The discharge process is a critical bottleneck for efficient patient flow through the hospital. Delayed discharges translate into delays in admissions and other patient transitions, often leading to excess costs, patient dissatisfaction, and even patient harm.^1-3 The emergency department is particularly impacted by these delays; bottlenecks there lead to overcrowding, increased overall hospital length of stay, and increased risks for bad outcomes during hospitalization.²

Academic medical centers in particular may struggle with delayed discharges. In a typical teaching hospital, a team composed of an attending physician and housestaff share responsibility for determining the discharge plan. Additionally, clinical teaching activities may affect the process and quality of discharge.^4-6

The prevalence and causes of delayed discharges vary greatly.^7-9 To improve efficiency around discharge, many hospitals have launched initiatives designed to discharge patients earlier in the day, including goal setting (“discharge by noon”), scheduling discharge appointments, and using quality-improvement methods, such as Lean Methodology (LEAN), to remove inefficiencies within discharge processes.^10-12 However, there are few data on the prevalence and effectiveness of different strategies.

The aim of this study was to survey academic hospitalist and general internal medicine physician leaders to elicit their perspectives on the factors contributing to discharge timing and the relative importance and effectiveness of early-discharge initiatives.

Study Design, Participants, and Oversight

We obtained a list of 115 university-affiliated hospitals associated with a residency program and, in most cases, a medical school from Vizient Inc. (formerly University HealthSystem Consortium), an alliance of academic medical centers and affiliated hospitals. Each member institution submits clinical data to allow for the benchmarking of outcomes to drive transparency and quality improvement.¹³ More than 95% of the nation’s academic medical centers and affiliated hospitals participate in this collaborative. Vizient works with members but does not set nor promote quality metrics, such as discharge timeliness. E-mail addresses for hospital medicine physician leaders (eg, division chief) of major academic medical centers were obtained from each institution via publicly available data (eg, the institution’s website). When an institution did not have a hospital medicine section, we identified the division chief of general internal medicine. The University of California, San Francisco Institutional Review Board approved this study.

Survey Development and Domains

We developed a 30-item survey to evaluate 5 main domains of interest: current discharge practices, degree of prioritization of early discharge on the inpatient service, barriers to timely discharge, prevalence and perceived effectiveness of implemented early-discharge initiatives, and barriers to implementation of early-discharge initiatives.

Respondents were first asked to identify their institutions’ goals for discharge time. They were then asked to compare the priority of early-discharge initiatives to other departmental quality-improvement initiatives, such as reducing 30-day readmissions, improving interpreter use, and improving patient satisfaction. Next, respondents were asked to estimate the degree to which clinical or patient factors contributed to delays in discharge. Respondents were then asked whether specific early-discharge initiatives, such as changes to rounding practices or communication interventions, were implemented at their institutions and, if so, the perceived effectiveness of these initiatives at meeting discharge targets. We piloted the questions locally with physicians and researchers prior to finalizing the survey.

Data Collection

We sent surveys via an online platform (Research Electronic Data Capture).¹⁴ Nonresponders were sent 2 e-mail reminders and then a follow-up telephone call asking them to complete the survey. Only 1 survey per academic medical center was collected. Any respondent who completed the survey within 2 weeks of receiving it was entered to win a Kindle Fire.

Data Analysis

We summarized survey responses using descriptive statistics. Analysis was completed in IBM SPSS version 22 (Armonk, NY).

Survey Respondent and Institutional Characteristics

Of the 115 institutions surveyed, we received 61 responses (response rate of 53%), with 39 (64%) respondents from divisions of hospital medicine and 22 (36%) from divisions of general internal medicine. A majority (n = 53; 87%) stated their medicine services have a combination of teaching (with residents) and nonteaching (without residents) teams. Thirty-nine (64%) reported having daily multidisciplinary rounds.

Early Discharge as a Priority

Forty-seven (77%) institutional representatives strongly agreed or agreed that early discharge was a priority, with discharge by noon being the most common target time (n = 23; 38%). Thirty (50%) respondents rated early discharge as more important than improving interpreter use for non-English-speaking patients and equally important as reducing 30-day readmissions (n = 29; 48%) and improving patient satisfaction (n = 27; 44%).

Factors Delaying Discharge

The most common factors perceived as delaying discharge were considered external to the hospital, such as postacute care bed availability or scheduled (eg, ambulance) transport delays (n = 48; 79%), followed by patient factors such as patient transport issues (n = 44; 72%). Less commonly reported were workflow issues, such as competing primary team priorities or case manager bandwidth (n = 38; 62%; Table 1).

Initiatives to Improve Discharge

The most commonly implemented initiatives perceived as effective at improving discharge times were the preemptive identification of early discharges to plan discharge paperwork (n = 34; 56%), communication with patients about anticipated discharge time on the day prior to discharge (n = 29; 48%), and the implementation of additional rounds between physician teams and case managers specifically around discharge planning (n = 28; 46%). Initiatives not commonly implemented included regular audit of and feedback on discharge times to providers and teams (n = 21; 34%), the use of a discharge readiness checklist (n = 26; 43%), incentives such as bonuses or penalties (n = 37; 61%), the use of a whiteboard to indicate discharge times (n = 23; 38%), and dedicated quality-improvement approaches such as LEAN (n = 37; 61%; Table 2).

Our study suggests early discharge for medicine patients is a priority among academic institutions. Hospitalist and general internal medicine physician leaders in our study generally attributed delayed discharges to external factors, particularly unavailability of postacute care facilities and transportation delays. Having issues with finding postacute care placements is consistent with previous findings by Selker et al.¹⁵ and Carey et al.⁸ This is despite the 20-year difference between Selker et al.’s study and the current study, reflecting a continued opportunity for improvement, including stronger partnerships with local and regional postacute care facilities to expedite care transition and stronger discharge-planning efforts early in the admission process. Efforts in postacute care placement may be particularly important for Medicaid-insured and uninsured patients.

Our responders, hospitalist and internal medicine physician leaders, did not perceive the additional responsibilities of teaching and supervising trainees to be factors that significantly delayed patient discharge. This is in contrast to previous studies, which attributed delays in discharge to prolonged clinical decision-making related to teaching and supervision.^4-6,8 This discrepancy may be due to the fact that we only surveyed single physician leaders at each institution and not residents. Our finding warrants further investigation to understand the degree to which resident skills may impact discharge planning and processes.

Institutions represented in our study have attempted a variety of initiatives promoting earlier discharge, with varying levels of perceived success. Initiatives perceived to be the most effective by hospital leaders centered on 2 main areas: (1) changing individual provider practice and (2) anticipatory discharge preparation. Interestingly, this is in discordance with the main factors labeled as causing delays in discharges, such as obtaining postacute care beds, busy case managers, and competing demands on primary teams. We hypothesize this may be because such changes require organization- or system-level changes and are perceived as more arduous than changes at the individual level. In addition, changes to individual provider behavior may be more cost- and time-effective than more systemic initiatives.

Our findings are consistent with the work published by Wertheimer and colleagues,¹¹ who show that additional afternoon interdisciplinary rounds can help identify patients who may be discharged before noon the next day. In their study, identifying such patients in advance improved the overall early-discharge rate the following day.

Our findings should be interpreted in light of several limitations. Our survey only considers the perspectives of hospitalist and general internal medicine physician leaders at academic medical centers that are part of the Vizient Inc. collaborative. They do not represent all academic or community-based medical centers. Although the perceived effectiveness of some initiatives was high, we did not collect empirical data to support these claims or to determine which initiative had the greatest relative impact on discharge timeliness. Lastly, we did not obtain resident, nursing, or case manager perspectives on discharge practices. Given their roles as frontline providers, we may have missed these alternative perspectives.

