of the acid‐base disturbance that impacts K+ concentration more than the change in pH itself.

In the kidney, the main site for regulation of K+ balance is the collecting duct. Factors that affect elimination include urinary sodium delivery, urine flow, and aldosterone.16 In order to adequately eliminate K+ these factors must be optimized in conjunction.

Treatment of hyperkalemia includes the sequential administration of agents that stabilize the cardiac membrane (calcium gluconate), shift the potassium intracellularly (insulin, ‐2 agonists), and remove the potassium (diuretics, sodium polystyrene, or dialysis).

The use of sodium bicarbonate for treatment of hyperkalemia has been long advocated.17 It was thought to act by translocation of potassium hence could be used to quickly lower K+ concentration. However, this dogma has been challenged recently.

To assess the true impact of sodium bicarbonate on potassium translocation, studies have been conducted on anephric patients with hyperkalemia. Bicarbonate infusion failed to elicit a significant rapid change in serum K+ concentration despite increase in bicarbonate concentration, arguing against a translocation mechanism.1821 After 60 minutes of treatment, neither isotonic nor hypertonic bicarbonate infusion affected Serum K+ levels in end‐stage renal disease (ESRD) patients.19, 20 On the contrary, hypertonic sodium bicarbonate increased the K+ concentration after 180 minutes of treatment,20 and it took a prolonged infusion of 4 hours to see a significant decrease in K+ concentration (0.6 mmol/L); half of which could be accounted for solely by volume administration. Moreover, this reduction was highly variable.

Rather, sodium bicarbonate seems to enhance potassium elimination by increasing sodium delivery to the distal tubule, increasing urinary pH and negative luminal charge and potentiating the action of diuretics.23 In an elegant study on normovolemic patients, the induction of bicarbonaturia practically doubled potassium excretion.23 However, such an effect is heterogeneous and usually takes place over 4 hours to 6 hours.17

At the cellular level, 2 ion exchange pumps cooperate to handle Na/K/H movement across the cellular membrane: an Na⁺/H⁺ exchanger (NHE) and the Na⁺/K⁺ ATP‐ase pump (Figure 1). The NHE is normally inactive and is only upregulated in cases of severe intracellular acidosis.24 The infusion of sodium bicarbonate to patients with severe metabolic acidosis could possibly decrease the serum potassium concentration by translocation if the NHE was significantly upregulated. However, this treatment can be associated with a drop in the ionized calcium level, a worsening of the intracellular acidosis, and a decreased peripheral oxygen delivery.25 Thus, the benefits should be balanced with the potential adverse effects and, even in cases of severe metabolic acidosis with hyperkalemia, we would advise the clinician to restrictively administer sodium bicarbonate.

Figure 1

In addition, in ESRD patients, the administration of sodium bicarbonate can be problematic owing to the osmotic and volume burden it carries. It should also be avoided in patients who are volume overload or in those with decreased ability to eliminate potassium.

When treating hyperkalemic patients, hospitalists should use sodium bicarbonate to potentiate urinary elimination of potassium and should consider administering it either with acetazolamide or a loop diuretic, anticipating a lowering effect after a few hours.26 It should be avoided in patients with volume overload and anuria. Immediate translocation of potassium into cells is best achieved by insulin and ‐2 agonists.

5‐Oxoprolinuria: A Newly Recognized Cause of High Anion Gap Metabolic Acidosis

There are several causes of metabolic high anion gap acidosis in hospitalized patients. However, despite careful investigations, the cause of that disorder is not always apparent.27 Recently, 5‐oxoprolinuria (also called pyroglutamic acidosis) has become increasingly recognized as a potential etiology.2832

The metabolism of glutamate (though the ‐Glutamyl cycle) generates glutathione which provides negative feedback to the ‐Glutamyl‐cysteine synthetase enzyme. The depletion of glutathione increases 5‐oxoproline production owing to the loss of that inhibition (Figures 2 and 3). Low glutathione levels can be seen with liver disease,33 chronic alcohol intake,34 acetaminophen use,35 malnutrition,36 renal dysfunction,29 use of vigabatrim (an antiepileptic that received Food and Drug Administration [FDA] approval for use in April 2009)37 and sepsis.38 Most of the reported cases were female and had more than one risk factor.39

Figure 2

Figure 3

Typically, patients present with high anion gap acidosis (often more than 20)28 with normal acetaminophen levels and all usual tests being negative. A history of chronic acetaminophen use with or without other risk factors can frequently be found. The true independent impact of this type of acidosis on outcomes is difficult to determine as all of the reported cases had many confounding factors.

