Hospital Medicine

In recent years, driven by accelerating health care costs and desire for improved health care value, major specialty group guidelines have incorporated resource utilization and value calculations into their recommendations. High-value care has the characteristics of enhancing outcomes, safety, and patient satisfaction at a reasonable cost. As one example, the American College of Cardiology (ACC) recently published a consensus statement on its clinical practice guidelines with a specific focus on cost and value.¹ This guideline acknowledges the difficulty in incorporating value into clinical decision making but stresses a need for increased transparency and consistency to boost value in everyday practice.

Chest pain and related symptoms were listed as the second leading principle reasons for emergency department visits in the US in 2011 with 14% of patients undergoing cardiac enzyme testing.² The ACC guidelines advocate use of troponin as the preferred laboratory test for the initial evaluation of acute coronary syndrome (ACS). Fractionated creatine kinase (CK-MB) is an acceptable alternative only when a cardiac troponin test is not available.³ Furthermore, troponins should be obtained no more than 3 times for the initial evaluation of a single event, and further trending provides no additional benefit or prognostic information.

A recent study from an academic hospital showed that process improvement interventions focused on eliminating unnecessary cardiac enzyme testing led to a 1-year cost savings of $1.25 million while increasing the rate of ACS diagnosis.⁴ Common clinical practice at Naval Medical Center Portsmouth (NMCP) in Virginia still routinely includes both troponin as well as a CK panel comprised of CK, CK-MB, and a calculated CK-MB/CK index. Our study focuses on the implementation of quality improvement efforts described by Larochelle and colleagues at NMCP.⁴ The study aimed to determine the impact of implementing interventions designed to improve the ordering practices and reduce the cost of cardiac enzyme testing.

Methods

The primary focus of the intervention was on ordering practices of the emergency medicine department (EMD), internal medicine (IM) inpatient services, and cardiology inpatient services. Specific interventions were: (1) removal of the CK panel from the chest pain order set in the EMD electronic health record (EHR); (2) removal of the CK panel from the inpatient cardiology order set; (3) education of staff on the changes in CK panel utility via direct communication during IM academic seminars; (4) education of nursing staff ordering laboratory results on behalf of physicians on the cardiology service at the morning and evening huddles; and (5) addition of “max of 3 tests indicated” comment to the inpatient EHR ordering page of the troponin test. Acknowledging that the CK-MB has some utility to interventional cardiologists in the setting of confirmed ACS, the laboratory instituted an automated, reflexive order of the CK-MB panel only if the troponin tests were positive. This test was automatically run on the same vial originally sent to the lab to mitigate any additional delay in determining results.

Data Source

The process improvement interventions were considered exempt from institutional review board (IRB) approval; however, we obtained expedited IRB approval with waiver of consent for the research aspect of the project. We obtained clinical administrative data from the Military Health System Data Repository (MDR). We identified all adult patients aged ≥ 18 years who had a troponin test, CK-MB, or both drawn at NMCP on the following services: the EMD, IM, and cardiology. A troponin or CK-MB test was defined using Current Procedural Terminology (CPT) codes and unique Logical Observation Identifiers Names and Codes (LOINC).

Measures

The study was divided into 3 periods: the preintervention period from August 1, 2013 to July 31, 2014; the intervention period from August 1, 2014 to January 31, 2015; and the postintervention period February 1, 2015 to January 31, 2016.

The primary outcomes measured were the frequency of guideline concordance and total costs for tests ordered per month using the Centers for Medicare and Medicaid Services (CMS) clinical laboratory fee schedule of $13.40 for troponin and $16.17 for CK-MB.⁵Concordance was defined as ≤ 3 troponin tests and no CK-MB tests ordered during 1 encounter for a patient without an ACS diagnosis in the preceding 7 days. Due to faster cellular release kinetics of CK-MB compared with that of troponin, this test has utility in evaluating new or worsening chest pain in the setting of a recent myocardial infarction (MI). Therefore, we excluded any patient who had a MI within the preceding 7 days of an order for either CK-MB or troponin tests. Additionally, the number of tests, both CK-MB and troponin, ordered per patient encounter (hereafter referred to as an episode) were measured. Finally, we measured the monthly prevalence of ACS diagnosis and percentage of visits having that diagnosis.

Data Analysis

Descriptive statistics were used to calculate population demographics of age group, sex, beneficiary category, sponsor service, and clinical setting. Monthly data were grouped into the preintervention and postintervention periods. The analysis was performed using t tests to compare mean values and CIs before and after the intervention. Simple linear regression with attention to correlation was used to create best fit lines with confidence bands before and after the intervention. Interrupted time series (ITS) regression was used to describe all data points throughout the study. Consistency between these various methods was verified. Mean values and CIs were reported from the t tests. Statistical significance was reported when appropriate. Equations and confidence predictions on the simple linear regressions were produced and reported. These were used to identify values at the start, midpoint, and end of the pre- and postintervention periods.

Results

There were a total of 6,281 patients in the study population. More patients were seen during the postintervention period than in the preintervention period. The mean age of patients was slightly higher during the preintervention period (Table 1).

Guideline Concordance

To determine whether ordering practices for cardiac enzyme testing improved, we assessed the changes in the frequency of guideline concordance during the pre- and postintervention period. On average during the preintervention year, the percentage of tests ordered that met guideline concordance was 10.1% (95% CI, 7.4%-12.9%), increasing by 0.80% (95% CI, 0.17%-1.42%) each month. 

Costs

We assessed changes in total dollars spent on cardiac enzyme testing during the pre- and postintervention periods. During the preintervention year, $9,400 (95% CI, $8,700-$10,100) was spent on average each month, which did not change significantly throughout the period. During the postintervention year, the cost was stable at $5,000 (95% CI, $4,600-$5,300) on average each month, a reduction of $4,400 (95% CI, $3,700-$5,100) (Figure 2).

CK-MB and Troponin Tests per Patient

To further assess ordering practices for cardiac enzyme testing, we compared the changes in the monthly number of tests and the average number of CK-MB and troponin tests ordered per episode pre- and postintervention. On average during the preintervention year, 297 tests (95% CI, 278-315) were run per month, with an average of 1.21 CK tests (95% CI, 1.15-1.27) per episode (Table 2, Figure 3). 

The changes in troponin testing were not as dramatic. The counts of tests each month remained similar, with a preintervention year average of 341 (95% CI, 306-377) and postintervention year average of 310 (95% CI, 287-332), which were not statistically different. However, there was a statistically significant decrease in the number of tests per episode. During the preintervention year, 1.38 troponin tests (95% CI, 1.31-1.45) were ordered per patient on average. This dropped by 0.17 (95% CI, 0.09-0.24) to the postintervention average of 1.21 (95% CI, 1.17-1.25) (Table 2, Figure 4). 

ACS Prevalence

To determine whether there was an impact on ACS diagnoses, we looked at the numbers of ACS diagnoses and their prevalence among visits before and after the intervention. During the preintervention year, the average monthly number of diagnoses was 29.7 (95% CI, 26.1-33.2), and prevalence of ACS was 0.56% (95% CI, 0.48%-0.63%) of all episodes. Although the monthly rate was statistically decreasing by 0.022% (95% CI, 0.003-0.41), this has little meaning since the level of correlation (r² = 0.2522, not displayed) was poor due to the essentially nonexistent correlation in number of visits each month (r² = 0.0112, not displayed). During the postintervention year, the average number of diagnoses was 32.2 (95% CI, 27.9-36.6), and the prevalence of ACS was 0.62% (95% CI, 0.54-0.65). Neither of these values changed significantly between the pre- and postintervention period. All ICD-9 and ICD-10 diagnosis codes used for the analysis are available upon request from the authors.

Discussion

Our data demonstrate the ability of simple process improvement interventions to decrease unnecessary testing in the workup of ACS, increasing the rate of guideline concordant testing by > 70% at a single military treatment facility (MTF). In particular, with the now widespread use of EHR, the order set presents a high-yield target for process improvement in an easily implemented, durable fashion. We had expected to see some decrease in the efficacy of the intervention at a time of staff turnover in the summer of 2015 because ongoing dedicated teaching sessions were not performed. Despite that, the intervention remained effective without further dedicated teaching sessions. This outcome was certainly attributable to the hardwired interventions made (mainly via order sets), but possibly indicates an institutional memory that can take hold after an initial concerted effort is made.

We reduced the estimated preintervention annual cost of $113,000 by $53,000 (95% CI, $42,000-$64,000). Although on a much smaller scale than the study by Larochelle, our study represents a nearly 50% reduction in the total cost of initial testing for possible ACS and a > 80% reduction in unnecessary CK-MB testing.⁴ This result was achieved with no statistical change in the prevalence of ACS. The cost reduction does not account for the labor costs to clinically follow-up and address additional unnecessary lab results. The estimated cost of intervention was limited to the time required to educate residents, interns, and nursing staff as well as the implementation of the automated, reflexive laboratory results ordering process.

Unique to our study, we also demonstrated an intervention that satisfied all the major stakeholders in the ordering of these laboratory results. By instituting the reflexive ordering of CK-MB tests for positive troponins, we obtained the support of the facility’s interventional cardiology department, which finds value in that data. Appreciating the time-sensitive nature of an ACS diagnosis, the reflexive ordering minimized the delay in receiving these data while still greatly reducing the number of tests performed. That being said, if the current trend away from CK-MB in favor of exclusively testing troponin continues, removing the reflexive ordering for positive laboratory results protocol would be an easy follow-on intervention.

Limitations

Our study presented several limitations. First, reporting errors due to improper or insufficient medical coding as well as data entry errors may exist within the MDR; therefore, the results of this analysis may be over- or underestimated. Specifically, CPT codes for troponin and CK-MB were available only in 1 of the 2 data sets used for this study, which primarily contains outpatient patient encounters. For this reason, most of the laboratory testing comes from the EMD rather than from inpatient services. However, because we excluded all patients who eventually had an ACS diagnosis (patients who likely had more inpatient time and better indication for repeat troponin), we feel that our intervention was still thoroughly investigated. Second, the number of tests drawn per patient was significantly < 2, the expected minimum number of tests to rule out ACS in patients with appropriate symptoms.

This study was not designed to answer the source of variation from guidelines. Many patients had only 1 test, which we feel represents an opportunity for future study to identify other ways cardiac enzyme testing is being used clinically. These tests might be used for patients without convincing symptoms and signs of coronary syndromes or for patients with other primary problems. Third, by using the ITS analysis, we assumed that the outcome during each intervention period follows a linear pattern. However, changes may follow a nonlinear pattern over a long period. Finally, our intervention was limited to only a single MTF, which may limit generalizability to other facilities across military medicine. However, we feel this study should serve as a guide for other MTFs as well as US Department of Veterans Affairs facilities that could institute similar process improvements.

Conclusion

We made easily implemented and durable process improvement interventions that changed institution-wide ordering practices. These changes dramatically increased the rate of guideline-concordant testing, decreasing cost and furthering the goal of high-value medical care.

In recent years, driven by accelerating health care costs and desire for improved health care value, major specialty group guidelines have incorporated resource utilization and value calculations into their recommendations. High-value care has the characteristics of enhancing outcomes, safety, and patient satisfaction at a reasonable cost. As one example, the American College of Cardiology (ACC) recently published a consensus statement on its clinical practice guidelines with a specific focus on cost and value.¹ This guideline acknowledges the difficulty in incorporating value into clinical decision making but stresses a need for increased transparency and consistency to boost value in everyday practice.

Chest pain and related symptoms were listed as the second leading principle reasons for emergency department visits in the US in 2011 with 14% of patients undergoing cardiac enzyme testing.² The ACC guidelines advocate use of troponin as the preferred laboratory test for the initial evaluation of acute coronary syndrome (ACS). Fractionated creatine kinase (CK-MB) is an acceptable alternative only when a cardiac troponin test is not available.³ Furthermore, troponins should be obtained no more than 3 times for the initial evaluation of a single event, and further trending provides no additional benefit or prognostic information.

A recent study from an academic hospital showed that process improvement interventions focused on eliminating unnecessary cardiac enzyme testing led to a 1-year cost savings of $1.25 million while increasing the rate of ACS diagnosis.⁴ Common clinical practice at Naval Medical Center Portsmouth (NMCP) in Virginia still routinely includes both troponin as well as a CK panel comprised of CK, CK-MB, and a calculated CK-MB/CK index. Our study focuses on the implementation of quality improvement efforts described by Larochelle and colleagues at NMCP.⁴ The study aimed to determine the impact of implementing interventions designed to improve the ordering practices and reduce the cost of cardiac enzyme testing.

Methods

The primary focus of the intervention was on ordering practices of the emergency medicine department (EMD), internal medicine (IM) inpatient services, and cardiology inpatient services. Specific interventions were: (1) removal of the CK panel from the chest pain order set in the EMD electronic health record (EHR); (2) removal of the CK panel from the inpatient cardiology order set; (3) education of staff on the changes in CK panel utility via direct communication during IM academic seminars; (4) education of nursing staff ordering laboratory results on behalf of physicians on the cardiology service at the morning and evening huddles; and (5) addition of “max of 3 tests indicated” comment to the inpatient EHR ordering page of the troponin test. Acknowledging that the CK-MB has some utility to interventional cardiologists in the setting of confirmed ACS, the laboratory instituted an automated, reflexive order of the CK-MB panel only if the troponin tests were positive. This test was automatically run on the same vial originally sent to the lab to mitigate any additional delay in determining results.

Data Source

The process improvement interventions were considered exempt from institutional review board (IRB) approval; however, we obtained expedited IRB approval with waiver of consent for the research aspect of the project. We obtained clinical administrative data from the Military Health System Data Repository (MDR). We identified all adult patients aged ≥ 18 years who had a troponin test, CK-MB, or both drawn at NMCP on the following services: the EMD, IM, and cardiology. A troponin or CK-MB test was defined using Current Procedural Terminology (CPT) codes and unique Logical Observation Identifiers Names and Codes (LOINC).

Measures

The study was divided into 3 periods: the preintervention period from August 1, 2013 to July 31, 2014; the intervention period from August 1, 2014 to January 31, 2015; and the postintervention period February 1, 2015 to January 31, 2016.

The primary outcomes measured were the frequency of guideline concordance and total costs for tests ordered per month using the Centers for Medicare and Medicaid Services (CMS) clinical laboratory fee schedule of $13.40 for troponin and $16.17 for CK-MB.⁵Concordance was defined as ≤ 3 troponin tests and no CK-MB tests ordered during 1 encounter for a patient without an ACS diagnosis in the preceding 7 days. Due to faster cellular release kinetics of CK-MB compared with that of troponin, this test has utility in evaluating new or worsening chest pain in the setting of a recent myocardial infarction (MI). Therefore, we excluded any patient who had a MI within the preceding 7 days of an order for either CK-MB or troponin tests. Additionally, the number of tests, both CK-MB and troponin, ordered per patient encounter (hereafter referred to as an episode) were measured. Finally, we measured the monthly prevalence of ACS diagnosis and percentage of visits having that diagnosis.

Data Analysis

Descriptive statistics were used to calculate population demographics of age group, sex, beneficiary category, sponsor service, and clinical setting. Monthly data were grouped into the preintervention and postintervention periods. The analysis was performed using t tests to compare mean values and CIs before and after the intervention. Simple linear regression with attention to correlation was used to create best fit lines with confidence bands before and after the intervention. Interrupted time series (ITS) regression was used to describe all data points throughout the study. Consistency between these various methods was verified. Mean values and CIs were reported from the t tests. Statistical significance was reported when appropriate. Equations and confidence predictions on the simple linear regressions were produced and reported. These were used to identify values at the start, midpoint, and end of the pre- and postintervention periods.

Results

There were a total of 6,281 patients in the study population. More patients were seen during the postintervention period than in the preintervention period. The mean age of patients was slightly higher during the preintervention period (Table 1).

Guideline Concordance

To determine whether ordering practices for cardiac enzyme testing improved, we assessed the changes in the frequency of guideline concordance during the pre- and postintervention period. On average during the preintervention year, the percentage of tests ordered that met guideline concordance was 10.1% (95% CI, 7.4%-12.9%), increasing by 0.80% (95% CI, 0.17%-1.42%) each month. 

Costs

We assessed changes in total dollars spent on cardiac enzyme testing during the pre- and postintervention periods. During the preintervention year, $9,400 (95% CI, $8,700-$10,100) was spent on average each month, which did not change significantly throughout the period. During the postintervention year, the cost was stable at $5,000 (95% CI, $4,600-$5,300) on average each month, a reduction of $4,400 (95% CI, $3,700-$5,100) (Figure 2).

CK-MB and Troponin Tests per Patient

To further assess ordering practices for cardiac enzyme testing, we compared the changes in the monthly number of tests and the average number of CK-MB and troponin tests ordered per episode pre- and postintervention. On average during the preintervention year, 297 tests (95% CI, 278-315) were run per month, with an average of 1.21 CK tests (95% CI, 1.15-1.27) per episode (Table 2, Figure 3). 

The changes in troponin testing were not as dramatic. The counts of tests each month remained similar, with a preintervention year average of 341 (95% CI, 306-377) and postintervention year average of 310 (95% CI, 287-332), which were not statistically different. However, there was a statistically significant decrease in the number of tests per episode. During the preintervention year, 1.38 troponin tests (95% CI, 1.31-1.45) were ordered per patient on average. This dropped by 0.17 (95% CI, 0.09-0.24) to the postintervention average of 1.21 (95% CI, 1.17-1.25) (Table 2, Figure 4). 

ACS Prevalence

To determine whether there was an impact on ACS diagnoses, we looked at the numbers of ACS diagnoses and their prevalence among visits before and after the intervention. During the preintervention year, the average monthly number of diagnoses was 29.7 (95% CI, 26.1-33.2), and prevalence of ACS was 0.56% (95% CI, 0.48%-0.63%) of all episodes. Although the monthly rate was statistically decreasing by 0.022% (95% CI, 0.003-0.41), this has little meaning since the level of correlation (r² = 0.2522, not displayed) was poor due to the essentially nonexistent correlation in number of visits each month (r² = 0.0112, not displayed). During the postintervention year, the average number of diagnoses was 32.2 (95% CI, 27.9-36.6), and the prevalence of ACS was 0.62% (95% CI, 0.54-0.65). Neither of these values changed significantly between the pre- and postintervention period. All ICD-9 and ICD-10 diagnosis codes used for the analysis are available upon request from the authors.

Discussion

Our data demonstrate the ability of simple process improvement interventions to decrease unnecessary testing in the workup of ACS, increasing the rate of guideline concordant testing by > 70% at a single military treatment facility (MTF). In particular, with the now widespread use of EHR, the order set presents a high-yield target for process improvement in an easily implemented, durable fashion. We had expected to see some decrease in the efficacy of the intervention at a time of staff turnover in the summer of 2015 because ongoing dedicated teaching sessions were not performed. Despite that, the intervention remained effective without further dedicated teaching sessions. This outcome was certainly attributable to the hardwired interventions made (mainly via order sets), but possibly indicates an institutional memory that can take hold after an initial concerted effort is made.

We reduced the estimated preintervention annual cost of $113,000 by $53,000 (95% CI, $42,000-$64,000). Although on a much smaller scale than the study by Larochelle, our study represents a nearly 50% reduction in the total cost of initial testing for possible ACS and a > 80% reduction in unnecessary CK-MB testing.⁴ This result was achieved with no statistical change in the prevalence of ACS. The cost reduction does not account for the labor costs to clinically follow-up and address additional unnecessary lab results. The estimated cost of intervention was limited to the time required to educate residents, interns, and nursing staff as well as the implementation of the automated, reflexive laboratory results ordering process.

Unique to our study, we also demonstrated an intervention that satisfied all the major stakeholders in the ordering of these laboratory results. By instituting the reflexive ordering of CK-MB tests for positive troponins, we obtained the support of the facility’s interventional cardiology department, which finds value in that data. Appreciating the time-sensitive nature of an ACS diagnosis, the reflexive ordering minimized the delay in receiving these data while still greatly reducing the number of tests performed. That being said, if the current trend away from CK-MB in favor of exclusively testing troponin continues, removing the reflexive ordering for positive laboratory results protocol would be an easy follow-on intervention.

Limitations

Our study presented several limitations. First, reporting errors due to improper or insufficient medical coding as well as data entry errors may exist within the MDR; therefore, the results of this analysis may be over- or underestimated. Specifically, CPT codes for troponin and CK-MB were available only in 1 of the 2 data sets used for this study, which primarily contains outpatient patient encounters. For this reason, most of the laboratory testing comes from the EMD rather than from inpatient services. However, because we excluded all patients who eventually had an ACS diagnosis (patients who likely had more inpatient time and better indication for repeat troponin), we feel that our intervention was still thoroughly investigated. Second, the number of tests drawn per patient was significantly < 2, the expected minimum number of tests to rule out ACS in patients with appropriate symptoms.

This study was not designed to answer the source of variation from guidelines. Many patients had only 1 test, which we feel represents an opportunity for future study to identify other ways cardiac enzyme testing is being used clinically. These tests might be used for patients without convincing symptoms and signs of coronary syndromes or for patients with other primary problems. Third, by using the ITS analysis, we assumed that the outcome during each intervention period follows a linear pattern. However, changes may follow a nonlinear pattern over a long period. Finally, our intervention was limited to only a single MTF, which may limit generalizability to other facilities across military medicine. However, we feel this study should serve as a guide for other MTFs as well as US Department of Veterans Affairs facilities that could institute similar process improvements.

Conclusion

We made easily implemented and durable process improvement interventions that changed institution-wide ordering practices. These changes dramatically increased the rate of guideline-concordant testing, decreasing cost and furthering the goal of high-value medical care.

In recent years, driven by accelerating health care costs and desire for improved health care value, major specialty group guidelines have incorporated resource utilization and value calculations into their recommendations. High-value care has the characteristics of enhancing outcomes, safety, and patient satisfaction at a reasonable cost. As one example, the American College of Cardiology (ACC) recently published a consensus statement on its clinical practice guidelines with a specific focus on cost and value.¹ This guideline acknowledges the difficulty in incorporating value into clinical decision making but stresses a need for increased transparency and consistency to boost value in everyday practice.

Chest pain and related symptoms were listed as the second leading principle reasons for emergency department visits in the US in 2011 with 14% of patients undergoing cardiac enzyme testing.² The ACC guidelines advocate use of troponin as the preferred laboratory test for the initial evaluation of acute coronary syndrome (ACS). Fractionated creatine kinase (CK-MB) is an acceptable alternative only when a cardiac troponin test is not available.³ Furthermore, troponins should be obtained no more than 3 times for the initial evaluation of a single event, and further trending provides no additional benefit or prognostic information.

A recent study from an academic hospital showed that process improvement interventions focused on eliminating unnecessary cardiac enzyme testing led to a 1-year cost savings of $1.25 million while increasing the rate of ACS diagnosis.⁴ Common clinical practice at Naval Medical Center Portsmouth (NMCP) in Virginia still routinely includes both troponin as well as a CK panel comprised of CK, CK-MB, and a calculated CK-MB/CK index. Our study focuses on the implementation of quality improvement efforts described by Larochelle and colleagues at NMCP.⁴ The study aimed to determine the impact of implementing interventions designed to improve the ordering practices and reduce the cost of cardiac enzyme testing.

Methods

The primary focus of the intervention was on ordering practices of the emergency medicine department (EMD), internal medicine (IM) inpatient services, and cardiology inpatient services. Specific interventions were: (1) removal of the CK panel from the chest pain order set in the EMD electronic health record (EHR); (2) removal of the CK panel from the inpatient cardiology order set; (3) education of staff on the changes in CK panel utility via direct communication during IM academic seminars; (4) education of nursing staff ordering laboratory results on behalf of physicians on the cardiology service at the morning and evening huddles; and (5) addition of “max of 3 tests indicated” comment to the inpatient EHR ordering page of the troponin test. Acknowledging that the CK-MB has some utility to interventional cardiologists in the setting of confirmed ACS, the laboratory instituted an automated, reflexive order of the CK-MB panel only if the troponin tests were positive. This test was automatically run on the same vial originally sent to the lab to mitigate any additional delay in determining results.

Data Source

The process improvement interventions were considered exempt from institutional review board (IRB) approval; however, we obtained expedited IRB approval with waiver of consent for the research aspect of the project. We obtained clinical administrative data from the Military Health System Data Repository (MDR). We identified all adult patients aged ≥ 18 years who had a troponin test, CK-MB, or both drawn at NMCP on the following services: the EMD, IM, and cardiology. A troponin or CK-MB test was defined using Current Procedural Terminology (CPT) codes and unique Logical Observation Identifiers Names and Codes (LOINC).

Measures

The study was divided into 3 periods: the preintervention period from August 1, 2013 to July 31, 2014; the intervention period from August 1, 2014 to January 31, 2015; and the postintervention period February 1, 2015 to January 31, 2016.

The primary outcomes measured were the frequency of guideline concordance and total costs for tests ordered per month using the Centers for Medicare and Medicaid Services (CMS) clinical laboratory fee schedule of $13.40 for troponin and $16.17 for CK-MB.⁵Concordance was defined as ≤ 3 troponin tests and no CK-MB tests ordered during 1 encounter for a patient without an ACS diagnosis in the preceding 7 days. Due to faster cellular release kinetics of CK-MB compared with that of troponin, this test has utility in evaluating new or worsening chest pain in the setting of a recent myocardial infarction (MI). Therefore, we excluded any patient who had a MI within the preceding 7 days of an order for either CK-MB or troponin tests. Additionally, the number of tests, both CK-MB and troponin, ordered per patient encounter (hereafter referred to as an episode) were measured. Finally, we measured the monthly prevalence of ACS diagnosis and percentage of visits having that diagnosis.

Data Analysis

Descriptive statistics were used to calculate population demographics of age group, sex, beneficiary category, sponsor service, and clinical setting. Monthly data were grouped into the preintervention and postintervention periods. The analysis was performed using t tests to compare mean values and CIs before and after the intervention. Simple linear regression with attention to correlation was used to create best fit lines with confidence bands before and after the intervention. Interrupted time series (ITS) regression was used to describe all data points throughout the study. Consistency between these various methods was verified. Mean values and CIs were reported from the t tests. Statistical significance was reported when appropriate. Equations and confidence predictions on the simple linear regressions were produced and reported. These were used to identify values at the start, midpoint, and end of the pre- and postintervention periods.

Results

There were a total of 6,281 patients in the study population. More patients were seen during the postintervention period than in the preintervention period. The mean age of patients was slightly higher during the preintervention period (Table 1).

Guideline Concordance

To determine whether ordering practices for cardiac enzyme testing improved, we assessed the changes in the frequency of guideline concordance during the pre- and postintervention period. On average during the preintervention year, the percentage of tests ordered that met guideline concordance was 10.1% (95% CI, 7.4%-12.9%), increasing by 0.80% (95% CI, 0.17%-1.42%) each month. 

Costs

We assessed changes in total dollars spent on cardiac enzyme testing during the pre- and postintervention periods. During the preintervention year, $9,400 (95% CI, $8,700-$10,100) was spent on average each month, which did not change significantly throughout the period. During the postintervention year, the cost was stable at $5,000 (95% CI, $4,600-$5,300) on average each month, a reduction of $4,400 (95% CI, $3,700-$5,100) (Figure 2).

CK-MB and Troponin Tests per Patient

To further assess ordering practices for cardiac enzyme testing, we compared the changes in the monthly number of tests and the average number of CK-MB and troponin tests ordered per episode pre- and postintervention. On average during the preintervention year, 297 tests (95% CI, 278-315) were run per month, with an average of 1.21 CK tests (95% CI, 1.15-1.27) per episode (Table 2, Figure 3). 

The changes in troponin testing were not as dramatic. The counts of tests each month remained similar, with a preintervention year average of 341 (95% CI, 306-377) and postintervention year average of 310 (95% CI, 287-332), which were not statistically different. However, there was a statistically significant decrease in the number of tests per episode. During the preintervention year, 1.38 troponin tests (95% CI, 1.31-1.45) were ordered per patient on average. This dropped by 0.17 (95% CI, 0.09-0.24) to the postintervention average of 1.21 (95% CI, 1.17-1.25) (Table 2, Figure 4). 

ACS Prevalence

To determine whether there was an impact on ACS diagnoses, we looked at the numbers of ACS diagnoses and their prevalence among visits before and after the intervention. During the preintervention year, the average monthly number of diagnoses was 29.7 (95% CI, 26.1-33.2), and prevalence of ACS was 0.56% (95% CI, 0.48%-0.63%) of all episodes. Although the monthly rate was statistically decreasing by 0.022% (95% CI, 0.003-0.41), this has little meaning since the level of correlation (r² = 0.2522, not displayed) was poor due to the essentially nonexistent correlation in number of visits each month (r² = 0.0112, not displayed). During the postintervention year, the average number of diagnoses was 32.2 (95% CI, 27.9-36.6), and the prevalence of ACS was 0.62% (95% CI, 0.54-0.65). Neither of these values changed significantly between the pre- and postintervention period. All ICD-9 and ICD-10 diagnosis codes used for the analysis are available upon request from the authors.

Discussion

Our data demonstrate the ability of simple process improvement interventions to decrease unnecessary testing in the workup of ACS, increasing the rate of guideline concordant testing by > 70% at a single military treatment facility (MTF). In particular, with the now widespread use of EHR, the order set presents a high-yield target for process improvement in an easily implemented, durable fashion. We had expected to see some decrease in the efficacy of the intervention at a time of staff turnover in the summer of 2015 because ongoing dedicated teaching sessions were not performed. Despite that, the intervention remained effective without further dedicated teaching sessions. This outcome was certainly attributable to the hardwired interventions made (mainly via order sets), but possibly indicates an institutional memory that can take hold after an initial concerted effort is made.

We reduced the estimated preintervention annual cost of $113,000 by $53,000 (95% CI, $42,000-$64,000). Although on a much smaller scale than the study by Larochelle, our study represents a nearly 50% reduction in the total cost of initial testing for possible ACS and a > 80% reduction in unnecessary CK-MB testing.⁴ This result was achieved with no statistical change in the prevalence of ACS. The cost reduction does not account for the labor costs to clinically follow-up and address additional unnecessary lab results. The estimated cost of intervention was limited to the time required to educate residents, interns, and nursing staff as well as the implementation of the automated, reflexive laboratory results ordering process.

Unique to our study, we also demonstrated an intervention that satisfied all the major stakeholders in the ordering of these laboratory results. By instituting the reflexive ordering of CK-MB tests for positive troponins, we obtained the support of the facility’s interventional cardiology department, which finds value in that data. Appreciating the time-sensitive nature of an ACS diagnosis, the reflexive ordering minimized the delay in receiving these data while still greatly reducing the number of tests performed. That being said, if the current trend away from CK-MB in favor of exclusively testing troponin continues, removing the reflexive ordering for positive laboratory results protocol would be an easy follow-on intervention.

Limitations

Our study presented several limitations. First, reporting errors due to improper or insufficient medical coding as well as data entry errors may exist within the MDR; therefore, the results of this analysis may be over- or underestimated. Specifically, CPT codes for troponin and CK-MB were available only in 1 of the 2 data sets used for this study, which primarily contains outpatient patient encounters. For this reason, most of the laboratory testing comes from the EMD rather than from inpatient services. However, because we excluded all patients who eventually had an ACS diagnosis (patients who likely had more inpatient time and better indication for repeat troponin), we feel that our intervention was still thoroughly investigated. Second, the number of tests drawn per patient was significantly < 2, the expected minimum number of tests to rule out ACS in patients with appropriate symptoms.

This study was not designed to answer the source of variation from guidelines. Many patients had only 1 test, which we feel represents an opportunity for future study to identify other ways cardiac enzyme testing is being used clinically. These tests might be used for patients without convincing symptoms and signs of coronary syndromes or for patients with other primary problems. Third, by using the ITS analysis, we assumed that the outcome during each intervention period follows a linear pattern. However, changes may follow a nonlinear pattern over a long period. Finally, our intervention was limited to only a single MTF, which may limit generalizability to other facilities across military medicine. However, we feel this study should serve as a guide for other MTFs as well as US Department of Veterans Affairs facilities that could institute similar process improvements.

Conclusion

We made easily implemented and durable process improvement interventions that changed institution-wide ordering practices. These changes dramatically increased the rate of guideline-concordant testing, decreasing cost and furthering the goal of high-value medical care.

The Red Lake Indian Health Service (IHS) health care facility is in north-central Minnesota within the Red Lake Nation. The facility supports primary care, emergency, urgent care, pharmacy, inpatient, optometry, dental, radiology, laboratory, physical therapy, and behavioral health services to about 10,000 Red Lake Band of Chippewa Indian patients. The Red Lake pharmacy provides inpatient and outpatient medication services and pharmacist-managed clinical patient care.

In 2013, the Red Lake IHS medical staff endorsed the implementation of comprehensive clinical pharmacy services to increase health care access and optimize clinical outcomes for patients. During the evolution of pharmacy-based patient-centric care, the clinical programs offered by Red Lake IHS pharmacy expanded from 1 anticoagulation clinic to multiple advanced-practice clinical pharmacy services. This included pharmacy primary care, medication-assisted therapy, naloxone, hepatitis C, and behavioral health medication management clinics.

The immense clinical growth of the pharmacy department demonstrated a need to assess and monitor pharmacist competency to ensure the delivery of quality patient care. Essential quality improvement processes were lacking. To fill these quality improvement gaps, a robust pharmacist credentialing and privileging program was implemented in 2015.

Patient Care

As efforts within health care establishments across the US focus on the delivery of efficient, high-quality, affordable health care, pharmacists have become increasingly instrumental in providing patient care within expanded clinical roles.^1-8 Many clinical pharmacy models have evolved into interdisciplinary approaches to care.⁹ Within these models, abiding by state and federal laws, pharmacists practice under the indirect supervision of licensed independent practitioners (LIPs), such as physicians, nurse practitioners, and physician assistants.⁸ Under collaborative practice agreements (CPAs), patients are initially diagnosed by LIPs, then referred to clinical pharmacists for therapeutic management.^5,7

Clinical pharmacist functions encompass comprehensive medication management (ie, prescribing, monitoring, and adjustment of medications), nonpharmacologic guidance, and coordination of care. Interdisciplinary collaboration allows pharmacists opportunities to provide direct patient care or consultations by telecommunication in many different clinical environments, including disease management, primary care, or specialty care. Pharmacists may manage chronic or acute illnesses associated with endocrine, cardiovascular, respiratory, gastrointestinal, or other systems.

Pharmacist Credentialing

With the advancement of modern clinical pharmacy practice, many pharmacists have undertaken responsibilities to fulfill the complex duties of clinical care and diverse patient situations, but with few or no requirements to prove initial or ongoing clinical competency.² Traditionally, pharmacist credentialing is limited to a onetime or periodic review of education and licensure, with little to no involvement in privileging and ongoing monitoring of clinical proficiency.¹⁰ These quality assurance disparities can be met and satisfied through credentialing and privileging processes. Credentialing and privileging are systematic, evidence-based processes that provide validation to HCPs, employers, and patients that pharmacists are qualified to practice clinically. ^2,9 According to the Council on Credentialing in Pharmacy, clinical pharmacists should be held accountable for demonstrating competency and providing quality care through credentialing and privileging, as required for other HCPs.^2,12

Credentialing and recredentialing is a primary source verification process. These processes ensure that there are no license restrictions or revocations; certifications are current; mandatory courses, certificates, and continuing education are complete; training and orientation are satisfactory; and any disciplinary action, malpractice claims, or history of impairment is reported. Privileging is the review of credentials and evaluation of clinical training and competence by the Clinical Director and Medical Executive Committee to determine whether a clinical pharmacist is competent to practice within requested privileges.¹¹

Credentialing and privileging processes are designed not only to initially confirm that a pharmacist is competent to practice clinically, but also monitor ongoing performance.^2,13 Participation in professional practice evaluations, which includes peer reviews, ongoing professional practice evaluations, and focused professional practice evaluations, is required for all credentialed and privileged practitioners. These evaluations are used to identify, assess, and correct unsatisfactory trends. Individual practices, documentation, and processes are evaluated against existing department standards (eg, CPAs, policies, processes)^11,13 The results of individual professional practice evaluations are reviewed with practitioners on a regular basis and performance improvement plans implemented as needed.

Since 2015, 17 pharmacists at the Red Lake IHS health care facility have been granted membership to the medical staff as credentialed and privileged practitioners. In a retrospective review of professional practice evaluations by the Red Lake IHS pharmacy clinical coordinator, 971 outpatient clinical peer reviews, including the evaluation of 21,526 peer-review elements were completed by pharmacists from fiscal year 2015 through 2018. Peer-review elements assessed visit documentation, patient care, and other clinic processes defined by department standards. Beginning in 2016, peer-review feedback was implemented and completed on a quarterly basis with each pharmacist. In fiscal years 2015, 2016, 2017, and 2018, the percentage of peer-review elements found as noncompliant with department standards were 18.0%, 11.6%, 3.7%, and 3.4%, respectively. Compared with the 2015 year baseline, these data correlate with a decrease of peer-review concerns by 35.5% in 2016, 79.4% in 2017, and 81.1% in 2018.