Our study shows there is a strong interest in increasing early discharges in an effort to improve hospital throughput and patient flow.

Acknowledgments

The authors thank all participants who completed the survey and Danielle Carrier at Vizient Inc. (formally University HealthSystem Consortium) for her assistance in obtaining data.

Disclosures

Hemali Patel, Margaret Fang, Michelle Mourad, Adrienne Green, Ryan Murphy, and James Harrison report no conflicts of interest. At the time the research was conducted, Robert Wachter reported that he is a member of the Lucian Leape Institute at the National Patient Safety Foundation (no compensation except travel expenses); recently chaired an advisory board to England’s National Health Service (NHS) reviewing the NHS’s digital health strategy (no compensation except travel expenses); has a contract with UCSF from the Agency for Healthcare Research and Quality to edit a patient-safety website; receives compensation from John Wiley & Sons for writing a blog; receives royalties from Lippincott Williams & Wilkins and McGraw-Hill Education for writing and/or editing several books; receives stock options for serving on the board of Acuity Medical Management Systems; receives a yearly stipend for serving on the board of The Doctors Company; serves on the scientific advisory boards for amino.com, PatientSafe Solutions Inc., Twine, and EarlySense (for which he receives stock options); has a small royalty stake in CareWeb, a hospital communication tool developed at UCSF; and holds the Marc and Lynne Benioff Endowed Chair in Hospital Medicine and the Holly Smith Distinguished Professorship in Science and Medicine at UCSF.

The discharge process is a critical bottleneck for efficient patient flow through the hospital. Delayed discharges translate into delays in admissions and other patient transitions, often leading to excess costs, patient dissatisfaction, and even patient harm.^1-3 The emergency department is particularly impacted by these delays; bottlenecks there lead to overcrowding, increased overall hospital length of stay, and increased risks for bad outcomes during hospitalization.²

Academic medical centers in particular may struggle with delayed discharges. In a typical teaching hospital, a team composed of an attending physician and housestaff share responsibility for determining the discharge plan. Additionally, clinical teaching activities may affect the process and quality of discharge.^4-6

The prevalence and causes of delayed discharges vary greatly.^7-9 To improve efficiency around discharge, many hospitals have launched initiatives designed to discharge patients earlier in the day, including goal setting (“discharge by noon”), scheduling discharge appointments, and using quality-improvement methods, such as Lean Methodology (LEAN), to remove inefficiencies within discharge processes.^10-12 However, there are few data on the prevalence and effectiveness of different strategies.

The aim of this study was to survey academic hospitalist and general internal medicine physician leaders to elicit their perspectives on the factors contributing to discharge timing and the relative importance and effectiveness of early-discharge initiatives.

Study Design, Participants, and Oversight

We obtained a list of 115 university-affiliated hospitals associated with a residency program and, in most cases, a medical school from Vizient Inc. (formerly University HealthSystem Consortium), an alliance of academic medical centers and affiliated hospitals. Each member institution submits clinical data to allow for the benchmarking of outcomes to drive transparency and quality improvement.¹³ More than 95% of the nation’s academic medical centers and affiliated hospitals participate in this collaborative. Vizient works with members but does not set nor promote quality metrics, such as discharge timeliness. E-mail addresses for hospital medicine physician leaders (eg, division chief) of major academic medical centers were obtained from each institution via publicly available data (eg, the institution’s website). When an institution did not have a hospital medicine section, we identified the division chief of general internal medicine. The University of California, San Francisco Institutional Review Board approved this study.

Survey Development and Domains

We developed a 30-item survey to evaluate 5 main domains of interest: current discharge practices, degree of prioritization of early discharge on the inpatient service, barriers to timely discharge, prevalence and perceived effectiveness of implemented early-discharge initiatives, and barriers to implementation of early-discharge initiatives.

Respondents were first asked to identify their institutions’ goals for discharge time. They were then asked to compare the priority of early-discharge initiatives to other departmental quality-improvement initiatives, such as reducing 30-day readmissions, improving interpreter use, and improving patient satisfaction. Next, respondents were asked to estimate the degree to which clinical or patient factors contributed to delays in discharge. Respondents were then asked whether specific early-discharge initiatives, such as changes to rounding practices or communication interventions, were implemented at their institutions and, if so, the perceived effectiveness of these initiatives at meeting discharge targets. We piloted the questions locally with physicians and researchers prior to finalizing the survey.

Data Collection

We sent surveys via an online platform (Research Electronic Data Capture).¹⁴ Nonresponders were sent 2 e-mail reminders and then a follow-up telephone call asking them to complete the survey. Only 1 survey per academic medical center was collected. Any respondent who completed the survey within 2 weeks of receiving it was entered to win a Kindle Fire.

Data Analysis

We summarized survey responses using descriptive statistics. Analysis was completed in IBM SPSS version 22 (Armonk, NY).

Survey Respondent and Institutional Characteristics

Of the 115 institutions surveyed, we received 61 responses (response rate of 53%), with 39 (64%) respondents from divisions of hospital medicine and 22 (36%) from divisions of general internal medicine. A majority (n = 53; 87%) stated their medicine services have a combination of teaching (with residents) and nonteaching (without residents) teams. Thirty-nine (64%) reported having daily multidisciplinary rounds.

Early Discharge as a Priority

Forty-seven (77%) institutional representatives strongly agreed or agreed that early discharge was a priority, with discharge by noon being the most common target time (n = 23; 38%). Thirty (50%) respondents rated early discharge as more important than improving interpreter use for non-English-speaking patients and equally important as reducing 30-day readmissions (n = 29; 48%) and improving patient satisfaction (n = 27; 44%).

Factors Delaying Discharge

The most common factors perceived as delaying discharge were considered external to the hospital, such as postacute care bed availability or scheduled (eg, ambulance) transport delays (n = 48; 79%), followed by patient factors such as patient transport issues (n = 44; 72%). Less commonly reported were workflow issues, such as competing primary team priorities or case manager bandwidth (n = 38; 62%; Table 1).

Initiatives to Improve Discharge

The most commonly implemented initiatives perceived as effective at improving discharge times were the preemptive identification of early discharges to plan discharge paperwork (n = 34; 56%), communication with patients about anticipated discharge time on the day prior to discharge (n = 29; 48%), and the implementation of additional rounds between physician teams and case managers specifically around discharge planning (n = 28; 46%). Initiatives not commonly implemented included regular audit of and feedback on discharge times to providers and teams (n = 21; 34%), the use of a discharge readiness checklist (n = 26; 43%), incentives such as bonuses or penalties (n = 37; 61%), the use of a whiteboard to indicate discharge times (n = 23; 38%), and dedicated quality-improvement approaches such as LEAN (n = 37; 61%; Table 2).

Our study suggests early discharge for medicine patients is a priority among academic institutions. Hospitalist and general internal medicine physician leaders in our study generally attributed delayed discharges to external factors, particularly unavailability of postacute care facilities and transportation delays. Having issues with finding postacute care placements is consistent with previous findings by Selker et al.¹⁵ and Carey et al.⁸ This is despite the 20-year difference between Selker et al.’s study and the current study, reflecting a continued opportunity for improvement, including stronger partnerships with local and regional postacute care facilities to expedite care transition and stronger discharge-planning efforts early in the admission process. Efforts in postacute care placement may be particularly important for Medicaid-insured and uninsured patients.