A urinary organic acid level is diagnostic and will reveal increased levels of pyroglutamic acid. Alternatively, the finding of a positive urinary anion gap (U_Na +U_K U_Cl) with a positive urinary osmolar gap (Uosm_measured‐Uosm_calculated) in the appropriate clinical setting (unexplained high anion gap acidosis with negative workup and presence of risk factors for 5‐oxoproliniuria) can point towards the diagnosis.40

A study of patients with unexplained metabolic acidosis did not find any cases of 5‐oxoprolinuria.41 Although this might suggest that the incidence of this disease is low, very few of those patients were actually taking acetaminophen (therefore had a reduced propensity for developing pyroglutamic acidosis).41 Thus, the actual incidence of 5‐oxoprolinuria is hard to determine.

Once recognized, acetaminophen should be withheld and N‐acetylcysteine (NAC) can be used to replete glutathione levels although there is no convincing evidence for this use.42 It is important for hospitalists to be aware of this disorder as it can pose a diagnostic challenge (negative usual work‐up), is easy to treat by stopping acetaminophen, and can (possibly) negatively affect outcomes.

Furosemide and Patients With Sulfa Allergy

Allergic reactions are a common occurrence with sulfa‐containing antibiotics (SCA) and reports estimates the incidence to be approximately 3% to 5%.43, 44

One misbelief is that patients who are allergic to SCAs should not receive sulfa containing diuretics or other sulfa‐containing medications.45 This leads some physicians to substitute commonly used diuretics (such as furosemide or thiazides) for ethacrynic acid. The use of ethacrynic acid has several challenges: the limited supply of the intravenous form, the discontinuation of the oral form, the increased cost, and the risk of permanent ototoxicity.

The evidence for potential allergic cross‐reactivity among medications containing the sulfa moiety has been primarily derived from Case Reports.4649

The molecular structures of sulfamethoxazole and furosemide are shown below. The allergic antigen is most often the N1 component,45 and sometimes N4 but not the sulfa moiety. Both of the incriminated antigens are not present in the furosemide structure (as well as all other sulfa containing diuretics) (Figures 4 and 5).

Figure 4

Figure 5

Experimental data showed that serum from patients allergic to SCAs did not bind to diuretics.50 In addition, clinical reports failed to demonstrate cross‐reactivity.5153 In a large clinical trial, Strom et al.53 showed that although there was a higher risk for allergic reaction to sulfa containing medications (SCM) in patients allergic to SCA (compared to those who were not), it was lower among patients with an allergy to sulfa antibiotic than among patients with a history of hypersensitivity to penicillins, suggesting this was due to a predisposition to allergic reactions in general rather than true cross‐reactivity. In another report, patients who were receiving ethacrynic acid for many years were successfully and uneventfully switched to furosemide.54

Taken together, these findings suggest that there is no evidence for withholding sulfa nonantibiotics in patients allergic to sulfa containing antibiotics.

Conclusion

Hypothyroidism, unlike myxedema, is not a cause of hyponatremia (although it can be sometimes seen in conjunction with the latter) and additional investigations should be done to determine its etiology. Sodium bicarbonate is effective for treatment of hyperkalemia by enhancing renal potassium elimination, rather than from shifting potassium into cells. The 5‐oxoprolinuria is a newly recognized cause of high anion‐gap metabolic acidosis and should be considered in patients who have taken acetaminophen. Furosemide (and sulfa containing diuretics) can be used safely in patients with an allergy to SCA.