Conclusion

Pharmacists have become increasingly instrumental in providing effective, cost-efficient, and accessible clinical services by continuing to move toward expanding and evolving roles within comprehensive, patient-centered clinical pharmacy practice settings.^5,6 Multifaceted clinical responsibilities associated with health care delivery necessitate assessment and monitoring of pharmacist performance. Credentialing and privileging is an established and trusted systematic process that assures HCPs, employers, and patients that pharmacists are qualified and competent to practice clinically.^2,4,12 Implementation of professional practice evaluations suggest improved staff compliance with visit documentation, patient care standards, and clinic processes required by CPAs, policies, and department standards to ensure the delivery of safe, high-quality patient care.

The Red Lake Indian Health Service (IHS) health care facility is in north-central Minnesota within the Red Lake Nation. The facility supports primary care, emergency, urgent care, pharmacy, inpatient, optometry, dental, radiology, laboratory, physical therapy, and behavioral health services to about 10,000 Red Lake Band of Chippewa Indian patients. The Red Lake pharmacy provides inpatient and outpatient medication services and pharmacist-managed clinical patient care.

In 2013, the Red Lake IHS medical staff endorsed the implementation of comprehensive clinical pharmacy services to increase health care access and optimize clinical outcomes for patients. During the evolution of pharmacy-based patient-centric care, the clinical programs offered by Red Lake IHS pharmacy expanded from 1 anticoagulation clinic to multiple advanced-practice clinical pharmacy services. This included pharmacy primary care, medication-assisted therapy, naloxone, hepatitis C, and behavioral health medication management clinics.

The immense clinical growth of the pharmacy department demonstrated a need to assess and monitor pharmacist competency to ensure the delivery of quality patient care. Essential quality improvement processes were lacking. To fill these quality improvement gaps, a robust pharmacist credentialing and privileging program was implemented in 2015.

Patient Care

As efforts within health care establishments across the US focus on the delivery of efficient, high-quality, affordable health care, pharmacists have become increasingly instrumental in providing patient care within expanded clinical roles.^1-8 Many clinical pharmacy models have evolved into interdisciplinary approaches to care.⁹ Within these models, abiding by state and federal laws, pharmacists practice under the indirect supervision of licensed independent practitioners (LIPs), such as physicians, nurse practitioners, and physician assistants.⁸ Under collaborative practice agreements (CPAs), patients are initially diagnosed by LIPs, then referred to clinical pharmacists for therapeutic management.^5,7

Clinical pharmacist functions encompass comprehensive medication management (ie, prescribing, monitoring, and adjustment of medications), nonpharmacologic guidance, and coordination of care. Interdisciplinary collaboration allows pharmacists opportunities to provide direct patient care or consultations by telecommunication in many different clinical environments, including disease management, primary care, or specialty care. Pharmacists may manage chronic or acute illnesses associated with endocrine, cardiovascular, respiratory, gastrointestinal, or other systems.

Pharmacist Credentialing

With the advancement of modern clinical pharmacy practice, many pharmacists have undertaken responsibilities to fulfill the complex duties of clinical care and diverse patient situations, but with few or no requirements to prove initial or ongoing clinical competency.² Traditionally, pharmacist credentialing is limited to a onetime or periodic review of education and licensure, with little to no involvement in privileging and ongoing monitoring of clinical proficiency.¹⁰ These quality assurance disparities can be met and satisfied through credentialing and privileging processes. Credentialing and privileging are systematic, evidence-based processes that provide validation to HCPs, employers, and patients that pharmacists are qualified to practice clinically. ^2,9 According to the Council on Credentialing in Pharmacy, clinical pharmacists should be held accountable for demonstrating competency and providing quality care through credentialing and privileging, as required for other HCPs.^2,12

Credentialing and recredentialing is a primary source verification process. These processes ensure that there are no license restrictions or revocations; certifications are current; mandatory courses, certificates, and continuing education are complete; training and orientation are satisfactory; and any disciplinary action, malpractice claims, or history of impairment is reported. Privileging is the review of credentials and evaluation of clinical training and competence by the Clinical Director and Medical Executive Committee to determine whether a clinical pharmacist is competent to practice within requested privileges.¹¹

Credentialing and privileging processes are designed not only to initially confirm that a pharmacist is competent to practice clinically, but also monitor ongoing performance.^2,13 Participation in professional practice evaluations, which includes peer reviews, ongoing professional practice evaluations, and focused professional practice evaluations, is required for all credentialed and privileged practitioners. These evaluations are used to identify, assess, and correct unsatisfactory trends. Individual practices, documentation, and processes are evaluated against existing department standards (eg, CPAs, policies, processes)^11,13 The results of individual professional practice evaluations are reviewed with practitioners on a regular basis and performance improvement plans implemented as needed.

Since 2015, 17 pharmacists at the Red Lake IHS health care facility have been granted membership to the medical staff as credentialed and privileged practitioners. In a retrospective review of professional practice evaluations by the Red Lake IHS pharmacy clinical coordinator, 971 outpatient clinical peer reviews, including the evaluation of 21,526 peer-review elements were completed by pharmacists from fiscal year 2015 through 2018. Peer-review elements assessed visit documentation, patient care, and other clinic processes defined by department standards. Beginning in 2016, peer-review feedback was implemented and completed on a quarterly basis with each pharmacist. In fiscal years 2015, 2016, 2017, and 2018, the percentage of peer-review elements found as noncompliant with department standards were 18.0%, 11.6%, 3.7%, and 3.4%, respectively. Compared with the 2015 year baseline, these data correlate with a decrease of peer-review concerns by 35.5% in 2016, 79.4% in 2017, and 81.1% in 2018.

Conclusion

Pharmacists have become increasingly instrumental in providing effective, cost-efficient, and accessible clinical services by continuing to move toward expanding and evolving roles within comprehensive, patient-centered clinical pharmacy practice settings.^5,6 Multifaceted clinical responsibilities associated with health care delivery necessitate assessment and monitoring of pharmacist performance. Credentialing and privileging is an established and trusted systematic process that assures HCPs, employers, and patients that pharmacists are qualified and competent to practice clinically.^2,4,12 Implementation of professional practice evaluations suggest improved staff compliance with visit documentation, patient care standards, and clinic processes required by CPAs, policies, and department standards to ensure the delivery of safe, high-quality patient care.

The Red Lake Indian Health Service (IHS) health care facility is in north-central Minnesota within the Red Lake Nation. The facility supports primary care, emergency, urgent care, pharmacy, inpatient, optometry, dental, radiology, laboratory, physical therapy, and behavioral health services to about 10,000 Red Lake Band of Chippewa Indian patients. The Red Lake pharmacy provides inpatient and outpatient medication services and pharmacist-managed clinical patient care.

In 2013, the Red Lake IHS medical staff endorsed the implementation of comprehensive clinical pharmacy services to increase health care access and optimize clinical outcomes for patients. During the evolution of pharmacy-based patient-centric care, the clinical programs offered by Red Lake IHS pharmacy expanded from 1 anticoagulation clinic to multiple advanced-practice clinical pharmacy services. This included pharmacy primary care, medication-assisted therapy, naloxone, hepatitis C, and behavioral health medication management clinics.

The immense clinical growth of the pharmacy department demonstrated a need to assess and monitor pharmacist competency to ensure the delivery of quality patient care. Essential quality improvement processes were lacking. To fill these quality improvement gaps, a robust pharmacist credentialing and privileging program was implemented in 2015.

Patient Care

As efforts within health care establishments across the US focus on the delivery of efficient, high-quality, affordable health care, pharmacists have become increasingly instrumental in providing patient care within expanded clinical roles.^1-8 Many clinical pharmacy models have evolved into interdisciplinary approaches to care.⁹ Within these models, abiding by state and federal laws, pharmacists practice under the indirect supervision of licensed independent practitioners (LIPs), such as physicians, nurse practitioners, and physician assistants.⁸ Under collaborative practice agreements (CPAs), patients are initially diagnosed by LIPs, then referred to clinical pharmacists for therapeutic management.^5,7

Clinical pharmacist functions encompass comprehensive medication management (ie, prescribing, monitoring, and adjustment of medications), nonpharmacologic guidance, and coordination of care. Interdisciplinary collaboration allows pharmacists opportunities to provide direct patient care or consultations by telecommunication in many different clinical environments, including disease management, primary care, or specialty care. Pharmacists may manage chronic or acute illnesses associated with endocrine, cardiovascular, respiratory, gastrointestinal, or other systems.

Pharmacist Credentialing

With the advancement of modern clinical pharmacy practice, many pharmacists have undertaken responsibilities to fulfill the complex duties of clinical care and diverse patient situations, but with few or no requirements to prove initial or ongoing clinical competency.² Traditionally, pharmacist credentialing is limited to a onetime or periodic review of education and licensure, with little to no involvement in privileging and ongoing monitoring of clinical proficiency.¹⁰ These quality assurance disparities can be met and satisfied through credentialing and privileging processes. Credentialing and privileging are systematic, evidence-based processes that provide validation to HCPs, employers, and patients that pharmacists are qualified to practice clinically. ^2,9 According to the Council on Credentialing in Pharmacy, clinical pharmacists should be held accountable for demonstrating competency and providing quality care through credentialing and privileging, as required for other HCPs.^2,12

Credentialing and recredentialing is a primary source verification process. These processes ensure that there are no license restrictions or revocations; certifications are current; mandatory courses, certificates, and continuing education are complete; training and orientation are satisfactory; and any disciplinary action, malpractice claims, or history of impairment is reported. Privileging is the review of credentials and evaluation of clinical training and competence by the Clinical Director and Medical Executive Committee to determine whether a clinical pharmacist is competent to practice within requested privileges.¹¹

Credentialing and privileging processes are designed not only to initially confirm that a pharmacist is competent to practice clinically, but also monitor ongoing performance.^2,13 Participation in professional practice evaluations, which includes peer reviews, ongoing professional practice evaluations, and focused professional practice evaluations, is required for all credentialed and privileged practitioners. These evaluations are used to identify, assess, and correct unsatisfactory trends. Individual practices, documentation, and processes are evaluated against existing department standards (eg, CPAs, policies, processes)^11,13 The results of individual professional practice evaluations are reviewed with practitioners on a regular basis and performance improvement plans implemented as needed.

Since 2015, 17 pharmacists at the Red Lake IHS health care facility have been granted membership to the medical staff as credentialed and privileged practitioners. In a retrospective review of professional practice evaluations by the Red Lake IHS pharmacy clinical coordinator, 971 outpatient clinical peer reviews, including the evaluation of 21,526 peer-review elements were completed by pharmacists from fiscal year 2015 through 2018. Peer-review elements assessed visit documentation, patient care, and other clinic processes defined by department standards. Beginning in 2016, peer-review feedback was implemented and completed on a quarterly basis with each pharmacist. In fiscal years 2015, 2016, 2017, and 2018, the percentage of peer-review elements found as noncompliant with department standards were 18.0%, 11.6%, 3.7%, and 3.4%, respectively. Compared with the 2015 year baseline, these data correlate with a decrease of peer-review concerns by 35.5% in 2016, 79.4% in 2017, and 81.1% in 2018.

Conclusion

Pharmacists have become increasingly instrumental in providing effective, cost-efficient, and accessible clinical services by continuing to move toward expanding and evolving roles within comprehensive, patient-centered clinical pharmacy practice settings.^5,6 Multifaceted clinical responsibilities associated with health care delivery necessitate assessment and monitoring of pharmacist performance. Credentialing and privileging is an established and trusted systematic process that assures HCPs, employers, and patients that pharmacists are qualified and competent to practice clinically.^2,4,12 Implementation of professional practice evaluations suggest improved staff compliance with visit documentation, patient care standards, and clinic processes required by CPAs, policies, and department standards to ensure the delivery of safe, high-quality patient care.

To the Editor:

We read with interest the case report of nephrogenic systemic fibrosis (NSF) by Chuang, Kaneshiro, and Betancourt in the June 2018 issue of Federal Practitioner.¹ It was reported that a 61-year-old Hispanic male patient with a history of IV heroin abuse with end-stage renal disease (ESRD) secondary to membranous glomerulonephritis on hemodialysis and chronic hepatitis C infection received 15 mL gadoversetamide, a linear gadolinium-based contrast agent (GBCA) during magnetic resonance imaging (MRI) of the brain. Hemodialysis was performed 18 hours after the contrast administration.

Eight weeks after his initial presentation, the patient developed pyoderma gangrenosum on his right forearm, which was treated with high-dose steroids. He then developed thickening and induration of his bilateral forearm skin with peau d’orange appearance. NSF was confirmed by a skin biopsy. The patient developed contractures of his upper and lower extremities and was finally wheelchair bound.

This case is very concerning since no NSF cases in patients receiving GBCA have been published since 2009. Unfortunately, the authors give no information on the occurrence of this particular case. Thus, it is unclear whether this case was observed before or after the switch to macrocyclic agents in patients with reduced renal function. The reported patient with ESRD was on hemodialysis and received 15 mL gadoversetamide during MRI of the brain. In 2007 the ESUR (European Society of Urogenital Radiology) published guidelines indicating linear GBCA (gadodiamide, gadoversetamide, gadopentetate dimeglumine) as high-risk agents that may not be used in patients with eGFR < 30 mL/min/1.73 m².^2,3

Consequently in 2007, the European Medicines Agency contraindicated these linear GBCA in patients with chronic kidney disease grades 4 and 5. Also in 2007 the US Food and Drug Administration (FDA) requested a revision of the prescribing information for all 5 GBCA approved in the US.⁴ In response to accumulating more informative data, in 2010 the FDA again used this class labeling approach to more explicitly describe differences in NSF risks among the agents.⁴ FDA regulation and contraindication of the use of low-stability GBCA in patients with advanced renal impairment and robust local policies on the safe use of these agents have resulted in marked reduction in the prevalence of NSF in the US. This case report needs to clarify why a high-risk linear agent was administered to a patient with ESRD.

In 2006 Grobner and Marckmann and colleagues reported their observations of a previously unrecognized link between exposure to gadodiamide and the development of NSF.^5,6 It soon became clear that NSF is a delayed adverse contrast reaction that may cause severe disability and even death. Advanced renal disease and high-risk linear GBCA are the main factors in the pathogenesis of NSF. Additionally, the dose of the agent may play a role. NSF can occur from hours to years after exposure to GBCA. Not all patients with severe kidney disease exposed to high-risk agents developed NSF. Thus, additional factors were proposed to play a role in the pathogenesis of NSF. Among those factors were erythropoietin, metabolic acidosis, anion gap, iron, increased phosphate, zinc loss, proinflammatory conditions/inflammation and angiotensin-converting enzyme (ACE) inhibitors.⁷ Although there is little proof with these assumptions, special care must be taken as shown by this reported patient with multiple inflammatory disorders.

- Gertraud Heinz, MD, MBA; Aart van der Molen, MD; and Giles Roditi, MD; on behalf of the ESUR Contrast Media Safety Committee

Author affiliations: Gertraud Heinz is former President ESUR and Head of the Department of Radiology, Diagnostics and Intervention University Hospital St. Pölten Karl Landsteiner University of Health Sciences.

Correspondence: Gertraud Heinz (gertraud.heinz@stpoelten .lknoe.at)

Disclosures: The authors report no conflict of interest with regard to this article.

References
1. Chuang K, Kaneshiro C, Betancourt J. Nephrogenic systemic fibrosis in a patient with multiple inflammatory disorders. Fed Pract. 2018;35(6):40-43.
2. Thomsen HS; European Society of Urogenital Radiology (ESUR). ESUR guideline: gadolinium based contrast media and nephrogenic systemic fibrosis. Eur Radiol. 2007;17(10):2692-2696.
3. Thomsen HS, Morcos SK, Almén T, et al; ESUR Contrast Medium Safety Committee. Nephrogenic systemic fibrosis and gadolinium-based contrast media: updated ESUR Contrast Media Safety Committee guidelines. Eur Radiol. 2013;23(2):307-318
4. Yang L, Krefting I, Gorovets A, et al. Nephrogenic systemic fibrosis and class labeling of gadolinium-based agents by the Food and Drug Administration. Radiology. 2012;265(1):248-253.
5. Grobner T. Gadolinium—a specific trigger for the development of nephrogenic fibrosing dermopathy and nephrogenic systemic fibrosis? Nephrol Dial Transplant. 2006;21(4):1104-1108.
6. Marckmann P, Skov L, Rossen K, et al. Nephrogenic systemic fibrosis: suspected causative role of gadodiamide used for contrast-enhanced magnetic resonance imaging. J Am Soc Nephrol. 2006;17(9):2359-2362.
7. Thomsen HS, Bennett CL. Six years after. Acta Radiol. 2012;53(8):827-829.

To the Editor:

With great interest, I read the case report by Chuang, Kaneshiro, and Betancourt.¹ Patients with nephrogenic systemic fibrosis (NSF) are of special interest because the disease is still unclear as mentioned by the authors. Although new cases may occur,² this case raises some concerns that I would like to address.

First, it would be of great interest to know the date when the patient received the high-risk gadolinium-based contrast agent (GBCA) gadoversetamide. Unfortunately, the authors did not mention the date of the injection of the GBCA that probably caused NSF. Due to the obvious association between the applications of special GBCAs in 2006, the US Food and Drug Administration (FDA) warned physicians not to inject these contrast agents in patients with compromised kidney function.³ Moreover, in 2007 the American College of Radiology (ACR) published guidelines for the safe use of GBCAs in patients with renal failure.⁴ Also, the European Medicines Agency (EMA) demanded that companies provide warning in product inserts about the acquisition of NSF in patients with severe kidney injury.⁵

Second, the clinical illustration of the case is inadequate. In the manuscript, we read that the patient acquired NSF-characteristic lesions like peau d’orange skin lesions and contractures of his extremities, but unfortunately, Chuang, Kaneshiro, and Betancourt did not provide figures that show them. On the other hand, Figure 1 shows an uncharacteristic dermal induration around inflammatory and ulcerated skin lesion (pyoderma gangrenosum).¹ Such clinical signs are well known and occur perilesional of different conditions independently of NSF.^6-8

Third, the histological features described as presence of fibrotic tissue in the deep dermis in Figure 2, and dermal fibrosis with thick collagen deposition in Figure 3¹ do not confirm the existence of NSF.

Taken together, the case presented by Chuang, Kaneshiro, and Betancourt contains some unclear aspects; therefore, it is questionable whether the published case describes a patient with NSF or not. In the current presentation, the diagnosis NSF seems to be an overestimation.

NSF still is a poorly understood disorder. Therefore, exactly documented new cases could be of clinical value when providing interesting information. Even single cases could shed some light in the darkness of the pathological mechanisms of this entity. On the other hand, we should not mix the existing cohort of published NSF cases with other scleroderma-like diseases, because this will lead to a confusion. Moreover, such a practice could inhibit the discovery of the pathophysiology of NSF.

- Ingrid Böhm, MD

Author affiliations: Ingrid Böhm is a Physician in the Department of Diagnostics, Interventional and Pediatric Radiology at the University Hospital of Bern, Inselspital, University of Bern in Bern, Switzerland.

Correspondence: Ingrid Böhm ([email protected])

Disclosures: The author reports no conflict of interest with regard to this article.

References
1. Chuang K, Kaneshiro C, Betancourt J. Nephrogenic systemic fibrosis in a patient with multiple inflammatory disorders. Fed Pract . 2018;35(6):40-43.
2. Larson KN, Gagnon AL, Darling MD, Patterson JW, Cropley TG. Nephrogenic systemic fibrosis manifesting a decade after exposure to gadolinium. JAMA Dermatol. 2015;151(10):1117-1120.
3. US Food and Drug Administration. A Public Health Advisory. Gadolinium-containing contrast agents for magnetic resonance imaging (MRI). http://wayback.archive-it.org/7993/20170112033022/http://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformation forPatientsandProviders/ucm053112.htm. Published June 8, 2006. Accessed March 15, 2019.
4. Kanal E, Barkovich AJ, Bell C, et al; ACR Blue Ribbon Panel on MR Safety. ACR guidance document for safe MR practices: 2007. AJR Am J Roentgenol. 2007;188(6):1447-1474.
5. European Medicines Agency. Public statement: Vasovist and nephrogenic systemic fibrosis (NSF). https://www.ema.europa.eu/en/news/public-statement-vasovist-nephrogenic-systemic-fibrosis-nsf. Published February 7, 2007. Accessed March 15, 2019.
6. Luke JC. The etiology and modern treatment of varicose ulcer. Can Med Assoc J. 1940;43(3):217-221.
7. Paulsen E, Bygum A. Keratin gel as an adjuvant in the treatment of recalcitrant pyoderma gangrenosum ulcers: a case report. Acta Derm Venereol. 2019;99(2):234-235.
8. Boehm I, Bauer R. Low-dose methotrexate controls a severe form of polyarteritis nodosa. Arch Dermatol. 2000; 136(2):167-169.

Response:

We thank Drs. Heinz, van der Molen, and Roditi for their valuable response. The following is the opinion of the authors and is not representative of the views or policies of our institution. The patient in this case received a gadolinium-based contrast agent (GBCA) in 2015 and was diagnosed with nephrogenic systemic fibrosis (NSF) 8 weeks later. We agree with the correspondents that linear GBCAs should not be used in patients with eGFR < 30 mL/min/1.73 m². To date, a few cases of patients who received GBCA and developed NSF since 2009 have unfortunately continued to be reported in the literature.^1-3 Our intention in publishing this case was to provide ongoing education to the medical community regarding this serious condition to ensure prevention of future cases.

We thank Dr. Böhm for her important inquiry. The patient received a histopathologic diagnosis of NSF. The report from the patient’s left dorsal forearm skin punch biopsy was read by our pathologist as “fibrosis and inflammation consistent with nephrogenic systemic fibrosis,” a diagnosis agreed upon by our colleagues in the dermatology and rheumatology departments based on the rapidity of his symptom onset and progression. While we acknowledge that this patient had other inflammatory disorders of the skin that may have coexisted with the diagnosis, after weighing the preponderance of clinical evidence in support of the biopsy results, we believe that this represents a case of NSF, which is associated with high morbidity and mortality. Thankfully, the patient in this case engaged extensively in physical and occupational therapy and is still alive nearly 4 years later. We would like to thank all the letter writers for their correspondence.

Author Affiliations: Kelley Chuang and Casey Kaneshiro are Hospitalists and Jaime Betancourt is a Pulmonologist, all in the Department of Medicine at the VA Greater Los Angeles Healthcare System in California.

Correspondence: Kelley Chuang ([email protected])

Disclosures: The authors report no conflict of interest with regard to this article.

References
1. Aggarwal A, Froehlich AA, Essah P, Brinster N, High WA, Downs RW. Complications of nephrogenic systemic fibrosis following repeated exposure to gadolinium in a man with hypothyroidism: a case report. J Med Case Rep. 2011;5:566.
2. Fuah KW, Lim CT. Erythema nodosum masking nephrogenic systemic fibrosis as initial skin manifestation. BMC Nephrol. 2017;18(1):249.
3. Koratala A, Bhatti V. Nephrogenic systemic fibrosis. Clin Case Rep. 2017;5(7):1184-1185.

To the Editor:

We read with interest the case report of nephrogenic systemic fibrosis (NSF) by Chuang, Kaneshiro, and Betancourt in the June 2018 issue of Federal Practitioner.¹ It was reported that a 61-year-old Hispanic male patient with a history of IV heroin abuse with end-stage renal disease (ESRD) secondary to membranous glomerulonephritis on hemodialysis and chronic hepatitis C infection received 15 mL gadoversetamide, a linear gadolinium-based contrast agent (GBCA) during magnetic resonance imaging (MRI) of the brain. Hemodialysis was performed 18 hours after the contrast administration.

Eight weeks after his initial presentation, the patient developed pyoderma gangrenosum on his right forearm, which was treated with high-dose steroids. He then developed thickening and induration of his bilateral forearm skin with peau d’orange appearance. NSF was confirmed by a skin biopsy. The patient developed contractures of his upper and lower extremities and was finally wheelchair bound.

This case is very concerning since no NSF cases in patients receiving GBCA have been published since 2009. Unfortunately, the authors give no information on the occurrence of this particular case. Thus, it is unclear whether this case was observed before or after the switch to macrocyclic agents in patients with reduced renal function. The reported patient with ESRD was on hemodialysis and received 15 mL gadoversetamide during MRI of the brain. In 2007 the ESUR (European Society of Urogenital Radiology) published guidelines indicating linear GBCA (gadodiamide, gadoversetamide, gadopentetate dimeglumine) as high-risk agents that may not be used in patients with eGFR < 30 mL/min/1.73 m².^2,3

Consequently in 2007, the European Medicines Agency contraindicated these linear GBCA in patients with chronic kidney disease grades 4 and 5. Also in 2007 the US Food and Drug Administration (FDA) requested a revision of the prescribing information for all 5 GBCA approved in the US.⁴ In response to accumulating more informative data, in 2010 the FDA again used this class labeling approach to more explicitly describe differences in NSF risks among the agents.⁴ FDA regulation and contraindication of the use of low-stability GBCA in patients with advanced renal impairment and robust local policies on the safe use of these agents have resulted in marked reduction in the prevalence of NSF in the US. This case report needs to clarify why a high-risk linear agent was administered to a patient with ESRD.

In 2006 Grobner and Marckmann and colleagues reported their observations of a previously unrecognized link between exposure to gadodiamide and the development of NSF.^5,6 It soon became clear that NSF is a delayed adverse contrast reaction that may cause severe disability and even death. Advanced renal disease and high-risk linear GBCA are the main factors in the pathogenesis of NSF. Additionally, the dose of the agent may play a role. NSF can occur from hours to years after exposure to GBCA. Not all patients with severe kidney disease exposed to high-risk agents developed NSF. Thus, additional factors were proposed to play a role in the pathogenesis of NSF. Among those factors were erythropoietin, metabolic acidosis, anion gap, iron, increased phosphate, zinc loss, proinflammatory conditions/inflammation and angiotensin-converting enzyme (ACE) inhibitors.⁷ Although there is little proof with these assumptions, special care must be taken as shown by this reported patient with multiple inflammatory disorders.

- Gertraud Heinz, MD, MBA; Aart van der Molen, MD; and Giles Roditi, MD; on behalf of the ESUR Contrast Media Safety Committee

Author affiliations: Gertraud Heinz is former President ESUR and Head of the Department of Radiology, Diagnostics and Intervention University Hospital St. Pölten Karl Landsteiner University of Health Sciences.

Correspondence: Gertraud Heinz (gertraud.heinz@stpoelten .lknoe.at)

Disclosures: The authors report no conflict of interest with regard to this article.

References
1. Chuang K, Kaneshiro C, Betancourt J. Nephrogenic systemic fibrosis in a patient with multiple inflammatory disorders. Fed Pract. 2018;35(6):40-43.
2. Thomsen HS; European Society of Urogenital Radiology (ESUR). ESUR guideline: gadolinium based contrast media and nephrogenic systemic fibrosis. Eur Radiol. 2007;17(10):2692-2696.
3. Thomsen HS, Morcos SK, Almén T, et al; ESUR Contrast Medium Safety Committee. Nephrogenic systemic fibrosis and gadolinium-based contrast media: updated ESUR Contrast Media Safety Committee guidelines. Eur Radiol. 2013;23(2):307-318
4. Yang L, Krefting I, Gorovets A, et al. Nephrogenic systemic fibrosis and class labeling of gadolinium-based agents by the Food and Drug Administration. Radiology. 2012;265(1):248-253.
5. Grobner T. Gadolinium—a specific trigger for the development of nephrogenic fibrosing dermopathy and nephrogenic systemic fibrosis? Nephrol Dial Transplant. 2006;21(4):1104-1108.
6. Marckmann P, Skov L, Rossen K, et al. Nephrogenic systemic fibrosis: suspected causative role of gadodiamide used for contrast-enhanced magnetic resonance imaging. J Am Soc Nephrol. 2006;17(9):2359-2362.
7. Thomsen HS, Bennett CL. Six years after. Acta Radiol. 2012;53(8):827-829.

To the Editor:

With great interest, I read the case report by Chuang, Kaneshiro, and Betancourt.¹ Patients with nephrogenic systemic fibrosis (NSF) are of special interest because the disease is still unclear as mentioned by the authors. Although new cases may occur,² this case raises some concerns that I would like to address.

First, it would be of great interest to know the date when the patient received the high-risk gadolinium-based contrast agent (GBCA) gadoversetamide. Unfortunately, the authors did not mention the date of the injection of the GBCA that probably caused NSF. Due to the obvious association between the applications of special GBCAs in 2006, the US Food and Drug Administration (FDA) warned physicians not to inject these contrast agents in patients with compromised kidney function.³ Moreover, in 2007 the American College of Radiology (ACR) published guidelines for the safe use of GBCAs in patients with renal failure.⁴ Also, the European Medicines Agency (EMA) demanded that companies provide warning in product inserts about the acquisition of NSF in patients with severe kidney injury.⁵

Second, the clinical illustration of the case is inadequate. In the manuscript, we read that the patient acquired NSF-characteristic lesions like peau d’orange skin lesions and contractures of his extremities, but unfortunately, Chuang, Kaneshiro, and Betancourt did not provide figures that show them. On the other hand, Figure 1 shows an uncharacteristic dermal induration around inflammatory and ulcerated skin lesion (pyoderma gangrenosum).¹ Such clinical signs are well known and occur perilesional of different conditions independently of NSF.^6-8

Third, the histological features described as presence of fibrotic tissue in the deep dermis in Figure 2, and dermal fibrosis with thick collagen deposition in Figure 3¹ do not confirm the existence of NSF.

Taken together, the case presented by Chuang, Kaneshiro, and Betancourt contains some unclear aspects; therefore, it is questionable whether the published case describes a patient with NSF or not. In the current presentation, the diagnosis NSF seems to be an overestimation.

NSF still is a poorly understood disorder. Therefore, exactly documented new cases could be of clinical value when providing interesting information. Even single cases could shed some light in the darkness of the pathological mechanisms of this entity. On the other hand, we should not mix the existing cohort of published NSF cases with other scleroderma-like diseases, because this will lead to a confusion. Moreover, such a practice could inhibit the discovery of the pathophysiology of NSF.

- Ingrid Böhm, MD

Author affiliations: Ingrid Böhm is a Physician in the Department of Diagnostics, Interventional and Pediatric Radiology at the University Hospital of Bern, Inselspital, University of Bern in Bern, Switzerland.

Correspondence: Ingrid Böhm ([email protected])

Disclosures: The author reports no conflict of interest with regard to this article.

References
1. Chuang K, Kaneshiro C, Betancourt J. Nephrogenic systemic fibrosis in a patient with multiple inflammatory disorders. Fed Pract . 2018;35(6):40-43.
2. Larson KN, Gagnon AL, Darling MD, Patterson JW, Cropley TG. Nephrogenic systemic fibrosis manifesting a decade after exposure to gadolinium. JAMA Dermatol. 2015;151(10):1117-1120.
3. US Food and Drug Administration. A Public Health Advisory. Gadolinium-containing contrast agents for magnetic resonance imaging (MRI). http://wayback.archive-it.org/7993/20170112033022/http://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformation forPatientsandProviders/ucm053112.htm. Published June 8, 2006. Accessed March 15, 2019.
4. Kanal E, Barkovich AJ, Bell C, et al; ACR Blue Ribbon Panel on MR Safety. ACR guidance document for safe MR practices: 2007. AJR Am J Roentgenol. 2007;188(6):1447-1474.
5. European Medicines Agency. Public statement: Vasovist and nephrogenic systemic fibrosis (NSF). https://www.ema.europa.eu/en/news/public-statement-vasovist-nephrogenic-systemic-fibrosis-nsf. Published February 7, 2007. Accessed March 15, 2019.
6. Luke JC. The etiology and modern treatment of varicose ulcer. Can Med Assoc J. 1940;43(3):217-221.
7. Paulsen E, Bygum A. Keratin gel as an adjuvant in the treatment of recalcitrant pyoderma gangrenosum ulcers: a case report. Acta Derm Venereol. 2019;99(2):234-235.
8. Boehm I, Bauer R. Low-dose methotrexate controls a severe form of polyarteritis nodosa. Arch Dermatol. 2000; 136(2):167-169.

Response:

We thank Drs. Heinz, van der Molen, and Roditi for their valuable response. The following is the opinion of the authors and is not representative of the views or policies of our institution. The patient in this case received a gadolinium-based contrast agent (GBCA) in 2015 and was diagnosed with nephrogenic systemic fibrosis (NSF) 8 weeks later. We agree with the correspondents that linear GBCAs should not be used in patients with eGFR < 30 mL/min/1.73 m². To date, a few cases of patients who received GBCA and developed NSF since 2009 have unfortunately continued to be reported in the literature.^1-3 Our intention in publishing this case was to provide ongoing education to the medical community regarding this serious condition to ensure prevention of future cases.

We thank Dr. Böhm for her important inquiry. The patient received a histopathologic diagnosis of NSF. The report from the patient’s left dorsal forearm skin punch biopsy was read by our pathologist as “fibrosis and inflammation consistent with nephrogenic systemic fibrosis,” a diagnosis agreed upon by our colleagues in the dermatology and rheumatology departments based on the rapidity of his symptom onset and progression. While we acknowledge that this patient had other inflammatory disorders of the skin that may have coexisted with the diagnosis, after weighing the preponderance of clinical evidence in support of the biopsy results, we believe that this represents a case of NSF, which is associated with high morbidity and mortality. Thankfully, the patient in this case engaged extensively in physical and occupational therapy and is still alive nearly 4 years later. We would like to thank all the letter writers for their correspondence.

Author Affiliations: Kelley Chuang and Casey Kaneshiro are Hospitalists and Jaime Betancourt is a Pulmonologist, all in the Department of Medicine at the VA Greater Los Angeles Healthcare System in California.

Correspondence: Kelley Chuang ([email protected])

Disclosures: The authors report no conflict of interest with regard to this article.

References
1. Aggarwal A, Froehlich AA, Essah P, Brinster N, High WA, Downs RW. Complications of nephrogenic systemic fibrosis following repeated exposure to gadolinium in a man with hypothyroidism: a case report. J Med Case Rep. 2011;5:566.
2. Fuah KW, Lim CT. Erythema nodosum masking nephrogenic systemic fibrosis as initial skin manifestation. BMC Nephrol. 2017;18(1):249.
3. Koratala A, Bhatti V. Nephrogenic systemic fibrosis. Clin Case Rep. 2017;5(7):1184-1185.

To the Editor:

We read with interest the case report of nephrogenic systemic fibrosis (NSF) by Chuang, Kaneshiro, and Betancourt in the June 2018 issue of Federal Practitioner.¹ It was reported that a 61-year-old Hispanic male patient with a history of IV heroin abuse with end-stage renal disease (ESRD) secondary to membranous glomerulonephritis on hemodialysis and chronic hepatitis C infection received 15 mL gadoversetamide, a linear gadolinium-based contrast agent (GBCA) during magnetic resonance imaging (MRI) of the brain. Hemodialysis was performed 18 hours after the contrast administration.

Eight weeks after his initial presentation, the patient developed pyoderma gangrenosum on his right forearm, which was treated with high-dose steroids. He then developed thickening and induration of his bilateral forearm skin with peau d’orange appearance. NSF was confirmed by a skin biopsy. The patient developed contractures of his upper and lower extremities and was finally wheelchair bound.

This case is very concerning since no NSF cases in patients receiving GBCA have been published since 2009. Unfortunately, the authors give no information on the occurrence of this particular case. Thus, it is unclear whether this case was observed before or after the switch to macrocyclic agents in patients with reduced renal function. The reported patient with ESRD was on hemodialysis and received 15 mL gadoversetamide during MRI of the brain. In 2007 the ESUR (European Society of Urogenital Radiology) published guidelines indicating linear GBCA (gadodiamide, gadoversetamide, gadopentetate dimeglumine) as high-risk agents that may not be used in patients with eGFR < 30 mL/min/1.73 m².^2,3

Consequently in 2007, the European Medicines Agency contraindicated these linear GBCA in patients with chronic kidney disease grades 4 and 5. Also in 2007 the US Food and Drug Administration (FDA) requested a revision of the prescribing information for all 5 GBCA approved in the US.⁴ In response to accumulating more informative data, in 2010 the FDA again used this class labeling approach to more explicitly describe differences in NSF risks among the agents.⁴ FDA regulation and contraindication of the use of low-stability GBCA in patients with advanced renal impairment and robust local policies on the safe use of these agents have resulted in marked reduction in the prevalence of NSF in the US. This case report needs to clarify why a high-risk linear agent was administered to a patient with ESRD.

In 2006 Grobner and Marckmann and colleagues reported their observations of a previously unrecognized link between exposure to gadodiamide and the development of NSF.^5,6 It soon became clear that NSF is a delayed adverse contrast reaction that may cause severe disability and even death. Advanced renal disease and high-risk linear GBCA are the main factors in the pathogenesis of NSF. Additionally, the dose of the agent may play a role. NSF can occur from hours to years after exposure to GBCA. Not all patients with severe kidney disease exposed to high-risk agents developed NSF. Thus, additional factors were proposed to play a role in the pathogenesis of NSF. Among those factors were erythropoietin, metabolic acidosis, anion gap, iron, increased phosphate, zinc loss, proinflammatory conditions/inflammation and angiotensin-converting enzyme (ACE) inhibitors.⁷ Although there is little proof with these assumptions, special care must be taken as shown by this reported patient with multiple inflammatory disorders.

- Gertraud Heinz, MD, MBA; Aart van der Molen, MD; and Giles Roditi, MD; on behalf of the ESUR Contrast Media Safety Committee

Author affiliations: Gertraud Heinz is former President ESUR and Head of the Department of Radiology, Diagnostics and Intervention University Hospital St. Pölten Karl Landsteiner University of Health Sciences.