Our responders, hospitalist and internal medicine physician leaders, did not perceive the additional responsibilities of teaching and supervising trainees to be factors that significantly delayed patient discharge. This is in contrast to previous studies, which attributed delays in discharge to prolonged clinical decision-making related to teaching and supervision.^4-6,8 This discrepancy may be due to the fact that we only surveyed single physician leaders at each institution and not residents. Our finding warrants further investigation to understand the degree to which resident skills may impact discharge planning and processes.

Institutions represented in our study have attempted a variety of initiatives promoting earlier discharge, with varying levels of perceived success. Initiatives perceived to be the most effective by hospital leaders centered on 2 main areas: (1) changing individual provider practice and (2) anticipatory discharge preparation. Interestingly, this is in discordance with the main factors labeled as causing delays in discharges, such as obtaining postacute care beds, busy case managers, and competing demands on primary teams. We hypothesize this may be because such changes require organization- or system-level changes and are perceived as more arduous than changes at the individual level. In addition, changes to individual provider behavior may be more cost- and time-effective than more systemic initiatives.

Our findings are consistent with the work published by Wertheimer and colleagues,¹¹ who show that additional afternoon interdisciplinary rounds can help identify patients who may be discharged before noon the next day. In their study, identifying such patients in advance improved the overall early-discharge rate the following day.

Our findings should be interpreted in light of several limitations. Our survey only considers the perspectives of hospitalist and general internal medicine physician leaders at academic medical centers that are part of the Vizient Inc. collaborative. They do not represent all academic or community-based medical centers. Although the perceived effectiveness of some initiatives was high, we did not collect empirical data to support these claims or to determine which initiative had the greatest relative impact on discharge timeliness. Lastly, we did not obtain resident, nursing, or case manager perspectives on discharge practices. Given their roles as frontline providers, we may have missed these alternative perspectives.

Our study shows there is a strong interest in increasing early discharges in an effort to improve hospital throughput and patient flow.

Acknowledgments

The authors thank all participants who completed the survey and Danielle Carrier at Vizient Inc. (formally University HealthSystem Consortium) for her assistance in obtaining data.

Disclosures

Hemali Patel, Margaret Fang, Michelle Mourad, Adrienne Green, Ryan Murphy, and James Harrison report no conflicts of interest. At the time the research was conducted, Robert Wachter reported that he is a member of the Lucian Leape Institute at the National Patient Safety Foundation (no compensation except travel expenses); recently chaired an advisory board to England’s National Health Service (NHS) reviewing the NHS’s digital health strategy (no compensation except travel expenses); has a contract with UCSF from the Agency for Healthcare Research and Quality to edit a patient-safety website; receives compensation from John Wiley & Sons for writing a blog; receives royalties from Lippincott Williams & Wilkins and McGraw-Hill Education for writing and/or editing several books; receives stock options for serving on the board of Acuity Medical Management Systems; receives a yearly stipend for serving on the board of The Doctors Company; serves on the scientific advisory boards for amino.com, PatientSafe Solutions Inc., Twine, and EarlySense (for which he receives stock options); has a small royalty stake in CareWeb, a hospital communication tool developed at UCSF; and holds the Marc and Lynne Benioff Endowed Chair in Hospital Medicine and the Holly Smith Distinguished Professorship in Science and Medicine at UCSF.

The discharge process is a critical bottleneck for efficient patient flow through the hospital. Delayed discharges translate into delays in admissions and other patient transitions, often leading to excess costs, patient dissatisfaction, and even patient harm.^1-3 The emergency department is particularly impacted by these delays; bottlenecks there lead to overcrowding, increased overall hospital length of stay, and increased risks for bad outcomes during hospitalization.²

Academic medical centers in particular may struggle with delayed discharges. In a typical teaching hospital, a team composed of an attending physician and housestaff share responsibility for determining the discharge plan. Additionally, clinical teaching activities may affect the process and quality of discharge.^4-6

The prevalence and causes of delayed discharges vary greatly.^7-9 To improve efficiency around discharge, many hospitals have launched initiatives designed to discharge patients earlier in the day, including goal setting (“discharge by noon”), scheduling discharge appointments, and using quality-improvement methods, such as Lean Methodology (LEAN), to remove inefficiencies within discharge processes.^10-12 However, there are few data on the prevalence and effectiveness of different strategies.

The aim of this study was to survey academic hospitalist and general internal medicine physician leaders to elicit their perspectives on the factors contributing to discharge timing and the relative importance and effectiveness of early-discharge initiatives.

Study Design, Participants, and Oversight

We obtained a list of 115 university-affiliated hospitals associated with a residency program and, in most cases, a medical school from Vizient Inc. (formerly University HealthSystem Consortium), an alliance of academic medical centers and affiliated hospitals. Each member institution submits clinical data to allow for the benchmarking of outcomes to drive transparency and quality improvement.¹³ More than 95% of the nation’s academic medical centers and affiliated hospitals participate in this collaborative. Vizient works with members but does not set nor promote quality metrics, such as discharge timeliness. E-mail addresses for hospital medicine physician leaders (eg, division chief) of major academic medical centers were obtained from each institution via publicly available data (eg, the institution’s website). When an institution did not have a hospital medicine section, we identified the division chief of general internal medicine. The University of California, San Francisco Institutional Review Board approved this study.

Survey Development and Domains

We developed a 30-item survey to evaluate 5 main domains of interest: current discharge practices, degree of prioritization of early discharge on the inpatient service, barriers to timely discharge, prevalence and perceived effectiveness of implemented early-discharge initiatives, and barriers to implementation of early-discharge initiatives.

Respondents were first asked to identify their institutions’ goals for discharge time. They were then asked to compare the priority of early-discharge initiatives to other departmental quality-improvement initiatives, such as reducing 30-day readmissions, improving interpreter use, and improving patient satisfaction. Next, respondents were asked to estimate the degree to which clinical or patient factors contributed to delays in discharge. Respondents were then asked whether specific early-discharge initiatives, such as changes to rounding practices or communication interventions, were implemented at their institutions and, if so, the perceived effectiveness of these initiatives at meeting discharge targets. We piloted the questions locally with physicians and researchers prior to finalizing the survey.

Data Collection

We sent surveys via an online platform (Research Electronic Data Capture).¹⁴ Nonresponders were sent 2 e-mail reminders and then a follow-up telephone call asking them to complete the survey. Only 1 survey per academic medical center was collected. Any respondent who completed the survey within 2 weeks of receiving it was entered to win a Kindle Fire.

Data Analysis

We summarized survey responses using descriptive statistics. Analysis was completed in IBM SPSS version 22 (Armonk, NY).

Survey Respondent and Institutional Characteristics

Of the 115 institutions surveyed, we received 61 responses (response rate of 53%), with 39 (64%) respondents from divisions of hospital medicine and 22 (36%) from divisions of general internal medicine. A majority (n = 53; 87%) stated their medicine services have a combination of teaching (with residents) and nonteaching (without residents) teams. Thirty-nine (64%) reported having daily multidisciplinary rounds.

Early Discharge as a Priority

Forty-seven (77%) institutional representatives strongly agreed or agreed that early discharge was a priority, with discharge by noon being the most common target time (n = 23; 38%). Thirty (50%) respondents rated early discharge as more important than improving interpreter use for non-English-speaking patients and equally important as reducing 30-day readmissions (n = 29; 48%) and improving patient satisfaction (n = 27; 44%).

Factors Delaying Discharge

The most common factors perceived as delaying discharge were considered external to the hospital, such as postacute care bed availability or scheduled (eg, ambulance) transport delays (n = 48; 79%), followed by patient factors such as patient transport issues (n = 44; 72%). Less commonly reported were workflow issues, such as competing primary team priorities or case manager bandwidth (n = 38; 62%; Table 1).