There are many controversial topics relating to renal disease in hospitalized patients. The aim of this review is to shed light on some important and often debated issues. We will first discuss topics related to electrolytes disorder commonly seen in hospitalized patients (hyponatremia, hyperkalemia, metabolic acidosis) then the use of diuretics in patients with allergy to sulfa containing antibiotics.

Hypothyroidism and Hyponatremia

Hyponatremia is common in hospitalized patients and is associated with worse outcomes.1 It can be seen with a variety of conditions ranging from congestive heart failure to volume depletion. Careful history and physical examination are paramount and the initial work‐up usually includes serum and urine osmolality and urine sodium concentration.

For euvolemic hyponatremia, the differential diagnosis includes the syndrome of inappropriate adenine dinucleotide (ADH) secretion (SIADH), hypoadrenalism, and beer potomania. Additionally, many authorities also include hypothyroidism.

Although the simultaneous finding of hypothyroidism and hyponatremia can occur in patients as both diseases are widely prevalent in the general population, causation has yet to be convincingly demonstrated.

ADH is released in response to effective volume depletion; consequently when hypothyroidism is encountered in the setting of complete pituitary failure there is often hyponatremia.2, 3 Alternatively, with myxedema, the ability of the kidney to handle a water load and concentrate urine can be impaired.4

However, the observation that thyroid hormone administration did not raise sodium values in newborns with congenital hypothyroidism or in adults supports the absence of causal effect.5, 6And in addition, large studies done in the hospital and outpatient setting showed no differences between the serum sodium values of hypothyroid patients and that of controls.7, 8 In the study of outpatients, among those with hypothyroidism, for every increase of 10 mU/L of thyroid‐stimulating hormone (TSH), there was a drop of only 0.14 mmol/L of Na concentration.8 Thus, the elevation of TSH required for a clinically meaningful drop in sodium to occur was considerable.

Hence in patients with hyponatremia, the hospitalist should look for etiologies other than hypothyroidism and should only consider thyroid hypofunction as a culprit in cases of myxedema, or panhypopituitarism.

Sodium Bicarbonate for Hyperkalemia

Hyperkalemia is one of the most feared electrolyte disorders encountered in hospitalized patients and can lead to dire outcomes.9, 10

Potassium (K+) homeostasis is maintained in the body by 2 complimentary systems: a short‐term system that regulates K+ variation by modifying translocation across the cellular membrane and a long‐term system that adjusts overall K+ balance. The translocation system is regulated primarily by insulin and ‐2 stimulation. Overall K+ balance is mainly controlled by the kidney (90‐95%) although the gastrointestinal (GI) tract can have a more preponderant role in anephric patients.

Hyperkalemia can ensue by either a dysregulation of the translocation system (as in diabetic Ketoacidosis secondary to insulin deficiency) or impairment of K+ elimination.

Acid‐base status was previously thought to have a prominent influence on K+ concentration, based on studies that demonstrated that. However, studies looking at metabolic acidosis revealed that contrary to the effect of mineral acidosis (excess of nonmetabolizable anions)11 where there is an inverse correlation between potassium concentration and pH; organic acidosis (excess of metabolizable anions)11 was not associated with hyperkalemia.1214 However, organic acidosis can be seen simultaneously and induced by a same underlying disease (such as organ ischemia with lactic acidosis or insulin deficiency complicated by ketoacidosis). Also, when changes in pH are induced by respiratory variations or with alkalosis, the impact on serum K+ concentration is less remarkable.15 Hence, it seems that it is the nature of the acid‐base disturbance that impacts K+ concentration more than the change in pH itself.

In the kidney, the main site for regulation of K+ balance is the collecting duct. Factors that affect elimination include urinary sodium delivery, urine flow, and aldosterone.16 In order to adequately eliminate K+ these factors must be optimized in conjunction.

Treatment of hyperkalemia includes the sequential administration of agents that stabilize the cardiac membrane (calcium gluconate), shift the potassium intracellularly (insulin, ‐2 agonists), and remove the potassium (diuretics, sodium polystyrene, or dialysis).