Correspondence: Gertraud Heinz (gertraud.heinz@stpoelten .lknoe.at)

Disclosures: The authors report no conflict of interest with regard to this article.

References
1. Chuang K, Kaneshiro C, Betancourt J. Nephrogenic systemic fibrosis in a patient with multiple inflammatory disorders. Fed Pract. 2018;35(6):40-43.
2. Thomsen HS; European Society of Urogenital Radiology (ESUR). ESUR guideline: gadolinium based contrast media and nephrogenic systemic fibrosis. Eur Radiol. 2007;17(10):2692-2696.
3. Thomsen HS, Morcos SK, Almén T, et al; ESUR Contrast Medium Safety Committee. Nephrogenic systemic fibrosis and gadolinium-based contrast media: updated ESUR Contrast Media Safety Committee guidelines. Eur Radiol. 2013;23(2):307-318
4. Yang L, Krefting I, Gorovets A, et al. Nephrogenic systemic fibrosis and class labeling of gadolinium-based agents by the Food and Drug Administration. Radiology. 2012;265(1):248-253.
5. Grobner T. Gadolinium—a specific trigger for the development of nephrogenic fibrosing dermopathy and nephrogenic systemic fibrosis? Nephrol Dial Transplant. 2006;21(4):1104-1108.
6. Marckmann P, Skov L, Rossen K, et al. Nephrogenic systemic fibrosis: suspected causative role of gadodiamide used for contrast-enhanced magnetic resonance imaging. J Am Soc Nephrol. 2006;17(9):2359-2362.
7. Thomsen HS, Bennett CL. Six years after. Acta Radiol. 2012;53(8):827-829.

To the Editor:

With great interest, I read the case report by Chuang, Kaneshiro, and Betancourt.¹ Patients with nephrogenic systemic fibrosis (NSF) are of special interest because the disease is still unclear as mentioned by the authors. Although new cases may occur,² this case raises some concerns that I would like to address.

First, it would be of great interest to know the date when the patient received the high-risk gadolinium-based contrast agent (GBCA) gadoversetamide. Unfortunately, the authors did not mention the date of the injection of the GBCA that probably caused NSF. Due to the obvious association between the applications of special GBCAs in 2006, the US Food and Drug Administration (FDA) warned physicians not to inject these contrast agents in patients with compromised kidney function.³ Moreover, in 2007 the American College of Radiology (ACR) published guidelines for the safe use of GBCAs in patients with renal failure.⁴ Also, the European Medicines Agency (EMA) demanded that companies provide warning in product inserts about the acquisition of NSF in patients with severe kidney injury.⁵

Second, the clinical illustration of the case is inadequate. In the manuscript, we read that the patient acquired NSF-characteristic lesions like peau d’orange skin lesions and contractures of his extremities, but unfortunately, Chuang, Kaneshiro, and Betancourt did not provide figures that show them. On the other hand, Figure 1 shows an uncharacteristic dermal induration around inflammatory and ulcerated skin lesion (pyoderma gangrenosum).¹ Such clinical signs are well known and occur perilesional of different conditions independently of NSF.^6-8

Third, the histological features described as presence of fibrotic tissue in the deep dermis in Figure 2, and dermal fibrosis with thick collagen deposition in Figure 3¹ do not confirm the existence of NSF.

Taken together, the case presented by Chuang, Kaneshiro, and Betancourt contains some unclear aspects; therefore, it is questionable whether the published case describes a patient with NSF or not. In the current presentation, the diagnosis NSF seems to be an overestimation.

NSF still is a poorly understood disorder. Therefore, exactly documented new cases could be of clinical value when providing interesting information. Even single cases could shed some light in the darkness of the pathological mechanisms of this entity. On the other hand, we should not mix the existing cohort of published NSF cases with other scleroderma-like diseases, because this will lead to a confusion. Moreover, such a practice could inhibit the discovery of the pathophysiology of NSF.

- Ingrid Böhm, MD

Author affiliations: Ingrid Böhm is a Physician in the Department of Diagnostics, Interventional and Pediatric Radiology at the University Hospital of Bern, Inselspital, University of Bern in Bern, Switzerland.

Correspondence: Ingrid Böhm ([email protected])

Disclosures: The author reports no conflict of interest with regard to this article.

References
1. Chuang K, Kaneshiro C, Betancourt J. Nephrogenic systemic fibrosis in a patient with multiple inflammatory disorders. Fed Pract . 2018;35(6):40-43.
2. Larson KN, Gagnon AL, Darling MD, Patterson JW, Cropley TG. Nephrogenic systemic fibrosis manifesting a decade after exposure to gadolinium. JAMA Dermatol. 2015;151(10):1117-1120.
3. US Food and Drug Administration. A Public Health Advisory. Gadolinium-containing contrast agents for magnetic resonance imaging (MRI). http://wayback.archive-it.org/7993/20170112033022/http://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformation forPatientsandProviders/ucm053112.htm. Published June 8, 2006. Accessed March 15, 2019.
4. Kanal E, Barkovich AJ, Bell C, et al; ACR Blue Ribbon Panel on MR Safety. ACR guidance document for safe MR practices: 2007. AJR Am J Roentgenol. 2007;188(6):1447-1474.
5. European Medicines Agency. Public statement: Vasovist and nephrogenic systemic fibrosis (NSF). https://www.ema.europa.eu/en/news/public-statement-vasovist-nephrogenic-systemic-fibrosis-nsf. Published February 7, 2007. Accessed March 15, 2019.
6. Luke JC. The etiology and modern treatment of varicose ulcer. Can Med Assoc J. 1940;43(3):217-221.
7. Paulsen E, Bygum A. Keratin gel as an adjuvant in the treatment of recalcitrant pyoderma gangrenosum ulcers: a case report. Acta Derm Venereol. 2019;99(2):234-235.
8. Boehm I, Bauer R. Low-dose methotrexate controls a severe form of polyarteritis nodosa. Arch Dermatol. 2000; 136(2):167-169.

Response:

We thank Drs. Heinz, van der Molen, and Roditi for their valuable response. The following is the opinion of the authors and is not representative of the views or policies of our institution. The patient in this case received a gadolinium-based contrast agent (GBCA) in 2015 and was diagnosed with nephrogenic systemic fibrosis (NSF) 8 weeks later. We agree with the correspondents that linear GBCAs should not be used in patients with eGFR < 30 mL/min/1.73 m². To date, a few cases of patients who received GBCA and developed NSF since 2009 have unfortunately continued to be reported in the literature.^1-3 Our intention in publishing this case was to provide ongoing education to the medical community regarding this serious condition to ensure prevention of future cases.

We thank Dr. Böhm for her important inquiry. The patient received a histopathologic diagnosis of NSF. The report from the patient’s left dorsal forearm skin punch biopsy was read by our pathologist as “fibrosis and inflammation consistent with nephrogenic systemic fibrosis,” a diagnosis agreed upon by our colleagues in the dermatology and rheumatology departments based on the rapidity of his symptom onset and progression. While we acknowledge that this patient had other inflammatory disorders of the skin that may have coexisted with the diagnosis, after weighing the preponderance of clinical evidence in support of the biopsy results, we believe that this represents a case of NSF, which is associated with high morbidity and mortality. Thankfully, the patient in this case engaged extensively in physical and occupational therapy and is still alive nearly 4 years later. We would like to thank all the letter writers for their correspondence.

Author Affiliations: Kelley Chuang and Casey Kaneshiro are Hospitalists and Jaime Betancourt is a Pulmonologist, all in the Department of Medicine at the VA Greater Los Angeles Healthcare System in California.

Correspondence: Kelley Chuang ([email protected])

Disclosures: The authors report no conflict of interest with regard to this article.

References
1. Aggarwal A, Froehlich AA, Essah P, Brinster N, High WA, Downs RW. Complications of nephrogenic systemic fibrosis following repeated exposure to gadolinium in a man with hypothyroidism: a case report. J Med Case Rep. 2011;5:566.
2. Fuah KW, Lim CT. Erythema nodosum masking nephrogenic systemic fibrosis as initial skin manifestation. BMC Nephrol. 2017;18(1):249.
3. Koratala A, Bhatti V. Nephrogenic systemic fibrosis. Clin Case Rep. 2017;5(7):1184-1185.

National K9 Veterans Day celebrates the loyalty, bravery, and sacrifice of canine warriors. On March 13, 1942, canines officially became members of the Armed Services, with the Army’s founding of its New War Dog Program, more popularly known as the K9 Corps. The dogs underwent basic training and then entered more specialized preparation just as human soldiers did.² There had been unofficial dogs of war who served courageously and selflessly in almost all of our armed conflicts.³ Indeed, the title of this column is taken from a wonderful article of the same name narrating the heroism of dogs in the 2 world wars.⁴

The dedication of canines to those who serve is not confined to combat or even active duty. Thousands of military and veteran men and women have benefited immensely from their relationship with service and emotional support dogs.

Before I continue, let me state 2 important limitations of this column. First, I am a dog person. Of course, veterans have formed healing and caring relationships with many types of companions. Equine therapy is increasingly recognized as a powerful means of helping veterans reduce distress and find purpose.⁵ Nevertheless, for this column, I will focus exclusively on dogs. Second, there are many worthy organizations, projects, and programs that pair veterans with therapeutic dogs inside and outside the VA. I am in no way an expert and will invariably neglect many of these positive initiatives in this brief review.

The long, proud history of canines in the military and the many moving stories of men and women in and out of uniform for whom dogs have been life changing, if not life-saving, have created 2 ethical dilemmas for the VA that I examine here. Both dilemmas pivot on the terms of official recognition of service dogs, the benefits, and who can qualify for them in the VA.

Under VA regulation and VHA policy, a service companion only can be a dog that is individually trained to do work or perform tasks to assist a person with a disability; dogs whose sole function is to provide emotional support, well-being, comfort, or companionship are not considered service pets.⁶

A valid question to ask is why couldn’t the most vulnerable of these veterans, for instance those with severe mental health conditions, have service dogs that could accompany them into at least most areas of the medical center? Part of the reason is cost: Some training organizations estimate it may cost as much as $27,000 to train service dogs.⁸ Though there are many wonderful volunteer and not-for-profit organizations that train mostly shelter dogs and their veteran handlers—a double rescue—the lengthy process and expense means that many veterans wait years for a companion.

Congressional representatives, ethicists, veterans advocates, and canine therapy groups claim that this was unjust discrimination against those suffering with the equally, if not more disabling, mental health conditions.⁹ For many years, the VA has done a very good deed: For those who qualify for a service dog, VA pays for veterinary care and the equipment to handle the dog, but not boarding, grooming, food, and other miscellaneous expenses.¹⁰ But until 2016, those veterans approved for service dogs in the main had sensory or physical disabilities.

A partial breakthrough emerged when the Center for Compassionate Care Innovation launched the Mental Health Mobility Service Dogs Program that expanded veterinary health benefits to veterans with a “substantial mobility limitation.” For example, veterans whose hypervigilance and hyperarousal are so severe that they cannot attend medical appointments.¹¹

VA experts argue that at this time there is insufficient evidence to fund service dogs as even adjunctive PTSD therapy for the hundreds of veterans who might potentially qualify. It becomes an ethical question of prudent stewardship of public funds and trust. There is certainly plenty of compelling anecdotal testimony that companion canines are a high-benefit, relatively low-risk form of complementary and integrated therapy for the spectrum of trauma disorders that afflict many of the men and women who served in our conflicts. Demonstrating those positive effects scientifically may be more difficult than it seems, although early evidence is promising, and the VA is intensively researching the question.¹² For some veterans and their legislators, the VA has not gone far enough, fast enough in mainstreaming therapy dogs, they are calling for VA to expand veterans’ benefits to include mental health service dogs and to define what benefits would be covered.

National K9 Veterans Day is an important step toward giving dogs of war the homage they have earned, as are increasing efforts to ensure care for military canines throughout their life cycle. But as the seventeenth century poet John Milton wrote when he reflected on his own worth despite his blindness, “Those also serve who only stand and wait.”¹³ The institutions charged to care for those the battle has most burdened are still trying to discover how to properly and proportionately revere that kind of furry valor.

National K9 Veterans Day celebrates the loyalty, bravery, and sacrifice of canine warriors. On March 13, 1942, canines officially became members of the Armed Services, with the Army’s founding of its New War Dog Program, more popularly known as the K9 Corps. The dogs underwent basic training and then entered more specialized preparation just as human soldiers did.² There had been unofficial dogs of war who served courageously and selflessly in almost all of our armed conflicts.³ Indeed, the title of this column is taken from a wonderful article of the same name narrating the heroism of dogs in the 2 world wars.⁴

The dedication of canines to those who serve is not confined to combat or even active duty. Thousands of military and veteran men and women have benefited immensely from their relationship with service and emotional support dogs.

Before I continue, let me state 2 important limitations of this column. First, I am a dog person. Of course, veterans have formed healing and caring relationships with many types of companions. Equine therapy is increasingly recognized as a powerful means of helping veterans reduce distress and find purpose.⁵ Nevertheless, for this column, I will focus exclusively on dogs. Second, there are many worthy organizations, projects, and programs that pair veterans with therapeutic dogs inside and outside the VA. I am in no way an expert and will invariably neglect many of these positive initiatives in this brief review.

The long, proud history of canines in the military and the many moving stories of men and women in and out of uniform for whom dogs have been life changing, if not life-saving, have created 2 ethical dilemmas for the VA that I examine here. Both dilemmas pivot on the terms of official recognition of service dogs, the benefits, and who can qualify for them in the VA.

Under VA regulation and VHA policy, a service companion only can be a dog that is individually trained to do work or perform tasks to assist a person with a disability; dogs whose sole function is to provide emotional support, well-being, comfort, or companionship are not considered service pets.⁶

A valid question to ask is why couldn’t the most vulnerable of these veterans, for instance those with severe mental health conditions, have service dogs that could accompany them into at least most areas of the medical center? Part of the reason is cost: Some training organizations estimate it may cost as much as $27,000 to train service dogs.⁸ Though there are many wonderful volunteer and not-for-profit organizations that train mostly shelter dogs and their veteran handlers—a double rescue—the lengthy process and expense means that many veterans wait years for a companion.

Congressional representatives, ethicists, veterans advocates, and canine therapy groups claim that this was unjust discrimination against those suffering with the equally, if not more disabling, mental health conditions.⁹ For many years, the VA has done a very good deed: For those who qualify for a service dog, VA pays for veterinary care and the equipment to handle the dog, but not boarding, grooming, food, and other miscellaneous expenses.¹⁰ But until 2016, those veterans approved for service dogs in the main had sensory or physical disabilities.

A partial breakthrough emerged when the Center for Compassionate Care Innovation launched the Mental Health Mobility Service Dogs Program that expanded veterinary health benefits to veterans with a “substantial mobility limitation.” For example, veterans whose hypervigilance and hyperarousal are so severe that they cannot attend medical appointments.¹¹

VA experts argue that at this time there is insufficient evidence to fund service dogs as even adjunctive PTSD therapy for the hundreds of veterans who might potentially qualify. It becomes an ethical question of prudent stewardship of public funds and trust. There is certainly plenty of compelling anecdotal testimony that companion canines are a high-benefit, relatively low-risk form of complementary and integrated therapy for the spectrum of trauma disorders that afflict many of the men and women who served in our conflicts. Demonstrating those positive effects scientifically may be more difficult than it seems, although early evidence is promising, and the VA is intensively researching the question.¹² For some veterans and their legislators, the VA has not gone far enough, fast enough in mainstreaming therapy dogs, they are calling for VA to expand veterans’ benefits to include mental health service dogs and to define what benefits would be covered.

National K9 Veterans Day is an important step toward giving dogs of war the homage they have earned, as are increasing efforts to ensure care for military canines throughout their life cycle. But as the seventeenth century poet John Milton wrote when he reflected on his own worth despite his blindness, “Those also serve who only stand and wait.”¹³ The institutions charged to care for those the battle has most burdened are still trying to discover how to properly and proportionately revere that kind of furry valor.

National K9 Veterans Day celebrates the loyalty, bravery, and sacrifice of canine warriors. On March 13, 1942, canines officially became members of the Armed Services, with the Army’s founding of its New War Dog Program, more popularly known as the K9 Corps. The dogs underwent basic training and then entered more specialized preparation just as human soldiers did.² There had been unofficial dogs of war who served courageously and selflessly in almost all of our armed conflicts.³ Indeed, the title of this column is taken from a wonderful article of the same name narrating the heroism of dogs in the 2 world wars.⁴

The dedication of canines to those who serve is not confined to combat or even active duty. Thousands of military and veteran men and women have benefited immensely from their relationship with service and emotional support dogs.

Before I continue, let me state 2 important limitations of this column. First, I am a dog person. Of course, veterans have formed healing and caring relationships with many types of companions. Equine therapy is increasingly recognized as a powerful means of helping veterans reduce distress and find purpose.⁵ Nevertheless, for this column, I will focus exclusively on dogs. Second, there are many worthy organizations, projects, and programs that pair veterans with therapeutic dogs inside and outside the VA. I am in no way an expert and will invariably neglect many of these positive initiatives in this brief review.

The long, proud history of canines in the military and the many moving stories of men and women in and out of uniform for whom dogs have been life changing, if not life-saving, have created 2 ethical dilemmas for the VA that I examine here. Both dilemmas pivot on the terms of official recognition of service dogs, the benefits, and who can qualify for them in the VA.

Under VA regulation and VHA policy, a service companion only can be a dog that is individually trained to do work or perform tasks to assist a person with a disability; dogs whose sole function is to provide emotional support, well-being, comfort, or companionship are not considered service pets.⁶

A valid question to ask is why couldn’t the most vulnerable of these veterans, for instance those with severe mental health conditions, have service dogs that could accompany them into at least most areas of the medical center? Part of the reason is cost: Some training organizations estimate it may cost as much as $27,000 to train service dogs.⁸ Though there are many wonderful volunteer and not-for-profit organizations that train mostly shelter dogs and their veteran handlers—a double rescue—the lengthy process and expense means that many veterans wait years for a companion.

Congressional representatives, ethicists, veterans advocates, and canine therapy groups claim that this was unjust discrimination against those suffering with the equally, if not more disabling, mental health conditions.⁹ For many years, the VA has done a very good deed: For those who qualify for a service dog, VA pays for veterinary care and the equipment to handle the dog, but not boarding, grooming, food, and other miscellaneous expenses.¹⁰ But until 2016, those veterans approved for service dogs in the main had sensory or physical disabilities.

A partial breakthrough emerged when the Center for Compassionate Care Innovation launched the Mental Health Mobility Service Dogs Program that expanded veterinary health benefits to veterans with a “substantial mobility limitation.” For example, veterans whose hypervigilance and hyperarousal are so severe that they cannot attend medical appointments.¹¹

VA experts argue that at this time there is insufficient evidence to fund service dogs as even adjunctive PTSD therapy for the hundreds of veterans who might potentially qualify. It becomes an ethical question of prudent stewardship of public funds and trust. There is certainly plenty of compelling anecdotal testimony that companion canines are a high-benefit, relatively low-risk form of complementary and integrated therapy for the spectrum of trauma disorders that afflict many of the men and women who served in our conflicts. Demonstrating those positive effects scientifically may be more difficult than it seems, although early evidence is promising, and the VA is intensively researching the question.¹² For some veterans and their legislators, the VA has not gone far enough, fast enough in mainstreaming therapy dogs, they are calling for VA to expand veterans’ benefits to include mental health service dogs and to define what benefits would be covered.

National K9 Veterans Day is an important step toward giving dogs of war the homage they have earned, as are increasing efforts to ensure care for military canines throughout their life cycle. But as the seventeenth century poet John Milton wrote when he reflected on his own worth despite his blindness, “Those also serve who only stand and wait.”¹³ The institutions charged to care for those the battle has most burdened are still trying to discover how to properly and proportionately revere that kind of furry valor.

Hospitalizations for nutritional rehabilitation of patients with restrictive eating disorders are increasing.¹ Among primary mental health admissions at free-standing children’s hospitals, eating disorders represent 5.5% of hospitalizations and are associated with the longest length of stay (LOS; mean 14.3 days) and costliest care (mean $46,130).² Admission is necessary to ensure initial weight restoration and monitoring for symptoms of refeeding syndrome, including electrolyte shifts and vital sign abnormalities.^3-5

Supervision is generally considered an essential element of caring for hospitalized patients with eating disorders, who may experience difficulty adhering to nutritional treatment, perform excessive movement or exercise, or demonstrate purging or self-harming behaviors. Supervision is presumed to prevent counterproductive behaviors, facilitating weight gain and earlier discharge to psychiatric treatment. Best practices for patient supervision to address these challenges have not been established but often include meal time or continuous one-to-one supervision by nursing assistants (NAs) or other staff.^6,7 While meal supervision has been shown to decrease medical LOS, it is costly, reduces staff availability for the care of other patient care, and can be a barrier to caring for patients with eating disorders in many institutions.⁸

Although not previously used in patients with eating disorders, centralized video monitoring (CVM) may provide an additional mode of supervision. CVM is an emerging technology consisting of real-time video streaming, without video recording, enabling tracking of patient movement, redirection of behaviors, and communication with unit nurses when necessary. CVM has been used in multiple patient safety initiatives to reduce falls, address staffing shortages, reduce costs,^9,10 supervise patients at risk for self-harm or elopement, and prevent controlled medication diversion.^10,11

We sought to pilot a novel use of CVM to replace our institution’s standard practice of continuous one-to-one nursing assistant (NA) supervision of patients admitted for medical stabilization of an eating disorder. Our objective was to evaluate the supervision cost and feasibility of CVM, using LOS and days to weight gain as balancing measures.

Setting and Participants

This retrospective cohort study included patients 12-18 years old admitted to the pediatric hospital medicine service on a general unit of an academic quaternary care children’s hospital for medical stabilization of an eating disorder between September 2013 and March 2017. Patients were identified using administrative data based on primary or secondary diagnosis of anorexia nervosa, eating disorder not other wise specified, or another specified eating disorder (ICD 9 3071, 20759, or ICD 10 f5000, 5001, f5089, f509).^12,13 This research study was considered exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Supervision Interventions

A standard medical stabilization protocol was used for patients admitted with an eating disorder throughout the study period (Appendix). All patients received continuous one-to-one NA supervision until they reached the target calorie intake and demonstrated the ability to follow the nutritional meal protocol. Beginning July 2015, patients received continuous CVM supervision unless they expressed suicidal ideation (SI), which triggered one-to-one NA supervision until they no longer endorsed suicidality.

Centralized Video Monitoring Implementation

Institutional CVM technology was AvaSys TeleSitter Solution (AvaSure, Inc). Our institution purchased CVM devices for use in adult settings, and one was assigned for pediatric CVM. Mobile CVM video carts were deployed to patient rooms and generated live video streams, without recorded capture, which were supervised by CVM technicians. These technicians were NAs hired and trained specifically for this role; worked four-, eight-, and 12-hour shifts; and observed up to eight camera feeds on a single monitor in a centralized room. Patients and family members could refuse CVM, which would trigger one-to-one NA supervision. Patients were not observed by CVM while in the restroom; staff were notified by either the patient or technician, and one-to-one supervision was provided. CVM had two-way audio communication, which allowed technicians to redirect patients verbally. Technicians could contact nursing staff directly by phone when additional intervention was needed.

Supervision Costs

NA supervision costs were estimated at $19/hour, based upon institutional human resources average NA salaries at that time. No additional mealtime supervision was included, as in-person supervision was already occurring.

CVM supervision costs were defined as the sum of the device cost plus CVM technician costs and two hours of one-to-one NA mealtime supervision per day. The CVM device cost was estimated at $2.10/hour, assuming a 10-year machine life expectancy (single unit cost $82,893 in 2015, 3,944 hours of use in fiscal year of 2018). CVM technician costs were $19/hour, based upon institutional human resources average CVM technician salaries at that time. Because technicians monitored an average of six patients simultaneously during this study, one-sixth of a CVM technician’s salary (ie, $3.17/hour) was used for each hour of CVM monitoring. Patients with mixed (NA and CVM) supervision were analyzed with those having CVM supervision. These patients’ costs were the sum of their NA supervision costs plus their CVM supervision costs.

Data Collection

Descriptive variables including age, gender, race/ethnicity, insurance, and LOS were collected from administrative data. The duration and type of supervision for all patients were collected from daily staffing logs. The eating disorder protocol standardized the process of obtaining daily weights (Appendix). Days to weight gain following admission were defined as the total number of days from admission to the first day of weight gain that was followed by another day of weight gain or maintaining the same weight. CVM acceptability and feasibility were assessed by family refusal of CVM, conversion from CVM to NA, technological failure, complaints, and unplanned discontinuation, which were prospectively documented by the unit nurse manager.

Data Analysis

Patient and hospitalization characteristics were summarized. A sample size of at least 14 in each group was estimated as necessary to detect a 50% reduction in supervision cost between the groups using alpha = 0.05, a power of 80%, a mean cost of $4,400 in the NA group, and a standard deviation of $1,600.Wilcoxon rank-sum tests were used to assess differences in median supervision cost between NA and CVM use. Differences in mean LOS and days to weight gain between NA and CVM use were assessed with t-tests because these data were normally distributed.

Patient Characteristics and Supervision Costs

The study included 37 consecutive admissions (NA = 23 and CVM = 14) with 35 unique patients. Patients were female, primarily non-Hispanic White, and privately insured (Table 1). Median supervision cost for the NA was statistically significantly more expensive at $4,104/admission versus $1,166/admission for CVM (P < .001, Table 2).

Balancing Measures, Acceptability, and Feasibility

Mean LOS was 11.7 days for NA and 9.8 days for CVM (P = .27; Table 2). The mean number of days to weight gain was 3.1 and 3.6 days, respectively (P = .28). No patients converted from CVM to NA supervision. One patient with SI converted to CVM after SI resolved and two patients required ongoing NA supervision due to continued SI. There were no reported refusals, technology failures, or unplanned discontinuations of CVM. One patient/family reported excessive CVM redirection of behavior.

This is the first description of CVM use in adolescent patients or patients with eating disorders. Our results suggest that CVM appears feasible and less costly in this population than one-to-one NA supervision, without statistically significant differences in LOS or time to weight gain. Patients with CVM with any NA supervision (except mealtime alone) were analyzed in the CVM group; therefore, this study may underestimate cost savings from CVM supervision. This innovative use of CVM may represent an opportunity for hospitals to repurpose monitoring technology for more efficient supervision of patients with eating disorders.

This pediatric pilot study adds to the growing body of literature in adult patients suggesting CVM supervision may be a feasible inpatient cost-reduction strategy.^9,10 One single-center study demonstrated that the use of CVM with adult inpatients led to fewer unsafe behaviors, eg, patient removal of intravenous catheters and oxygen therapy. Personnel savings exceeded the original investment cost of the monitor within one fiscal quarter.⁹ Results of another study suggest that CVM use with hospitalized adults who required supervision to prevent falls was associated with improved patient and family satisfaction.¹⁴ In the absence of a gold standard for supervision of patients hospitalized with eating disorders, CVM technology is a tool that may balance cost, care quality, and patient experience. Given the upfront investment in CVM units, this technology may be most appropriate for institutions already using CVM for other inpatient indications.

The results of this pilot study suggest that supervision costs for patients admitted for medical stabilization of eating disorders were statistically significantly lower with CVM when compared with one-to-one NA supervision, without a change in hospitalization LOS or time to weight gain. These findings are particularly important as hospitals seek opportunities to reduce costs while providing safe and effective care. Future efforts should focus on evaluating clinical outcomes and patient experiences with this technology and strategies to maximize efficiency to offset the initial device cost.

Disclosures

The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

Hospitalizations for nutritional rehabilitation of patients with restrictive eating disorders are increasing.¹ Among primary mental health admissions at free-standing children’s hospitals, eating disorders represent 5.5% of hospitalizations and are associated with the longest length of stay (LOS; mean 14.3 days) and costliest care (mean $46,130).² Admission is necessary to ensure initial weight restoration and monitoring for symptoms of refeeding syndrome, including electrolyte shifts and vital sign abnormalities.^3-5

Supervision is generally considered an essential element of caring for hospitalized patients with eating disorders, who may experience difficulty adhering to nutritional treatment, perform excessive movement or exercise, or demonstrate purging or self-harming behaviors. Supervision is presumed to prevent counterproductive behaviors, facilitating weight gain and earlier discharge to psychiatric treatment. Best practices for patient supervision to address these challenges have not been established but often include meal time or continuous one-to-one supervision by nursing assistants (NAs) or other staff.^6,7 While meal supervision has been shown to decrease medical LOS, it is costly, reduces staff availability for the care of other patient care, and can be a barrier to caring for patients with eating disorders in many institutions.⁸

Although not previously used in patients with eating disorders, centralized video monitoring (CVM) may provide an additional mode of supervision. CVM is an emerging technology consisting of real-time video streaming, without video recording, enabling tracking of patient movement, redirection of behaviors, and communication with unit nurses when necessary. CVM has been used in multiple patient safety initiatives to reduce falls, address staffing shortages, reduce costs,^9,10 supervise patients at risk for self-harm or elopement, and prevent controlled medication diversion.^10,11

We sought to pilot a novel use of CVM to replace our institution’s standard practice of continuous one-to-one nursing assistant (NA) supervision of patients admitted for medical stabilization of an eating disorder. Our objective was to evaluate the supervision cost and feasibility of CVM, using LOS and days to weight gain as balancing measures.

Setting and Participants

This retrospective cohort study included patients 12-18 years old admitted to the pediatric hospital medicine service on a general unit of an academic quaternary care children’s hospital for medical stabilization of an eating disorder between September 2013 and March 2017. Patients were identified using administrative data based on primary or secondary diagnosis of anorexia nervosa, eating disorder not other wise specified, or another specified eating disorder (ICD 9 3071, 20759, or ICD 10 f5000, 5001, f5089, f509).^12,13 This research study was considered exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Supervision Interventions

A standard medical stabilization protocol was used for patients admitted with an eating disorder throughout the study period (Appendix). All patients received continuous one-to-one NA supervision until they reached the target calorie intake and demonstrated the ability to follow the nutritional meal protocol. Beginning July 2015, patients received continuous CVM supervision unless they expressed suicidal ideation (SI), which triggered one-to-one NA supervision until they no longer endorsed suicidality.

Centralized Video Monitoring Implementation

Institutional CVM technology was AvaSys TeleSitter Solution (AvaSure, Inc). Our institution purchased CVM devices for use in adult settings, and one was assigned for pediatric CVM. Mobile CVM video carts were deployed to patient rooms and generated live video streams, without recorded capture, which were supervised by CVM technicians. These technicians were NAs hired and trained specifically for this role; worked four-, eight-, and 12-hour shifts; and observed up to eight camera feeds on a single monitor in a centralized room. Patients and family members could refuse CVM, which would trigger one-to-one NA supervision. Patients were not observed by CVM while in the restroom; staff were notified by either the patient or technician, and one-to-one supervision was provided. CVM had two-way audio communication, which allowed technicians to redirect patients verbally. Technicians could contact nursing staff directly by phone when additional intervention was needed.

Supervision Costs

NA supervision costs were estimated at $19/hour, based upon institutional human resources average NA salaries at that time. No additional mealtime supervision was included, as in-person supervision was already occurring.

CVM supervision costs were defined as the sum of the device cost plus CVM technician costs and two hours of one-to-one NA mealtime supervision per day. The CVM device cost was estimated at $2.10/hour, assuming a 10-year machine life expectancy (single unit cost $82,893 in 2015, 3,944 hours of use in fiscal year of 2018). CVM technician costs were $19/hour, based upon institutional human resources average CVM technician salaries at that time. Because technicians monitored an average of six patients simultaneously during this study, one-sixth of a CVM technician’s salary (ie, $3.17/hour) was used for each hour of CVM monitoring. Patients with mixed (NA and CVM) supervision were analyzed with those having CVM supervision. These patients’ costs were the sum of their NA supervision costs plus their CVM supervision costs.

Data Collection

Descriptive variables including age, gender, race/ethnicity, insurance, and LOS were collected from administrative data. The duration and type of supervision for all patients were collected from daily staffing logs. The eating disorder protocol standardized the process of obtaining daily weights (Appendix). Days to weight gain following admission were defined as the total number of days from admission to the first day of weight gain that was followed by another day of weight gain or maintaining the same weight. CVM acceptability and feasibility were assessed by family refusal of CVM, conversion from CVM to NA, technological failure, complaints, and unplanned discontinuation, which were prospectively documented by the unit nurse manager.

Data Analysis

Patient and hospitalization characteristics were summarized. A sample size of at least 14 in each group was estimated as necessary to detect a 50% reduction in supervision cost between the groups using alpha = 0.05, a power of 80%, a mean cost of $4,400 in the NA group, and a standard deviation of $1,600.Wilcoxon rank-sum tests were used to assess differences in median supervision cost between NA and CVM use. Differences in mean LOS and days to weight gain between NA and CVM use were assessed with t-tests because these data were normally distributed.

Patient Characteristics and Supervision Costs

The study included 37 consecutive admissions (NA = 23 and CVM = 14) with 35 unique patients. Patients were female, primarily non-Hispanic White, and privately insured (Table 1). Median supervision cost for the NA was statistically significantly more expensive at $4,104/admission versus $1,166/admission for CVM (P < .001, Table 2).

Balancing Measures, Acceptability, and Feasibility

Mean LOS was 11.7 days for NA and 9.8 days for CVM (P = .27; Table 2). The mean number of days to weight gain was 3.1 and 3.6 days, respectively (P = .28). No patients converted from CVM to NA supervision. One patient with SI converted to CVM after SI resolved and two patients required ongoing NA supervision due to continued SI. There were no reported refusals, technology failures, or unplanned discontinuations of CVM. One patient/family reported excessive CVM redirection of behavior.

This is the first description of CVM use in adolescent patients or patients with eating disorders. Our results suggest that CVM appears feasible and less costly in this population than one-to-one NA supervision, without statistically significant differences in LOS or time to weight gain. Patients with CVM with any NA supervision (except mealtime alone) were analyzed in the CVM group; therefore, this study may underestimate cost savings from CVM supervision. This innovative use of CVM may represent an opportunity for hospitals to repurpose monitoring technology for more efficient supervision of patients with eating disorders.

This pediatric pilot study adds to the growing body of literature in adult patients suggesting CVM supervision may be a feasible inpatient cost-reduction strategy.^9,10 One single-center study demonstrated that the use of CVM with adult inpatients led to fewer unsafe behaviors, eg, patient removal of intravenous catheters and oxygen therapy. Personnel savings exceeded the original investment cost of the monitor within one fiscal quarter.⁹ Results of another study suggest that CVM use with hospitalized adults who required supervision to prevent falls was associated with improved patient and family satisfaction.¹⁴ In the absence of a gold standard for supervision of patients hospitalized with eating disorders, CVM technology is a tool that may balance cost, care quality, and patient experience. Given the upfront investment in CVM units, this technology may be most appropriate for institutions already using CVM for other inpatient indications.

The results of this pilot study suggest that supervision costs for patients admitted for medical stabilization of eating disorders were statistically significantly lower with CVM when compared with one-to-one NA supervision, without a change in hospitalization LOS or time to weight gain. These findings are particularly important as hospitals seek opportunities to reduce costs while providing safe and effective care. Future efforts should focus on evaluating clinical outcomes and patient experiences with this technology and strategies to maximize efficiency to offset the initial device cost.

Disclosures

The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

Hospitalizations for nutritional rehabilitation of patients with restrictive eating disorders are increasing.¹ Among primary mental health admissions at free-standing children’s hospitals, eating disorders represent 5.5% of hospitalizations and are associated with the longest length of stay (LOS; mean 14.3 days) and costliest care (mean $46,130).² Admission is necessary to ensure initial weight restoration and monitoring for symptoms of refeeding syndrome, including electrolyte shifts and vital sign abnormalities.^3-5

Supervision is generally considered an essential element of caring for hospitalized patients with eating disorders, who may experience difficulty adhering to nutritional treatment, perform excessive movement or exercise, or demonstrate purging or self-harming behaviors. Supervision is presumed to prevent counterproductive behaviors, facilitating weight gain and earlier discharge to psychiatric treatment. Best practices for patient supervision to address these challenges have not been established but often include meal time or continuous one-to-one supervision by nursing assistants (NAs) or other staff.^6,7 While meal supervision has been shown to decrease medical LOS, it is costly, reduces staff availability for the care of other patient care, and can be a barrier to caring for patients with eating disorders in many institutions.⁸

Although not previously used in patients with eating disorders, centralized video monitoring (CVM) may provide an additional mode of supervision. CVM is an emerging technology consisting of real-time video streaming, without video recording, enabling tracking of patient movement, redirection of behaviors, and communication with unit nurses when necessary. CVM has been used in multiple patient safety initiatives to reduce falls, address staffing shortages, reduce costs,^9,10 supervise patients at risk for self-harm or elopement, and prevent controlled medication diversion.^10,11

We sought to pilot a novel use of CVM to replace our institution’s standard practice of continuous one-to-one nursing assistant (NA) supervision of patients admitted for medical stabilization of an eating disorder. Our objective was to evaluate the supervision cost and feasibility of CVM, using LOS and days to weight gain as balancing measures.