Initiatives to Improve Discharge

The most commonly implemented initiatives perceived as effective at improving discharge times were the preemptive identification of early discharges to plan discharge paperwork (n = 34; 56%), communication with patients about anticipated discharge time on the day prior to discharge (n = 29; 48%), and the implementation of additional rounds between physician teams and case managers specifically around discharge planning (n = 28; 46%). Initiatives not commonly implemented included regular audit of and feedback on discharge times to providers and teams (n = 21; 34%), the use of a discharge readiness checklist (n = 26; 43%), incentives such as bonuses or penalties (n = 37; 61%), the use of a whiteboard to indicate discharge times (n = 23; 38%), and dedicated quality-improvement approaches such as LEAN (n = 37; 61%; Table 2).

Our study suggests early discharge for medicine patients is a priority among academic institutions. Hospitalist and general internal medicine physician leaders in our study generally attributed delayed discharges to external factors, particularly unavailability of postacute care facilities and transportation delays. Having issues with finding postacute care placements is consistent with previous findings by Selker et al.¹⁵ and Carey et al.⁸ This is despite the 20-year difference between Selker et al.’s study and the current study, reflecting a continued opportunity for improvement, including stronger partnerships with local and regional postacute care facilities to expedite care transition and stronger discharge-planning efforts early in the admission process. Efforts in postacute care placement may be particularly important for Medicaid-insured and uninsured patients.

Our responders, hospitalist and internal medicine physician leaders, did not perceive the additional responsibilities of teaching and supervising trainees to be factors that significantly delayed patient discharge. This is in contrast to previous studies, which attributed delays in discharge to prolonged clinical decision-making related to teaching and supervision.^4-6,8 This discrepancy may be due to the fact that we only surveyed single physician leaders at each institution and not residents. Our finding warrants further investigation to understand the degree to which resident skills may impact discharge planning and processes.

Institutions represented in our study have attempted a variety of initiatives promoting earlier discharge, with varying levels of perceived success. Initiatives perceived to be the most effective by hospital leaders centered on 2 main areas: (1) changing individual provider practice and (2) anticipatory discharge preparation. Interestingly, this is in discordance with the main factors labeled as causing delays in discharges, such as obtaining postacute care beds, busy case managers, and competing demands on primary teams. We hypothesize this may be because such changes require organization- or system-level changes and are perceived as more arduous than changes at the individual level. In addition, changes to individual provider behavior may be more cost- and time-effective than more systemic initiatives.

Our findings are consistent with the work published by Wertheimer and colleagues,¹¹ who show that additional afternoon interdisciplinary rounds can help identify patients who may be discharged before noon the next day. In their study, identifying such patients in advance improved the overall early-discharge rate the following day.

Our findings should be interpreted in light of several limitations. Our survey only considers the perspectives of hospitalist and general internal medicine physician leaders at academic medical centers that are part of the Vizient Inc. collaborative. They do not represent all academic or community-based medical centers. Although the perceived effectiveness of some initiatives was high, we did not collect empirical data to support these claims or to determine which initiative had the greatest relative impact on discharge timeliness. Lastly, we did not obtain resident, nursing, or case manager perspectives on discharge practices. Given their roles as frontline providers, we may have missed these alternative perspectives.

Our study shows there is a strong interest in increasing early discharges in an effort to improve hospital throughput and patient flow.

Acknowledgments

The authors thank all participants who completed the survey and Danielle Carrier at Vizient Inc. (formally University HealthSystem Consortium) for her assistance in obtaining data.

Disclosures

Hemali Patel, Margaret Fang, Michelle Mourad, Adrienne Green, Ryan Murphy, and James Harrison report no conflicts of interest. At the time the research was conducted, Robert Wachter reported that he is a member of the Lucian Leape Institute at the National Patient Safety Foundation (no compensation except travel expenses); recently chaired an advisory board to England’s National Health Service (NHS) reviewing the NHS’s digital health strategy (no compensation except travel expenses); has a contract with UCSF from the Agency for Healthcare Research and Quality to edit a patient-safety website; receives compensation from John Wiley & Sons for writing a blog; receives royalties from Lippincott Williams & Wilkins and McGraw-Hill Education for writing and/or editing several books; receives stock options for serving on the board of Acuity Medical Management Systems; receives a yearly stipend for serving on the board of The Doctors Company; serves on the scientific advisory boards for amino.com, PatientSafe Solutions Inc., Twine, and EarlySense (for which he receives stock options); has a small royalty stake in CareWeb, a hospital communication tool developed at UCSF; and holds the Marc and Lynne Benioff Endowed Chair in Hospital Medicine and the Holly Smith Distinguished Professorship in Science and Medicine at UCSF.

Frailty is a common geriatric syndrome characterized by decreased physiological reserves leading to increased vulnerability to stressors.¹ Frail individuals are at increased risk of adverse health outcomes including falls, disability, hospitalization, and mortality.¹ Discharge to skilled nursing facilities (SNFs) is also associated with adverse outcomes,^2,3 but limited data exist on the utility of frailty in predicting discharge location in medical elders. We aimed to evaluate the association of frailty assessed by the Reported Edmonton Frailty Scale (REFS) with discharge disposition in hospitalized medical patients who were previously living in the community.

We conducted a prospective study of community dwelling elders (≥65 years) hospitalized to the medical service from January 2014 to April 2016. Trained research assistants interviewed patients and/or caregivers on hospital day 1; the REFS was used to screen for frailty and the Mini-Cog assessment for cognitive impairment (supplementary Appendixes 1 and 2). The primary outcome was discharge disposition categorized as discharge to home (with or without home health services) or discharge to a postacute care (PAC) facility (SNF or inpatient rehabilitation). Multivariable Poisson regression analysis was used to estimate the relative risk of discharge to a PAC facility. Frailty was grouped into the following 3 categories: (1) not frail, (2) apparently vulnerable/mildly frail, and (3) moderately/severely frail.

Among the 775 patients screened, 272 declined to participate, were non-English speakers, were transferred from another facility, were admitted under observation status, had advanced dementia, or died during hospitalization. Five hundred and three medical patients were included: median age was 80 years (interquartile range 75-86 years); 54.1% were female and 82.9% were white. The most common comorbidities were hypertension (51.7%), diabetes (26.0%), and renal failure (26.0%). Of the included patients, 11.1% had a known diagnosis of dementia and 52.1% screened positive for cognitive impairment (Table).

Overall, 24.9% were not frail, 49.5% were apparently vulnerable/mildly frail, and 25.6% were moderately/severely frail. About two-thirds (64.8%) returned home (40.0% with home healthcare) and 35% were discharged to a PAC facility (97.1% of them to SNF). Compared with patients who were discharged home, those discharged to a PAC facility were older (≥85 years; 26.7% vs 40.1%) and more likely to have dementia (7.7% vs 17.5%) and be frail (apparently vulnerable/mild frailty = 48.5% vs 51.4%%, moderate/severe frailty = 19.9% vs 36.2%; P < .001). Median length of hospital stay was shorter in those returning home (4 vs 5 days, P < .001).

In the multivariate analysis, which was adjusted for demographics, comorbidities, and principal diagnosis, frailty was strongly associated with discharge to PAC facility (apparently vulnerable/mild frailty vs no frailty, relative ratio [RR] = 2.00; 95% confidence interval [CI], 1.28-3.27, and moderate/severe frailty vs no frailty; RR = 2.66, 95% CI, 1.67-4.43). When the frailty score was included as a continuous variable, 1 unit increase in the score was associated with a 12% higher risk for discharge to a PAC facility (RR = 1.12; 95% CI, 1.07-1.17).