The use of sodium bicarbonate for treatment of hyperkalemia has been long advocated.17 It was thought to act by translocation of potassium hence could be used to quickly lower K+ concentration. However, this dogma has been challenged recently.

To assess the true impact of sodium bicarbonate on potassium translocation, studies have been conducted on anephric patients with hyperkalemia. Bicarbonate infusion failed to elicit a significant rapid change in serum K+ concentration despite increase in bicarbonate concentration, arguing against a translocation mechanism.1821 After 60 minutes of treatment, neither isotonic nor hypertonic bicarbonate infusion affected Serum K+ levels in end‐stage renal disease (ESRD) patients.19, 20 On the contrary, hypertonic sodium bicarbonate increased the K+ concentration after 180 minutes of treatment,20 and it took a prolonged infusion of 4 hours to see a significant decrease in K+ concentration (0.6 mmol/L); half of which could be accounted for solely by volume administration. Moreover, this reduction was highly variable.

Rather, sodium bicarbonate seems to enhance potassium elimination by increasing sodium delivery to the distal tubule, increasing urinary pH and negative luminal charge and potentiating the action of diuretics.23 In an elegant study on normovolemic patients, the induction of bicarbonaturia practically doubled potassium excretion.23 However, such an effect is heterogeneous and usually takes place over 4 hours to 6 hours.17

At the cellular level, 2 ion exchange pumps cooperate to handle Na/K/H movement across the cellular membrane: an Na⁺/H⁺ exchanger (NHE) and the Na⁺/K⁺ ATP‐ase pump (Figure 1). The NHE is normally inactive and is only upregulated in cases of severe intracellular acidosis.24 The infusion of sodium bicarbonate to patients with severe metabolic acidosis could possibly decrease the serum potassium concentration by translocation if the NHE was significantly upregulated. However, this treatment can be associated with a drop in the ionized calcium level, a worsening of the intracellular acidosis, and a decreased peripheral oxygen delivery.25 Thus, the benefits should be balanced with the potential adverse effects and, even in cases of severe metabolic acidosis with hyperkalemia, we would advise the clinician to restrictively administer sodium bicarbonate.

Figure 1

In addition, in ESRD patients, the administration of sodium bicarbonate can be problematic owing to the osmotic and volume burden it carries. It should also be avoided in patients who are volume overload or in those with decreased ability to eliminate potassium.

When treating hyperkalemic patients, hospitalists should use sodium bicarbonate to potentiate urinary elimination of potassium and should consider administering it either with acetazolamide or a loop diuretic, anticipating a lowering effect after a few hours.26 It should be avoided in patients with volume overload and anuria. Immediate translocation of potassium into cells is best achieved by insulin and ‐2 agonists.

5‐Oxoprolinuria: A Newly Recognized Cause of High Anion Gap Metabolic Acidosis

There are several causes of metabolic high anion gap acidosis in hospitalized patients. However, despite careful investigations, the cause of that disorder is not always apparent.27 Recently, 5‐oxoprolinuria (also called pyroglutamic acidosis) has become increasingly recognized as a potential etiology.2832

The metabolism of glutamate (though the ‐Glutamyl cycle) generates glutathione which provides negative feedback to the ‐Glutamyl‐cysteine synthetase enzyme. The depletion of glutathione increases 5‐oxoproline production owing to the loss of that inhibition (Figures 2 and 3). Low glutathione levels can be seen with liver disease,33 chronic alcohol intake,34 acetaminophen use,35 malnutrition,36 renal dysfunction,29 use of vigabatrim (an antiepileptic that received Food and Drug Administration [FDA] approval for use in April 2009)37 and sepsis.38 Most of the reported cases were female and had more than one risk factor.39

Figure 2

Figure 3

Typically, patients present with high anion gap acidosis (often more than 20)28 with normal acetaminophen levels and all usual tests being negative. A history of chronic acetaminophen use with or without other risk factors can frequently be found. The true independent impact of this type of acidosis on outcomes is difficult to determine as all of the reported cases had many confounding factors.