Setting and Participants

This retrospective cohort study included patients 12-18 years old admitted to the pediatric hospital medicine service on a general unit of an academic quaternary care children’s hospital for medical stabilization of an eating disorder between September 2013 and March 2017. Patients were identified using administrative data based on primary or secondary diagnosis of anorexia nervosa, eating disorder not other wise specified, or another specified eating disorder (ICD 9 3071, 20759, or ICD 10 f5000, 5001, f5089, f509).^12,13 This research study was considered exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Supervision Interventions

A standard medical stabilization protocol was used for patients admitted with an eating disorder throughout the study period (Appendix). All patients received continuous one-to-one NA supervision until they reached the target calorie intake and demonstrated the ability to follow the nutritional meal protocol. Beginning July 2015, patients received continuous CVM supervision unless they expressed suicidal ideation (SI), which triggered one-to-one NA supervision until they no longer endorsed suicidality.

Centralized Video Monitoring Implementation

Institutional CVM technology was AvaSys TeleSitter Solution (AvaSure, Inc). Our institution purchased CVM devices for use in adult settings, and one was assigned for pediatric CVM. Mobile CVM video carts were deployed to patient rooms and generated live video streams, without recorded capture, which were supervised by CVM technicians. These technicians were NAs hired and trained specifically for this role; worked four-, eight-, and 12-hour shifts; and observed up to eight camera feeds on a single monitor in a centralized room. Patients and family members could refuse CVM, which would trigger one-to-one NA supervision. Patients were not observed by CVM while in the restroom; staff were notified by either the patient or technician, and one-to-one supervision was provided. CVM had two-way audio communication, which allowed technicians to redirect patients verbally. Technicians could contact nursing staff directly by phone when additional intervention was needed.

Supervision Costs

NA supervision costs were estimated at $19/hour, based upon institutional human resources average NA salaries at that time. No additional mealtime supervision was included, as in-person supervision was already occurring.

CVM supervision costs were defined as the sum of the device cost plus CVM technician costs and two hours of one-to-one NA mealtime supervision per day. The CVM device cost was estimated at $2.10/hour, assuming a 10-year machine life expectancy (single unit cost $82,893 in 2015, 3,944 hours of use in fiscal year of 2018). CVM technician costs were $19/hour, based upon institutional human resources average CVM technician salaries at that time. Because technicians monitored an average of six patients simultaneously during this study, one-sixth of a CVM technician’s salary (ie, $3.17/hour) was used for each hour of CVM monitoring. Patients with mixed (NA and CVM) supervision were analyzed with those having CVM supervision. These patients’ costs were the sum of their NA supervision costs plus their CVM supervision costs.

Data Collection

Descriptive variables including age, gender, race/ethnicity, insurance, and LOS were collected from administrative data. The duration and type of supervision for all patients were collected from daily staffing logs. The eating disorder protocol standardized the process of obtaining daily weights (Appendix). Days to weight gain following admission were defined as the total number of days from admission to the first day of weight gain that was followed by another day of weight gain or maintaining the same weight. CVM acceptability and feasibility were assessed by family refusal of CVM, conversion from CVM to NA, technological failure, complaints, and unplanned discontinuation, which were prospectively documented by the unit nurse manager.

Data Analysis

Patient and hospitalization characteristics were summarized. A sample size of at least 14 in each group was estimated as necessary to detect a 50% reduction in supervision cost between the groups using alpha = 0.05, a power of 80%, a mean cost of $4,400 in the NA group, and a standard deviation of $1,600.Wilcoxon rank-sum tests were used to assess differences in median supervision cost between NA and CVM use. Differences in mean LOS and days to weight gain between NA and CVM use were assessed with t-tests because these data were normally distributed.

Patient Characteristics and Supervision Costs

The study included 37 consecutive admissions (NA = 23 and CVM = 14) with 35 unique patients. Patients were female, primarily non-Hispanic White, and privately insured (Table 1). Median supervision cost for the NA was statistically significantly more expensive at $4,104/admission versus $1,166/admission for CVM (P < .001, Table 2).

Balancing Measures, Acceptability, and Feasibility

Mean LOS was 11.7 days for NA and 9.8 days for CVM (P = .27; Table 2). The mean number of days to weight gain was 3.1 and 3.6 days, respectively (P = .28). No patients converted from CVM to NA supervision. One patient with SI converted to CVM after SI resolved and two patients required ongoing NA supervision due to continued SI. There were no reported refusals, technology failures, or unplanned discontinuations of CVM. One patient/family reported excessive CVM redirection of behavior.

This is the first description of CVM use in adolescent patients or patients with eating disorders. Our results suggest that CVM appears feasible and less costly in this population than one-to-one NA supervision, without statistically significant differences in LOS or time to weight gain. Patients with CVM with any NA supervision (except mealtime alone) were analyzed in the CVM group; therefore, this study may underestimate cost savings from CVM supervision. This innovative use of CVM may represent an opportunity for hospitals to repurpose monitoring technology for more efficient supervision of patients with eating disorders.

This pediatric pilot study adds to the growing body of literature in adult patients suggesting CVM supervision may be a feasible inpatient cost-reduction strategy.^9,10 One single-center study demonstrated that the use of CVM with adult inpatients led to fewer unsafe behaviors, eg, patient removal of intravenous catheters and oxygen therapy. Personnel savings exceeded the original investment cost of the monitor within one fiscal quarter.⁹ Results of another study suggest that CVM use with hospitalized adults who required supervision to prevent falls was associated with improved patient and family satisfaction.¹⁴ In the absence of a gold standard for supervision of patients hospitalized with eating disorders, CVM technology is a tool that may balance cost, care quality, and patient experience. Given the upfront investment in CVM units, this technology may be most appropriate for institutions already using CVM for other inpatient indications.

The results of this pilot study suggest that supervision costs for patients admitted for medical stabilization of eating disorders were statistically significantly lower with CVM when compared with one-to-one NA supervision, without a change in hospitalization LOS or time to weight gain. These findings are particularly important as hospitals seek opportunities to reduce costs while providing safe and effective care. Future efforts should focus on evaluating clinical outcomes and patient experiences with this technology and strategies to maximize efficiency to offset the initial device cost.

Disclosures

The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

The transfer of patients between acute care hospitals (interhospital transfer [IHT]) occurs regularly among patients with a variety of diagnoses, in theory, to gain access to unique specialty services and/or a higher level of care, among other reasons.^1,2

However, the practice of IHT is variable and nonstandardized,^3,4 and existing data largely suggests that transferred patients experience worse outcomes, including longer length of stay, higher hospitalization costs, longer ICU time, and greater mortality, even with rigorous adjustment for confounding by indication.^5,6 Though there are many possible reasons for these findings, existing literature suggests that there may be aspects of the transfer process itself which contribute to these outcomes.^2,6,7

Understanding which aspects of the transfer process contribute to poor patient outcomes is a key first step toward the development of targeted quality improvement initiatives to improve this process of care. In this study, we aim to examine the association between select characteristics of the transfer process, including the timing of transfer and workload of the admitting physician team, and clinical outcomes among patients undergoing IHT.

Data and Study Population

We performed a retrospective analysis of patients ≥age 18 years who transferred to Brigham and Women’s Hospital (BWH), a 777-bed tertiary care hospital, from another acute care hospital between January 2005, and September 2013. Dates of inclusion were purposefully chosen prior to BWH implementation of a new electronic health records system to avoid potential information bias. As at most academic medical centers, night coverage at BWH differs by service and includes a combination of long-call admitting teams and night float coverage. On weekends, many services are less well staffed, and some procedures may only be available if needed emergently. Some services have caps on the daily number of admissions or total patient census, but none have caps on the number of discharges per day. Patients were excluded from analysis if they left BWH against medical advice, were transferred from closely affiliated hospitals with shared personnel and electronic health records (Brigham and Women’s Faulkner Hospital, Dana Farber Cancer Institute), transferred from inpatient psychiatric or inpatient hospice facilities, or transferred to obstetrics or nursery services. Data were obtained from administrative sources and the research patient data repository (RPDR), a centralized clinical data repository that gathers data from various hospital legacy systems and stores them in one data warehouse.⁸ Our study was approved by the Partners Institutional Review Board (IRB) with a waiver of patient consent.

Transfer Process Characteristics

Predictors included select characteristics of the transfer process, including (1) Day of week of transfer, dichotomized into Friday through Sunday (“weekend”), versus Monday through Thursday (“weekday”);⁹ Friday was included with “weekend” given the suggestion of increased volume of transfers in advance of the weekend; (2) Time of arrival of the transferred patient, categorized into “daytime” (7 am-5 pm), “evening” (5 pm -10 pm), and “nighttime” (10 pm -7 am), with daytime as the reference group; (3) Admitting team “busyness” on day of patient transfer, defined as the total number of additional patient admissions and patient discharges performed by the admitting team on the calendar day of patient arrival, as has been used in prior research,¹⁰ and categorized into quartiles with lowest quartile as the reference group. Service-specific quartiles were calculated and used for stratified analyses (described below); and (4) “Time delay” between patient acceptance for transfer and patient arrival at BWH, categorized into 0-12 hours, 12-24 hours, 24-48 hours, and >48 hours, with 12-24 hours as the reference group (anticipating that time delay of 0-12 hours would be reflective of “sicker” patients in need of expedited transfer).

Outcomes

Outcomes included transfer to the intensive care unit (ICU) within 48 hours of arrival and 30-day mortality from date of index admission.^5,6

Patient Characteristics

Covariates for adjustment included: patient age, sex, race, Elixhauser comorbidity score,¹¹ Diagnosis-Related Group (DRG)-weight, insurance status, year of admission, number of preadmission medications, and service of admission.

Statistical Analyses

We used descriptive statistics to display baseline characteristics and performed a series of univariable and multivariable logistic regression models to obtain the adjusted odds of each transfer process characteristic on each outcome, adjusting for all covariates (proc logistic, SAS Statistical Software, Cary, North Carolina). For analyses of ICU transfer within 48 hours of arrival, all patients initially admitted to the ICU at time of transfer were excluded.

In the secondary analyses, we used a combined day-of-week and time-of-day variable (ie, Monday day, Monday evening, Monday night, Tuesday day, and so on, with Monday day as the reference group) to obtain a more detailed evaluation of timing of transfer on patient outcomes. We also performed stratified analyses to evaluate each transfer process characteristic on adjusted odds of 30-day mortality stratified by service of admission (ie, at the time of transfer to BWH), adjusting for all covariates. For all analyses, two-sided P values < .05 were considered significant.

Overall, 24,352 patients met our inclusion criteria and underwent IHT, of whom 2,174 (8.9%) died within 30 days. Of the 22,910 transferred patients originally admitted to a non-ICU service, 5,464 (23.8%) underwent ICU transfer within 48 hours of arrival. Cohort characteristics are shown in Table 1.

Multivariable regression analyses demonstrated no significant association between weekend (versus weekday) transfer or increased time delay between patient acceptance and arrival (>48 hours) and adjusted odds of ICU transfer within 48 hours or 30-day mortality. However, they did demonstrate that nighttime (versus daytime) transfer was associated with greater adjusted odds of both ICU transfer and 30-day mortality. Increased admitting team busyness was associated with lower adjusted odds of ICU transfer but was not significantly associated with adjusted odds of 30-day mortality (Table 2). As expected, decreased time delay between patient acceptance and arrival (0-12 hours) was associated with increased adjusted odds of both ICU transfer (adjusted OR 2.68; 95% CI 2.29, 3.15) and 30-day mortality (adjusted OR 1.25; 95% CI 1.03, 1.53) compared with 12-24 hours (results not shown). Time delay >48 hours was not associated with either outcome.

Regression analyses with the combined day/time variable demonstrated that compared with Monday daytime transfer, Sunday night transfer was significantly associated with increased adjusted odds of 30-day mortality, and Friday night transfer was associated with a trend toward increased 30-day mortality (adjusted OR [aOR] 1.88; 95% CI 1.25, 2.82, and aOR 1.43; 95% CI 0.99, 2.06, respectively). We also found that all nighttime transfers (ie, Monday through Sunday night) were associated with increased adjusted odds of ICU transfer within 48 hours (as compared with Monday daytime transfer). Other days/time analyses were not significant.

Univariable and multivariable analyses stratified by service were performed (Appendix). Multivariable stratified analyses demonstrated that weekend transfer, nighttime transfer, and increased admitting team busyness were associated with increased adjusted odds of 30-day mortality among cardiothoracic (CT) and gastrointestinal (GI) surgical service patients. Increased admitting team busyness was also associated with increased mortality among ICU service patients but was associated with decreased mortality among cardiology service patients. An increased time delay between patient acceptance and arrival was associated with decreased mortality among CT and GI surgical service patients (Figure; Appendix). Other adjusted stratified outcomes were not significant.

In this study of 24,352 patients undergoing IHT, we found no significant association between weekend transfer or increased time delay between transfer acceptance and arrival and patient outcomes in the cohort as a whole; but we found that nighttime transfer is associated with increased adjusted odds of both ICU transfer within 48 hours and 30-day mortality. Our analyses combining day-of-week and time-of-day demonstrate that Sunday night transfer is particularly associated with increased adjusted odds of 30-day mortality (as compared with Monday daytime transfer), and show a trend toward increased mortality with Friday night transfers. These detailed analyses otherwise reinforce that nighttime transfer across all nights of the week is associated with increased adjusted odds of ICU transfer within 48 hours. We also found that increased admitting team busyness on the day of patient transfer is associated with decreased odds of ICU transfer, though this may solely be reflective of higher turnover services (ie, cardiology) caring for lower acuity patients, as suggested by secondary analyses stratified by service. In addition, secondary analyses demonstrated differential associations between weekend transfers, nighttime transfers, and increased team busyness on the odds of 30-day mortality based on service of transfer. These analyses showed that patients transferred to higher acuity services requiring procedural care, including CT surgery, GI surgery, and Medical ICU, do worse under all three circumstances as compared with patients transferred to other services. Secondary analyses also demonstrated that increased time delay between patient acceptance and arrival is inversely associated with 30-day mortality among CT and GI surgery service patients, likely reflecting lower acuity patients (ie, less sick patients are less rapidly transferred).

There are several possible explanations for these findings. Patients transferred to surgical services at night may reflect a more urgent need for surgery and include a sicker cohort of patients, possibly explaining these findings. Alternatively, or in addition, both weekend and nighttime hospital admission expose patients to similar potential risks, ie, limited resources available during off-peak hours. Our findings could, therefore, reflect the possibility that patients transferred to higher acuity services in need of procedural care are most vulnerable to off-peak timing of transfer. Similar data looking at patients admitted through the emergency room (ER) find the strongest effect of off-peak admissions on patients in need of procedures, including GI hemorrhage,¹² atrial fibrillation¹³ and acute myocardial infarction (AMI),¹⁴ arguably because of the limited availability of necessary interventions. Patients undergoing IHT are a sicker cohort of patients than those admitted through the ER, and, therefore, may be even more vulnerable to these issues.^3,5 This is supported by our findings that Sunday night transfers (and trend toward Friday night transfers) are associated with greater mortality compared with Monday daytime transfers, when at-the-ready resources and/or specialty personnel may be less available (Sunday night), and delays until receipt of necessary procedures may be longer (Friday night). Though we did not observe similar results among cardiology service transfers, as may be expected based on existing literature,^13,14 this subset of patients includes more heterogeneous diagnoses, (ie, not solely those that require acute intervention) and exhibited a low level of acuity (low Elixhauser score and DRG-weight, data not shown).

Disclosures

Dr. Mueller and Dr. Schnipper have nothing to disclose. Ms. Fiskio has nothing to disclose. Dr. Schnipper is the recipient of grant funding from Mallinckrodt Pharmaceuticals to conduct an investigator-initiated study of predictors and impact of opioid-related adverse drug events.

The transfer of patients between acute care hospitals (interhospital transfer [IHT]) occurs regularly among patients with a variety of diagnoses, in theory, to gain access to unique specialty services and/or a higher level of care, among other reasons.^1,2

However, the practice of IHT is variable and nonstandardized,^3,4 and existing data largely suggests that transferred patients experience worse outcomes, including longer length of stay, higher hospitalization costs, longer ICU time, and greater mortality, even with rigorous adjustment for confounding by indication.^5,6 Though there are many possible reasons for these findings, existing literature suggests that there may be aspects of the transfer process itself which contribute to these outcomes.^2,6,7

Understanding which aspects of the transfer process contribute to poor patient outcomes is a key first step toward the development of targeted quality improvement initiatives to improve this process of care. In this study, we aim to examine the association between select characteristics of the transfer process, including the timing of transfer and workload of the admitting physician team, and clinical outcomes among patients undergoing IHT.

Data and Study Population

We performed a retrospective analysis of patients ≥age 18 years who transferred to Brigham and Women’s Hospital (BWH), a 777-bed tertiary care hospital, from another acute care hospital between January 2005, and September 2013. Dates of inclusion were purposefully chosen prior to BWH implementation of a new electronic health records system to avoid potential information bias. As at most academic medical centers, night coverage at BWH differs by service and includes a combination of long-call admitting teams and night float coverage. On weekends, many services are less well staffed, and some procedures may only be available if needed emergently. Some services have caps on the daily number of admissions or total patient census, but none have caps on the number of discharges per day. Patients were excluded from analysis if they left BWH against medical advice, were transferred from closely affiliated hospitals with shared personnel and electronic health records (Brigham and Women’s Faulkner Hospital, Dana Farber Cancer Institute), transferred from inpatient psychiatric or inpatient hospice facilities, or transferred to obstetrics or nursery services. Data were obtained from administrative sources and the research patient data repository (RPDR), a centralized clinical data repository that gathers data from various hospital legacy systems and stores them in one data warehouse.⁸ Our study was approved by the Partners Institutional Review Board (IRB) with a waiver of patient consent.

Transfer Process Characteristics

Predictors included select characteristics of the transfer process, including (1) Day of week of transfer, dichotomized into Friday through Sunday (“weekend”), versus Monday through Thursday (“weekday”);⁹ Friday was included with “weekend” given the suggestion of increased volume of transfers in advance of the weekend; (2) Time of arrival of the transferred patient, categorized into “daytime” (7 am-5 pm), “evening” (5 pm -10 pm), and “nighttime” (10 pm -7 am), with daytime as the reference group; (3) Admitting team “busyness” on day of patient transfer, defined as the total number of additional patient admissions and patient discharges performed by the admitting team on the calendar day of patient arrival, as has been used in prior research,¹⁰ and categorized into quartiles with lowest quartile as the reference group. Service-specific quartiles were calculated and used for stratified analyses (described below); and (4) “Time delay” between patient acceptance for transfer and patient arrival at BWH, categorized into 0-12 hours, 12-24 hours, 24-48 hours, and >48 hours, with 12-24 hours as the reference group (anticipating that time delay of 0-12 hours would be reflective of “sicker” patients in need of expedited transfer).

Outcomes

Outcomes included transfer to the intensive care unit (ICU) within 48 hours of arrival and 30-day mortality from date of index admission.^5,6

Patient Characteristics

Covariates for adjustment included: patient age, sex, race, Elixhauser comorbidity score,¹¹ Diagnosis-Related Group (DRG)-weight, insurance status, year of admission, number of preadmission medications, and service of admission.

Statistical Analyses

We used descriptive statistics to display baseline characteristics and performed a series of univariable and multivariable logistic regression models to obtain the adjusted odds of each transfer process characteristic on each outcome, adjusting for all covariates (proc logistic, SAS Statistical Software, Cary, North Carolina). For analyses of ICU transfer within 48 hours of arrival, all patients initially admitted to the ICU at time of transfer were excluded.

In the secondary analyses, we used a combined day-of-week and time-of-day variable (ie, Monday day, Monday evening, Monday night, Tuesday day, and so on, with Monday day as the reference group) to obtain a more detailed evaluation of timing of transfer on patient outcomes. We also performed stratified analyses to evaluate each transfer process characteristic on adjusted odds of 30-day mortality stratified by service of admission (ie, at the time of transfer to BWH), adjusting for all covariates. For all analyses, two-sided P values < .05 were considered significant.

Overall, 24,352 patients met our inclusion criteria and underwent IHT, of whom 2,174 (8.9%) died within 30 days. Of the 22,910 transferred patients originally admitted to a non-ICU service, 5,464 (23.8%) underwent ICU transfer within 48 hours of arrival. Cohort characteristics are shown in Table 1.

Multivariable regression analyses demonstrated no significant association between weekend (versus weekday) transfer or increased time delay between patient acceptance and arrival (>48 hours) and adjusted odds of ICU transfer within 48 hours or 30-day mortality. However, they did demonstrate that nighttime (versus daytime) transfer was associated with greater adjusted odds of both ICU transfer and 30-day mortality. Increased admitting team busyness was associated with lower adjusted odds of ICU transfer but was not significantly associated with adjusted odds of 30-day mortality (Table 2). As expected, decreased time delay between patient acceptance and arrival (0-12 hours) was associated with increased adjusted odds of both ICU transfer (adjusted OR 2.68; 95% CI 2.29, 3.15) and 30-day mortality (adjusted OR 1.25; 95% CI 1.03, 1.53) compared with 12-24 hours (results not shown). Time delay >48 hours was not associated with either outcome.

Regression analyses with the combined day/time variable demonstrated that compared with Monday daytime transfer, Sunday night transfer was significantly associated with increased adjusted odds of 30-day mortality, and Friday night transfer was associated with a trend toward increased 30-day mortality (adjusted OR [aOR] 1.88; 95% CI 1.25, 2.82, and aOR 1.43; 95% CI 0.99, 2.06, respectively). We also found that all nighttime transfers (ie, Monday through Sunday night) were associated with increased adjusted odds of ICU transfer within 48 hours (as compared with Monday daytime transfer). Other days/time analyses were not significant.

Univariable and multivariable analyses stratified by service were performed (Appendix). Multivariable stratified analyses demonstrated that weekend transfer, nighttime transfer, and increased admitting team busyness were associated with increased adjusted odds of 30-day mortality among cardiothoracic (CT) and gastrointestinal (GI) surgical service patients. Increased admitting team busyness was also associated with increased mortality among ICU service patients but was associated with decreased mortality among cardiology service patients. An increased time delay between patient acceptance and arrival was associated with decreased mortality among CT and GI surgical service patients (Figure; Appendix). Other adjusted stratified outcomes were not significant.

In this study of 24,352 patients undergoing IHT, we found no significant association between weekend transfer or increased time delay between transfer acceptance and arrival and patient outcomes in the cohort as a whole; but we found that nighttime transfer is associated with increased adjusted odds of both ICU transfer within 48 hours and 30-day mortality. Our analyses combining day-of-week and time-of-day demonstrate that Sunday night transfer is particularly associated with increased adjusted odds of 30-day mortality (as compared with Monday daytime transfer), and show a trend toward increased mortality with Friday night transfers. These detailed analyses otherwise reinforce that nighttime transfer across all nights of the week is associated with increased adjusted odds of ICU transfer within 48 hours. We also found that increased admitting team busyness on the day of patient transfer is associated with decreased odds of ICU transfer, though this may solely be reflective of higher turnover services (ie, cardiology) caring for lower acuity patients, as suggested by secondary analyses stratified by service. In addition, secondary analyses demonstrated differential associations between weekend transfers, nighttime transfers, and increased team busyness on the odds of 30-day mortality based on service of transfer. These analyses showed that patients transferred to higher acuity services requiring procedural care, including CT surgery, GI surgery, and Medical ICU, do worse under all three circumstances as compared with patients transferred to other services. Secondary analyses also demonstrated that increased time delay between patient acceptance and arrival is inversely associated with 30-day mortality among CT and GI surgery service patients, likely reflecting lower acuity patients (ie, less sick patients are less rapidly transferred).

There are several possible explanations for these findings. Patients transferred to surgical services at night may reflect a more urgent need for surgery and include a sicker cohort of patients, possibly explaining these findings. Alternatively, or in addition, both weekend and nighttime hospital admission expose patients to similar potential risks, ie, limited resources available during off-peak hours. Our findings could, therefore, reflect the possibility that patients transferred to higher acuity services in need of procedural care are most vulnerable to off-peak timing of transfer. Similar data looking at patients admitted through the emergency room (ER) find the strongest effect of off-peak admissions on patients in need of procedures, including GI hemorrhage,¹² atrial fibrillation¹³ and acute myocardial infarction (AMI),¹⁴ arguably because of the limited availability of necessary interventions. Patients undergoing IHT are a sicker cohort of patients than those admitted through the ER, and, therefore, may be even more vulnerable to these issues.^3,5 This is supported by our findings that Sunday night transfers (and trend toward Friday night transfers) are associated with greater mortality compared with Monday daytime transfers, when at-the-ready resources and/or specialty personnel may be less available (Sunday night), and delays until receipt of necessary procedures may be longer (Friday night). Though we did not observe similar results among cardiology service transfers, as may be expected based on existing literature,^13,14 this subset of patients includes more heterogeneous diagnoses, (ie, not solely those that require acute intervention) and exhibited a low level of acuity (low Elixhauser score and DRG-weight, data not shown).

Disclosures

Dr. Mueller and Dr. Schnipper have nothing to disclose. Ms. Fiskio has nothing to disclose. Dr. Schnipper is the recipient of grant funding from Mallinckrodt Pharmaceuticals to conduct an investigator-initiated study of predictors and impact of opioid-related adverse drug events.

The transfer of patients between acute care hospitals (interhospital transfer [IHT]) occurs regularly among patients with a variety of diagnoses, in theory, to gain access to unique specialty services and/or a higher level of care, among other reasons.^1,2

However, the practice of IHT is variable and nonstandardized,^3,4 and existing data largely suggests that transferred patients experience worse outcomes, including longer length of stay, higher hospitalization costs, longer ICU time, and greater mortality, even with rigorous adjustment for confounding by indication.^5,6 Though there are many possible reasons for these findings, existing literature suggests that there may be aspects of the transfer process itself which contribute to these outcomes.^2,6,7

Understanding which aspects of the transfer process contribute to poor patient outcomes is a key first step toward the development of targeted quality improvement initiatives to improve this process of care. In this study, we aim to examine the association between select characteristics of the transfer process, including the timing of transfer and workload of the admitting physician team, and clinical outcomes among patients undergoing IHT.

Data and Study Population

We performed a retrospective analysis of patients ≥age 18 years who transferred to Brigham and Women’s Hospital (BWH), a 777-bed tertiary care hospital, from another acute care hospital between January 2005, and September 2013. Dates of inclusion were purposefully chosen prior to BWH implementation of a new electronic health records system to avoid potential information bias. As at most academic medical centers, night coverage at BWH differs by service and includes a combination of long-call admitting teams and night float coverage. On weekends, many services are less well staffed, and some procedures may only be available if needed emergently. Some services have caps on the daily number of admissions or total patient census, but none have caps on the number of discharges per day. Patients were excluded from analysis if they left BWH against medical advice, were transferred from closely affiliated hospitals with shared personnel and electronic health records (Brigham and Women’s Faulkner Hospital, Dana Farber Cancer Institute), transferred from inpatient psychiatric or inpatient hospice facilities, or transferred to obstetrics or nursery services. Data were obtained from administrative sources and the research patient data repository (RPDR), a centralized clinical data repository that gathers data from various hospital legacy systems and stores them in one data warehouse.⁸ Our study was approved by the Partners Institutional Review Board (IRB) with a waiver of patient consent.

Transfer Process Characteristics

Predictors included select characteristics of the transfer process, including (1) Day of week of transfer, dichotomized into Friday through Sunday (“weekend”), versus Monday through Thursday (“weekday”);⁹ Friday was included with “weekend” given the suggestion of increased volume of transfers in advance of the weekend; (2) Time of arrival of the transferred patient, categorized into “daytime” (7 am-5 pm), “evening” (5 pm -10 pm), and “nighttime” (10 pm -7 am), with daytime as the reference group; (3) Admitting team “busyness” on day of patient transfer, defined as the total number of additional patient admissions and patient discharges performed by the admitting team on the calendar day of patient arrival, as has been used in prior research,¹⁰ and categorized into quartiles with lowest quartile as the reference group. Service-specific quartiles were calculated and used for stratified analyses (described below); and (4) “Time delay” between patient acceptance for transfer and patient arrival at BWH, categorized into 0-12 hours, 12-24 hours, 24-48 hours, and >48 hours, with 12-24 hours as the reference group (anticipating that time delay of 0-12 hours would be reflective of “sicker” patients in need of expedited transfer).

Outcomes

Outcomes included transfer to the intensive care unit (ICU) within 48 hours of arrival and 30-day mortality from date of index admission.^5,6

Patient Characteristics

Covariates for adjustment included: patient age, sex, race, Elixhauser comorbidity score,¹¹ Diagnosis-Related Group (DRG)-weight, insurance status, year of admission, number of preadmission medications, and service of admission.

Statistical Analyses

We used descriptive statistics to display baseline characteristics and performed a series of univariable and multivariable logistic regression models to obtain the adjusted odds of each transfer process characteristic on each outcome, adjusting for all covariates (proc logistic, SAS Statistical Software, Cary, North Carolina). For analyses of ICU transfer within 48 hours of arrival, all patients initially admitted to the ICU at time of transfer were excluded.

In the secondary analyses, we used a combined day-of-week and time-of-day variable (ie, Monday day, Monday evening, Monday night, Tuesday day, and so on, with Monday day as the reference group) to obtain a more detailed evaluation of timing of transfer on patient outcomes. We also performed stratified analyses to evaluate each transfer process characteristic on adjusted odds of 30-day mortality stratified by service of admission (ie, at the time of transfer to BWH), adjusting for all covariates. For all analyses, two-sided P values < .05 were considered significant.

Overall, 24,352 patients met our inclusion criteria and underwent IHT, of whom 2,174 (8.9%) died within 30 days. Of the 22,910 transferred patients originally admitted to a non-ICU service, 5,464 (23.8%) underwent ICU transfer within 48 hours of arrival. Cohort characteristics are shown in Table 1.

Multivariable regression analyses demonstrated no significant association between weekend (versus weekday) transfer or increased time delay between patient acceptance and arrival (>48 hours) and adjusted odds of ICU transfer within 48 hours or 30-day mortality. However, they did demonstrate that nighttime (versus daytime) transfer was associated with greater adjusted odds of both ICU transfer and 30-day mortality. Increased admitting team busyness was associated with lower adjusted odds of ICU transfer but was not significantly associated with adjusted odds of 30-day mortality (Table 2). As expected, decreased time delay between patient acceptance and arrival (0-12 hours) was associated with increased adjusted odds of both ICU transfer (adjusted OR 2.68; 95% CI 2.29, 3.15) and 30-day mortality (adjusted OR 1.25; 95% CI 1.03, 1.53) compared with 12-24 hours (results not shown). Time delay >48 hours was not associated with either outcome.

Regression analyses with the combined day/time variable demonstrated that compared with Monday daytime transfer, Sunday night transfer was significantly associated with increased adjusted odds of 30-day mortality, and Friday night transfer was associated with a trend toward increased 30-day mortality (adjusted OR [aOR] 1.88; 95% CI 1.25, 2.82, and aOR 1.43; 95% CI 0.99, 2.06, respectively). We also found that all nighttime transfers (ie, Monday through Sunday night) were associated with increased adjusted odds of ICU transfer within 48 hours (as compared with Monday daytime transfer). Other days/time analyses were not significant.

Univariable and multivariable analyses stratified by service were performed (Appendix). Multivariable stratified analyses demonstrated that weekend transfer, nighttime transfer, and increased admitting team busyness were associated with increased adjusted odds of 30-day mortality among cardiothoracic (CT) and gastrointestinal (GI) surgical service patients. Increased admitting team busyness was also associated with increased mortality among ICU service patients but was associated with decreased mortality among cardiology service patients. An increased time delay between patient acceptance and arrival was associated with decreased mortality among CT and GI surgical service patients (Figure; Appendix). Other adjusted stratified outcomes were not significant.

In this study of 24,352 patients undergoing IHT, we found no significant association between weekend transfer or increased time delay between transfer acceptance and arrival and patient outcomes in the cohort as a whole; but we found that nighttime transfer is associated with increased adjusted odds of both ICU transfer within 48 hours and 30-day mortality. Our analyses combining day-of-week and time-of-day demonstrate that Sunday night transfer is particularly associated with increased adjusted odds of 30-day mortality (as compared with Monday daytime transfer), and show a trend toward increased mortality with Friday night transfers. These detailed analyses otherwise reinforce that nighttime transfer across all nights of the week is associated with increased adjusted odds of ICU transfer within 48 hours. We also found that increased admitting team busyness on the day of patient transfer is associated with decreased odds of ICU transfer, though this may solely be reflective of higher turnover services (ie, cardiology) caring for lower acuity patients, as suggested by secondary analyses stratified by service. In addition, secondary analyses demonstrated differential associations between weekend transfers, nighttime transfers, and increased team busyness on the odds of 30-day mortality based on service of transfer. These analyses showed that patients transferred to higher acuity services requiring procedural care, including CT surgery, GI surgery, and Medical ICU, do worse under all three circumstances as compared with patients transferred to other services. Secondary analyses also demonstrated that increased time delay between patient acceptance and arrival is inversely associated with 30-day mortality among CT and GI surgery service patients, likely reflecting lower acuity patients (ie, less sick patients are less rapidly transferred).

There are several possible explanations for these findings. Patients transferred to surgical services at night may reflect a more urgent need for surgery and include a sicker cohort of patients, possibly explaining these findings. Alternatively, or in addition, both weekend and nighttime hospital admission expose patients to similar potential risks, ie, limited resources available during off-peak hours. Our findings could, therefore, reflect the possibility that patients transferred to higher acuity services in need of procedural care are most vulnerable to off-peak timing of transfer. Similar data looking at patients admitted through the emergency room (ER) find the strongest effect of off-peak admissions on patients in need of procedures, including GI hemorrhage,¹² atrial fibrillation¹³ and acute myocardial infarction (AMI),¹⁴ arguably because of the limited availability of necessary interventions. Patients undergoing IHT are a sicker cohort of patients than those admitted through the ER, and, therefore, may be even more vulnerable to these issues.^3,5 This is supported by our findings that Sunday night transfers (and trend toward Friday night transfers) are associated with greater mortality compared with Monday daytime transfers, when at-the-ready resources and/or specialty personnel may be less available (Sunday night), and delays until receipt of necessary procedures may be longer (Friday night). Though we did not observe similar results among cardiology service transfers, as may be expected based on existing literature,^13,14 this subset of patients includes more heterogeneous diagnoses, (ie, not solely those that require acute intervention) and exhibited a low level of acuity (low Elixhauser score and DRG-weight, data not shown).

Disclosures

Dr. Mueller and Dr. Schnipper have nothing to disclose. Ms. Fiskio has nothing to disclose. Dr. Schnipper is the recipient of grant funding from Mallinckrodt Pharmaceuticals to conduct an investigator-initiated study of predictors and impact of opioid-related adverse drug events.

Many studies have shown that improved control can be achieved for most children with asthma if inhaled medications are taken correctly and adequately.^1-3 Drug delivery studies have shown that bioavailability of medication with a pressurized metered-dose inhaler (MDI) improves from 34% to 83% with the addition of spacer devices. This difference is largely due to the decrease in oropharyngeal deposition,^1,4,5 and therefore, the use of a spacer with proper technique has been recommended in all pediatric patients.^1,6

Poor inhaler technique is common among children.^1,7 Previous studies of children with asthma have evaluated inhaler technique, primarily in the outpatient and community settings, and reported variable rates of error (from 45% to >90%).^8,9 No studies have evaluated children hospitalized with asthma. As these children represent a particularly high-risk group for morbidity and mortality,^10,11 the objectives of this study were to assess errors in inhaler technique in hospitalized asthmatic children and identify risk factors for improper use.

As part of a larger interventional study, we conducted a prospective cross-sectional study at a tertiary urban children’s hospital. We enrolled a convenience sample of children aged 2-16 years admitted to the inpatient ward with an asthma exacerbation Monday-Friday from 8 AM to 6 PM. Participants were required to have a diagnosis of asthma (an established diagnosis by their primary care provider or meets the National Heart, Lung, and Blood Institute [NHLBI] criteria¹), have a consenting adult available, and speak English. Patients were excluded if they had a codiagnosis of an additional respiratory disease (ie, pneumonia), cardiac disease, or sickle cell anemia. The Institutional Review Board approved this study.

We asked caregivers, or children >10 years old if they independently use their inhaler, to demonstrate their typical home inhaler technique using a spacer with mask (SM), spacer with mouthpiece (SMP), or no spacer (per their usual home practice). Inhaler technique was scored using a previously validated asthma checklist (Table 1).¹² Certain steps in the checklist were identified as critical: (Step 1) removing the cap, (Step 3) attaching to a spacer, (Step 7) taking six breaths (SM), and (Step 9) holding breath for five seconds (SMP). Caregivers only were also asked to complete questionnaires assessing their literacy (Brief Health Literacy Screen [BHLS]), confidence (Parent Asthma Management Self-Efficacy scale [PAMSE]), and any barriers to managing their child’s asthma (Barriers to Asthma Care). Demographic and medical history information was extracted from the medical chart.

Participants

From October 2016 to June 2017, 380 participants were assessed for participation; 215 were excluded for not having a parent available (59%), not speaking English (27%), not having an asthma diagnosis (ie, viral wheezing; 14%), and 52 (14%) declined to participate. Therefore, a total of 113 participants were enrolled, with demonstrations provided by 100 caregivers and 13 children. The mean age of the patients overall was 6.6 ± 3.4 years and over half (55%) of the participants had uncontrolled asthma (NHLBI criteria¹).