In this analysis of over 500 community-dwelling elderly medical patients hospitalized at one large tertiary center, we found that almost half of the patients were frail and over one-third had a new discharge to a PAC facility. Frailty, as assessed by REFS, was strongly associated with discharge to a PAC facility after adjusting for possible confounders.

Frailty is increasingly recognized as a useful tool to risk stratify the highly heterogeneous population of elderly people.⁴ Previous studies reported that frailty was predictive of discharge to PAC facilities in geriatric trauma and burn injury patients.^5,6 We found similar results in a population of elderly medical patients. A recent study showed that the Hospital Admission Risk Profile score comprising of age, modified Mini-Mental State Examination (MMSE), and functionality prior to admission was associated with discharge disposition in elderly patients admitted to a single geriatric unit in a rural hospital.⁷ Our study supports this finding by using a validated measure of frailty, the RFS, and does not include the lengthy MMSE.

Our study has several limitations. First, it a single-center study and results may not be generalizable; however, we included a large sample of patients with a variety of medical diagnoses. Second, the REFS is self-reported posing the risks of recall, respondent bias, and interview bias. We chose the REFS to assess frailty due to its practicality and ease of administration but also its completeness of assessing multiple important geriatric domains. Lastly, we did not collect the reason for discharge to PAC and it may have been a potential confounder.

In conclusion, our study demonstrates that frailty assessed by a practical validated scale, the REFS, is a strong predictor of a new discharge to PAC facilities in older medical patients. Accurate identification of elders at risk for discharge to PAC facilities provides the potential to counsel patients and families and plan for complex post discharge needs. Future studies should identify potential interventions targeting frail patients in which PAC is not obligatory, aiming to increase their chance of being discharged home.

Disclosure

Drs. Stefan and Ramdass had full access to all the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Stefan, Starr, Brennan, and Ramdass conceived the study. Ms. Liu and Dr. Pekow analyzed the data. Dr. Ramdass prepared the manuscript. Drs. Stefan, Brennan, Lindenauer, and Starr critically reviewed the manuscript for important intellectual content. A subset of the patients included in this study was part of a Health Resources and Services Administration funded Geri-Pal Transformation through Learning and Collaboration project awarded to Baystate Medical Center, grant number U1QHP28702 (PI: Maura J. Brennan). The investigators retained full independence in the conduct of this research. The authors have no conflicts of interest.

Frailty is a common geriatric syndrome characterized by decreased physiological reserves leading to increased vulnerability to stressors.¹ Frail individuals are at increased risk of adverse health outcomes including falls, disability, hospitalization, and mortality.¹ Discharge to skilled nursing facilities (SNFs) is also associated with adverse outcomes,^2,3 but limited data exist on the utility of frailty in predicting discharge location in medical elders. We aimed to evaluate the association of frailty assessed by the Reported Edmonton Frailty Scale (REFS) with discharge disposition in hospitalized medical patients who were previously living in the community.

We conducted a prospective study of community dwelling elders (≥65 years) hospitalized to the medical service from January 2014 to April 2016. Trained research assistants interviewed patients and/or caregivers on hospital day 1; the REFS was used to screen for frailty and the Mini-Cog assessment for cognitive impairment (supplementary Appendixes 1 and 2). The primary outcome was discharge disposition categorized as discharge to home (with or without home health services) or discharge to a postacute care (PAC) facility (SNF or inpatient rehabilitation). Multivariable Poisson regression analysis was used to estimate the relative risk of discharge to a PAC facility. Frailty was grouped into the following 3 categories: (1) not frail, (2) apparently vulnerable/mildly frail, and (3) moderately/severely frail.

Among the 775 patients screened, 272 declined to participate, were non-English speakers, were transferred from another facility, were admitted under observation status, had advanced dementia, or died during hospitalization. Five hundred and three medical patients were included: median age was 80 years (interquartile range 75-86 years); 54.1% were female and 82.9% were white. The most common comorbidities were hypertension (51.7%), diabetes (26.0%), and renal failure (26.0%). Of the included patients, 11.1% had a known diagnosis of dementia and 52.1% screened positive for cognitive impairment (Table).

Overall, 24.9% were not frail, 49.5% were apparently vulnerable/mildly frail, and 25.6% were moderately/severely frail. About two-thirds (64.8%) returned home (40.0% with home healthcare) and 35% were discharged to a PAC facility (97.1% of them to SNF). Compared with patients who were discharged home, those discharged to a PAC facility were older (≥85 years; 26.7% vs 40.1%) and more likely to have dementia (7.7% vs 17.5%) and be frail (apparently vulnerable/mild frailty = 48.5% vs 51.4%%, moderate/severe frailty = 19.9% vs 36.2%; P < .001). Median length of hospital stay was shorter in those returning home (4 vs 5 days, P < .001).

In the multivariate analysis, which was adjusted for demographics, comorbidities, and principal diagnosis, frailty was strongly associated with discharge to PAC facility (apparently vulnerable/mild frailty vs no frailty, relative ratio [RR] = 2.00; 95% confidence interval [CI], 1.28-3.27, and moderate/severe frailty vs no frailty; RR = 2.66, 95% CI, 1.67-4.43). When the frailty score was included as a continuous variable, 1 unit increase in the score was associated with a 12% higher risk for discharge to a PAC facility (RR = 1.12; 95% CI, 1.07-1.17).

In this analysis of over 500 community-dwelling elderly medical patients hospitalized at one large tertiary center, we found that almost half of the patients were frail and over one-third had a new discharge to a PAC facility. Frailty, as assessed by REFS, was strongly associated with discharge to a PAC facility after adjusting for possible confounders.

Frailty is increasingly recognized as a useful tool to risk stratify the highly heterogeneous population of elderly people.⁴ Previous studies reported that frailty was predictive of discharge to PAC facilities in geriatric trauma and burn injury patients.^5,6 We found similar results in a population of elderly medical patients. A recent study showed that the Hospital Admission Risk Profile score comprising of age, modified Mini-Mental State Examination (MMSE), and functionality prior to admission was associated with discharge disposition in elderly patients admitted to a single geriatric unit in a rural hospital.⁷ Our study supports this finding by using a validated measure of frailty, the RFS, and does not include the lengthy MMSE.

Our study has several limitations. First, it a single-center study and results may not be generalizable; however, we included a large sample of patients with a variety of medical diagnoses. Second, the REFS is self-reported posing the risks of recall, respondent bias, and interview bias. We chose the REFS to assess frailty due to its practicality and ease of administration but also its completeness of assessing multiple important geriatric domains. Lastly, we did not collect the reason for discharge to PAC and it may have been a potential confounder.

In conclusion, our study demonstrates that frailty assessed by a practical validated scale, the REFS, is a strong predictor of a new discharge to PAC facilities in older medical patients. Accurate identification of elders at risk for discharge to PAC facilities provides the potential to counsel patients and families and plan for complex post discharge needs. Future studies should identify potential interventions targeting frail patients in which PAC is not obligatory, aiming to increase their chance of being discharged home.

Disclosure

Drs. Stefan and Ramdass had full access to all the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Stefan, Starr, Brennan, and Ramdass conceived the study. Ms. Liu and Dr. Pekow analyzed the data. Dr. Ramdass prepared the manuscript. Drs. Stefan, Brennan, Lindenauer, and Starr critically reviewed the manuscript for important intellectual content. A subset of the patients included in this study was part of a Health Resources and Services Administration funded Geri-Pal Transformation through Learning and Collaboration project awarded to Baystate Medical Center, grant number U1QHP28702 (PI: Maura J. Brennan). The investigators retained full independence in the conduct of this research. The authors have no conflicts of interest.