A urinary organic acid level is diagnostic and will reveal increased levels of pyroglutamic acid. Alternatively, the finding of a positive urinary anion gap (U_Na +U_K U_Cl) with a positive urinary osmolar gap (Uosm_measured‐Uosm_calculated) in the appropriate clinical setting (unexplained high anion gap acidosis with negative workup and presence of risk factors for 5‐oxoproliniuria) can point towards the diagnosis.40

A study of patients with unexplained metabolic acidosis did not find any cases of 5‐oxoprolinuria.41 Although this might suggest that the incidence of this disease is low, very few of those patients were actually taking acetaminophen (therefore had a reduced propensity for developing pyroglutamic acidosis).41 Thus, the actual incidence of 5‐oxoprolinuria is hard to determine.

Once recognized, acetaminophen should be withheld and N‐acetylcysteine (NAC) can be used to replete glutathione levels although there is no convincing evidence for this use.42 It is important for hospitalists to be aware of this disorder as it can pose a diagnostic challenge (negative usual work‐up), is easy to treat by stopping acetaminophen, and can (possibly) negatively affect outcomes.

Furosemide and Patients With Sulfa Allergy

Allergic reactions are a common occurrence with sulfa‐containing antibiotics (SCA) and reports estimates the incidence to be approximately 3% to 5%.43, 44

One misbelief is that patients who are allergic to SCAs should not receive sulfa containing diuretics or other sulfa‐containing medications.45 This leads some physicians to substitute commonly used diuretics (such as furosemide or thiazides) for ethacrynic acid. The use of ethacrynic acid has several challenges: the limited supply of the intravenous form, the discontinuation of the oral form, the increased cost, and the risk of permanent ototoxicity.

The evidence for potential allergic cross‐reactivity among medications containing the sulfa moiety has been primarily derived from Case Reports.4649

The molecular structures of sulfamethoxazole and furosemide are shown below. The allergic antigen is most often the N1 component,45 and sometimes N4 but not the sulfa moiety. Both of the incriminated antigens are not present in the furosemide structure (as well as all other sulfa containing diuretics) (Figures 4 and 5).

Figure 4

Figure 5

Experimental data showed that serum from patients allergic to SCAs did not bind to diuretics.50 In addition, clinical reports failed to demonstrate cross‐reactivity.5153 In a large clinical trial, Strom et al.53 showed that although there was a higher risk for allergic reaction to sulfa containing medications (SCM) in patients allergic to SCA (compared to those who were not), it was lower among patients with an allergy to sulfa antibiotic than among patients with a history of hypersensitivity to penicillins, suggesting this was due to a predisposition to allergic reactions in general rather than true cross‐reactivity. In another report, patients who were receiving ethacrynic acid for many years were successfully and uneventfully switched to furosemide.54

Taken together, these findings suggest that there is no evidence for withholding sulfa nonantibiotics in patients allergic to sulfa containing antibiotics.

Conclusion

Hypothyroidism, unlike myxedema, is not a cause of hyponatremia (although it can be sometimes seen in conjunction with the latter) and additional investigations should be done to determine its etiology. Sodium bicarbonate is effective for treatment of hyperkalemia by enhancing renal potassium elimination, rather than from shifting potassium into cells. The 5‐oxoprolinuria is a newly recognized cause of high anion‐gap metabolic acidosis and should be considered in patients who have taken acetaminophen. Furosemide (and sulfa containing diuretics) can be used safely in patients with an allergy to SCA.

The United States spends more money per capita on healthcare than any other industrialized nation,1 yet patients are the least satisfied with their care.2 Patient satisfaction is associated in both cross‐sectional3 and longitudinal studies4 with improved physical and mental health outcomes. Conversely, dissatisfaction with care hampers future medical interactions, prevents sharing of information, and impairs the building of trust.5 The increasing recognition that a patient's experience of care affects patient outcomes has furthered efforts to evaluate satisfaction with care.6, 7