Errors in Inhaler Technique

The mean asthma checklist score was 6.7 (maximum score of 10 for SM and 12 for SMP). A third (35%) scored <7 on the asthma checklist and 42% of participants missed at least one critical step. Overall, children who missed a critical step were significantly older (7.8 [6.7-8.9] vs 5.8 [5.1-6.5] years; P = .002). More participants missed a critical step with the SMP than the SM (75% [51%-90%] vs 36% [27%-46%]; P = .003), and this was the most prominent factor for missing a critical step in the adjusted regression analysis (OR 6.95 [1.71-28.23], P = .007). The most commonly missed steps were breathing normally for 30 seconds for SM, and for SMP, it was breathing out fully and breathing away from the spacer (Table 1). Twenty participants (18%) did not use a spacer device; these patients were older than those who did use a spacer (mean age 8.5 [6.7-10.4] vs 6.2 [5.6-6.9] years; P = .005); however, no other significant differences were identified.

Demographic, Medical History, and Socioeconomic Characteristics

Overall, race, ethnicity, and insurance status did not vary significantly based on asthma checklist score ≥7 or missing a critical step. Patients in the SM group who had received inpatient asthma education during a previous admission, had a history of pediatric intensive care unit (PICU) admission, and had been prescribed a daily controller were less likely to miss a critical step (Table 2). Parental education level varied, with 33% having a high school degree or less, but was not associated with asthma checklist score or missing critical steps. Parental BHLS and parental confidence (PAMSE) were not significantly associated with inhaler proficiency. However, transportation-related barriers were more common in patients with checklist scores <7 and more missed critical steps (OR 1.62 [1.06-2.46]; P = .02).

Nearly half of the participants in this study missed at least one critical step in inhaler use. In addition, 18% did not use a spacer when demonstrating their inhaler technique. Despite robust studies demonstrating how asthma education can improve both asthma skills and clinical outcomes,¹³ our study demonstrates that a large gap remains in proper inhaler technique among asthmatic patients presenting for inpatient care. Specifically, in the mouthpiece group, steps related to breathing technique were the most commonly missed. Our results also show that inhaler technique errors were most prominent in the adolescent population, possibly coinciding with the process of transitioning to a mouthpiece and more independence in medication administration. Adolescents may be a high-impact population on which to focus inpatient asthma education. Additionally, we found that a previous PICU admission and previous inpatient asthma education were associated with missing fewer critical steps in inhaler technique. This finding is consistent with those of another study that evaluated inhaler technique in the emergency department and found that previous hospitalization for asthma was inversely related to improper inhaler use (RR 0.55, 95% CI 0.36-0.84).¹⁴ This supports that when provided, inpatient education can increase inhaler administration skills.

Previous studies conducted in the outpatient setting have demonstrated variable rates of inhaler skill, from 0% to approximately 89% of children performing all steps of inhalation correctly.⁸ This wide range may be related to variations in the number and definition of critical steps between the different studies. In our study, we highlighted removing the cap, attaching a spacer, and adequate breathing technique as critical steps, because failure to complete them would significantly reduce lung deposition of medication. While past studies did evaluate both MDIs and discuss the devices, our study is the first to report difference in problems with technique between SM and SMP. As asthma educational interventions are developed and/or implemented, it is important to stress that different steps in inhaler technique are being missed in those using a mask versus mouthpiece.

The limitations of this study include that it was at a single center with a primarily urban and English-speaking population; however, this study population reflects the racial diversity of pediatric asthma patients. Further studies may explore the reproducibility of these findings at multiple centers and with non-English-speaking families. This study included younger patients than in some previous publications investigating asthma; however, all patients met the criteria for asthma diagnosis and this age range is reflective of patients presenting for inpatient asthma care. Furthermore, because of our daytime research hours, 59% of patients were excluded because a primary caregiver was not available. It is possible that these families have decreased access to inpatient asthma educators as well and may be another target group for future studies. Finally, a large proportion of parents had a college education or greater in our sample. However, there was no association within our analysis between parental education level and inhaler proficiency.

The findings from this study indicate that continued efforts are needed to establish that inhaler technique is adequate for all families regardless of their educational status or socioeconomic background, especially for adolescents and in the setting of poor asthma control. Furthermore, our findings support that inhaler technique education may be beneficial in the inpatient setting and that acute care settings can provide a valuable “teachable moment.”^14,15

CONCLUSION

Errors in inhaler technique are prevalent in pediatric inpatients with asthma, primarily those using a mouthpiece device. Educational efforts in both inpatient and outpatient settings have the potential to improve drug delivery and therefore asthma control. Inpatient hospitalization may serve as a platform for further studies to investigate innovative educational interventions.

Acknowledgments

The authors thank Tina Carter for her assistance in the recruitment and data collection and Ashley Hull and Susannah Butters for training the study staff on the use of the asthma checklist.

Disclosures

Dr. Gupta receives research grant support from the National Institutes of Health and the United Healthcare Group. Dr. Gupta serves as a consultant for DBV Technology, Aimmune Therapeutics, Kaleo & BEFORE Brands. Dr. Gupta has received lecture fees/honorariums from the Allergy Asthma Network & the American College of Asthma, Allergy & Immunology. Dr. Press reports research support from the Chicago Center for Diabetes Translation Research Pilot and Feasibility Grant, the Bucksbaum Institute for Clinical Excellence Pilot Grant Program, the Academy of Distinguished Medical Educators, the Development of Novel Hospital-initiated Care Bundle in Adults Hospitalized for Acute Asthma: the 41st Multicenter Airway Research Collaboration (MARC-41) Study, UCM’s Innovation Grant Program, the University of Chicago-Chapin Hall Join Research Fund, the NIH/NHLBI Loan Repayment Program, 1 K23 HL118151 01, NIH NLBHI R03 (RFA-HL-18-025), the George and Carol Abramson Pilot Awards, the COPD Foundation Green Shoots Grant, the University of Chicago Women’s Board Grant, NIH NHLBI UG1 (RFA-HL-17-009), and the CTSA Pilot Award, outside the submitted work. These disclosures have been reported to Dr. Press’ institutional IRB board. Additionally, a management plan is on file that details how to address conflicts such as these which are sources of research support but do not directly support the work at hand. The remaining authors have no conflicts of interest relevant to the article to disclose.

Funding

This study was funded by internal grants from Ann and Robert H. Lurie Children’s Hospital of Chicago. Dr. Press was funded by a K23HL118151.

Many studies have shown that improved control can be achieved for most children with asthma if inhaled medications are taken correctly and adequately.^1-3 Drug delivery studies have shown that bioavailability of medication with a pressurized metered-dose inhaler (MDI) improves from 34% to 83% with the addition of spacer devices. This difference is largely due to the decrease in oropharyngeal deposition,^1,4,5 and therefore, the use of a spacer with proper technique has been recommended in all pediatric patients.^1,6

Poor inhaler technique is common among children.^1,7 Previous studies of children with asthma have evaluated inhaler technique, primarily in the outpatient and community settings, and reported variable rates of error (from 45% to >90%).^8,9 No studies have evaluated children hospitalized with asthma. As these children represent a particularly high-risk group for morbidity and mortality,^10,11 the objectives of this study were to assess errors in inhaler technique in hospitalized asthmatic children and identify risk factors for improper use.

As part of a larger interventional study, we conducted a prospective cross-sectional study at a tertiary urban children’s hospital. We enrolled a convenience sample of children aged 2-16 years admitted to the inpatient ward with an asthma exacerbation Monday-Friday from 8 AM to 6 PM. Participants were required to have a diagnosis of asthma (an established diagnosis by their primary care provider or meets the National Heart, Lung, and Blood Institute [NHLBI] criteria¹), have a consenting adult available, and speak English. Patients were excluded if they had a codiagnosis of an additional respiratory disease (ie, pneumonia), cardiac disease, or sickle cell anemia. The Institutional Review Board approved this study.

We asked caregivers, or children >10 years old if they independently use their inhaler, to demonstrate their typical home inhaler technique using a spacer with mask (SM), spacer with mouthpiece (SMP), or no spacer (per their usual home practice). Inhaler technique was scored using a previously validated asthma checklist (Table 1).¹² Certain steps in the checklist were identified as critical: (Step 1) removing the cap, (Step 3) attaching to a spacer, (Step 7) taking six breaths (SM), and (Step 9) holding breath for five seconds (SMP). Caregivers only were also asked to complete questionnaires assessing their literacy (Brief Health Literacy Screen [BHLS]), confidence (Parent Asthma Management Self-Efficacy scale [PAMSE]), and any barriers to managing their child’s asthma (Barriers to Asthma Care). Demographic and medical history information was extracted from the medical chart.

Participants

From October 2016 to June 2017, 380 participants were assessed for participation; 215 were excluded for not having a parent available (59%), not speaking English (27%), not having an asthma diagnosis (ie, viral wheezing; 14%), and 52 (14%) declined to participate. Therefore, a total of 113 participants were enrolled, with demonstrations provided by 100 caregivers and 13 children. The mean age of the patients overall was 6.6 ± 3.4 years and over half (55%) of the participants had uncontrolled asthma (NHLBI criteria¹).

Errors in Inhaler Technique

The mean asthma checklist score was 6.7 (maximum score of 10 for SM and 12 for SMP). A third (35%) scored <7 on the asthma checklist and 42% of participants missed at least one critical step. Overall, children who missed a critical step were significantly older (7.8 [6.7-8.9] vs 5.8 [5.1-6.5] years; P = .002). More participants missed a critical step with the SMP than the SM (75% [51%-90%] vs 36% [27%-46%]; P = .003), and this was the most prominent factor for missing a critical step in the adjusted regression analysis (OR 6.95 [1.71-28.23], P = .007). The most commonly missed steps were breathing normally for 30 seconds for SM, and for SMP, it was breathing out fully and breathing away from the spacer (Table 1). Twenty participants (18%) did not use a spacer device; these patients were older than those who did use a spacer (mean age 8.5 [6.7-10.4] vs 6.2 [5.6-6.9] years; P = .005); however, no other significant differences were identified.

Demographic, Medical History, and Socioeconomic Characteristics

Overall, race, ethnicity, and insurance status did not vary significantly based on asthma checklist score ≥7 or missing a critical step. Patients in the SM group who had received inpatient asthma education during a previous admission, had a history of pediatric intensive care unit (PICU) admission, and had been prescribed a daily controller were less likely to miss a critical step (Table 2). Parental education level varied, with 33% having a high school degree or less, but was not associated with asthma checklist score or missing critical steps. Parental BHLS and parental confidence (PAMSE) were not significantly associated with inhaler proficiency. However, transportation-related barriers were more common in patients with checklist scores <7 and more missed critical steps (OR 1.62 [1.06-2.46]; P = .02).

Nearly half of the participants in this study missed at least one critical step in inhaler use. In addition, 18% did not use a spacer when demonstrating their inhaler technique. Despite robust studies demonstrating how asthma education can improve both asthma skills and clinical outcomes,¹³ our study demonstrates that a large gap remains in proper inhaler technique among asthmatic patients presenting for inpatient care. Specifically, in the mouthpiece group, steps related to breathing technique were the most commonly missed. Our results also show that inhaler technique errors were most prominent in the adolescent population, possibly coinciding with the process of transitioning to a mouthpiece and more independence in medication administration. Adolescents may be a high-impact population on which to focus inpatient asthma education. Additionally, we found that a previous PICU admission and previous inpatient asthma education were associated with missing fewer critical steps in inhaler technique. This finding is consistent with those of another study that evaluated inhaler technique in the emergency department and found that previous hospitalization for asthma was inversely related to improper inhaler use (RR 0.55, 95% CI 0.36-0.84).¹⁴ This supports that when provided, inpatient education can increase inhaler administration skills.

Previous studies conducted in the outpatient setting have demonstrated variable rates of inhaler skill, from 0% to approximately 89% of children performing all steps of inhalation correctly.⁸ This wide range may be related to variations in the number and definition of critical steps between the different studies. In our study, we highlighted removing the cap, attaching a spacer, and adequate breathing technique as critical steps, because failure to complete them would significantly reduce lung deposition of medication. While past studies did evaluate both MDIs and discuss the devices, our study is the first to report difference in problems with technique between SM and SMP. As asthma educational interventions are developed and/or implemented, it is important to stress that different steps in inhaler technique are being missed in those using a mask versus mouthpiece.

The limitations of this study include that it was at a single center with a primarily urban and English-speaking population; however, this study population reflects the racial diversity of pediatric asthma patients. Further studies may explore the reproducibility of these findings at multiple centers and with non-English-speaking families. This study included younger patients than in some previous publications investigating asthma; however, all patients met the criteria for asthma diagnosis and this age range is reflective of patients presenting for inpatient asthma care. Furthermore, because of our daytime research hours, 59% of patients were excluded because a primary caregiver was not available. It is possible that these families have decreased access to inpatient asthma educators as well and may be another target group for future studies. Finally, a large proportion of parents had a college education or greater in our sample. However, there was no association within our analysis between parental education level and inhaler proficiency.

The findings from this study indicate that continued efforts are needed to establish that inhaler technique is adequate for all families regardless of their educational status or socioeconomic background, especially for adolescents and in the setting of poor asthma control. Furthermore, our findings support that inhaler technique education may be beneficial in the inpatient setting and that acute care settings can provide a valuable “teachable moment.”^14,15

CONCLUSION

Errors in inhaler technique are prevalent in pediatric inpatients with asthma, primarily those using a mouthpiece device. Educational efforts in both inpatient and outpatient settings have the potential to improve drug delivery and therefore asthma control. Inpatient hospitalization may serve as a platform for further studies to investigate innovative educational interventions.

Acknowledgments

The authors thank Tina Carter for her assistance in the recruitment and data collection and Ashley Hull and Susannah Butters for training the study staff on the use of the asthma checklist.

Disclosures

Dr. Gupta receives research grant support from the National Institutes of Health and the United Healthcare Group. Dr. Gupta serves as a consultant for DBV Technology, Aimmune Therapeutics, Kaleo & BEFORE Brands. Dr. Gupta has received lecture fees/honorariums from the Allergy Asthma Network & the American College of Asthma, Allergy & Immunology. Dr. Press reports research support from the Chicago Center for Diabetes Translation Research Pilot and Feasibility Grant, the Bucksbaum Institute for Clinical Excellence Pilot Grant Program, the Academy of Distinguished Medical Educators, the Development of Novel Hospital-initiated Care Bundle in Adults Hospitalized for Acute Asthma: the 41st Multicenter Airway Research Collaboration (MARC-41) Study, UCM’s Innovation Grant Program, the University of Chicago-Chapin Hall Join Research Fund, the NIH/NHLBI Loan Repayment Program, 1 K23 HL118151 01, NIH NLBHI R03 (RFA-HL-18-025), the George and Carol Abramson Pilot Awards, the COPD Foundation Green Shoots Grant, the University of Chicago Women’s Board Grant, NIH NHLBI UG1 (RFA-HL-17-009), and the CTSA Pilot Award, outside the submitted work. These disclosures have been reported to Dr. Press’ institutional IRB board. Additionally, a management plan is on file that details how to address conflicts such as these which are sources of research support but do not directly support the work at hand. The remaining authors have no conflicts of interest relevant to the article to disclose.

Funding

This study was funded by internal grants from Ann and Robert H. Lurie Children’s Hospital of Chicago. Dr. Press was funded by a K23HL118151.

Many studies have shown that improved control can be achieved for most children with asthma if inhaled medications are taken correctly and adequately.^1-3 Drug delivery studies have shown that bioavailability of medication with a pressurized metered-dose inhaler (MDI) improves from 34% to 83% with the addition of spacer devices. This difference is largely due to the decrease in oropharyngeal deposition,^1,4,5 and therefore, the use of a spacer with proper technique has been recommended in all pediatric patients.^1,6

Poor inhaler technique is common among children.^1,7 Previous studies of children with asthma have evaluated inhaler technique, primarily in the outpatient and community settings, and reported variable rates of error (from 45% to >90%).^8,9 No studies have evaluated children hospitalized with asthma. As these children represent a particularly high-risk group for morbidity and mortality,^10,11 the objectives of this study were to assess errors in inhaler technique in hospitalized asthmatic children and identify risk factors for improper use.

As part of a larger interventional study, we conducted a prospective cross-sectional study at a tertiary urban children’s hospital. We enrolled a convenience sample of children aged 2-16 years admitted to the inpatient ward with an asthma exacerbation Monday-Friday from 8 AM to 6 PM. Participants were required to have a diagnosis of asthma (an established diagnosis by their primary care provider or meets the National Heart, Lung, and Blood Institute [NHLBI] criteria¹), have a consenting adult available, and speak English. Patients were excluded if they had a codiagnosis of an additional respiratory disease (ie, pneumonia), cardiac disease, or sickle cell anemia. The Institutional Review Board approved this study.

We asked caregivers, or children >10 years old if they independently use their inhaler, to demonstrate their typical home inhaler technique using a spacer with mask (SM), spacer with mouthpiece (SMP), or no spacer (per their usual home practice). Inhaler technique was scored using a previously validated asthma checklist (Table 1).¹² Certain steps in the checklist were identified as critical: (Step 1) removing the cap, (Step 3) attaching to a spacer, (Step 7) taking six breaths (SM), and (Step 9) holding breath for five seconds (SMP). Caregivers only were also asked to complete questionnaires assessing their literacy (Brief Health Literacy Screen [BHLS]), confidence (Parent Asthma Management Self-Efficacy scale [PAMSE]), and any barriers to managing their child’s asthma (Barriers to Asthma Care). Demographic and medical history information was extracted from the medical chart.

Participants

From October 2016 to June 2017, 380 participants were assessed for participation; 215 were excluded for not having a parent available (59%), not speaking English (27%), not having an asthma diagnosis (ie, viral wheezing; 14%), and 52 (14%) declined to participate. Therefore, a total of 113 participants were enrolled, with demonstrations provided by 100 caregivers and 13 children. The mean age of the patients overall was 6.6 ± 3.4 years and over half (55%) of the participants had uncontrolled asthma (NHLBI criteria¹).

Errors in Inhaler Technique

The mean asthma checklist score was 6.7 (maximum score of 10 for SM and 12 for SMP). A third (35%) scored <7 on the asthma checklist and 42% of participants missed at least one critical step. Overall, children who missed a critical step were significantly older (7.8 [6.7-8.9] vs 5.8 [5.1-6.5] years; P = .002). More participants missed a critical step with the SMP than the SM (75% [51%-90%] vs 36% [27%-46%]; P = .003), and this was the most prominent factor for missing a critical step in the adjusted regression analysis (OR 6.95 [1.71-28.23], P = .007). The most commonly missed steps were breathing normally for 30 seconds for SM, and for SMP, it was breathing out fully and breathing away from the spacer (Table 1). Twenty participants (18%) did not use a spacer device; these patients were older than those who did use a spacer (mean age 8.5 [6.7-10.4] vs 6.2 [5.6-6.9] years; P = .005); however, no other significant differences were identified.

Demographic, Medical History, and Socioeconomic Characteristics

Overall, race, ethnicity, and insurance status did not vary significantly based on asthma checklist score ≥7 or missing a critical step. Patients in the SM group who had received inpatient asthma education during a previous admission, had a history of pediatric intensive care unit (PICU) admission, and had been prescribed a daily controller were less likely to miss a critical step (Table 2). Parental education level varied, with 33% having a high school degree or less, but was not associated with asthma checklist score or missing critical steps. Parental BHLS and parental confidence (PAMSE) were not significantly associated with inhaler proficiency. However, transportation-related barriers were more common in patients with checklist scores <7 and more missed critical steps (OR 1.62 [1.06-2.46]; P = .02).

Nearly half of the participants in this study missed at least one critical step in inhaler use. In addition, 18% did not use a spacer when demonstrating their inhaler technique. Despite robust studies demonstrating how asthma education can improve both asthma skills and clinical outcomes,¹³ our study demonstrates that a large gap remains in proper inhaler technique among asthmatic patients presenting for inpatient care. Specifically, in the mouthpiece group, steps related to breathing technique were the most commonly missed. Our results also show that inhaler technique errors were most prominent in the adolescent population, possibly coinciding with the process of transitioning to a mouthpiece and more independence in medication administration. Adolescents may be a high-impact population on which to focus inpatient asthma education. Additionally, we found that a previous PICU admission and previous inpatient asthma education were associated with missing fewer critical steps in inhaler technique. This finding is consistent with those of another study that evaluated inhaler technique in the emergency department and found that previous hospitalization for asthma was inversely related to improper inhaler use (RR 0.55, 95% CI 0.36-0.84).¹⁴ This supports that when provided, inpatient education can increase inhaler administration skills.

Previous studies conducted in the outpatient setting have demonstrated variable rates of inhaler skill, from 0% to approximately 89% of children performing all steps of inhalation correctly.⁸ This wide range may be related to variations in the number and definition of critical steps between the different studies. In our study, we highlighted removing the cap, attaching a spacer, and adequate breathing technique as critical steps, because failure to complete them would significantly reduce lung deposition of medication. While past studies did evaluate both MDIs and discuss the devices, our study is the first to report difference in problems with technique between SM and SMP. As asthma educational interventions are developed and/or implemented, it is important to stress that different steps in inhaler technique are being missed in those using a mask versus mouthpiece.

The limitations of this study include that it was at a single center with a primarily urban and English-speaking population; however, this study population reflects the racial diversity of pediatric asthma patients. Further studies may explore the reproducibility of these findings at multiple centers and with non-English-speaking families. This study included younger patients than in some previous publications investigating asthma; however, all patients met the criteria for asthma diagnosis and this age range is reflective of patients presenting for inpatient asthma care. Furthermore, because of our daytime research hours, 59% of patients were excluded because a primary caregiver was not available. It is possible that these families have decreased access to inpatient asthma educators as well and may be another target group for future studies. Finally, a large proportion of parents had a college education or greater in our sample. However, there was no association within our analysis between parental education level and inhaler proficiency.

The findings from this study indicate that continued efforts are needed to establish that inhaler technique is adequate for all families regardless of their educational status or socioeconomic background, especially for adolescents and in the setting of poor asthma control. Furthermore, our findings support that inhaler technique education may be beneficial in the inpatient setting and that acute care settings can provide a valuable “teachable moment.”^14,15

CONCLUSION

Errors in inhaler technique are prevalent in pediatric inpatients with asthma, primarily those using a mouthpiece device. Educational efforts in both inpatient and outpatient settings have the potential to improve drug delivery and therefore asthma control. Inpatient hospitalization may serve as a platform for further studies to investigate innovative educational interventions.

Acknowledgments

The authors thank Tina Carter for her assistance in the recruitment and data collection and Ashley Hull and Susannah Butters for training the study staff on the use of the asthma checklist.

Disclosures

Dr. Gupta receives research grant support from the National Institutes of Health and the United Healthcare Group. Dr. Gupta serves as a consultant for DBV Technology, Aimmune Therapeutics, Kaleo & BEFORE Brands. Dr. Gupta has received lecture fees/honorariums from the Allergy Asthma Network & the American College of Asthma, Allergy & Immunology. Dr. Press reports research support from the Chicago Center for Diabetes Translation Research Pilot and Feasibility Grant, the Bucksbaum Institute for Clinical Excellence Pilot Grant Program, the Academy of Distinguished Medical Educators, the Development of Novel Hospital-initiated Care Bundle in Adults Hospitalized for Acute Asthma: the 41st Multicenter Airway Research Collaboration (MARC-41) Study, UCM’s Innovation Grant Program, the University of Chicago-Chapin Hall Join Research Fund, the NIH/NHLBI Loan Repayment Program, 1 K23 HL118151 01, NIH NLBHI R03 (RFA-HL-18-025), the George and Carol Abramson Pilot Awards, the COPD Foundation Green Shoots Grant, the University of Chicago Women’s Board Grant, NIH NHLBI UG1 (RFA-HL-17-009), and the CTSA Pilot Award, outside the submitted work. These disclosures have been reported to Dr. Press’ institutional IRB board. Additionally, a management plan is on file that details how to address conflicts such as these which are sources of research support but do not directly support the work at hand. The remaining authors have no conflicts of interest relevant to the article to disclose.

Funding

This study was funded by internal grants from Ann and Robert H. Lurie Children’s Hospital of Chicago. Dr. Press was funded by a K23HL118151.

Postgraduate training for physician assistants (PAs) and nurse practitioners (NPs) is a rapidly evolving field. It has been estimated that the number of these advanced practice providers (APPs) almost doubled between 2000 and 2016 (from 15.3 to 28.2 per 100 physicians) and is expected to double again by 2030.¹ As APPs continue to become a progressively larger part of the healthcare workforce, medical organizations are seeking more comprehensive strategies to train and mentor them.² This has led to the development of formal postgraduate programs, often called APP fellowships.

Historically, postgraduate APP fellowships have functioned to help bridge the gap in clinical practice experience between physicians and APPs.³ This gap is evident in hours of clinical training. Whereas NPs are generally expected to complete 500-1,500 hours of clinical practice before graduating,⁴ and PAs are expected to complete 2,000 hours,⁵ most physicians will complete over 15,000 hours of clinical training by the end of residency.⁶ As increasing patient complexity continues to challenge the healthcare workforce,⁷ both the NP and the PA leadership have recommended increased training of graduates and outcome studies of formal postgraduate fellowships.^8,9 In 2007, there were over 60 of these programs in the United States,¹⁰ most of them offering training in surgical specialties.

First described in 2010 by the Mayo Clinic,¹¹ APP fellowships in hospital medicine are also being developed. These programs are built to improve the training of nonphysician hospitalists, who often work independently¹² and manage medically complex patients.¹³ However, little is known about the number or structure of these fellowships. The limited understanding of the current APP fellowship environment is partly due to the lack of an administrative body overseeing these programs.¹⁴ The Accreditation Review Commission on Education for the Physician Assistant (ARC-PA) pioneered a model in 2007 for postgraduate PA programs, but it has been held in abeyance since 2014.¹⁵ Both the American Nurses Credentialing Center and the National Nurse Practitioner Residency and Fellowship Training Consortium have fellowship accreditation review processes, but they are not specific to hospital medicine.¹⁶ The Society of Hospital Medicine (SHM) has several resources for the training of APPs;¹⁷ however, it neither reviews nor accredits fellowship programs. Without standards, guidelines, or active accrediting bodies, APP fellowships in hospital medicine are poorly understood and are of unknown efficacy. The purpose of this study was to identify and describe the active APP fellowships in hospital medicine.

This was a cross-sectional study of all APP adult and pediatric fellowships in hospital medicine, in the United States, that were identifiable through May 2018. Multiple methods were used to identify all active fellowships. First, all training programs offering a Hospital Medicine Fellowship in the ARC-PA and Association of Postgraduate PA Programs databases were noted. Second, questionnaires were given out at the NP/PA forum at the national SHM conference in 2018 to gather information on existing APP fellowships. Third, similar online requests to identify known programs were posted to the SHM web forum Hospital Medicine Exchange (HMX). Fourth, Internet searches were used to discover additional programs. Once those fellowships were identified, surveys were sent to their program directors (PDs). These surveys not only asked the PDs about their fellowship but also asked them to identify additional APP fellowships beyond those that we had captured. Once additional programs were identified, a second round of surveys was sent to their PDs. This was performed in an iterative fashion until no additional fellowships were discovered.

The survey tool was developed and validated internally in the AAMC Survey Development style¹⁸ and was influenced by prior validated surveys of postgraduate medical fellowships.^10,19-21 Each question was developed by a team that had expertise in survey design (Wright and Tackett), and two survey design team members were themselves PDs of APP fellowships in hospital medicine (Kisuule and Franco). The survey was revised iteratively by the team on the basis of meetings and pilot testing with PDs of other programs. All qualitative or descriptive questions had a free response option available to allow PDs to answer the survey accurately and exhaustively. The final version of the survey was approved by consensus of all authors. It consisted of 25 multiple choice questions which were created to gather information about the following key areas of APP hospital medicine fellowships: fellowship and learner characteristics, program rationales, curricula, and methods of fellow assessment.

A web-based survey format (Qualtrics) was used to distribute the questionnaire e-mail to the PDs. Follow up e-mail reminders were sent to all nonresponders to encourage full participation. Survey completion was voluntary; no financial incentives or gifts were offered. IRB approval was obtained at Johns Hopkins Bayview (IRB number 00181629). Descriptive statistics (proportions, means, and ranges as appropriate) were calculated for all variables. Stata 13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, Texas. StataCorp LP) was used for data analysis.

In total, 11 fellowships were identified using our multimethod approach. We found four (36%) programs by utilizing existing online databases, two (18%) through the SHM questionnaire and HMX forum, three (27%) through internet searches, and the remaining two (18%) were referred to us by the other PDs who were surveyed. Of the programs surveyed, 10 were adult programs and one was a pediatric program. Surveys were sent to the PDs of the 11 fellowships, and all but one of them (10/11, 91%) responded. Respondent programs were given alphabetical designations A through J (Table). 

Fellowship and Individual Characteristics

Most programs have been in existence for five years or fewer. Eighty percent of the programs are about one year in duration; two outlier programs have fellowship lengths of six months and 18 months. The main hospital where training occurs has a mean of 496 beds (range 213 to 900). Ninety percent of the hospitals also have physician residency training programs. Sixty percent of programs enroll two to four fellows per year while 40% enroll five or more. The salary range paid by the programs is $55,000 to >$70,000, and half the programs pay more than $65,000.

The majority of fellows accepted into APP fellowships in hospital medicine are women. Eighty percent of fellows are 26-30 years old, and 90% of fellows have been out of NP or PA school for one year or less. Both NP and PA applicants are accepted in 80% of fellowships.

Program Rationales

All programs reported that training and retaining applicants is the main driver for developing their fellowship, and 50% of them offer financial incentives for retention upon successful completion of the program. Forty percent of PDs stated that there is an implicit or explicit understanding that successful completion of the fellowship would result in further employment. Over the last five years, 89% (range: 71%-100%) of graduates were asked to remain for a full-time position after program completion.

In addition to training and retention, building an interprofessional team (50%), managing patient volume (30%), and reducing overhead (20%) were also reported as rationales for program development. The majority of programs (80%) have fellows bill for clinical services, and five of those eight programs do so after their fellows become more clinically competent.

Curricula

Of the nine adult programs, 67% teach explicitly to SHM core competencies and 33% send their fellows to the SHM NP/PA Boot Camp. Thirty percent of fellowships partner formally with either a physician residency or a local PA program to develop educational content. Six of the nine programs with active physician residencies, including the pediatric fellowship, offer shared educational experiences for the residents and APPs.

There are notable differences in clinical rotations between the programs (Figure 1). No single rotation is universally required, although general hospital internal medicine is required in all adult fellowships. The majority (80%) of programs offer at least one elective. Six programs reported mandatory rotations outside the department of medicine, most commonly neurology or the stroke service (four programs). Only one program reported only general medicine rotations, with no subspecialty electives.

Methods of Fellow Assessment

Each program surveyed has a unique method of fellow assessment. Ninety percent of the programs use more than one method to assess their fellows. Faculty reviews are most commonly used and are conducted in all rotations in 80% of fellowships. Both self-assessment exercises and written examinations are used in some rotations by the majority of programs. Capstone projects are required infrequently (30%).

We found several commonalities between the fellowships surveyed. Many of the program characteristics, such as years in operation, salary, duration, and lack of accreditation, are quite similar. Most fellowships also have a similar rationale for building their programs and use resources from the SHM to inform their curricula. Fellows, on average, share several demographic characteristics, such as age, gender, and time out of schooling. Conversely, we found wide variability in clinical rotations, the general teaching structure, and methods of fellow evaluation.

There have been several publications detailing successful individual APP fellowships in medical subspecialties,²² psychiatry,²³ and surgical specialties,²⁴ all of which describe the benefits to the institution. One study found that physician hospitalists have a poor understanding of the training PAs undergo and would favor a standardized curriculum for PA hospitalists.²⁵ Another study compared all PA postgraduate training programs in emergency medicine;¹⁹ it also described a small number of relatively young programs with variable curricula and a need for standardization. Yet another paper¹⁰ surveyed postgraduate PA programs across all specialties; however, that study only captured two hospital medicine programs, and it was not focused on several key areas studied in this paper—such as the program rationale, curricular elements, and assessment.

It is noteworthy that every program surveyed was created with training and retention in mind, rather than other factors like decreasing overhead or managing patient volume. Training one’s own APPs so that they can learn on the job, come to understand expectations within a group, and witness the culture is extremely valuable. From a patient safety standpoint, it has been documented that physician hospitalists straight out of residency have a higher patient mortality compared with more experienced providers.²⁶ Given the findings that on a national level, the majority of hospitalist NPs and PAs practice autonomously or somewhat autonomously,¹² it is reasonable to assume that similar trends of more experienced providers delivering safer care would be expected for APPs, but this remains speculative. From a retention standpoint, it has been well described that high APP turnover is often due to decreased feelings of competence and confidence during their transition from trainees to medical providers.²⁷ APPs who have completed fellowships feel more confident and able to succeed in their field.²⁸ To this point, in one survey of hospitalist PAs, almost all reported that they would have been interested in completing a fellowship, even it meant a lower initial salary.²⁹Despite having the same general goals and using similar national resources, our study reveals that APP fellows are trained and assessed very differently between programs. This might represent an area of future growth in the field of hospitalist APP education. For physician learning, competency-based medical education (CBME) has emerged as a learner centric, outcomes-based model of teaching and assessment that emphasizes mastery of skills and progression through milestones.³⁰ Both the ACGME³¹ and the SHM³² have described core competencies that provide a framework within CBME for determining readiness for independent practice. While we were not surprised to find that each fellowship has its own unique method of determining readiness for practice, these findings suggest that graduates from different programs likely have very different skill sets and aptitude levels. In the future, an active accrediting body could offer guidance in defining hospitalist APP core competencies and help standardize education.

Several limitations to this study should be considered. While we used multiple strategies to locate as many fellowships as possible, it is unlikely that we successfully captured all existing programs, and new programs are being developed annually. We also relied on self-reported data from PDs. While we would expect PDs to provide accurate data, we could not externally validate their answers. Additionally, although our survey tool was reviewed extensively and validated internally, it was developed de novo for this study.

APP fellowships in hospital medicine have experienced marked growth since the first program was described in 2010. The majority of programs are 12 months long, operate in existing teaching centers, and are intended to further enhance the training and retention of newly graduated PAs and NPs. Despite their similarities, fellowships have striking variability in their methods of teaching and assessing their learners. Best practices have yet to be identified, and further study is required to determine how to standardize curricula across the board.

Acknowledgments

Disclosures

The authors report no conflicts of interest.

Funding

This project was supported by the Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core. Dr. Wright is the Anne Gaines and G. Thomas Miller Professor of Medicine, which is supported through the Johns Hopkins’ Center for Innovative Medicine.

Postgraduate training for physician assistants (PAs) and nurse practitioners (NPs) is a rapidly evolving field. It has been estimated that the number of these advanced practice providers (APPs) almost doubled between 2000 and 2016 (from 15.3 to 28.2 per 100 physicians) and is expected to double again by 2030.¹ As APPs continue to become a progressively larger part of the healthcare workforce, medical organizations are seeking more comprehensive strategies to train and mentor them.² This has led to the development of formal postgraduate programs, often called APP fellowships.

Historically, postgraduate APP fellowships have functioned to help bridge the gap in clinical practice experience between physicians and APPs.³ This gap is evident in hours of clinical training. Whereas NPs are generally expected to complete 500-1,500 hours of clinical practice before graduating,⁴ and PAs are expected to complete 2,000 hours,⁵ most physicians will complete over 15,000 hours of clinical training by the end of residency.⁶ As increasing patient complexity continues to challenge the healthcare workforce,⁷ both the NP and the PA leadership have recommended increased training of graduates and outcome studies of formal postgraduate fellowships.^8,9 In 2007, there were over 60 of these programs in the United States,¹⁰ most of them offering training in surgical specialties.

First described in 2010 by the Mayo Clinic,¹¹ APP fellowships in hospital medicine are also being developed. These programs are built to improve the training of nonphysician hospitalists, who often work independently¹² and manage medically complex patients.¹³ However, little is known about the number or structure of these fellowships. The limited understanding of the current APP fellowship environment is partly due to the lack of an administrative body overseeing these programs.¹⁴ The Accreditation Review Commission on Education for the Physician Assistant (ARC-PA) pioneered a model in 2007 for postgraduate PA programs, but it has been held in abeyance since 2014.¹⁵ Both the American Nurses Credentialing Center and the National Nurse Practitioner Residency and Fellowship Training Consortium have fellowship accreditation review processes, but they are not specific to hospital medicine.¹⁶ The Society of Hospital Medicine (SHM) has several resources for the training of APPs;¹⁷ however, it neither reviews nor accredits fellowship programs. Without standards, guidelines, or active accrediting bodies, APP fellowships in hospital medicine are poorly understood and are of unknown efficacy. The purpose of this study was to identify and describe the active APP fellowships in hospital medicine.