Frailty is a common geriatric syndrome characterized by decreased physiological reserves leading to increased vulnerability to stressors.¹ Frail individuals are at increased risk of adverse health outcomes including falls, disability, hospitalization, and mortality.¹ Discharge to skilled nursing facilities (SNFs) is also associated with adverse outcomes,^2,3 but limited data exist on the utility of frailty in predicting discharge location in medical elders. We aimed to evaluate the association of frailty assessed by the Reported Edmonton Frailty Scale (REFS) with discharge disposition in hospitalized medical patients who were previously living in the community.

We conducted a prospective study of community dwelling elders (≥65 years) hospitalized to the medical service from January 2014 to April 2016. Trained research assistants interviewed patients and/or caregivers on hospital day 1; the REFS was used to screen for frailty and the Mini-Cog assessment for cognitive impairment (supplementary Appendixes 1 and 2). The primary outcome was discharge disposition categorized as discharge to home (with or without home health services) or discharge to a postacute care (PAC) facility (SNF or inpatient rehabilitation). Multivariable Poisson regression analysis was used to estimate the relative risk of discharge to a PAC facility. Frailty was grouped into the following 3 categories: (1) not frail, (2) apparently vulnerable/mildly frail, and (3) moderately/severely frail.

Among the 775 patients screened, 272 declined to participate, were non-English speakers, were transferred from another facility, were admitted under observation status, had advanced dementia, or died during hospitalization. Five hundred and three medical patients were included: median age was 80 years (interquartile range 75-86 years); 54.1% were female and 82.9% were white. The most common comorbidities were hypertension (51.7%), diabetes (26.0%), and renal failure (26.0%). Of the included patients, 11.1% had a known diagnosis of dementia and 52.1% screened positive for cognitive impairment (Table).

Overall, 24.9% were not frail, 49.5% were apparently vulnerable/mildly frail, and 25.6% were moderately/severely frail. About two-thirds (64.8%) returned home (40.0% with home healthcare) and 35% were discharged to a PAC facility (97.1% of them to SNF). Compared with patients who were discharged home, those discharged to a PAC facility were older (≥85 years; 26.7% vs 40.1%) and more likely to have dementia (7.7% vs 17.5%) and be frail (apparently vulnerable/mild frailty = 48.5% vs 51.4%%, moderate/severe frailty = 19.9% vs 36.2%; P < .001). Median length of hospital stay was shorter in those returning home (4 vs 5 days, P < .001).

In the multivariate analysis, which was adjusted for demographics, comorbidities, and principal diagnosis, frailty was strongly associated with discharge to PAC facility (apparently vulnerable/mild frailty vs no frailty, relative ratio [RR] = 2.00; 95% confidence interval [CI], 1.28-3.27, and moderate/severe frailty vs no frailty; RR = 2.66, 95% CI, 1.67-4.43). When the frailty score was included as a continuous variable, 1 unit increase in the score was associated with a 12% higher risk for discharge to a PAC facility (RR = 1.12; 95% CI, 1.07-1.17).

In this analysis of over 500 community-dwelling elderly medical patients hospitalized at one large tertiary center, we found that almost half of the patients were frail and over one-third had a new discharge to a PAC facility. Frailty, as assessed by REFS, was strongly associated with discharge to a PAC facility after adjusting for possible confounders.

Frailty is increasingly recognized as a useful tool to risk stratify the highly heterogeneous population of elderly people.⁴ Previous studies reported that frailty was predictive of discharge to PAC facilities in geriatric trauma and burn injury patients.^5,6 We found similar results in a population of elderly medical patients. A recent study showed that the Hospital Admission Risk Profile score comprising of age, modified Mini-Mental State Examination (MMSE), and functionality prior to admission was associated with discharge disposition in elderly patients admitted to a single geriatric unit in a rural hospital.⁷ Our study supports this finding by using a validated measure of frailty, the RFS, and does not include the lengthy MMSE.

Our study has several limitations. First, it a single-center study and results may not be generalizable; however, we included a large sample of patients with a variety of medical diagnoses. Second, the REFS is self-reported posing the risks of recall, respondent bias, and interview bias. We chose the REFS to assess frailty due to its practicality and ease of administration but also its completeness of assessing multiple important geriatric domains. Lastly, we did not collect the reason for discharge to PAC and it may have been a potential confounder.

In conclusion, our study demonstrates that frailty assessed by a practical validated scale, the REFS, is a strong predictor of a new discharge to PAC facilities in older medical patients. Accurate identification of elders at risk for discharge to PAC facilities provides the potential to counsel patients and families and plan for complex post discharge needs. Future studies should identify potential interventions targeting frail patients in which PAC is not obligatory, aiming to increase their chance of being discharged home.

Disclosure

Drs. Stefan and Ramdass had full access to all the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Stefan, Starr, Brennan, and Ramdass conceived the study. Ms. Liu and Dr. Pekow analyzed the data. Dr. Ramdass prepared the manuscript. Drs. Stefan, Brennan, Lindenauer, and Starr critically reviewed the manuscript for important intellectual content. A subset of the patients included in this study was part of a Health Resources and Services Administration funded Geri-Pal Transformation through Learning and Collaboration project awarded to Baystate Medical Center, grant number U1QHP28702 (PI: Maura J. Brennan). The investigators retained full independence in the conduct of this research. The authors have no conflicts of interest.

Reducing the treatment of asymptomatic bacteriuria (ASB), or isolation of bacteria from a urine specimen in a patient without urinary tract infection (UTI) symptoms, is a key goal of antibiotic stewardship programs.¹ Treatment of ASB has been associated with the emergence of resistant organisms and subsequent UTI risk among women with recurrent UTI.^2,3 The Infectious Diseases Society of America and the American Board of Internal Medicine Foundation’s Choosing Wisely campaign recommend against treating ASB, with the exception of pregnant patients and urogenital surgical patients.^1,4

Obtaining urinalyses and urine cultures (UC) in asymptomatic patients may contribute to the unnecessary treatment of ASB. In a study of hospitalized patients, 62% received urinalysis testing, even though 82% of these patients did not have UTI symptoms.⁵ Of the patients found to have ASB, 30% were given antibiotics.⁵ Therefore, interventions aimed at reducing urine testing may reduce ASB treatment.

Electronic passive clinical decision support (CDS) alerts and electronic education may be effective interventions to reduce urine testing.⁶ While CDS tools are recommended in antibiotic stewardship guidelines,⁷ they have led to only modest improvements in appropriate antibiotic prescribing and are typically bundled with time-intensive educational interventions.⁸ Furthermore, most in-hospital interventions to decrease ASB treatment have focused on intensive care units (ICUs).⁹ We hypothesized that CDS and electronic education would decrease (1) urinalysis and UC ordering and (2) antibiotic orders for urinalyses and UCs in hospitalized adult patients.

Population

We conducted a prospective time series analysis (preintervention: September 2014 to June 2015; postintervention: September 2015 to June 2016) at a large tertiary medical center. All hospitalized patients ≥18 years old were eligible except those admitted to services requiring specialized ASB management (eg, leukemia and lymphoma, solid organ transplant, and obstetrics).¹ The study was declared quality improvement by the Johns Hopkins Institutional Review Board.