However, patient satisfaction is challenging to define and understand. Even the definition of satisfaction is ambiguous, for to satisfy can mean both to make happy and the lesser, to be adequate. To dissatisfy is to displease or disappoint, but dissatisfaction is not the opposite of satisfaction: qualitative studies give little if any indication that patients evaluate satisfaction on a continuum ranging from dissatisfied at one end to very satisfied at the other.8 Instead, it appears that satisfaction and dissatisfaction are different constructs, such that patients may simultaneously be both satisfied and dissatisfied.9, 10 Patients often express overall satisfaction with a service or encounter while also reporting specific criticisms about its shortcomings.11, 12 Alternatively, consumers may be generally satisfied unless something unpleasant or improper happens.13 Thus, dissatisfaction and satisfaction may require different methods of measurement.

The most common model for measuring patient satisfaction is a quantitative survey of patients' experiences in specific predetermined domains. Of 54 hospital satisfaction surveys in common use, only 11 included patient input in their development,14 casting doubt on the relevance of these attributes to patients' priorities of care. Since it is well recognized that patient expectations influence satisfaction,8, 13, 15 it is important to identify patient expectations and priorities up front. However, these have not been clearly established. Furthermore, focusing purely on satisfaction with particular domains of care may miss the separate but illuminating construct of patient dissatisfaction.

In this study we therefore aim to understand patient dissatisfaction with hospitalization more fully as a means of elucidating implicit expectations for hospital care. Using qualitative techniques, we analyzed a large volume of patient responses to a single open‐ended study question to identify determinants and patterns of patient dissatisfaction.

Methods

Results

A total of 976 surveys was randomly selected from 9,764 postdischarge phone interviews completed between July 1, 2007 and June 30, 2008. A total of 56.3% of patients was female. Nearly half the patients were discharged from medical units (Table 1). Of the 976 patients, 439 (45.0%) gave at least one suggestion for improvement, yielding a total of 579 suggestions. Patients also offered numerous positive comments about their care, but these comments were not included in the analysis.

Through qualitative analysis, we assigned suggestions for improvement to six major categories of dissatisfaction: 1) ineptitude, 2) disrespect, 3) prolonged waits, 4) ineffective communication, 5) lack of environmental control, and 6) substandard amenities. We considered the inverse of these problems to represent six implicit expectations of good hospital care: 1) safety, 2) treatment with respect and dignity, 3) prompt and efficient care, 4) successful exchange of information, 5) environmental autonomy and control, and 6) high‐quality amenities (Table 2). The number of patient suggestions related to each domain is detailed in Table 3.

Discussion

We analyzed 439 patient suggestions for improving hospital care and found that dissatisfaction resulted from six categories of negative experiences: 1) ineptitude, 2) disrespect, 3) prolonged wait times, 4) ineffective communication, 5) lack of environmental control, and 6) substandard amenities. These domains represented a corresponding set of implicit patient expectations for: 1) safety, 2) treatment with respect and dignity, 3) prompt and efficient care, 4) successful exchange of information, 5) environmental autonomy and control, and 6) high‐quality amenities. Each of these categories suggests avenues by which both the assessment and provision of hospital care can be made more patient‐centered.

The most widely used patient satisfaction survey in use in the United States today is the Hospital Consumer Assessment of Healthcare Providers & Systems (HCAHPS), which includes eight domains: communication with doctors, communication with nurses, responsiveness of hospital staff, pain management, communication about medicines, discharge information, cleanliness of the hospital environment, and quietness of the hospital environment.17 The dissatisfaction domains found in this study closely overlap the HCAHPS satisfaction domains, but with a few key differences.

First, dissatisfaction with ineptitude in our study encompassed concerns over adverse events and near misses, in addition to the cleanliness of the environment. Other research has shown that dissatisfaction with hospitalization can be predicted by the number of reported problems18 and the perception of receiving incorrect treatment.19 While elaborate methods have been devised to assess and compare the hospital quality and safety, patient satisfaction surveys including the HCAHPS survey often fail to ask patients directly about their perceptions of safety. In fact, this study and others20, 21 show that patients are able to recognize adverse events during hospitalization. Patient report may be a useful adjunct to other methods of adverse event case finding and outcomes reporting.