This was a cross-sectional study of all APP adult and pediatric fellowships in hospital medicine, in the United States, that were identifiable through May 2018. Multiple methods were used to identify all active fellowships. First, all training programs offering a Hospital Medicine Fellowship in the ARC-PA and Association of Postgraduate PA Programs databases were noted. Second, questionnaires were given out at the NP/PA forum at the national SHM conference in 2018 to gather information on existing APP fellowships. Third, similar online requests to identify known programs were posted to the SHM web forum Hospital Medicine Exchange (HMX). Fourth, Internet searches were used to discover additional programs. Once those fellowships were identified, surveys were sent to their program directors (PDs). These surveys not only asked the PDs about their fellowship but also asked them to identify additional APP fellowships beyond those that we had captured. Once additional programs were identified, a second round of surveys was sent to their PDs. This was performed in an iterative fashion until no additional fellowships were discovered.

The survey tool was developed and validated internally in the AAMC Survey Development style¹⁸ and was influenced by prior validated surveys of postgraduate medical fellowships.^10,19-21 Each question was developed by a team that had expertise in survey design (Wright and Tackett), and two survey design team members were themselves PDs of APP fellowships in hospital medicine (Kisuule and Franco). The survey was revised iteratively by the team on the basis of meetings and pilot testing with PDs of other programs. All qualitative or descriptive questions had a free response option available to allow PDs to answer the survey accurately and exhaustively. The final version of the survey was approved by consensus of all authors. It consisted of 25 multiple choice questions which were created to gather information about the following key areas of APP hospital medicine fellowships: fellowship and learner characteristics, program rationales, curricula, and methods of fellow assessment.

A web-based survey format (Qualtrics) was used to distribute the questionnaire e-mail to the PDs. Follow up e-mail reminders were sent to all nonresponders to encourage full participation. Survey completion was voluntary; no financial incentives or gifts were offered. IRB approval was obtained at Johns Hopkins Bayview (IRB number 00181629). Descriptive statistics (proportions, means, and ranges as appropriate) were calculated for all variables. Stata 13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, Texas. StataCorp LP) was used for data analysis.

In total, 11 fellowships were identified using our multimethod approach. We found four (36%) programs by utilizing existing online databases, two (18%) through the SHM questionnaire and HMX forum, three (27%) through internet searches, and the remaining two (18%) were referred to us by the other PDs who were surveyed. Of the programs surveyed, 10 were adult programs and one was a pediatric program. Surveys were sent to the PDs of the 11 fellowships, and all but one of them (10/11, 91%) responded. Respondent programs were given alphabetical designations A through J (Table). 

Fellowship and Individual Characteristics

Most programs have been in existence for five years or fewer. Eighty percent of the programs are about one year in duration; two outlier programs have fellowship lengths of six months and 18 months. The main hospital where training occurs has a mean of 496 beds (range 213 to 900). Ninety percent of the hospitals also have physician residency training programs. Sixty percent of programs enroll two to four fellows per year while 40% enroll five or more. The salary range paid by the programs is $55,000 to >$70,000, and half the programs pay more than $65,000.

The majority of fellows accepted into APP fellowships in hospital medicine are women. Eighty percent of fellows are 26-30 years old, and 90% of fellows have been out of NP or PA school for one year or less. Both NP and PA applicants are accepted in 80% of fellowships.

Program Rationales

All programs reported that training and retaining applicants is the main driver for developing their fellowship, and 50% of them offer financial incentives for retention upon successful completion of the program. Forty percent of PDs stated that there is an implicit or explicit understanding that successful completion of the fellowship would result in further employment. Over the last five years, 89% (range: 71%-100%) of graduates were asked to remain for a full-time position after program completion.

In addition to training and retention, building an interprofessional team (50%), managing patient volume (30%), and reducing overhead (20%) were also reported as rationales for program development. The majority of programs (80%) have fellows bill for clinical services, and five of those eight programs do so after their fellows become more clinically competent.

Curricula

Of the nine adult programs, 67% teach explicitly to SHM core competencies and 33% send their fellows to the SHM NP/PA Boot Camp. Thirty percent of fellowships partner formally with either a physician residency or a local PA program to develop educational content. Six of the nine programs with active physician residencies, including the pediatric fellowship, offer shared educational experiences for the residents and APPs.

There are notable differences in clinical rotations between the programs (Figure 1). No single rotation is universally required, although general hospital internal medicine is required in all adult fellowships. The majority (80%) of programs offer at least one elective. Six programs reported mandatory rotations outside the department of medicine, most commonly neurology or the stroke service (four programs). Only one program reported only general medicine rotations, with no subspecialty electives.

Methods of Fellow Assessment

Each program surveyed has a unique method of fellow assessment. Ninety percent of the programs use more than one method to assess their fellows. Faculty reviews are most commonly used and are conducted in all rotations in 80% of fellowships. Both self-assessment exercises and written examinations are used in some rotations by the majority of programs. Capstone projects are required infrequently (30%).

We found several commonalities between the fellowships surveyed. Many of the program characteristics, such as years in operation, salary, duration, and lack of accreditation, are quite similar. Most fellowships also have a similar rationale for building their programs and use resources from the SHM to inform their curricula. Fellows, on average, share several demographic characteristics, such as age, gender, and time out of schooling. Conversely, we found wide variability in clinical rotations, the general teaching structure, and methods of fellow evaluation.

There have been several publications detailing successful individual APP fellowships in medical subspecialties,²² psychiatry,²³ and surgical specialties,²⁴ all of which describe the benefits to the institution. One study found that physician hospitalists have a poor understanding of the training PAs undergo and would favor a standardized curriculum for PA hospitalists.²⁵ Another study compared all PA postgraduate training programs in emergency medicine;¹⁹ it also described a small number of relatively young programs with variable curricula and a need for standardization. Yet another paper¹⁰ surveyed postgraduate PA programs across all specialties; however, that study only captured two hospital medicine programs, and it was not focused on several key areas studied in this paper—such as the program rationale, curricular elements, and assessment.

It is noteworthy that every program surveyed was created with training and retention in mind, rather than other factors like decreasing overhead or managing patient volume. Training one’s own APPs so that they can learn on the job, come to understand expectations within a group, and witness the culture is extremely valuable. From a patient safety standpoint, it has been documented that physician hospitalists straight out of residency have a higher patient mortality compared with more experienced providers.²⁶ Given the findings that on a national level, the majority of hospitalist NPs and PAs practice autonomously or somewhat autonomously,¹² it is reasonable to assume that similar trends of more experienced providers delivering safer care would be expected for APPs, but this remains speculative. From a retention standpoint, it has been well described that high APP turnover is often due to decreased feelings of competence and confidence during their transition from trainees to medical providers.²⁷ APPs who have completed fellowships feel more confident and able to succeed in their field.²⁸ To this point, in one survey of hospitalist PAs, almost all reported that they would have been interested in completing a fellowship, even it meant a lower initial salary.²⁹Despite having the same general goals and using similar national resources, our study reveals that APP fellows are trained and assessed very differently between programs. This might represent an area of future growth in the field of hospitalist APP education. For physician learning, competency-based medical education (CBME) has emerged as a learner centric, outcomes-based model of teaching and assessment that emphasizes mastery of skills and progression through milestones.³⁰ Both the ACGME³¹ and the SHM³² have described core competencies that provide a framework within CBME for determining readiness for independent practice. While we were not surprised to find that each fellowship has its own unique method of determining readiness for practice, these findings suggest that graduates from different programs likely have very different skill sets and aptitude levels. In the future, an active accrediting body could offer guidance in defining hospitalist APP core competencies and help standardize education.

Several limitations to this study should be considered. While we used multiple strategies to locate as many fellowships as possible, it is unlikely that we successfully captured all existing programs, and new programs are being developed annually. We also relied on self-reported data from PDs. While we would expect PDs to provide accurate data, we could not externally validate their answers. Additionally, although our survey tool was reviewed extensively and validated internally, it was developed de novo for this study.

APP fellowships in hospital medicine have experienced marked growth since the first program was described in 2010. The majority of programs are 12 months long, operate in existing teaching centers, and are intended to further enhance the training and retention of newly graduated PAs and NPs. Despite their similarities, fellowships have striking variability in their methods of teaching and assessing their learners. Best practices have yet to be identified, and further study is required to determine how to standardize curricula across the board.

Acknowledgments

Disclosures

The authors report no conflicts of interest.

Funding

This project was supported by the Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core. Dr. Wright is the Anne Gaines and G. Thomas Miller Professor of Medicine, which is supported through the Johns Hopkins’ Center for Innovative Medicine.

Postgraduate training for physician assistants (PAs) and nurse practitioners (NPs) is a rapidly evolving field. It has been estimated that the number of these advanced practice providers (APPs) almost doubled between 2000 and 2016 (from 15.3 to 28.2 per 100 physicians) and is expected to double again by 2030.¹ As APPs continue to become a progressively larger part of the healthcare workforce, medical organizations are seeking more comprehensive strategies to train and mentor them.² This has led to the development of formal postgraduate programs, often called APP fellowships.

Historically, postgraduate APP fellowships have functioned to help bridge the gap in clinical practice experience between physicians and APPs.³ This gap is evident in hours of clinical training. Whereas NPs are generally expected to complete 500-1,500 hours of clinical practice before graduating,⁴ and PAs are expected to complete 2,000 hours,⁵ most physicians will complete over 15,000 hours of clinical training by the end of residency.⁶ As increasing patient complexity continues to challenge the healthcare workforce,⁷ both the NP and the PA leadership have recommended increased training of graduates and outcome studies of formal postgraduate fellowships.^8,9 In 2007, there were over 60 of these programs in the United States,¹⁰ most of them offering training in surgical specialties.

First described in 2010 by the Mayo Clinic,¹¹ APP fellowships in hospital medicine are also being developed. These programs are built to improve the training of nonphysician hospitalists, who often work independently¹² and manage medically complex patients.¹³ However, little is known about the number or structure of these fellowships. The limited understanding of the current APP fellowship environment is partly due to the lack of an administrative body overseeing these programs.¹⁴ The Accreditation Review Commission on Education for the Physician Assistant (ARC-PA) pioneered a model in 2007 for postgraduate PA programs, but it has been held in abeyance since 2014.¹⁵ Both the American Nurses Credentialing Center and the National Nurse Practitioner Residency and Fellowship Training Consortium have fellowship accreditation review processes, but they are not specific to hospital medicine.¹⁶ The Society of Hospital Medicine (SHM) has several resources for the training of APPs;¹⁷ however, it neither reviews nor accredits fellowship programs. Without standards, guidelines, or active accrediting bodies, APP fellowships in hospital medicine are poorly understood and are of unknown efficacy. The purpose of this study was to identify and describe the active APP fellowships in hospital medicine.

This was a cross-sectional study of all APP adult and pediatric fellowships in hospital medicine, in the United States, that were identifiable through May 2018. Multiple methods were used to identify all active fellowships. First, all training programs offering a Hospital Medicine Fellowship in the ARC-PA and Association of Postgraduate PA Programs databases were noted. Second, questionnaires were given out at the NP/PA forum at the national SHM conference in 2018 to gather information on existing APP fellowships. Third, similar online requests to identify known programs were posted to the SHM web forum Hospital Medicine Exchange (HMX). Fourth, Internet searches were used to discover additional programs. Once those fellowships were identified, surveys were sent to their program directors (PDs). These surveys not only asked the PDs about their fellowship but also asked them to identify additional APP fellowships beyond those that we had captured. Once additional programs were identified, a second round of surveys was sent to their PDs. This was performed in an iterative fashion until no additional fellowships were discovered.

The survey tool was developed and validated internally in the AAMC Survey Development style¹⁸ and was influenced by prior validated surveys of postgraduate medical fellowships.^10,19-21 Each question was developed by a team that had expertise in survey design (Wright and Tackett), and two survey design team members were themselves PDs of APP fellowships in hospital medicine (Kisuule and Franco). The survey was revised iteratively by the team on the basis of meetings and pilot testing with PDs of other programs. All qualitative or descriptive questions had a free response option available to allow PDs to answer the survey accurately and exhaustively. The final version of the survey was approved by consensus of all authors. It consisted of 25 multiple choice questions which were created to gather information about the following key areas of APP hospital medicine fellowships: fellowship and learner characteristics, program rationales, curricula, and methods of fellow assessment.

A web-based survey format (Qualtrics) was used to distribute the questionnaire e-mail to the PDs. Follow up e-mail reminders were sent to all nonresponders to encourage full participation. Survey completion was voluntary; no financial incentives or gifts were offered. IRB approval was obtained at Johns Hopkins Bayview (IRB number 00181629). Descriptive statistics (proportions, means, and ranges as appropriate) were calculated for all variables. Stata 13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, Texas. StataCorp LP) was used for data analysis.

In total, 11 fellowships were identified using our multimethod approach. We found four (36%) programs by utilizing existing online databases, two (18%) through the SHM questionnaire and HMX forum, three (27%) through internet searches, and the remaining two (18%) were referred to us by the other PDs who were surveyed. Of the programs surveyed, 10 were adult programs and one was a pediatric program. Surveys were sent to the PDs of the 11 fellowships, and all but one of them (10/11, 91%) responded. Respondent programs were given alphabetical designations A through J (Table). 

Fellowship and Individual Characteristics

Most programs have been in existence for five years or fewer. Eighty percent of the programs are about one year in duration; two outlier programs have fellowship lengths of six months and 18 months. The main hospital where training occurs has a mean of 496 beds (range 213 to 900). Ninety percent of the hospitals also have physician residency training programs. Sixty percent of programs enroll two to four fellows per year while 40% enroll five or more. The salary range paid by the programs is $55,000 to >$70,000, and half the programs pay more than $65,000.

The majority of fellows accepted into APP fellowships in hospital medicine are women. Eighty percent of fellows are 26-30 years old, and 90% of fellows have been out of NP or PA school for one year or less. Both NP and PA applicants are accepted in 80% of fellowships.

Program Rationales

All programs reported that training and retaining applicants is the main driver for developing their fellowship, and 50% of them offer financial incentives for retention upon successful completion of the program. Forty percent of PDs stated that there is an implicit or explicit understanding that successful completion of the fellowship would result in further employment. Over the last five years, 89% (range: 71%-100%) of graduates were asked to remain for a full-time position after program completion.

In addition to training and retention, building an interprofessional team (50%), managing patient volume (30%), and reducing overhead (20%) were also reported as rationales for program development. The majority of programs (80%) have fellows bill for clinical services, and five of those eight programs do so after their fellows become more clinically competent.

Curricula

Of the nine adult programs, 67% teach explicitly to SHM core competencies and 33% send their fellows to the SHM NP/PA Boot Camp. Thirty percent of fellowships partner formally with either a physician residency or a local PA program to develop educational content. Six of the nine programs with active physician residencies, including the pediatric fellowship, offer shared educational experiences for the residents and APPs.

There are notable differences in clinical rotations between the programs (Figure 1). No single rotation is universally required, although general hospital internal medicine is required in all adult fellowships. The majority (80%) of programs offer at least one elective. Six programs reported mandatory rotations outside the department of medicine, most commonly neurology or the stroke service (four programs). Only one program reported only general medicine rotations, with no subspecialty electives.

Methods of Fellow Assessment

Each program surveyed has a unique method of fellow assessment. Ninety percent of the programs use more than one method to assess their fellows. Faculty reviews are most commonly used and are conducted in all rotations in 80% of fellowships. Both self-assessment exercises and written examinations are used in some rotations by the majority of programs. Capstone projects are required infrequently (30%).

We found several commonalities between the fellowships surveyed. Many of the program characteristics, such as years in operation, salary, duration, and lack of accreditation, are quite similar. Most fellowships also have a similar rationale for building their programs and use resources from the SHM to inform their curricula. Fellows, on average, share several demographic characteristics, such as age, gender, and time out of schooling. Conversely, we found wide variability in clinical rotations, the general teaching structure, and methods of fellow evaluation.

There have been several publications detailing successful individual APP fellowships in medical subspecialties,²² psychiatry,²³ and surgical specialties,²⁴ all of which describe the benefits to the institution. One study found that physician hospitalists have a poor understanding of the training PAs undergo and would favor a standardized curriculum for PA hospitalists.²⁵ Another study compared all PA postgraduate training programs in emergency medicine;¹⁹ it also described a small number of relatively young programs with variable curricula and a need for standardization. Yet another paper¹⁰ surveyed postgraduate PA programs across all specialties; however, that study only captured two hospital medicine programs, and it was not focused on several key areas studied in this paper—such as the program rationale, curricular elements, and assessment.

It is noteworthy that every program surveyed was created with training and retention in mind, rather than other factors like decreasing overhead or managing patient volume. Training one’s own APPs so that they can learn on the job, come to understand expectations within a group, and witness the culture is extremely valuable. From a patient safety standpoint, it has been documented that physician hospitalists straight out of residency have a higher patient mortality compared with more experienced providers.²⁶ Given the findings that on a national level, the majority of hospitalist NPs and PAs practice autonomously or somewhat autonomously,¹² it is reasonable to assume that similar trends of more experienced providers delivering safer care would be expected for APPs, but this remains speculative. From a retention standpoint, it has been well described that high APP turnover is often due to decreased feelings of competence and confidence during their transition from trainees to medical providers.²⁷ APPs who have completed fellowships feel more confident and able to succeed in their field.²⁸ To this point, in one survey of hospitalist PAs, almost all reported that they would have been interested in completing a fellowship, even it meant a lower initial salary.²⁹Despite having the same general goals and using similar national resources, our study reveals that APP fellows are trained and assessed very differently between programs. This might represent an area of future growth in the field of hospitalist APP education. For physician learning, competency-based medical education (CBME) has emerged as a learner centric, outcomes-based model of teaching and assessment that emphasizes mastery of skills and progression through milestones.³⁰ Both the ACGME³¹ and the SHM³² have described core competencies that provide a framework within CBME for determining readiness for independent practice. While we were not surprised to find that each fellowship has its own unique method of determining readiness for practice, these findings suggest that graduates from different programs likely have very different skill sets and aptitude levels. In the future, an active accrediting body could offer guidance in defining hospitalist APP core competencies and help standardize education.

Several limitations to this study should be considered. While we used multiple strategies to locate as many fellowships as possible, it is unlikely that we successfully captured all existing programs, and new programs are being developed annually. We also relied on self-reported data from PDs. While we would expect PDs to provide accurate data, we could not externally validate their answers. Additionally, although our survey tool was reviewed extensively and validated internally, it was developed de novo for this study.

APP fellowships in hospital medicine have experienced marked growth since the first program was described in 2010. The majority of programs are 12 months long, operate in existing teaching centers, and are intended to further enhance the training and retention of newly graduated PAs and NPs. Despite their similarities, fellowships have striking variability in their methods of teaching and assessing their learners. Best practices have yet to be identified, and further study is required to determine how to standardize curricula across the board.

Acknowledgments

Disclosures

The authors report no conflicts of interest.

Funding

This project was supported by the Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core. Dr. Wright is the Anne Gaines and G. Thomas Miller Professor of Medicine, which is supported through the Johns Hopkins’ Center for Innovative Medicine.

Inadequate bowel preparation (IBP) at the time of inpatient colonoscopy is common and associated with increased length of stay and cost of care.¹ The factors that contribute to IBP can be categorized into those that are modifiable and those that are nonmodifiable. While many factors have been associated with IBP, studies have been limited by small sample size or have combined inpatient/outpatient populations, thus limiting generalizability.^1-5 Moreover, most factors associated with IBP, such as socioeconomic status, male gender, increased age, and comorbidities, are nonmodifiable. No studies have explicitly focused on modifiable risk factors, such as medication use, colonoscopy timing, or assessed the potential impact of modifying these factors.

In a large, multihospital system, we examine the frequency of IBP among inpatients undergoing colonoscopy along with factors associated with IBP. We attempted to identify modifiable risk factors that were associated with IBP.

Potential Predictors of IBP

Demographic data such as patient age, gender, ethnicity, body mass index (BMI), and insurance/payor status were obtained from the electronic health record (EHR). International Classification of Disease 9^th and 10^th revision, Clinical Modifications (ICD-9/10-CM) codes were used to obtain patient comorbidities including diabetes, coronary artery disease, heart failure, cirrhosis, gastroparesis, hypothyroidism, inflammatory bowel disease, constipation, stroke, dementia, dysphagia, and nausea/vomiting. Use of opioid medications within three days before colonoscopy was extracted from the medication administration record. These variables were chosen as biologically plausible modifiers of bowel preparation or had previously been assessed in the literature.^1-6 The name and volume, classified as 4 L (GoLytely^®) and < 4 liters (MoviPrep^®) of bowel preparation, time of day when colonoscopy was performed, solid diet the day prior to colonoscopy, type of sedation used (conscious sedation or general anesthesia), and total colonoscopy time (defined as the time from scope insertion to removal) was recorded. Hospitalization-related variables, including the number of hospitalizations in the year before the current hospitalization, the year in which the colonoscopy was performed, and the number of days from admission to colonoscopy, were also obtained from the EHR.

Outcome Measures

An internally validated natural language algorithm, using Structured Queried Language was used to search through colonoscopy reports to identify adequacy of bowel preparation. ProVation^® software allows the gastroenterologist to use some terms to describe bowel preparation in a drop-down menu format. In addition to the Aronchik scale (which allows the gastroenterologist to rate bowel preparation on a five-point scale: “excellent,” “good,” “fair,” “poor,” and “inadequate”) it also allows the provider to use terms such as “adequate” or “adequate to detect polyps >5 mm” as well as “unsatisfactory.”⁷ Mirroring prior literature, bowel preparation quality was classified into “adequate” and “inadequate”; “good” and “excellent” on the Aronchik scale were categorized as adequate as was the term “adequate” in any form; “fair,” “poor,” or “inadequate” on the Aronchik scale were classified as inadequate as was the term “unsatisfactory.” We evaluated the hospital length of stay (LOS) as a secondary outcome measure.

Statistical Analysis

After describing the frequency of IBP, the quality of bowel preparation (adequate vs inadequate) was compared based on the predictors described above. Categorical variables were reported as frequencies with percentages and continuous variables were reported as medians with 25^th-75^th percentile values. The significance of the difference between the proportion or median values of those who had inadequate versus adequate bowel preparation was assessed. Two-sided chi-square analysis was used to assess the significance of differences between categorical variables and the Wilcoxon Rank-Sum test was used to assess the significance of differences between continuous variables.

Multivariate logistic regression analysis was performed to assess factors associated with hospital predictors and outcomes, after adjusting for all the aforementioned factors and clustering the effect based on the endoscopist. To evaluate the potential impact of modifiable factors on IBP, we performed counterfactual analysis, in which the observed distribution was compared to a hypothetical population in which all the modifiable risk factors were optimal.

Overall, 8,819 patients were included in our study population. They had a median age of 64 [53-76] years; 50.5% were female and 51% had an IBP. Patient characteristics and rates of IBP are presented in Table 1.

In unadjusted analyses, with regards to modifiable factors, opiate use within three days of colonoscopy was associated with a higher rate of IBP (55.4% vs 47.3%, P <.001), as was a lower volume (<4L) bowel preparation (55.3% vs 50.4%, P = .003). IBP was less frequent when colonoscopy was performed before noon vs afternoon (50.3% vs 57.4%, P < .001), and when patients were documented to receive a clear liquid diet or nil per os vs a solid diet the day prior to colonoscopy (50.3% vs 57.4%, P < .001). Overall bowel preparation quality improved over time (Figure 1). Median LOS was five [3-11] days. Patients who had IBP on their initial colonoscopy had a LOS one day longer than patients without IBP (six days vs five days, P < .001).

Multivariate Analysis

Table 2 shows the results of the multivariate analysis. The following modifiable factors were associated with IBP: opiate used within three days of the procedure (OR 1.31; 95% CI 1.8, 1.45), having the colonoscopy performed after12:00 pm (OR, 1.25; 95% CI, 1.10, 1.41), and consuming a solid diet the day prior to the colonoscopy (OR, 1.37; 95% CI, 1.18, 1.59). However, the volume of bowel preparation was not associated with IBP. The selected nonmodifiable factors that were found to be associated with IBP included age (increment of five years; OR, 1.04; 95% CI, 1.02, 1.05), male gender (OR, 1.33; 95% CI, 1.23, 1.44), Medicare insurance (OR, 1.17; 95% CI, 1.07, 1.28), Medicaid insurance (OR, 1.34; 95% CI, 1.07, 1.28), gastroparesis (OR, 1.62; 95% CI, 1.16, 2.27), nausea/vomiting (OR 1.21; 95% CI, 1.09, 1.34), and dysphagia (OR, 1.16; 95% CI, 1.01, 1.34).

Potential Impact of Modifiable Variables

We conducted a counterfactual analysis based on a multivariate model to assess the impact of each modifiable risk factor on the IBP rate (Figure 1). In the included study population, 44.9% received an opiate, 39.3% had a colonoscopy after 12:00 pm, and 9.1% received solid food the day prior to the procedure. Holding all other factors constant, if all patients were not prescribed opiates within three days of the procedure a 2.9% reduction in IBP would be expected. Similarly, if all patients underwent colonoscopy before noon, a 2.1% reduction in IBP rate would be expected. A 0.7% reduction would be expected if all patients were maintained on a liquid diet or nil per os. Combined, instituting all these changes (no opiates or solid diet before colonoscopy and performing all colonoscopies before noon) would produce a 5.6% reduction in IBP rate.

In this large, multihospital cohort, IBP was documented in half (51%) of 8,819 inpatient colonoscopies performed. Nonmodifiable patient characteristics independently associated with IBP were age, male gender, white race, Medicare and Medicaid insurance, nausea/vomiting, dysphagia, and gastroparesis. Modifiable factors included not consuming opiates within three days of colonoscopy, avoidance of a solid diet the day prior to colonoscopy and performing the colonoscopy before noon. The volume of bowel preparation consumed was not associated with IBP. In a counterfactual analysis, we found that if all three modifiable factors were optimized, the predicted rate of IBP would drop to 45%.

Many studies, including our analysis, have shown significant differences between the frequency of IBP in inpatient versus outpatient bowel preparations.^8-11 Therefore, it is crucial to study IBP in these settings separately. Three single-institution studies, including a total of 898 patients, have identified risk factors for inpatient IBP. Individual studies ranged in size from 130 to 524 patients with rates of IBP ranging from 22%-57%.^1-3 They found IBP to be associated with increasing age, lower income, ASA Grade >3, diabetes, coronary artery disease (CAD), nausea or vomiting, BMI >25, and chronic constipation. Modifiable factors included opiates, afternoon procedures, and runway times >6 hours.

We also found IBP to be associated with increasing age and male gender. However, we found no association with diabetes, chronic constipation, CAD or BMI. As we were able to adjust for a wider variety of variables, it is possible that we were able to account for residual confounding better than previous studies. For example, we found that having nausea/vomiting, dysphagia, and gastroparesis was associated with IBP. Gastroparesis with associated nausea and vomiting may be the mechanism by which diabetes increases the risk for IBP. Further studies are needed to assess if interventions or alternative bowel cleansing in these patients can result in improved IBP. Finally, in contrast to studies with smaller cohorts which found that lower volume bowel preps improved IBP in the right colon,^4,12 we found no association between IBP based and volume of bowel preparation consumed. Our impact analysis suggests that avoidance of opiates for at least three days before colonoscopy, avoidance of solid diet on the day before colonoscopy and performing all colonoscopies before noon would reduce the rate of IBP by 5.6%. While at first glance this does not appear to be a significant change, from a public health perspective with thousands of inpatient colonoscopies performed every year, it is crucial. We found that IBP was associated with an increased inpatient LOS of approximately one day. Assuming an average cost of one hospital day to be $2,000,¹³ this 5.6% improvement among our almost 9,000 patients, would translate into eliminating 494 unnecessary hospital days, or approximately $1 million in savings. More importantly, this savings comes without risk to patients and would result in an improvement in quality.

The factors mentioned above may not always be amenable to modification. For example, for patients with active gastrointestinal bleeding, postponing colonoscopy by one day for the sake of maintaining a patient on a clear diet may not be feasible. Similarly, performing colonoscopies in the morning is highly dependent on endoscopy suite availability and hospital logistics. Denying opiates to patients experiencing severe pain is not ethical. In many scenarios, however, these variables could be modified, and institutional efforts to support these practices could yield considerable savings. Future prospective studies are needed to verify the real impact of these changes.

Further discussion is needed to contextualize the finding that colonoscopies scheduled in the afternoon are associated with improved bowel preparation quality. Previous research—albeit in the outpatient setting—has demonstrated 11.8 hours as the maximum upper time limit for the time elapsed between the end of bowel preparation to colonoscopy.¹⁴ Another study found an inverse relationship between the quality of bowel preparation and the time after completion of the bowel preparation.¹⁵ This makes sense from a physiological perspective as delaying the time between completion of bowel preparation, and the procedure allows chyme from the small intestine to reaccumulate in the colon. Anecdotally, at our institution as well as at many others, the bowel preparations are ordered to start in the evening to allow the consumption of complete bowel preparation by midnight. As a result of this practice, only patients who have their colonoscopies scheduled before noon fall within the optimal period of 11.8 hours. In the outpatient setting, the use of split preparations has led to the obliteration of the difference in the quality of bowel preparation between morning and afternoon colonoscopies.¹⁶ Prospective trials are needed to evaluate the use of split preparations to improve the quality of afternoon inpatient colonoscopies.

In this large retrospective study evaluating bowel preparation quality in inpatients undergoing colonoscopy, we found that more than half of the patients have IBP and that IBP was associated with an extra day of hospitalization. Our study identifies those patients at highest risk and identifies modifiable risk factors for IBP. Specifically, we found that abstinence from opiates or solid diet before the colonoscopy, along with performing colonoscopies before noon were associated with improved outcomes. Prospective studies are needed to confirm the effects of these interventions on bowel preparation quality.

Disclosures

Carol A Burke, MD has received research funding from Ferring Pharmaceuticals. Other authors have no conflicts of interest to disclose.

Inadequate bowel preparation (IBP) at the time of inpatient colonoscopy is common and associated with increased length of stay and cost of care.¹ The factors that contribute to IBP can be categorized into those that are modifiable and those that are nonmodifiable. While many factors have been associated with IBP, studies have been limited by small sample size or have combined inpatient/outpatient populations, thus limiting generalizability.^1-5 Moreover, most factors associated with IBP, such as socioeconomic status, male gender, increased age, and comorbidities, are nonmodifiable. No studies have explicitly focused on modifiable risk factors, such as medication use, colonoscopy timing, or assessed the potential impact of modifying these factors.

In a large, multihospital system, we examine the frequency of IBP among inpatients undergoing colonoscopy along with factors associated with IBP. We attempted to identify modifiable risk factors that were associated with IBP.

Potential Predictors of IBP

Demographic data such as patient age, gender, ethnicity, body mass index (BMI), and insurance/payor status were obtained from the electronic health record (EHR). International Classification of Disease 9^th and 10^th revision, Clinical Modifications (ICD-9/10-CM) codes were used to obtain patient comorbidities including diabetes, coronary artery disease, heart failure, cirrhosis, gastroparesis, hypothyroidism, inflammatory bowel disease, constipation, stroke, dementia, dysphagia, and nausea/vomiting. Use of opioid medications within three days before colonoscopy was extracted from the medication administration record. These variables were chosen as biologically plausible modifiers of bowel preparation or had previously been assessed in the literature.^1-6 The name and volume, classified as 4 L (GoLytely^®) and < 4 liters (MoviPrep^®) of bowel preparation, time of day when colonoscopy was performed, solid diet the day prior to colonoscopy, type of sedation used (conscious sedation or general anesthesia), and total colonoscopy time (defined as the time from scope insertion to removal) was recorded. Hospitalization-related variables, including the number of hospitalizations in the year before the current hospitalization, the year in which the colonoscopy was performed, and the number of days from admission to colonoscopy, were also obtained from the EHR.

Outcome Measures

An internally validated natural language algorithm, using Structured Queried Language was used to search through colonoscopy reports to identify adequacy of bowel preparation. ProVation^® software allows the gastroenterologist to use some terms to describe bowel preparation in a drop-down menu format. In addition to the Aronchik scale (which allows the gastroenterologist to rate bowel preparation on a five-point scale: “excellent,” “good,” “fair,” “poor,” and “inadequate”) it also allows the provider to use terms such as “adequate” or “adequate to detect polyps >5 mm” as well as “unsatisfactory.”⁷ Mirroring prior literature, bowel preparation quality was classified into “adequate” and “inadequate”; “good” and “excellent” on the Aronchik scale were categorized as adequate as was the term “adequate” in any form; “fair,” “poor,” or “inadequate” on the Aronchik scale were classified as inadequate as was the term “unsatisfactory.” We evaluated the hospital length of stay (LOS) as a secondary outcome measure.

Statistical Analysis

After describing the frequency of IBP, the quality of bowel preparation (adequate vs inadequate) was compared based on the predictors described above. Categorical variables were reported as frequencies with percentages and continuous variables were reported as medians with 25^th-75^th percentile values. The significance of the difference between the proportion or median values of those who had inadequate versus adequate bowel preparation was assessed. Two-sided chi-square analysis was used to assess the significance of differences between categorical variables and the Wilcoxon Rank-Sum test was used to assess the significance of differences between continuous variables.

Multivariate logistic regression analysis was performed to assess factors associated with hospital predictors and outcomes, after adjusting for all the aforementioned factors and clustering the effect based on the endoscopist. To evaluate the potential impact of modifiable factors on IBP, we performed counterfactual analysis, in which the observed distribution was compared to a hypothetical population in which all the modifiable risk factors were optimal.

Overall, 8,819 patients were included in our study population. They had a median age of 64 [53-76] years; 50.5% were female and 51% had an IBP. Patient characteristics and rates of IBP are presented in Table 1.

In unadjusted analyses, with regards to modifiable factors, opiate use within three days of colonoscopy was associated with a higher rate of IBP (55.4% vs 47.3%, P <.001), as was a lower volume (<4L) bowel preparation (55.3% vs 50.4%, P = .003). IBP was less frequent when colonoscopy was performed before noon vs afternoon (50.3% vs 57.4%, P < .001), and when patients were documented to receive a clear liquid diet or nil per os vs a solid diet the day prior to colonoscopy (50.3% vs 57.4%, P < .001). Overall bowel preparation quality improved over time (Figure 1). Median LOS was five [3-11] days. Patients who had IBP on their initial colonoscopy had a LOS one day longer than patients without IBP (six days vs five days, P < .001).

Multivariate Analysis

Table 2 shows the results of the multivariate analysis. The following modifiable factors were associated with IBP: opiate used within three days of the procedure (OR 1.31; 95% CI 1.8, 1.45), having the colonoscopy performed after12:00 pm (OR, 1.25; 95% CI, 1.10, 1.41), and consuming a solid diet the day prior to the colonoscopy (OR, 1.37; 95% CI, 1.18, 1.59). However, the volume of bowel preparation was not associated with IBP. The selected nonmodifiable factors that were found to be associated with IBP included age (increment of five years; OR, 1.04; 95% CI, 1.02, 1.05), male gender (OR, 1.33; 95% CI, 1.23, 1.44), Medicare insurance (OR, 1.17; 95% CI, 1.07, 1.28), Medicaid insurance (OR, 1.34; 95% CI, 1.07, 1.28), gastroparesis (OR, 1.62; 95% CI, 1.16, 2.27), nausea/vomiting (OR 1.21; 95% CI, 1.09, 1.34), and dysphagia (OR, 1.16; 95% CI, 1.01, 1.34).

Potential Impact of Modifiable Variables

We conducted a counterfactual analysis based on a multivariate model to assess the impact of each modifiable risk factor on the IBP rate (Figure 1). In the included study population, 44.9% received an opiate, 39.3% had a colonoscopy after 12:00 pm, and 9.1% received solid food the day prior to the procedure. Holding all other factors constant, if all patients were not prescribed opiates within three days of the procedure a 2.9% reduction in IBP would be expected. Similarly, if all patients underwent colonoscopy before noon, a 2.1% reduction in IBP rate would be expected. A 0.7% reduction would be expected if all patients were maintained on a liquid diet or nil per os. Combined, instituting all these changes (no opiates or solid diet before colonoscopy and performing all colonoscopies before noon) would produce a 5.6% reduction in IBP rate.

In this large, multihospital cohort, IBP was documented in half (51%) of 8,819 inpatient colonoscopies performed. Nonmodifiable patient characteristics independently associated with IBP were age, male gender, white race, Medicare and Medicaid insurance, nausea/vomiting, dysphagia, and gastroparesis. Modifiable factors included not consuming opiates within three days of colonoscopy, avoidance of a solid diet the day prior to colonoscopy and performing the colonoscopy before noon. The volume of bowel preparation consumed was not associated with IBP. In a counterfactual analysis, we found that if all three modifiable factors were optimized, the predicted rate of IBP would drop to 45%.

Many studies, including our analysis, have shown significant differences between the frequency of IBP in inpatient versus outpatient bowel preparations.^8-11 Therefore, it is crucial to study IBP in these settings separately. Three single-institution studies, including a total of 898 patients, have identified risk factors for inpatient IBP. Individual studies ranged in size from 130 to 524 patients with rates of IBP ranging from 22%-57%.^1-3 They found IBP to be associated with increasing age, lower income, ASA Grade >3, diabetes, coronary artery disease (CAD), nausea or vomiting, BMI >25, and chronic constipation. Modifiable factors included opiates, afternoon procedures, and runway times >6 hours.