Intervention

In August 2015, we implemented a multifaceted intervention that included provider education and passive electronic CDS (supplementary Appendix 1 and supplementary Appendix 2). Materials were disseminated through hospital-wide computer workstation screensavers and a 1-page e-mailed newsletter to department of medicine clinicians. The CDS tool included simple informational messages recommending against urine testing without symptoms and against treating ASB; these messages accompanied electronic health record (EHR; Allscripts Sunrise Clinical Manager, Chicago, IL) orders for urinalysis, UC, and antibiotics commonly used within our institution to treat UTI (cefazolin, cephalexin, ceftriaxone, trimethoprim-sulfamethoxazole, nitrofurantoin, and ciprofloxacin). The information was displayed automatically when orders for these tests and antibiotics were selected; provider acknowledgment was not required to proceed.

Data Collection

The services within our hospital are geographically located. We collected orders for urinalysis, UC, and the associated antibiotics for all units except those housing patients excluded from our study. As the CDS tool appeared only in the inpatient EHR, only postadmission orders were included, excluding emergency department orders. For admissions with multiple urinalyses, urinalysis orders placed ≥72 hours apart were eligible. Only antibiotics ordered for ≥24 hours were included, excluding on-call and 1-time antibiotic orders.

Our approach to data collection attempted to model a clinician’s decision-making pathway from (1) ordering a urinalysis, to (2) ordering a UC in response to a urinalysis result, to (3) ordering antibiotics in response to a urinalysis or UC result. We focused on order placement rather than results to prioritize avoiding testing in asymptomatic patients, as our institution does not require positive urinalyses for UC testing (reflex testing). Urinalyses resulted within 1 to 2 hours, allowing for clinicians to quickly order UCs after urinalysis result review. Urinalysis and UC orders per monthly admissions were defined as (1) urinalyses, (2) UCs, (3) simultaneous urinalysis and UC (within 1 hour of each other), and (4) UCs ordered 1 to 24 hours after urinalysis. We also analyzed the following antibiotic orders per monthly admissions: (1) simultaneous urinalysis and antibiotic orders, (2) antibiotics ordered 1 to 24 hours after urinalysis order, and (3) antibiotics ordered within 24 hours of the UC result.

Outcome Measures

All outcome measures were calculated as the change over time per total monthly admissions in the preintervention and postintervention periods. In addition to symptoms, urinalysis is a critical, measurable early step in determining the presence of ASB. Therefore, the primary outcome measure was the postintervention change in monthly urinalysis orders, and the secondary outcome measure was the postintervention change in monthly UC orders. Additional outcome measures included monthly postintervention changes in (1) UC ordered 1 to 24 hours after urinalyses, (2) urinalyses and antibiotics ordered simultaneously, (3) antibiotic orders within 1 to 24 hours of urinalyses, and (4) antibiotics ordered within 24 hours of UC result.

Statistical Analysis

Statistical analyses were performed by using Stata (version 14.2; StataCorp LLC, College Station, TX). An interrupted time series analysis was performed to compare the change in orders per 100 monthly admissions in preintervention and postintervention periods. To do this, we created 2 separate segmented linear regression models for each dependent variable, pre- and postintervention. Normality was assumed because of large numbers. Rate differences per 100 monthly admissions are also calculated as the total number of orders divided by the total number of admissions in postintervention and preintervention periods with Mantel-Haenszel estimators. Differences were considered statistically significant at P ≤ .05.

A multifaceted but simple bundle of CDS and provider education reduced UC testing but not urinalyses in a large tertiary care hospital. The bundle also reduced antibiotic ordering in response to urinalyses as well as antibiotic ordering in response to UC results.

Other in-hospital CDS tools to decrease ASB treatment have focused only on ICUs.^9,10 Our intervention was evaluated hospital-wide and included urinalyses and UCs. Our intervention was clinician directed and not laboratory directed, such as a positive urinalysis reflexing to a UC. Simultaneous urinalysis and UC testing may lead to ASB treatment, as clinicians treat the positive UC and ignore the negative urinalysis.^11,12 Therefore, we focused on UCs being sent in response to urinalyses.

We chose to focus on laboratory testing data instead of administrative diagnoses for UTI. The sensitivity of administrative data to determine similar conditions such as catheter-associated UTIs is low (0%).¹³

Our single-center study may not be generalizable to other settings. We did not include emergency department patients, as this location used a different EHR. In addition, given the 600,000 yearly hospital admissions, it was impractical to assess the appropriateness of each antibiotic-based documentation of symptoms. Instead of focusing on symptoms of ASB or UTI diagnoses, we focused on ordering urinalysis, UC, and antibiotics. In investigating the antibiotics most frequently used to treat UTI in our hospital, we may have both missed some patients who were treated with other antibiotics for ASB (eg, 4th generation cephalosporins, penicillins, carbapenems, etc) and captured patients receiving antibiotics for indications other than UTI (eg, pneumonia). In our focus on overall ordering practices across a hospital, we did not capture data on bladder catheterization status or the predominant organism seen in UC. At the time of the intervention, the laboratory did not have the resources for urinalysis testing reflexing to UC. However, our intervention did not prevent ordering simultaneous urinalysis and UC in symptomatic patients in general or urosepsis in particular. With only 12 total time points, the interrupted time series analysis may have been underpowered.¹⁴ We also do not know if the intervention’s effect would decay over time.

Although the intervention took very little staff time and resources, alert fatigue was a risk.¹⁵ We attempted to mitigate this alert fatigue by making the CDS passive (in the form of a brief informational message) with no provider action required. In conversations with providers in our institution, there has been dissatisfaction with alerts requiring action, as these are thought to be overly intrusive. We are also not clear on which element of the intervention bundle (ie, the CDS or the educational intervention) may have had more of an impact, as the elements of the intervention bundle were rolled out simultaneously. It is possible and even probable that both elements are needed to raise awareness of the problem. Also, as our EHR required all interventions to be rolled out hospital-wide simultaneously, we were unable to randomize certain floors or providers to the CDS portion of the intervention bundle. Other analyses including the type of hospital unit were beyond the scope of this brief report.

Our intervention bundle was associated with reduced UC orders and reduced antibiotics ordered after urinalyses. If a provider does not know there is bacteriuria, then the provider will not be tempted to order antibiotics. This easily implementable bundle may play an important role as an antimicrobial stewardship strategy for ASB.

Acknowledgments

The authors acknowledge the support of Erin Fanning, BS, and Angel Florentin, BS, in providing data for analysis. SCK received funding from the Johns Hopkins Institute for Clinical and Translational Research (ICTR), which is funded in part by grant number KL2TR001077 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), and the NIH Roadmap for Medical Research. These contents are solely the responsibility of the authors and do not necessarily represent the official view of the Johns Hopkins ICTR, NCATS, or NIH. We also acknowledge support from the Centers for Disease Control and Prevention’s Prevention Epicenter Program Q8377 (collaborative agreement U54 CK000447 to SEC). SEC has received support for consulting from Novartis and Theravance, and her institution has received a grant from Pfizer Grants for Learning and Change/The Joint Commission. This work was supported by the NIH T32 HL116275 to NC. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Disclosure

No conflicts of interest have been reported by any author.

Reducing the treatment of asymptomatic bacteriuria (ASB), or isolation of bacteria from a urine specimen in a patient without urinary tract infection (UTI) symptoms, is a key goal of antibiotic stewardship programs.¹ Treatment of ASB has been associated with the emergence of resistant organisms and subsequent UTI risk among women with recurrent UTI.^2,3 The Infectious Diseases Society of America and the American Board of Internal Medicine Foundation’s Choosing Wisely campaign recommend against treating ASB, with the exception of pregnant patients and urogenital surgical patients.^1,4

Obtaining urinalyses and urine cultures (UC) in asymptomatic patients may contribute to the unnecessary treatment of ASB. In a study of hospitalized patients, 62% received urinalysis testing, even though 82% of these patients did not have UTI symptoms.⁵ Of the patients found to have ASB, 30% were given antibiotics.⁵ Therefore, interventions aimed at reducing urine testing may reduce ASB treatment.