Second, while HCAHPS and others identify warmth, courtesy, concern, and respect as dimensions of patient‐centered care,14, 17, 22, 23 the ability of quantitative satisfaction surveys to capture the experience of disrespectful treatment may be limited, especially during hospitalization. Most respondents who commented on feeling disrespected identified only a single encounter, which can be masked by otherwise satisfying interactions with numerous care providers. Directly asking patients whether any experience during hospitalization caused them to feel disrespected, and allowing room for explanation, might more efficiently identify problem areas. This is particularly important because even one episode of disrespectful treatment, particularly when perceived to be racially motivated, increases the likelihood of not following a doctor's advice or putting off care.24

Third, HCAHPS emphasizes two aspects of communication: that between patients and doctors, and that between patients and nurses. Our patients confirmed that these are important, but they also noted a third dimension of communication contributing to dissatisfaction: provider‐provider communication. Communication and coordination failures among providers are key contributors to adverse events or near misses,2528 but their influence on patient satisfaction has not been widely assessed. Furthermore, patient input is rarely utilized to identify poor interprovider communication. Our study suggests that, just as patients can identify adverse events, they are also able to recognize poor provider‐provider communication.

Patients' reports of dissatisfying events also highlight areas in which small changes in hospital practice might greatly improve the patient experience. For instance, concerns over environment, food, sleep, hygiene, and pain appeared to be representative of a broader dissatisfaction with loss of autonomy and control. Hospitalized patients are often obliged to room with strangers, are subject to noise and interruptions, and cede control of their medication management at a time when they are feeling particularly vulnerable. The importance of this lack of autonomy to patients suggests a variety of small interventions that could improve satisfaction, such as individual control of noise and temperature, a visible commitment to a quiet hospital environment, and minimized interruptions and sleep disturbance.2932 Single‐occupancy hospital rooms have been associated with lower rates of nosocomial infection, medication errors, and patient stress, as well as increased privacy, rest, visitor involvement, and doctor‐patient communication.33, 34 The most sophisticated intervention, acuity‐adaptable private hospital rooms, allows hospitals to maintain patients in the same private hospital room during an entire admission, regardless of changes to level of acuity.35

In‐depth analysis of suggestions for improvement, as gathered by telephone surveys of recently discharged patients, was a particularly well‐suited approach to identifying explicit expectations for care that were violated by dissatisfying incidents. When allowed to express dissatisfaction in terms of suggestions for improvement, patients talked freely about specific dissatisfying experiences. Using telephone interviews allowed a large volume of patient responses to be included, unlike smaller focus groups. Our study was oral and did not rely on the literacy level of patients. Additionally, the open‐ended nature of questioning avoided some of the usual pitfalls of satisfaction surveys. We did not rely on predetermined satisfaction categories or presume the inherent value of particular attributes of care. Nonetheless, our study does have important limitations.

Patient perceptions were not compared with chart data or clinician report. Caregivers were allowed to participate in lieu of patients, which may have reduced identification of some dissatisfying events. Likewise, patients discharged to nursing homes or who were not English or Spanish speaking were excluded and may have had different dissatisfying experiences. Interviews were brief and dissatisfying events were not explored in detail. Although nearly half of respondents reported dissatisfying events, some patients may have been reluctant to criticize their care directly to a hospital representative. Finally, patients generally confined their comments to one or two dissatisfying events, even though there may have been others. We therefore cannot draw any conclusions about the relative frequency of dissatisfying events by domain.

Conclusions

All hospitalized patients bring expectations for their hospital experience. While specific expectations vary between patients, expectations for: 1) safety, 2) treatment with respect and dignity, 3) prompt and efficient care, 4) successful exchange of information, 5) environmental autonomy and control, and 6) high‐quality amenities were found in this study to encompass core expectations for hospitalization. It may be useful to ensure that postdischarge surveys explicitly address these expectations. Efforts to address and manage these core expectations of hospital care may help to reduce patient dissatisfaction with hospitalization and improve the delivery and quality of hospital care.