We also found IBP to be associated with increasing age and male gender. However, we found no association with diabetes, chronic constipation, CAD or BMI. As we were able to adjust for a wider variety of variables, it is possible that we were able to account for residual confounding better than previous studies. For example, we found that having nausea/vomiting, dysphagia, and gastroparesis was associated with IBP. Gastroparesis with associated nausea and vomiting may be the mechanism by which diabetes increases the risk for IBP. Further studies are needed to assess if interventions or alternative bowel cleansing in these patients can result in improved IBP. Finally, in contrast to studies with smaller cohorts which found that lower volume bowel preps improved IBP in the right colon,^4,12 we found no association between IBP based and volume of bowel preparation consumed. Our impact analysis suggests that avoidance of opiates for at least three days before colonoscopy, avoidance of solid diet on the day before colonoscopy and performing all colonoscopies before noon would reduce the rate of IBP by 5.6%. While at first glance this does not appear to be a significant change, from a public health perspective with thousands of inpatient colonoscopies performed every year, it is crucial. We found that IBP was associated with an increased inpatient LOS of approximately one day. Assuming an average cost of one hospital day to be $2,000,¹³ this 5.6% improvement among our almost 9,000 patients, would translate into eliminating 494 unnecessary hospital days, or approximately $1 million in savings. More importantly, this savings comes without risk to patients and would result in an improvement in quality.

The factors mentioned above may not always be amenable to modification. For example, for patients with active gastrointestinal bleeding, postponing colonoscopy by one day for the sake of maintaining a patient on a clear diet may not be feasible. Similarly, performing colonoscopies in the morning is highly dependent on endoscopy suite availability and hospital logistics. Denying opiates to patients experiencing severe pain is not ethical. In many scenarios, however, these variables could be modified, and institutional efforts to support these practices could yield considerable savings. Future prospective studies are needed to verify the real impact of these changes.

Further discussion is needed to contextualize the finding that colonoscopies scheduled in the afternoon are associated with improved bowel preparation quality. Previous research—albeit in the outpatient setting—has demonstrated 11.8 hours as the maximum upper time limit for the time elapsed between the end of bowel preparation to colonoscopy.¹⁴ Another study found an inverse relationship between the quality of bowel preparation and the time after completion of the bowel preparation.¹⁵ This makes sense from a physiological perspective as delaying the time between completion of bowel preparation, and the procedure allows chyme from the small intestine to reaccumulate in the colon. Anecdotally, at our institution as well as at many others, the bowel preparations are ordered to start in the evening to allow the consumption of complete bowel preparation by midnight. As a result of this practice, only patients who have their colonoscopies scheduled before noon fall within the optimal period of 11.8 hours. In the outpatient setting, the use of split preparations has led to the obliteration of the difference in the quality of bowel preparation between morning and afternoon colonoscopies.¹⁶ Prospective trials are needed to evaluate the use of split preparations to improve the quality of afternoon inpatient colonoscopies.

In this large retrospective study evaluating bowel preparation quality in inpatients undergoing colonoscopy, we found that more than half of the patients have IBP and that IBP was associated with an extra day of hospitalization. Our study identifies those patients at highest risk and identifies modifiable risk factors for IBP. Specifically, we found that abstinence from opiates or solid diet before the colonoscopy, along with performing colonoscopies before noon were associated with improved outcomes. Prospective studies are needed to confirm the effects of these interventions on bowel preparation quality.

Disclosures

Carol A Burke, MD has received research funding from Ferring Pharmaceuticals. Other authors have no conflicts of interest to disclose.

Inadequate bowel preparation (IBP) at the time of inpatient colonoscopy is common and associated with increased length of stay and cost of care.¹ The factors that contribute to IBP can be categorized into those that are modifiable and those that are nonmodifiable. While many factors have been associated with IBP, studies have been limited by small sample size or have combined inpatient/outpatient populations, thus limiting generalizability.^1-5 Moreover, most factors associated with IBP, such as socioeconomic status, male gender, increased age, and comorbidities, are nonmodifiable. No studies have explicitly focused on modifiable risk factors, such as medication use, colonoscopy timing, or assessed the potential impact of modifying these factors.

In a large, multihospital system, we examine the frequency of IBP among inpatients undergoing colonoscopy along with factors associated with IBP. We attempted to identify modifiable risk factors that were associated with IBP.

Potential Predictors of IBP

Demographic data such as patient age, gender, ethnicity, body mass index (BMI), and insurance/payor status were obtained from the electronic health record (EHR). International Classification of Disease 9^th and 10^th revision, Clinical Modifications (ICD-9/10-CM) codes were used to obtain patient comorbidities including diabetes, coronary artery disease, heart failure, cirrhosis, gastroparesis, hypothyroidism, inflammatory bowel disease, constipation, stroke, dementia, dysphagia, and nausea/vomiting. Use of opioid medications within three days before colonoscopy was extracted from the medication administration record. These variables were chosen as biologically plausible modifiers of bowel preparation or had previously been assessed in the literature.^1-6 The name and volume, classified as 4 L (GoLytely^®) and < 4 liters (MoviPrep^®) of bowel preparation, time of day when colonoscopy was performed, solid diet the day prior to colonoscopy, type of sedation used (conscious sedation or general anesthesia), and total colonoscopy time (defined as the time from scope insertion to removal) was recorded. Hospitalization-related variables, including the number of hospitalizations in the year before the current hospitalization, the year in which the colonoscopy was performed, and the number of days from admission to colonoscopy, were also obtained from the EHR.

Outcome Measures

An internally validated natural language algorithm, using Structured Queried Language was used to search through colonoscopy reports to identify adequacy of bowel preparation. ProVation^® software allows the gastroenterologist to use some terms to describe bowel preparation in a drop-down menu format. In addition to the Aronchik scale (which allows the gastroenterologist to rate bowel preparation on a five-point scale: “excellent,” “good,” “fair,” “poor,” and “inadequate”) it also allows the provider to use terms such as “adequate” or “adequate to detect polyps >5 mm” as well as “unsatisfactory.”⁷ Mirroring prior literature, bowel preparation quality was classified into “adequate” and “inadequate”; “good” and “excellent” on the Aronchik scale were categorized as adequate as was the term “adequate” in any form; “fair,” “poor,” or “inadequate” on the Aronchik scale were classified as inadequate as was the term “unsatisfactory.” We evaluated the hospital length of stay (LOS) as a secondary outcome measure.

Statistical Analysis

After describing the frequency of IBP, the quality of bowel preparation (adequate vs inadequate) was compared based on the predictors described above. Categorical variables were reported as frequencies with percentages and continuous variables were reported as medians with 25^th-75^th percentile values. The significance of the difference between the proportion or median values of those who had inadequate versus adequate bowel preparation was assessed. Two-sided chi-square analysis was used to assess the significance of differences between categorical variables and the Wilcoxon Rank-Sum test was used to assess the significance of differences between continuous variables.

Multivariate logistic regression analysis was performed to assess factors associated with hospital predictors and outcomes, after adjusting for all the aforementioned factors and clustering the effect based on the endoscopist. To evaluate the potential impact of modifiable factors on IBP, we performed counterfactual analysis, in which the observed distribution was compared to a hypothetical population in which all the modifiable risk factors were optimal.

Overall, 8,819 patients were included in our study population. They had a median age of 64 [53-76] years; 50.5% were female and 51% had an IBP. Patient characteristics and rates of IBP are presented in Table 1.

In unadjusted analyses, with regards to modifiable factors, opiate use within three days of colonoscopy was associated with a higher rate of IBP (55.4% vs 47.3%, P <.001), as was a lower volume (<4L) bowel preparation (55.3% vs 50.4%, P = .003). IBP was less frequent when colonoscopy was performed before noon vs afternoon (50.3% vs 57.4%, P < .001), and when patients were documented to receive a clear liquid diet or nil per os vs a solid diet the day prior to colonoscopy (50.3% vs 57.4%, P < .001). Overall bowel preparation quality improved over time (Figure 1). Median LOS was five [3-11] days. Patients who had IBP on their initial colonoscopy had a LOS one day longer than patients without IBP (six days vs five days, P < .001).

Multivariate Analysis

Table 2 shows the results of the multivariate analysis. The following modifiable factors were associated with IBP: opiate used within three days of the procedure (OR 1.31; 95% CI 1.8, 1.45), having the colonoscopy performed after12:00 pm (OR, 1.25; 95% CI, 1.10, 1.41), and consuming a solid diet the day prior to the colonoscopy (OR, 1.37; 95% CI, 1.18, 1.59). However, the volume of bowel preparation was not associated with IBP. The selected nonmodifiable factors that were found to be associated with IBP included age (increment of five years; OR, 1.04; 95% CI, 1.02, 1.05), male gender (OR, 1.33; 95% CI, 1.23, 1.44), Medicare insurance (OR, 1.17; 95% CI, 1.07, 1.28), Medicaid insurance (OR, 1.34; 95% CI, 1.07, 1.28), gastroparesis (OR, 1.62; 95% CI, 1.16, 2.27), nausea/vomiting (OR 1.21; 95% CI, 1.09, 1.34), and dysphagia (OR, 1.16; 95% CI, 1.01, 1.34).

Potential Impact of Modifiable Variables

We conducted a counterfactual analysis based on a multivariate model to assess the impact of each modifiable risk factor on the IBP rate (Figure 1). In the included study population, 44.9% received an opiate, 39.3% had a colonoscopy after 12:00 pm, and 9.1% received solid food the day prior to the procedure. Holding all other factors constant, if all patients were not prescribed opiates within three days of the procedure a 2.9% reduction in IBP would be expected. Similarly, if all patients underwent colonoscopy before noon, a 2.1% reduction in IBP rate would be expected. A 0.7% reduction would be expected if all patients were maintained on a liquid diet or nil per os. Combined, instituting all these changes (no opiates or solid diet before colonoscopy and performing all colonoscopies before noon) would produce a 5.6% reduction in IBP rate.

In this large, multihospital cohort, IBP was documented in half (51%) of 8,819 inpatient colonoscopies performed. Nonmodifiable patient characteristics independently associated with IBP were age, male gender, white race, Medicare and Medicaid insurance, nausea/vomiting, dysphagia, and gastroparesis. Modifiable factors included not consuming opiates within three days of colonoscopy, avoidance of a solid diet the day prior to colonoscopy and performing the colonoscopy before noon. The volume of bowel preparation consumed was not associated with IBP. In a counterfactual analysis, we found that if all three modifiable factors were optimized, the predicted rate of IBP would drop to 45%.

Many studies, including our analysis, have shown significant differences between the frequency of IBP in inpatient versus outpatient bowel preparations.^8-11 Therefore, it is crucial to study IBP in these settings separately. Three single-institution studies, including a total of 898 patients, have identified risk factors for inpatient IBP. Individual studies ranged in size from 130 to 524 patients with rates of IBP ranging from 22%-57%.^1-3 They found IBP to be associated with increasing age, lower income, ASA Grade >3, diabetes, coronary artery disease (CAD), nausea or vomiting, BMI >25, and chronic constipation. Modifiable factors included opiates, afternoon procedures, and runway times >6 hours.

We also found IBP to be associated with increasing age and male gender. However, we found no association with diabetes, chronic constipation, CAD or BMI. As we were able to adjust for a wider variety of variables, it is possible that we were able to account for residual confounding better than previous studies. For example, we found that having nausea/vomiting, dysphagia, and gastroparesis was associated with IBP. Gastroparesis with associated nausea and vomiting may be the mechanism by which diabetes increases the risk for IBP. Further studies are needed to assess if interventions or alternative bowel cleansing in these patients can result in improved IBP. Finally, in contrast to studies with smaller cohorts which found that lower volume bowel preps improved IBP in the right colon,^4,12 we found no association between IBP based and volume of bowel preparation consumed. Our impact analysis suggests that avoidance of opiates for at least three days before colonoscopy, avoidance of solid diet on the day before colonoscopy and performing all colonoscopies before noon would reduce the rate of IBP by 5.6%. While at first glance this does not appear to be a significant change, from a public health perspective with thousands of inpatient colonoscopies performed every year, it is crucial. We found that IBP was associated with an increased inpatient LOS of approximately one day. Assuming an average cost of one hospital day to be $2,000,¹³ this 5.6% improvement among our almost 9,000 patients, would translate into eliminating 494 unnecessary hospital days, or approximately $1 million in savings. More importantly, this savings comes without risk to patients and would result in an improvement in quality.

The factors mentioned above may not always be amenable to modification. For example, for patients with active gastrointestinal bleeding, postponing colonoscopy by one day for the sake of maintaining a patient on a clear diet may not be feasible. Similarly, performing colonoscopies in the morning is highly dependent on endoscopy suite availability and hospital logistics. Denying opiates to patients experiencing severe pain is not ethical. In many scenarios, however, these variables could be modified, and institutional efforts to support these practices could yield considerable savings. Future prospective studies are needed to verify the real impact of these changes.

Further discussion is needed to contextualize the finding that colonoscopies scheduled in the afternoon are associated with improved bowel preparation quality. Previous research—albeit in the outpatient setting—has demonstrated 11.8 hours as the maximum upper time limit for the time elapsed between the end of bowel preparation to colonoscopy.¹⁴ Another study found an inverse relationship between the quality of bowel preparation and the time after completion of the bowel preparation.¹⁵ This makes sense from a physiological perspective as delaying the time between completion of bowel preparation, and the procedure allows chyme from the small intestine to reaccumulate in the colon. Anecdotally, at our institution as well as at many others, the bowel preparations are ordered to start in the evening to allow the consumption of complete bowel preparation by midnight. As a result of this practice, only patients who have their colonoscopies scheduled before noon fall within the optimal period of 11.8 hours. In the outpatient setting, the use of split preparations has led to the obliteration of the difference in the quality of bowel preparation between morning and afternoon colonoscopies.¹⁶ Prospective trials are needed to evaluate the use of split preparations to improve the quality of afternoon inpatient colonoscopies.

In this large retrospective study evaluating bowel preparation quality in inpatients undergoing colonoscopy, we found that more than half of the patients have IBP and that IBP was associated with an extra day of hospitalization. Our study identifies those patients at highest risk and identifies modifiable risk factors for IBP. Specifically, we found that abstinence from opiates or solid diet before the colonoscopy, along with performing colonoscopies before noon were associated with improved outcomes. Prospective studies are needed to confirm the effects of these interventions on bowel preparation quality.

Disclosures

Carol A Burke, MD has received research funding from Ferring Pharmaceuticals. Other authors have no conflicts of interest to disclose.

Sepsis is both the most expensive condition treated and the most common cause of death in hospitals in the United States.^1-3 Most sepsis patients (as many as 80% to 90%) meet sepsis criteria on hospital arrival, but mortality and costs are higher when meeting criteria after admission.^3-6 Mechanisms of this increased mortality for these distinct populations are not well explored. Patients who present septic in the emergency department (ED) and patients who present as inpatients likely present very different challenges for recognition, treatment, and monitoring.⁷ Yet, how these groups differ by demographic and clinical characteristics, the etiology and severity of infection, and patterns of resuscitation care are not well described. Literature on sepsis epidemiology on hospital wards is particularly limited.⁸

This knowledge gap is important. If hospital-presenting sepsis (HPS) contributes disproportionately to disease burdCHFens, it reflects a high-yield population deserving the focus of quality improvement (QI) initiatives. If specific causes of disparities were identified—eg, poor initial resuscitation— they could be specifically targeted for correction. Given that current treatment guidelines are uniform for the two populations,^9,10 characterizing phenotypic differences could also have implications for both diagnostic and therapeutic recommendations, particularly if the groups display substantially differing clinical presentations. Our prior work has not probed these effects specifically, but suggested ED versus inpatient setting at the time of initial sepsis presentation might be an effect modifier for the association between several elements of fluid resuscitation and patient outcomes.^11,12

We, therefore, conducted a retrospective analysis to ask four sequential questions: (1) Do patients with HPS, compared with EDPS, contribute adverse outcome out of proportion to case prevalence? (2) At the time of initial presentation, how do HPS patients differ from EDPS patients with respect to demographics, comorbidities, infectious etiologies, clinical presentations, and severity of illness (3) If holding observed baseline factors constant, does the physical location of sepsis presentation inherently increase the risk for treatment delays and mortality? (4) To what extent can differences in the likelihood for timely initial treatment between the ED and inpatient settings explain differences in mortality and patient outcomes?

We hypothesized a priori that HPS would reflect chronically sicker patients whom both received less timely resuscitation and who contributed disproportionately frequent bad outcomes. We expected disparities in timely resuscitation care would explain a large proportion of this difference.

We performed a retrospective analysis of the Northwell Sepsis Database, a prospectively captured, multisite, real world, consecutive-sample cohort of all “severe sepsis” and septic shock patients treated at nine tertiary and community hospitals in New York from October 1, 2014, to March 31, 2016. We analyzed all patients from a previously published cohort.¹¹

Database Design and Structure

The Northwell Sepsis Database has previously been described in detail.^11,13,14 Briefly, all patients met clinical sepsis criteria: (1) infection AND (2) ≥2 (SIRS) criteria AND (3) ≥1 acute organ dysfunction criterion. Organ dysfunction criteria were hypotension, acute kidney injury (AKI), coagulopathy, altered gas exchange, elevated bilirubin (≥2.0 mg/dL), or altered mental status (AMS; clarified in Supplemental Table 1). All organ dysfunction was not otherwise explained by patients’ medical histories; eg, patients on warfarin anticoagulation were not documented to have coagulopathy based on international normalized ratio > 1.5. The time of the sepsis episode (and database inclusion) was the time of the first vital sign measurement or laboratory result where a patient simultaneously met all three inclusion criteria: infection, SIRS, and organ dysfunction. The database excludes patients who were <18 years, declined bundle interventions, had advance directives precluding interventions, or were admitted directly to palliative care or hospice. Abstractors assumed comorbidities were absent if not documented within the medical record and that physiologic abnormalities were absent if not measured by the treatment team. There were no missing data for the variables analyzed. We report analysis in adherence with the STROBE statement guidelines for observational research.

Exposure

The primary exposure was whether patients had EDPS versus HPS. We defined EDPS patients as meeting all objective clinical inclusion criteria while physically in the ED. We defined HPS as first meeting sepsis inclusion criteria outside the ED, regardless of the reason for admission, and regardless of whether patients were admitted through the ED or directly to the hospital. All ED patients were admitted to the hospital.

Outcomes

Process outcomes were full 3-hour bundle compliance, time to antibiotic administration, blood cultures before antibiotics, time to fluid initiation, the volume of administered fluid resuscitation, lactate result time, and whether repeat lactate was obtained (Supplemental Table 2). Treatment times were times of administration (rather than order time). The primary patient outcome was hospital mortality. Secondary patient outcomes were mechanical ventilation, ICU admission, ICU days, hospital length of stay (LOS). We discounted HPS patients’ LOS to include only days after meeting the inclusion criteria. Patients were excluded from the analysis of the ICU admission outcome if they were already in the ICU prior to meeting sepsis criteria.

Statistical Analysis

We report continuous variables as means (standard deviation) or medians (interquartile range), and categorical variables as frequencies (proportions), as appropriate. Summative statistics with 95% confidence intervals (CI) describe overall group contributions. We used generalized linear models to determine patient factors associated with EDPS versus HPS, entering random effects for individual study sites to control for intercenter variability.

Next, to generate a propensity-matched cohort, we computed propensity scores adjusted from a priori selected variables: age, sex, tertiary versus community hospital, congestive heart failure (CHF), renal failure, COPD, diabetes, liver failure, immunocompromise, primary source of infection, nosocomial source, temperature, initial lactate, presenting hypotension, altered gas exchange, AMS, AKI, and coagulopathy. We then matched subjects 1:1 without optimization or replacement, imposing a caliper width of 0.01; ie, we required matched pairs to have a <1.0% difference in propensity scores. The macro used to match subjects is publically available.¹⁵

We then compared resuscitation and patient outcomes in the matched cohort using generalized linear models, ie, doubly-robust estimation (DRE).¹⁶ When assessing patient outcomes corrected for resuscitation, we used mixed DRE/multivariable regression. We did this for two reasons: first, DRE has the advantage of only requiring only one approach (propensity vs covariate adjustments) to be correctly specified.¹⁶ Second, computing propensity scores adjusted for resuscitation would be inappropriate given that resuscitation occurs after the exposure allocation (HPS vs EDPS). However, these factors could still impact the outcome and in fact, we hypothesized they were potential mediators of the exposure effect. To interrogate this mediating relationship, we recapitulated the DRE modeling but added covariates for resuscitation factors. Resuscitation-adjusted models controlled for timeliness of antibiotics, fluids, and lactate results; blood cultures before antibiotics; repeat lactate obtained, and fluid volume in the first six hours. Since ICU days and LOS are subject to competing risks bias (LOS could be shorter if patients died earlier), we used proportional hazards models where “the event” was defined as a live discharge to censor for mortality and we report output as inverse hazard ratios. We also tested interaction coefficients for discrete bundle elements and HPS to determine if specific bundle elements were effect modifiers for the association between the presenting location and mortality risk. Finally, we estimated attributable risk differences by comparing adjusted odds ratios of adverse outcome with and without adjustment for resuscitation variables, as described by Sahai et al.¹⁷

As sensitivity analyses, we recomputed propensity scores and generated a new matched cohort that excluded HPS patients who met criteria for sepsis while already in the ICU for another reason (ie, excluding ICU-presenting sepsis). We then recapitulated all analyses as above for this cohort. We performed analyses using SAS version 9.4 (SAS Institute, Cary, North Carolina).

Prevalence and Outcome Contributions

Of the 11,182 sepsis patients in the database, we classified 2,509 (22%) as HPS (Figure 1). HPS contributed 785 (35%) of 2,241 sepsis-related mortalities, 1,241 (38%) mechanical ventilations, and 1,762 (34%) ICU admissions. Of 39,263 total ICU days and 127,178 hospital days, HPS contributed 18,104 (46.1%) and 44,412 (34.9%) days, respectively.

Patient Characteristics

Most HPS presented early in the hospital course, with 1,352 (53.9%) cases meeting study criteria within three days of admission. Median time from admission to meeting study criteria for HPS was two days (interquartile range: one to seven days). We report selected baseline patient characteristics in Table 1 and adjusted associations of baseline variables with HPS versus EDPS in Table 2. The full cohort characterization is available in Supplemental Table 3. Notably, HPS patients more often had CHF (aOR [adjusted odds ratio}: 1.31, CI: 1.18-1.47) or renal failure (aOR: 1.62, CI: 1.38-1.91), gastrointestinal source of infection (aOR: 1.84, CI: 1.48-2.29), hypothermia (aOR: 1.56, CI: 1.28-1.90) hypotension (aOR: 1.85, CI: 1.65-2.08), or altered gas exchange (aOR: 2.46, CI: 1.43-4.24). In contrast, HPS patients less frequently were admitted from skilled nursing facilities (aOR: 0.44, CI: 0.32-0.60), or had COPD (aOR: 0.53, CI: 0.36-0.76), fever (aOR: 0.70, CI: 0.52-0.91), tachypnea (aOR: 0.76, CI: 0.58-0.98), or AKI (aOR: 082, CI: 0.68-0.97). Other baseline variables were similar, including respiratory source, tachycardia, white cell abnormalities, AMS, and coagulopathies. These associations were preserved in the sensitivity analysis excluding ICU-presenting sepsis.

Propensity Matching

Propensity score matching yielded 1,942 matched pairs (n = 3,884, 77% of HPS patients, 22% of EDPS patients). Table 1 and Supplemental Table 3 show patient characteristics after propensity matching. Supplemental Table 4 shows the propensity model. The frequency densities are shown for the cohort as a function of propensity score in Supplemental Figure 1. After matching, frequencies between groups differed by <5% for all categorical variables assessed. In the sensitivity analysis, propensity matching (model in Supplemental Table 5) resulted in 1,233 matched pairs (n = 2,466, 49% of HPS patients, 14% of EDPS patients), with group differences comparable to the primary analysis.

Process Outcomes

We present propensity-matched differences in initial resuscitation in Figure 2A for all HPS patients, as well as non-ICU-presenting HPS, versus EDPS. HPS patients were roughly half as likely to receive fully 3-hour bundle compliant care (17.0% vs 30.3%, aOR: 0.47, CI: 0.40-0.57), to have blood cultures drawn within three hours prior to antibiotics (44.9% vs 67.2%, aOR: 0.40, CI: 0.35-0.46), or to receive fluid resuscitation initiated within two hours (11.1% vs 26.1%, aOR: 0.35, CI: 0.29-0.42). Antibiotic receipt within one hour was comparable (45.3% vs 48.1%, aOR: 0.89, CI: 0.79-1.01). However, differences emerged for antibiotics within three hours (66.2% vs 83.8%, aOR: 0.38, CI: 0.32-0.44) and persisted at six hours (77.0% vs 92.5%, aOR: 0.27, CI: 0.22-33). Excluding ICU-presenting sepsis from propensity matching exaggerated disparities in antibiotic receipt at one hour (43.4% vs 49.1%, aOR: 0.80, CI: 0.68-0.93), three hours (64.2% vs 86.1%, aOR: 0.29, CI: 0.24-0.35), and six hours (75.7% vs 93.0%, aOR: 0.23, CI: 0.18-0.30). HPS patients more frequently had repeat lactate obtained within 24 hours (62.4% vs 54.3%, aOR: 1.40, CI: 1.23-1.59).

Patient Outcomes

HPS patients had higher mortality (31.2% vs19.3%), mechanical ventilation (51.5% vs27.4%), and ICU admission (60.6% vs 46.5%) (Table 1 and Supplemental Table 6). Figure 2b shows propensity-matched and covariate-adjusted differences in patient outcomes before and after adjusting for initial resuscitation. aORs corresponded to approximate relative risk differences¹⁸ of 1.38 (CI: 1.28-1.48), 1.68 (CI: 1.57-1.79), and 1.72 (CI: 1.61-1.84) for mortality, mechanical ventilation, and ICU admission, respectively. HPS was associated with 83% longer mortality-censored ICU stays (five vs nine days, HR^–1: 1.83, CI: 1.65-2.03), and 108% longer hospital stay (eight vs 17 days, HR^–1: 2.08, CI: 1.93-2.24). After adjustment for resuscitation, all effect sizes decreased but persisted. The initial crystalloid volume was a significant negative effect modifier for mortality (Supplemental Table 7). That is, the magnitude of the association between HPS and greater mortality decreased by a factor of 0.89 per 10 mL/kg given (CI: 0.82-0.97). We did not observe significant interaction from other interventions, or overall bundle compliance, meaning these interventions’ association with mortality did not significantly differ between HPS versus EDPS.

The implied attributable risk difference from discrepancies in initial resuscitation was 23.3% for mortality, 35.2% for mechanical ventilation, and 7.6% for ICU admission (Figure 2B). Resuscitation explained 26.5% of longer ICU LOS and 16.7% of longer hospital LOS associated with HPS.

Figure 2C shows sensitivity analysis excluding ICU-presenting sepsis from propensity matching (ie, limiting HPS to hospital ward presentations). Again, HPS was associated with all adverse outcomes, though effect sizes were smaller than in the primary cohort for all outcomes except hospital LOS. In this cohort, resuscitation factors now explained 16.5% of HPS’ association with mortality, and 14.5% of the association with longer ICU LOS. However, they explained a greater proportion (13.0%) of ICU admissions. Attributable risk differences were comparable to the primary cohort for mechanical ventilation (37.6%) and hospital LOS (15.3%).

In this analysis of 11,182 sepsis and septic shock patients, HPS contributed 22% of prevalence but >35% of total sepsis mortalities, ICU utilization, and hospital days. HPS patients had higher comorbidity burdens and had clinical presentations less obviously attributable to infection with more severe organ dysfunction. EDPS received antibiotics within three hours about 1.62 times more often than do HPS patients. EDPS patients also receive fluids initiated within two hours about 1.82 times more often than HPS patients do. HPS had nearly 1.5-fold greater mortality and LOS, and nearly two-fold greater mechanical ventilation and ICU utilization. Resuscitation disparities could partially explain these associations. These patterns persisted when comparing only wards presenting HPS with EDPS.

Our analysis revealed several notable findings. First, these data confirm that HPS represents a potentially high-impact target population that contributes adverse outcomes disproportionately frequently with respect to case prevalence.

Our findings, unsurprisingly, revealed HPS and EDPS reflect dramatically different patient populations. We found that the two groups significantly differed by the majority of the baseline factors we compared. It may be worth asking if and how these substantial differences in illness etiology, chronic health, and acute physiology impact what we consider an optimal approach to management. Significant interaction effects of fluid volume on the association between HPS and mortality suggest differential treatment effects may exist between patients. Indeed, patients who newly arrive from the community and those who are several days into admission likely have different volume status. However, no interactions were noted with other bundle elements, such as timeliness of antibiotics or timeliness of initial fluids.

Another potentially concerning observation was that HPS patients were admitted much less frequently from skilled nursing facilities, as it could imply that this poorer-fairing population had a comparatively higher baseline functional status. The fact that 25% of EDPS cases were admitted from these facilities also underscores the need to engage skilled nursing facility providers in future sepsis initiatives.

We found marked disparities in resuscitation. Timely delivery of interventions, such as antibiotics and initial fluid resuscitation, occurred less than half as often for HPS, especially on hospital wards. While evidence supporting the efficacy of specific 3-hour bundle elements remains unsettled,¹⁹ a wealth of literature demonstrates a correlation between bundle uptake and decreased sepsis mortality, especially for early antibiotic administration.^13,20-26 Some analysis suggests that differing initial resuscitation practices explain different mortality rates in the early goal-directed therapy trials.²⁷ The comparatively poor performance for non-ICU HPS indicates further QI efforts are better focused on inpatient wards, rather than on EDs or ICUs where resuscitation is already delivered with substantially greater fidelity.

While resuscitation differences partially explained outcome discrepancies between groups, they did not account for as much variation as expected. Though resuscitation accounted for >35% of attributable mechanical ventilation risk, it explained only 16.5% of mortality differences for non-ICU HPS vs EDPS. We speculate that several factors may contribute.

First, HPS patients are already hospitalized for another acute insult and may be too physiologically brittle to derive equal benefit from initial resuscitation. Some literature suggests protocolized sepsis resuscitation may paradoxically be more effective in milder/earlier disease.²⁸

Second, clinical information indicating septic organ dysfunction may become available too late in HPS—a possible data limitation where inpatient providers are counterintuitively more likely to miss early signs of patients’ deterioration and a subsequent therapeutic window. Several studies found that fluid resuscitation is associated with improved sepsis outcomes only when it is administered very early.^11,29-31 In inpatient wards, decreased monitoring³² and human factors (eg, hospital workflow, provider-to-patient ratios, electronic documentation burdens)^33,34 may hinder early diagnosis. In contrast, ED environments are explicitly designed to identify acutely ill patients and deliver intervention rapidly. If HPS patients were sicker when they were identified, this would also explain their more severe organ dysfunctions. Our data seems to support this possibility. HPS patients had tachypnea less frequently but more often had impaired gas exchange. This finding may suggest that early tachypnea was either less often detected or documented, or that it had progressed further by the time of detection.

Third, inpatients with sepsis may more often present with greater diagnostic complexity. We observed that HPS patients were more often euthermic and less often tachypneic. Beyond suggesting a greater diagnostic challenge, this also raises questions as to whether differences reflect patient physiology (response to infection) or iatrogenic factors (eg, prior antipyretics). Higher comorbidity and acute physiological burdens also limit the degree to which new organ dysfunction can be clearly attributed to infection. We note differences in the proportion of patients who received antibiotics increased over time, suggesting that HPS patients who received delayed antibiotics did so much later than their EDPS counterparts. This lag could also arise from diagnostic difficulty.

All three possibilities highlight a potential lead time effect, where the same measured three-hour period on the wards, between meeting sepsis criteria and starting treatment, actually reflects a longer period between (as yet unmeasurable) pathobiologic “time zero” and treatment versus the ED. The time of sepsis detection, as distinct from the time of sepsis onset, therefore proves difficult to evaluate and impossible to account for statistically.

Regardless, our findings suggest additional difficulty in both the recognition and resuscitation of inpatient sepsis. Inpatients, especially with infections, may need closer monitoring. How to cost effectively implement this monitoring is a challenge that deserves attention.

A more rational systems approach to HPS likely combines efforts to improve initial resuscitation with other initiatives aimed at both improving monitoring and preventing infection.

To be clear, we do not imply that timely initial resuscitation does not matter on the wards. Rather, resuscitation-focused QI alone does not appear to be sufficient to overcome differences in outcomes for HPS. The 23.3% attributable mortality risk we observed still implies that resuscitation differences could explain nearly one in four excess HPS mortalities. We previously showed that timely resuscitation is strongly associated with better outcomes.^11,13,30 As discussed above, the unclear degree to which better resuscitation is a marker for more obvious presentations is a persistent limitation of prior investigations and the present study.

Taken together, the ultimate question that this study raises but cannot answer is whether the timely recognition of sepsis, rather than any specific treatment, is what truly improves outcomes.

In addition to those above, this study has several limitations. Our study did not differentiate HPS with respect to patients admitted for noninfectious reasons and who subsequently became septic versus nonseptic patients admitted for an infection who subsequently became septic from that infection. Nor could we discriminate between missed ED diagnoses and true delayed presentations. We note distinguishing these entities clinically can be equally challenging. Additionally, this was a propensity-matched retrospective analysis of an existing sepsis cohort, and the many limitations of both retrospective study and propensity matching apply.^35,36 We note that randomizing patients to develop sepsis in the community versus hospital is not feasible and that two of our aims intended to describe overall patterns rather than causal effects. We could not ascertain robust measures of severity of illness (eg, SOFA) because a real world setting precludes required data points—eg, urine output is unreliably recorded. We also note incomplete overlap between inclusion criteria and either Sepsis-2 or -3 definitions,^1,37 because we designed and populated our database prior to publication of Sepsis-3. Further, we could not account for surgical source control, the appropriateness of antimicrobial therapy, mechanical ventilation before sepsis onset, or most treatments given after initial resuscitation.

In conclusion, hospital-presenting sepsis accounted for adverse patient outcomes disproportionately to prevalence. HPS patients had more complex presentations, received timely antibiotics half as often ED-presenting sepsis, and had nearly twice the mortality odds. Resuscitation disparities explained roughly 25% of this difference.

Disclosures

The authors have no conflicts of interest to disclose.

Funding

This investigation was funded in part by a grant from the Center for Medicare and Medicaid Innovation to the High Value Healthcare Collaborative, of which the study sites’ umbrella health system was a part. This grant helped fund the underlying QI program and database in this study.

Sepsis is both the most expensive condition treated and the most common cause of death in hospitals in the United States.^1-3 Most sepsis patients (as many as 80% to 90%) meet sepsis criteria on hospital arrival, but mortality and costs are higher when meeting criteria after admission.^3-6 Mechanisms of this increased mortality for these distinct populations are not well explored. Patients who present septic in the emergency department (ED) and patients who present as inpatients likely present very different challenges for recognition, treatment, and monitoring.⁷ Yet, how these groups differ by demographic and clinical characteristics, the etiology and severity of infection, and patterns of resuscitation care are not well described. Literature on sepsis epidemiology on hospital wards is particularly limited.⁸

This knowledge gap is important. If hospital-presenting sepsis (HPS) contributes disproportionately to disease burdCHFens, it reflects a high-yield population deserving the focus of quality improvement (QI) initiatives. If specific causes of disparities were identified—eg, poor initial resuscitation— they could be specifically targeted for correction. Given that current treatment guidelines are uniform for the two populations,^9,10 characterizing phenotypic differences could also have implications for both diagnostic and therapeutic recommendations, particularly if the groups display substantially differing clinical presentations. Our prior work has not probed these effects specifically, but suggested ED versus inpatient setting at the time of initial sepsis presentation might be an effect modifier for the association between several elements of fluid resuscitation and patient outcomes.^11,12

We, therefore, conducted a retrospective analysis to ask four sequential questions: (1) Do patients with HPS, compared with EDPS, contribute adverse outcome out of proportion to case prevalence? (2) At the time of initial presentation, how do HPS patients differ from EDPS patients with respect to demographics, comorbidities, infectious etiologies, clinical presentations, and severity of illness (3) If holding observed baseline factors constant, does the physical location of sepsis presentation inherently increase the risk for treatment delays and mortality? (4) To what extent can differences in the likelihood for timely initial treatment between the ED and inpatient settings explain differences in mortality and patient outcomes?

We hypothesized a priori that HPS would reflect chronically sicker patients whom both received less timely resuscitation and who contributed disproportionately frequent bad outcomes. We expected disparities in timely resuscitation care would explain a large proportion of this difference.

We performed a retrospective analysis of the Northwell Sepsis Database, a prospectively captured, multisite, real world, consecutive-sample cohort of all “severe sepsis” and septic shock patients treated at nine tertiary and community hospitals in New York from October 1, 2014, to March 31, 2016. We analyzed all patients from a previously published cohort.¹¹

Database Design and Structure

The Northwell Sepsis Database has previously been described in detail.^11,13,14 Briefly, all patients met clinical sepsis criteria: (1) infection AND (2) ≥2 (SIRS) criteria AND (3) ≥1 acute organ dysfunction criterion. Organ dysfunction criteria were hypotension, acute kidney injury (AKI), coagulopathy, altered gas exchange, elevated bilirubin (≥2.0 mg/dL), or altered mental status (AMS; clarified in Supplemental Table 1). All organ dysfunction was not otherwise explained by patients’ medical histories; eg, patients on warfarin anticoagulation were not documented to have coagulopathy based on international normalized ratio > 1.5. The time of the sepsis episode (and database inclusion) was the time of the first vital sign measurement or laboratory result where a patient simultaneously met all three inclusion criteria: infection, SIRS, and organ dysfunction. The database excludes patients who were <18 years, declined bundle interventions, had advance directives precluding interventions, or were admitted directly to palliative care or hospice. Abstractors assumed comorbidities were absent if not documented within the medical record and that physiologic abnormalities were absent if not measured by the treatment team. There were no missing data for the variables analyzed. We report analysis in adherence with the STROBE statement guidelines for observational research.

Exposure

The primary exposure was whether patients had EDPS versus HPS. We defined EDPS patients as meeting all objective clinical inclusion criteria while physically in the ED. We defined HPS as first meeting sepsis inclusion criteria outside the ED, regardless of the reason for admission, and regardless of whether patients were admitted through the ED or directly to the hospital. All ED patients were admitted to the hospital.

Outcomes

Process outcomes were full 3-hour bundle compliance, time to antibiotic administration, blood cultures before antibiotics, time to fluid initiation, the volume of administered fluid resuscitation, lactate result time, and whether repeat lactate was obtained (Supplemental Table 2). Treatment times were times of administration (rather than order time). The primary patient outcome was hospital mortality. Secondary patient outcomes were mechanical ventilation, ICU admission, ICU days, hospital length of stay (LOS). We discounted HPS patients’ LOS to include only days after meeting the inclusion criteria. Patients were excluded from the analysis of the ICU admission outcome if they were already in the ICU prior to meeting sepsis criteria.

Statistical Analysis

We report continuous variables as means (standard deviation) or medians (interquartile range), and categorical variables as frequencies (proportions), as appropriate. Summative statistics with 95% confidence intervals (CI) describe overall group contributions. We used generalized linear models to determine patient factors associated with EDPS versus HPS, entering random effects for individual study sites to control for intercenter variability.

Next, to generate a propensity-matched cohort, we computed propensity scores adjusted from a priori selected variables: age, sex, tertiary versus community hospital, congestive heart failure (CHF), renal failure, COPD, diabetes, liver failure, immunocompromise, primary source of infection, nosocomial source, temperature, initial lactate, presenting hypotension, altered gas exchange, AMS, AKI, and coagulopathy. We then matched subjects 1:1 without optimization or replacement, imposing a caliper width of 0.01; ie, we required matched pairs to have a <1.0% difference in propensity scores. The macro used to match subjects is publically available.¹⁵

We then compared resuscitation and patient outcomes in the matched cohort using generalized linear models, ie, doubly-robust estimation (DRE).¹⁶ When assessing patient outcomes corrected for resuscitation, we used mixed DRE/multivariable regression. We did this for two reasons: first, DRE has the advantage of only requiring only one approach (propensity vs covariate adjustments) to be correctly specified.¹⁶ Second, computing propensity scores adjusted for resuscitation would be inappropriate given that resuscitation occurs after the exposure allocation (HPS vs EDPS). However, these factors could still impact the outcome and in fact, we hypothesized they were potential mediators of the exposure effect. To interrogate this mediating relationship, we recapitulated the DRE modeling but added covariates for resuscitation factors. Resuscitation-adjusted models controlled for timeliness of antibiotics, fluids, and lactate results; blood cultures before antibiotics; repeat lactate obtained, and fluid volume in the first six hours. Since ICU days and LOS are subject to competing risks bias (LOS could be shorter if patients died earlier), we used proportional hazards models where “the event” was defined as a live discharge to censor for mortality and we report output as inverse hazard ratios. We also tested interaction coefficients for discrete bundle elements and HPS to determine if specific bundle elements were effect modifiers for the association between the presenting location and mortality risk. Finally, we estimated attributable risk differences by comparing adjusted odds ratios of adverse outcome with and without adjustment for resuscitation variables, as described by Sahai et al.¹⁷

As sensitivity analyses, we recomputed propensity scores and generated a new matched cohort that excluded HPS patients who met criteria for sepsis while already in the ICU for another reason (ie, excluding ICU-presenting sepsis). We then recapitulated all analyses as above for this cohort. We performed analyses using SAS version 9.4 (SAS Institute, Cary, North Carolina).

Prevalence and Outcome Contributions

Of the 11,182 sepsis patients in the database, we classified 2,509 (22%) as HPS (Figure 1). HPS contributed 785 (35%) of 2,241 sepsis-related mortalities, 1,241 (38%) mechanical ventilations, and 1,762 (34%) ICU admissions. Of 39,263 total ICU days and 127,178 hospital days, HPS contributed 18,104 (46.1%) and 44,412 (34.9%) days, respectively.

Patient Characteristics

Most HPS presented early in the hospital course, with 1,352 (53.9%) cases meeting study criteria within three days of admission. Median time from admission to meeting study criteria for HPS was two days (interquartile range: one to seven days). We report selected baseline patient characteristics in Table 1 and adjusted associations of baseline variables with HPS versus EDPS in Table 2. The full cohort characterization is available in Supplemental Table 3. Notably, HPS patients more often had CHF (aOR [adjusted odds ratio}: 1.31, CI: 1.18-1.47) or renal failure (aOR: 1.62, CI: 1.38-1.91), gastrointestinal source of infection (aOR: 1.84, CI: 1.48-2.29), hypothermia (aOR: 1.56, CI: 1.28-1.90) hypotension (aOR: 1.85, CI: 1.65-2.08), or altered gas exchange (aOR: 2.46, CI: 1.43-4.24). In contrast, HPS patients less frequently were admitted from skilled nursing facilities (aOR: 0.44, CI: 0.32-0.60), or had COPD (aOR: 0.53, CI: 0.36-0.76), fever (aOR: 0.70, CI: 0.52-0.91), tachypnea (aOR: 0.76, CI: 0.58-0.98), or AKI (aOR: 082, CI: 0.68-0.97). Other baseline variables were similar, including respiratory source, tachycardia, white cell abnormalities, AMS, and coagulopathies. These associations were preserved in the sensitivity analysis excluding ICU-presenting sepsis.

Propensity Matching

Propensity score matching yielded 1,942 matched pairs (n = 3,884, 77% of HPS patients, 22% of EDPS patients). Table 1 and Supplemental Table 3 show patient characteristics after propensity matching. Supplemental Table 4 shows the propensity model. The frequency densities are shown for the cohort as a function of propensity score in Supplemental Figure 1. After matching, frequencies between groups differed by <5% for all categorical variables assessed. In the sensitivity analysis, propensity matching (model in Supplemental Table 5) resulted in 1,233 matched pairs (n = 2,466, 49% of HPS patients, 14% of EDPS patients), with group differences comparable to the primary analysis.

Process Outcomes

We present propensity-matched differences in initial resuscitation in Figure 2A for all HPS patients, as well as non-ICU-presenting HPS, versus EDPS. HPS patients were roughly half as likely to receive fully 3-hour bundle compliant care (17.0% vs 30.3%, aOR: 0.47, CI: 0.40-0.57), to have blood cultures drawn within three hours prior to antibiotics (44.9% vs 67.2%, aOR: 0.40, CI: 0.35-0.46), or to receive fluid resuscitation initiated within two hours (11.1% vs 26.1%, aOR: 0.35, CI: 0.29-0.42). Antibiotic receipt within one hour was comparable (45.3% vs 48.1%, aOR: 0.89, CI: 0.79-1.01). However, differences emerged for antibiotics within three hours (66.2% vs 83.8%, aOR: 0.38, CI: 0.32-0.44) and persisted at six hours (77.0% vs 92.5%, aOR: 0.27, CI: 0.22-33). Excluding ICU-presenting sepsis from propensity matching exaggerated disparities in antibiotic receipt at one hour (43.4% vs 49.1%, aOR: 0.80, CI: 0.68-0.93), three hours (64.2% vs 86.1%, aOR: 0.29, CI: 0.24-0.35), and six hours (75.7% vs 93.0%, aOR: 0.23, CI: 0.18-0.30). HPS patients more frequently had repeat lactate obtained within 24 hours (62.4% vs 54.3%, aOR: 1.40, CI: 1.23-1.59).

Patient Outcomes

HPS patients had higher mortality (31.2% vs19.3%), mechanical ventilation (51.5% vs27.4%), and ICU admission (60.6% vs 46.5%) (Table 1 and Supplemental Table 6). Figure 2b shows propensity-matched and covariate-adjusted differences in patient outcomes before and after adjusting for initial resuscitation. aORs corresponded to approximate relative risk differences¹⁸ of 1.38 (CI: 1.28-1.48), 1.68 (CI: 1.57-1.79), and 1.72 (CI: 1.61-1.84) for mortality, mechanical ventilation, and ICU admission, respectively. HPS was associated with 83% longer mortality-censored ICU stays (five vs nine days, HR^–1: 1.83, CI: 1.65-2.03), and 108% longer hospital stay (eight vs 17 days, HR^–1: 2.08, CI: 1.93-2.24). After adjustment for resuscitation, all effect sizes decreased but persisted. The initial crystalloid volume was a significant negative effect modifier for mortality (Supplemental Table 7). That is, the magnitude of the association between HPS and greater mortality decreased by a factor of 0.89 per 10 mL/kg given (CI: 0.82-0.97). We did not observe significant interaction from other interventions, or overall bundle compliance, meaning these interventions’ association with mortality did not significantly differ between HPS versus EDPS.

The implied attributable risk difference from discrepancies in initial resuscitation was 23.3% for mortality, 35.2% for mechanical ventilation, and 7.6% for ICU admission (Figure 2B). Resuscitation explained 26.5% of longer ICU LOS and 16.7% of longer hospital LOS associated with HPS.

Figure 2C shows sensitivity analysis excluding ICU-presenting sepsis from propensity matching (ie, limiting HPS to hospital ward presentations). Again, HPS was associated with all adverse outcomes, though effect sizes were smaller than in the primary cohort for all outcomes except hospital LOS. In this cohort, resuscitation factors now explained 16.5% of HPS’ association with mortality, and 14.5% of the association with longer ICU LOS. However, they explained a greater proportion (13.0%) of ICU admissions. Attributable risk differences were comparable to the primary cohort for mechanical ventilation (37.6%) and hospital LOS (15.3%).

In this analysis of 11,182 sepsis and septic shock patients, HPS contributed 22% of prevalence but >35% of total sepsis mortalities, ICU utilization, and hospital days. HPS patients had higher comorbidity burdens and had clinical presentations less obviously attributable to infection with more severe organ dysfunction. EDPS received antibiotics within three hours about 1.62 times more often than do HPS patients. EDPS patients also receive fluids initiated within two hours about 1.82 times more often than HPS patients do. HPS had nearly 1.5-fold greater mortality and LOS, and nearly two-fold greater mechanical ventilation and ICU utilization. Resuscitation disparities could partially explain these associations. These patterns persisted when comparing only wards presenting HPS with EDPS.

Our analysis revealed several notable findings. First, these data confirm that HPS represents a potentially high-impact target population that contributes adverse outcomes disproportionately frequently with respect to case prevalence.

Our findings, unsurprisingly, revealed HPS and EDPS reflect dramatically different patient populations. We found that the two groups significantly differed by the majority of the baseline factors we compared. It may be worth asking if and how these substantial differences in illness etiology, chronic health, and acute physiology impact what we consider an optimal approach to management. Significant interaction effects of fluid volume on the association between HPS and mortality suggest differential treatment effects may exist between patients. Indeed, patients who newly arrive from the community and those who are several days into admission likely have different volume status. However, no interactions were noted with other bundle elements, such as timeliness of antibiotics or timeliness of initial fluids.

Another potentially concerning observation was that HPS patients were admitted much less frequently from skilled nursing facilities, as it could imply that this poorer-fairing population had a comparatively higher baseline functional status. The fact that 25% of EDPS cases were admitted from these facilities also underscores the need to engage skilled nursing facility providers in future sepsis initiatives.

We found marked disparities in resuscitation. Timely delivery of interventions, such as antibiotics and initial fluid resuscitation, occurred less than half as often for HPS, especially on hospital wards. While evidence supporting the efficacy of specific 3-hour bundle elements remains unsettled,¹⁹ a wealth of literature demonstrates a correlation between bundle uptake and decreased sepsis mortality, especially for early antibiotic administration.^13,20-26 Some analysis suggests that differing initial resuscitation practices explain different mortality rates in the early goal-directed therapy trials.²⁷ The comparatively poor performance for non-ICU HPS indicates further QI efforts are better focused on inpatient wards, rather than on EDs or ICUs where resuscitation is already delivered with substantially greater fidelity.

While resuscitation differences partially explained outcome discrepancies between groups, they did not account for as much variation as expected. Though resuscitation accounted for >35% of attributable mechanical ventilation risk, it explained only 16.5% of mortality differences for non-ICU HPS vs EDPS. We speculate that several factors may contribute.

First, HPS patients are already hospitalized for another acute insult and may be too physiologically brittle to derive equal benefit from initial resuscitation. Some literature suggests protocolized sepsis resuscitation may paradoxically be more effective in milder/earlier disease.²⁸

Second, clinical information indicating septic organ dysfunction may become available too late in HPS—a possible data limitation where inpatient providers are counterintuitively more likely to miss early signs of patients’ deterioration and a subsequent therapeutic window. Several studies found that fluid resuscitation is associated with improved sepsis outcomes only when it is administered very early.^11,29-31 In inpatient wards, decreased monitoring³² and human factors (eg, hospital workflow, provider-to-patient ratios, electronic documentation burdens)^33,34 may hinder early diagnosis. In contrast, ED environments are explicitly designed to identify acutely ill patients and deliver intervention rapidly. If HPS patients were sicker when they were identified, this would also explain their more severe organ dysfunctions. Our data seems to support this possibility. HPS patients had tachypnea less frequently but more often had impaired gas exchange. This finding may suggest that early tachypnea was either less often detected or documented, or that it had progressed further by the time of detection.

Third, inpatients with sepsis may more often present with greater diagnostic complexity. We observed that HPS patients were more often euthermic and less often tachypneic. Beyond suggesting a greater diagnostic challenge, this also raises questions as to whether differences reflect patient physiology (response to infection) or iatrogenic factors (eg, prior antipyretics). Higher comorbidity and acute physiological burdens also limit the degree to which new organ dysfunction can be clearly attributed to infection. We note differences in the proportion of patients who received antibiotics increased over time, suggesting that HPS patients who received delayed antibiotics did so much later than their EDPS counterparts. This lag could also arise from diagnostic difficulty.

All three possibilities highlight a potential lead time effect, where the same measured three-hour period on the wards, between meeting sepsis criteria and starting treatment, actually reflects a longer period between (as yet unmeasurable) pathobiologic “time zero” and treatment versus the ED. The time of sepsis detection, as distinct from the time of sepsis onset, therefore proves difficult to evaluate and impossible to account for statistically.

Regardless, our findings suggest additional difficulty in both the recognition and resuscitation of inpatient sepsis. Inpatients, especially with infections, may need closer monitoring. How to cost effectively implement this monitoring is a challenge that deserves attention.

A more rational systems approach to HPS likely combines efforts to improve initial resuscitation with other initiatives aimed at both improving monitoring and preventing infection.

To be clear, we do not imply that timely initial resuscitation does not matter on the wards. Rather, resuscitation-focused QI alone does not appear to be sufficient to overcome differences in outcomes for HPS. The 23.3% attributable mortality risk we observed still implies that resuscitation differences could explain nearly one in four excess HPS mortalities. We previously showed that timely resuscitation is strongly associated with better outcomes.^11,13,30 As discussed above, the unclear degree to which better resuscitation is a marker for more obvious presentations is a persistent limitation of prior investigations and the present study.

Taken together, the ultimate question that this study raises but cannot answer is whether the timely recognition of sepsis, rather than any specific treatment, is what truly improves outcomes.

In addition to those above, this study has several limitations. Our study did not differentiate HPS with respect to patients admitted for noninfectious reasons and who subsequently became septic versus nonseptic patients admitted for an infection who subsequently became septic from that infection. Nor could we discriminate between missed ED diagnoses and true delayed presentations. We note distinguishing these entities clinically can be equally challenging. Additionally, this was a propensity-matched retrospective analysis of an existing sepsis cohort, and the many limitations of both retrospective study and propensity matching apply.^35,36 We note that randomizing patients to develop sepsis in the community versus hospital is not feasible and that two of our aims intended to describe overall patterns rather than causal effects. We could not ascertain robust measures of severity of illness (eg, SOFA) because a real world setting precludes required data points—eg, urine output is unreliably recorded. We also note incomplete overlap between inclusion criteria and either Sepsis-2 or -3 definitions,^1,37 because we designed and populated our database prior to publication of Sepsis-3. Further, we could not account for surgical source control, the appropriateness of antimicrobial therapy, mechanical ventilation before sepsis onset, or most treatments given after initial resuscitation.

In conclusion, hospital-presenting sepsis accounted for adverse patient outcomes disproportionately to prevalence. HPS patients had more complex presentations, received timely antibiotics half as often ED-presenting sepsis, and had nearly twice the mortality odds. Resuscitation disparities explained roughly 25% of this difference.

Disclosures

The authors have no conflicts of interest to disclose.

Funding

This investigation was funded in part by a grant from the Center for Medicare and Medicaid Innovation to the High Value Healthcare Collaborative, of which the study sites’ umbrella health system was a part. This grant helped fund the underlying QI program and database in this study.

Sepsis is both the most expensive condition treated and the most common cause of death in hospitals in the United States.^1-3 Most sepsis patients (as many as 80% to 90%) meet sepsis criteria on hospital arrival, but mortality and costs are higher when meeting criteria after admission.^3-6 Mechanisms of this increased mortality for these distinct populations are not well explored. Patients who present septic in the emergency department (ED) and patients who present as inpatients likely present very different challenges for recognition, treatment, and monitoring.⁷ Yet, how these groups differ by demographic and clinical characteristics, the etiology and severity of infection, and patterns of resuscitation care are not well described. Literature on sepsis epidemiology on hospital wards is particularly limited.⁸

This knowledge gap is important. If hospital-presenting sepsis (HPS) contributes disproportionately to disease burdCHFens, it reflects a high-yield population deserving the focus of quality improvement (QI) initiatives. If specific causes of disparities were identified—eg, poor initial resuscitation— they could be specifically targeted for correction. Given that current treatment guidelines are uniform for the two populations,^9,10 characterizing phenotypic differences could also have implications for both diagnostic and therapeutic recommendations, particularly if the groups display substantially differing clinical presentations. Our prior work has not probed these effects specifically, but suggested ED versus inpatient setting at the time of initial sepsis presentation might be an effect modifier for the association between several elements of fluid resuscitation and patient outcomes.^11,12

We, therefore, conducted a retrospective analysis to ask four sequential questions: (1) Do patients with HPS, compared with EDPS, contribute adverse outcome out of proportion to case prevalence? (2) At the time of initial presentation, how do HPS patients differ from EDPS patients with respect to demographics, comorbidities, infectious etiologies, clinical presentations, and severity of illness (3) If holding observed baseline factors constant, does the physical location of sepsis presentation inherently increase the risk for treatment delays and mortality? (4) To what extent can differences in the likelihood for timely initial treatment between the ED and inpatient settings explain differences in mortality and patient outcomes?

We hypothesized a priori that HPS would reflect chronically sicker patients whom both received less timely resuscitation and who contributed disproportionately frequent bad outcomes. We expected disparities in timely resuscitation care would explain a large proportion of this difference.

We performed a retrospective analysis of the Northwell Sepsis Database, a prospectively captured, multisite, real world, consecutive-sample cohort of all “severe sepsis” and septic shock patients treated at nine tertiary and community hospitals in New York from October 1, 2014, to March 31, 2016. We analyzed all patients from a previously published cohort.¹¹

Database Design and Structure

The Northwell Sepsis Database has previously been described in detail.^11,13,14 Briefly, all patients met clinical sepsis criteria: (1) infection AND (2) ≥2 (SIRS) criteria AND (3) ≥1 acute organ dysfunction criterion. Organ dysfunction criteria were hypotension, acute kidney injury (AKI), coagulopathy, altered gas exchange, elevated bilirubin (≥2.0 mg/dL), or altered mental status (AMS; clarified in Supplemental Table 1). All organ dysfunction was not otherwise explained by patients’ medical histories; eg, patients on warfarin anticoagulation were not documented to have coagulopathy based on international normalized ratio > 1.5. The time of the sepsis episode (and database inclusion) was the time of the first vital sign measurement or laboratory result where a patient simultaneously met all three inclusion criteria: infection, SIRS, and organ dysfunction. The database excludes patients who were <18 years, declined bundle interventions, had advance directives precluding interventions, or were admitted directly to palliative care or hospice. Abstractors assumed comorbidities were absent if not documented within the medical record and that physiologic abnormalities were absent if not measured by the treatment team. There were no missing data for the variables analyzed. We report analysis in adherence with the STROBE statement guidelines for observational research.

Exposure

The primary exposure was whether patients had EDPS versus HPS. We defined EDPS patients as meeting all objective clinical inclusion criteria while physically in the ED. We defined HPS as first meeting sepsis inclusion criteria outside the ED, regardless of the reason for admission, and regardless of whether patients were admitted through the ED or directly to the hospital. All ED patients were admitted to the hospital.

Outcomes

Process outcomes were full 3-hour bundle compliance, time to antibiotic administration, blood cultures before antibiotics, time to fluid initiation, the volume of administered fluid resuscitation, lactate result time, and whether repeat lactate was obtained (Supplemental Table 2). Treatment times were times of administration (rather than order time). The primary patient outcome was hospital mortality. Secondary patient outcomes were mechanical ventilation, ICU admission, ICU days, hospital length of stay (LOS). We discounted HPS patients’ LOS to include only days after meeting the inclusion criteria. Patients were excluded from the analysis of the ICU admission outcome if they were already in the ICU prior to meeting sepsis criteria.

Statistical Analysis

We report continuous variables as means (standard deviation) or medians (interquartile range), and categorical variables as frequencies (proportions), as appropriate. Summative statistics with 95% confidence intervals (CI) describe overall group contributions. We used generalized linear models to determine patient factors associated with EDPS versus HPS, entering random effects for individual study sites to control for intercenter variability.

Next, to generate a propensity-matched cohort, we computed propensity scores adjusted from a priori selected variables: age, sex, tertiary versus community hospital, congestive heart failure (CHF), renal failure, COPD, diabetes, liver failure, immunocompromise, primary source of infection, nosocomial source, temperature, initial lactate, presenting hypotension, altered gas exchange, AMS, AKI, and coagulopathy. We then matched subjects 1:1 without optimization or replacement, imposing a caliper width of 0.01; ie, we required matched pairs to have a <1.0% difference in propensity scores. The macro used to match subjects is publically available.¹⁵

We then compared resuscitation and patient outcomes in the matched cohort using generalized linear models, ie, doubly-robust estimation (DRE).¹⁶ When assessing patient outcomes corrected for resuscitation, we used mixed DRE/multivariable regression. We did this for two reasons: first, DRE has the advantage of only requiring only one approach (propensity vs covariate adjustments) to be correctly specified.¹⁶ Second, computing propensity scores adjusted for resuscitation would be inappropriate given that resuscitation occurs after the exposure allocation (HPS vs EDPS). However, these factors could still impact the outcome and in fact, we hypothesized they were potential mediators of the exposure effect. To interrogate this mediating relationship, we recapitulated the DRE modeling but added covariates for resuscitation factors. Resuscitation-adjusted models controlled for timeliness of antibiotics, fluids, and lactate results; blood cultures before antibiotics; repeat lactate obtained, and fluid volume in the first six hours. Since ICU days and LOS are subject to competing risks bias (LOS could be shorter if patients died earlier), we used proportional hazards models where “the event” was defined as a live discharge to censor for mortality and we report output as inverse hazard ratios. We also tested interaction coefficients for discrete bundle elements and HPS to determine if specific bundle elements were effect modifiers for the association between the presenting location and mortality risk. Finally, we estimated attributable risk differences by comparing adjusted odds ratios of adverse outcome with and without adjustment for resuscitation variables, as described by Sahai et al.¹⁷

As sensitivity analyses, we recomputed propensity scores and generated a new matched cohort that excluded HPS patients who met criteria for sepsis while already in the ICU for another reason (ie, excluding ICU-presenting sepsis). We then recapitulated all analyses as above for this cohort. We performed analyses using SAS version 9.4 (SAS Institute, Cary, North Carolina).

Prevalence and Outcome Contributions

Of the 11,182 sepsis patients in the database, we classified 2,509 (22%) as HPS (Figure 1). HPS contributed 785 (35%) of 2,241 sepsis-related mortalities, 1,241 (38%) mechanical ventilations, and 1,762 (34%) ICU admissions. Of 39,263 total ICU days and 127,178 hospital days, HPS contributed 18,104 (46.1%) and 44,412 (34.9%) days, respectively.

Patient Characteristics

Most HPS presented early in the hospital course, with 1,352 (53.9%) cases meeting study criteria within three days of admission. Median time from admission to meeting study criteria for HPS was two days (interquartile range: one to seven days). We report selected baseline patient characteristics in Table 1 and adjusted associations of baseline variables with HPS versus EDPS in Table 2. The full cohort characterization is available in Supplemental Table 3. Notably, HPS patients more often had CHF (aOR [adjusted odds ratio}: 1.31, CI: 1.18-1.47) or renal failure (aOR: 1.62, CI: 1.38-1.91), gastrointestinal source of infection (aOR: 1.84, CI: 1.48-2.29), hypothermia (aOR: 1.56, CI: 1.28-1.90) hypotension (aOR: 1.85, CI: 1.65-2.08), or altered gas exchange (aOR: 2.46, CI: 1.43-4.24). In contrast, HPS patients less frequently were admitted from skilled nursing facilities (aOR: 0.44, CI: 0.32-0.60), or had COPD (aOR: 0.53, CI: 0.36-0.76), fever (aOR: 0.70, CI: 0.52-0.91), tachypnea (aOR: 0.76, CI: 0.58-0.98), or AKI (aOR: 082, CI: 0.68-0.97). Other baseline variables were similar, including respiratory source, tachycardia, white cell abnormalities, AMS, and coagulopathies. These associations were preserved in the sensitivity analysis excluding ICU-presenting sepsis.

Propensity Matching

Propensity score matching yielded 1,942 matched pairs (n = 3,884, 77% of HPS patients, 22% of EDPS patients). Table 1 and Supplemental Table 3 show patient characteristics after propensity matching. Supplemental Table 4 shows the propensity model. The frequency densities are shown for the cohort as a function of propensity score in Supplemental Figure 1. After matching, frequencies between groups differed by <5% for all categorical variables assessed. In the sensitivity analysis, propensity matching (model in Supplemental Table 5) resulted in 1,233 matched pairs (n = 2,466, 49% of HPS patients, 14% of EDPS patients), with group differences comparable to the primary analysis.

Process Outcomes

We present propensity-matched differences in initial resuscitation in Figure 2A for all HPS patients, as well as non-ICU-presenting HPS, versus EDPS. HPS patients were roughly half as likely to receive fully 3-hour bundle compliant care (17.0% vs 30.3%, aOR: 0.47, CI: 0.40-0.57), to have blood cultures drawn within three hours prior to antibiotics (44.9% vs 67.2%, aOR: 0.40, CI: 0.35-0.46), or to receive fluid resuscitation initiated within two hours (11.1% vs 26.1%, aOR: 0.35, CI: 0.29-0.42). Antibiotic receipt within one hour was comparable (45.3% vs 48.1%, aOR: 0.89, CI: 0.79-1.01). However, differences emerged for antibiotics within three hours (66.2% vs 83.8%, aOR: 0.38, CI: 0.32-0.44) and persisted at six hours (77.0% vs 92.5%, aOR: 0.27, CI: 0.22-33). Excluding ICU-presenting sepsis from propensity matching exaggerated disparities in antibiotic receipt at one hour (43.4% vs 49.1%, aOR: 0.80, CI: 0.68-0.93), three hours (64.2% vs 86.1%, aOR: 0.29, CI: 0.24-0.35), and six hours (75.7% vs 93.0%, aOR: 0.23, CI: 0.18-0.30). HPS patients more frequently had repeat lactate obtained within 24 hours (62.4% vs 54.3%, aOR: 1.40, CI: 1.23-1.59).

Patient Outcomes

HPS patients had higher mortality (31.2% vs19.3%), mechanical ventilation (51.5% vs27.4%), and ICU admission (60.6% vs 46.5%) (Table 1 and Supplemental Table 6). Figure 2b shows propensity-matched and covariate-adjusted differences in patient outcomes before and after adjusting for initial resuscitation. aORs corresponded to approximate relative risk differences¹⁸ of 1.38 (CI: 1.28-1.48), 1.68 (CI: 1.57-1.79), and 1.72 (CI: 1.61-1.84) for mortality, mechanical ventilation, and ICU admission, respectively. HPS was associated with 83% longer mortality-censored ICU stays (five vs nine days, HR^–1: 1.83, CI: 1.65-2.03), and 108% longer hospital stay (eight vs 17 days, HR^–1: 2.08, CI: 1.93-2.24). After adjustment for resuscitation, all effect sizes decreased but persisted. The initial crystalloid volume was a significant negative effect modifier for mortality (Supplemental Table 7). That is, the magnitude of the association between HPS and greater mortality decreased by a factor of 0.89 per 10 mL/kg given (CI: 0.82-0.97). We did not observe significant interaction from other interventions, or overall bundle compliance, meaning these interventions’ association with mortality did not significantly differ between HPS versus EDPS.

The implied attributable risk difference from discrepancies in initial resuscitation was 23.3% for mortality, 35.2% for mechanical ventilation, and 7.6% for ICU admission (Figure 2B). Resuscitation explained 26.5% of longer ICU LOS and 16.7% of longer hospital LOS associated with HPS.

Figure 2C shows sensitivity analysis excluding ICU-presenting sepsis from propensity matching (ie, limiting HPS to hospital ward presentations). Again, HPS was associated with all adverse outcomes, though effect sizes were smaller than in the primary cohort for all outcomes except hospital LOS. In this cohort, resuscitation factors now explained 16.5% of HPS’ association with mortality, and 14.5% of the association with longer ICU LOS. However, they explained a greater proportion (13.0%) of ICU admissions. Attributable risk differences were comparable to the primary cohort for mechanical ventilation (37.6%) and hospital LOS (15.3%).

In this analysis of 11,182 sepsis and septic shock patients, HPS contributed 22% of prevalence but >35% of total sepsis mortalities, ICU utilization, and hospital days. HPS patients had higher comorbidity burdens and had clinical presentations less obviously attributable to infection with more severe organ dysfunction. EDPS received antibiotics within three hours about 1.62 times more often than do HPS patients. EDPS patients also receive fluids initiated within two hours about 1.82 times more often than HPS patients do. HPS had nearly 1.5-fold greater mortality and LOS, and nearly two-fold greater mechanical ventilation and ICU utilization. Resuscitation disparities could partially explain these associations. These patterns persisted when comparing only wards presenting HPS with EDPS.

Our analysis revealed several notable findings. First, these data confirm that HPS represents a potentially high-impact target population that contributes adverse outcomes disproportionately frequently with respect to case prevalence.

Our findings, unsurprisingly, revealed HPS and EDPS reflect dramatically different patient populations. We found that the two groups significantly differed by the majority of the baseline factors we compared. It may be worth asking if and how these substantial differences in illness etiology, chronic health, and acute physiology impact what we consider an optimal approach to management. Significant interaction effects of fluid volume on the association between HPS and mortality suggest differential treatment effects may exist between patients. Indeed, patients who newly arrive from the community and those who are several days into admission likely have different volume status. However, no interactions were noted with other bundle elements, such as timeliness of antibiotics or timeliness of initial fluids.

Another potentially concerning observation was that HPS patients were admitted much less frequently from skilled nursing facilities, as it could imply that this poorer-fairing population had a comparatively higher baseline functional status. The fact that 25% of EDPS cases were admitted from these facilities also underscores the need to engage skilled nursing facility providers in future sepsis initiatives.

We found marked disparities in resuscitation. Timely delivery of interventions, such as antibiotics and initial fluid resuscitation, occurred less than half as often for HPS, especially on hospital wards. While evidence supporting the efficacy of specific 3-hour bundle elements remains unsettled,¹⁹ a wealth of literature demonstrates a correlation between bundle uptake and decreased sepsis mortality, especially for early antibiotic administration.^13,20-26 Some analysis suggests that differing initial resuscitation practices explain different mortality rates in the early goal-directed therapy trials.²⁷ The comparatively poor performance for non-ICU HPS indicates further QI efforts are better focused on inpatient wards, rather than on EDs or ICUs where resuscitation is already delivered with substantially greater fidelity.

While resuscitation differences partially explained outcome discrepancies between groups, they did not account for as much variation as expected. Though resuscitation accounted for >35% of attributable mechanical ventilation risk, it explained only 16.5% of mortality differences for non-ICU HPS vs EDPS. We speculate that several factors may contribute.

First, HPS patients are already hospitalized for another acute insult and may be too physiologically brittle to derive equal benefit from initial resuscitation. Some literature suggests protocolized sepsis resuscitation may paradoxically be more effective in milder/earlier disease.²⁸

Second, clinical information indicating septic organ dysfunction may become available too late in HPS—a possible data limitation where inpatient providers are counterintuitively more likely to miss early signs of patients’ deterioration and a subsequent therapeutic window. Several studies found that fluid resuscitation is associated with improved sepsis outcomes only when it is administered very early.^11,29-31 In inpatient wards, decreased monitoring³² and human factors (eg, hospital workflow, provider-to-patient ratios, electronic documentation burdens)^33,34 may hinder early diagnosis. In contrast, ED environments are explicitly designed to identify acutely ill patients and deliver intervention rapidly. If HPS patients were sicker when they were identified, this would also explain their more severe organ dysfunctions. Our data seems to support this possibility. HPS patients had tachypnea less frequently but more often had impaired gas exchange. This finding may suggest that early tachypnea was either less often detected or documented, or that it had progressed further by the time of detection.

Third, inpatients with sepsis may more often present with greater diagnostic complexity. We observed that HPS patients were more often euthermic and less often tachypneic. Beyond suggesting a greater diagnostic challenge, this also raises questions as to whether differences reflect patient physiology (response to infection) or iatrogenic factors (eg, prior antipyretics). Higher comorbidity and acute physiological burdens also limit the degree to which new organ dysfunction can be clearly attributed to infection. We note differences in the proportion of patients who received antibiotics increased over time, suggesting that HPS patients who received delayed antibiotics did so much later than their EDPS counterparts. This lag could also arise from diagnostic difficulty.

All three possibilities highlight a potential lead time effect, where the same measured three-hour period on the wards, between meeting sepsis criteria and starting treatment, actually reflects a longer period between (as yet unmeasurable) pathobiologic “time zero” and treatment versus the ED. The time of sepsis detection, as distinct from the time of sepsis onset, therefore proves difficult to evaluate and impossible to account for statistically.

Regardless, our findings suggest additional difficulty in both the recognition and resuscitation of inpatient sepsis. Inpatients, especially with infections, may need closer monitoring. How to cost effectively implement this monitoring is a challenge that deserves attention.

A more rational systems approach to HPS likely combines efforts to improve initial resuscitation with other initiatives aimed at both improving monitoring and preventing infection.

To be clear, we do not imply that timely initial resuscitation does not matter on the wards. Rather, resuscitation-focused QI alone does not appear to be sufficient to overcome differences in outcomes for HPS. The 23.3% attributable mortality risk we observed still implies that resuscitation differences could explain nearly one in four excess HPS mortalities. We previously showed that timely resuscitation is strongly associated with better outcomes.^11,13,30 As discussed above, the unclear degree to which better resuscitation is a marker for more obvious presentations is a persistent limitation of prior investigations and the present study.

Taken together, the ultimate question that this study raises but cannot answer is whether the timely recognition of sepsis, rather than any specific treatment, is what truly improves outcomes.

In addition to those above, this study has several limitations. Our study did not differentiate HPS with respect to patients admitted for noninfectious reasons and who subsequently became septic versus nonseptic patients admitted for an infection who subsequently became septic from that infection. Nor could we discriminate between missed ED diagnoses and true delayed presentations. We note distinguishing these entities clinically can be equally challenging. Additionally, this was a propensity-matched retrospective analysis of an existing sepsis cohort, and the many limitations of both retrospective study and propensity matching apply.^35,36 We note that randomizing patients to develop sepsis in the community versus hospital is not feasible and that two of our aims intended to describe overall patterns rather than causal effects. We could not ascertain robust measures of severity of illness (eg, SOFA) because a real world setting precludes required data points—eg, urine output is unreliably recorded. We also note incomplete overlap between inclusion criteria and either Sepsis-2 or -3 definitions,^1,37 because we designed and populated our database prior to publication of Sepsis-3. Further, we could not account for surgical source control, the appropriateness of antimicrobial therapy, mechanical ventilation before sepsis onset, or most treatments given after initial resuscitation.

In conclusion, hospital-presenting sepsis accounted for adverse patient outcomes disproportionately to prevalence. HPS patients had more complex presentations, received timely antibiotics half as often ED-presenting sepsis, and had nearly twice the mortality odds. Resuscitation disparities explained roughly 25% of this difference.

Disclosures

The authors have no conflicts of interest to disclose.

Funding

This investigation was funded in part by a grant from the Center for Medicare and Medicaid Innovation to the High Value Healthcare Collaborative, of which the study sites’ umbrella health system was a part. This grant helped fund the underlying QI program and database in this study.