Electronic passive clinical decision support (CDS) alerts and electronic education may be effective interventions to reduce urine testing.⁶ While CDS tools are recommended in antibiotic stewardship guidelines,⁷ they have led to only modest improvements in appropriate antibiotic prescribing and are typically bundled with time-intensive educational interventions.⁸ Furthermore, most in-hospital interventions to decrease ASB treatment have focused on intensive care units (ICUs).⁹ We hypothesized that CDS and electronic education would decrease (1) urinalysis and UC ordering and (2) antibiotic orders for urinalyses and UCs in hospitalized adult patients.

Population

We conducted a prospective time series analysis (preintervention: September 2014 to June 2015; postintervention: September 2015 to June 2016) at a large tertiary medical center. All hospitalized patients ≥18 years old were eligible except those admitted to services requiring specialized ASB management (eg, leukemia and lymphoma, solid organ transplant, and obstetrics).¹ The study was declared quality improvement by the Johns Hopkins Institutional Review Board.

Intervention

In August 2015, we implemented a multifaceted intervention that included provider education and passive electronic CDS (supplementary Appendix 1 and supplementary Appendix 2). Materials were disseminated through hospital-wide computer workstation screensavers and a 1-page e-mailed newsletter to department of medicine clinicians. The CDS tool included simple informational messages recommending against urine testing without symptoms and against treating ASB; these messages accompanied electronic health record (EHR; Allscripts Sunrise Clinical Manager, Chicago, IL) orders for urinalysis, UC, and antibiotics commonly used within our institution to treat UTI (cefazolin, cephalexin, ceftriaxone, trimethoprim-sulfamethoxazole, nitrofurantoin, and ciprofloxacin). The information was displayed automatically when orders for these tests and antibiotics were selected; provider acknowledgment was not required to proceed.

Data Collection

The services within our hospital are geographically located. We collected orders for urinalysis, UC, and the associated antibiotics for all units except those housing patients excluded from our study. As the CDS tool appeared only in the inpatient EHR, only postadmission orders were included, excluding emergency department orders. For admissions with multiple urinalyses, urinalysis orders placed ≥72 hours apart were eligible. Only antibiotics ordered for ≥24 hours were included, excluding on-call and 1-time antibiotic orders.

Our approach to data collection attempted to model a clinician’s decision-making pathway from (1) ordering a urinalysis, to (2) ordering a UC in response to a urinalysis result, to (3) ordering antibiotics in response to a urinalysis or UC result. We focused on order placement rather than results to prioritize avoiding testing in asymptomatic patients, as our institution does not require positive urinalyses for UC testing (reflex testing). Urinalyses resulted within 1 to 2 hours, allowing for clinicians to quickly order UCs after urinalysis result review. Urinalysis and UC orders per monthly admissions were defined as (1) urinalyses, (2) UCs, (3) simultaneous urinalysis and UC (within 1 hour of each other), and (4) UCs ordered 1 to 24 hours after urinalysis. We also analyzed the following antibiotic orders per monthly admissions: (1) simultaneous urinalysis and antibiotic orders, (2) antibiotics ordered 1 to 24 hours after urinalysis order, and (3) antibiotics ordered within 24 hours of the UC result.

Outcome Measures

All outcome measures were calculated as the change over time per total monthly admissions in the preintervention and postintervention periods. In addition to symptoms, urinalysis is a critical, measurable early step in determining the presence of ASB. Therefore, the primary outcome measure was the postintervention change in monthly urinalysis orders, and the secondary outcome measure was the postintervention change in monthly UC orders. Additional outcome measures included monthly postintervention changes in (1) UC ordered 1 to 24 hours after urinalyses, (2) urinalyses and antibiotics ordered simultaneously, (3) antibiotic orders within 1 to 24 hours of urinalyses, and (4) antibiotics ordered within 24 hours of UC result.

Statistical Analysis

Statistical analyses were performed by using Stata (version 14.2; StataCorp LLC, College Station, TX). An interrupted time series analysis was performed to compare the change in orders per 100 monthly admissions in preintervention and postintervention periods. To do this, we created 2 separate segmented linear regression models for each dependent variable, pre- and postintervention. Normality was assumed because of large numbers. Rate differences per 100 monthly admissions are also calculated as the total number of orders divided by the total number of admissions in postintervention and preintervention periods with Mantel-Haenszel estimators. Differences were considered statistically significant at P ≤ .05.

A multifaceted but simple bundle of CDS and provider education reduced UC testing but not urinalyses in a large tertiary care hospital. The bundle also reduced antibiotic ordering in response to urinalyses as well as antibiotic ordering in response to UC results.

Other in-hospital CDS tools to decrease ASB treatment have focused only on ICUs.^9,10 Our intervention was evaluated hospital-wide and included urinalyses and UCs. Our intervention was clinician directed and not laboratory directed, such as a positive urinalysis reflexing to a UC. Simultaneous urinalysis and UC testing may lead to ASB treatment, as clinicians treat the positive UC and ignore the negative urinalysis.^11,12 Therefore, we focused on UCs being sent in response to urinalyses.

We chose to focus on laboratory testing data instead of administrative diagnoses for UTI. The sensitivity of administrative data to determine similar conditions such as catheter-associated UTIs is low (0%).¹³

Our single-center study may not be generalizable to other settings. We did not include emergency department patients, as this location used a different EHR. In addition, given the 600,000 yearly hospital admissions, it was impractical to assess the appropriateness of each antibiotic-based documentation of symptoms. Instead of focusing on symptoms of ASB or UTI diagnoses, we focused on ordering urinalysis, UC, and antibiotics. In investigating the antibiotics most frequently used to treat UTI in our hospital, we may have both missed some patients who were treated with other antibiotics for ASB (eg, 4th generation cephalosporins, penicillins, carbapenems, etc) and captured patients receiving antibiotics for indications other than UTI (eg, pneumonia). In our focus on overall ordering practices across a hospital, we did not capture data on bladder catheterization status or the predominant organism seen in UC. At the time of the intervention, the laboratory did not have the resources for urinalysis testing reflexing to UC. However, our intervention did not prevent ordering simultaneous urinalysis and UC in symptomatic patients in general or urosepsis in particular. With only 12 total time points, the interrupted time series analysis may have been underpowered.¹⁴ We also do not know if the intervention’s effect would decay over time.

Although the intervention took very little staff time and resources, alert fatigue was a risk.¹⁵ We attempted to mitigate this alert fatigue by making the CDS passive (in the form of a brief informational message) with no provider action required. In conversations with providers in our institution, there has been dissatisfaction with alerts requiring action, as these are thought to be overly intrusive. We are also not clear on which element of the intervention bundle (ie, the CDS or the educational intervention) may have had more of an impact, as the elements of the intervention bundle were rolled out simultaneously. It is possible and even probable that both elements are needed to raise awareness of the problem. Also, as our EHR required all interventions to be rolled out hospital-wide simultaneously, we were unable to randomize certain floors or providers to the CDS portion of the intervention bundle. Other analyses including the type of hospital unit were beyond the scope of this brief report.

Our intervention bundle was associated with reduced UC orders and reduced antibiotics ordered after urinalyses. If a provider does not know there is bacteriuria, then the provider will not be tempted to order antibiotics. This easily implementable bundle may play an important role as an antimicrobial stewardship strategy for ASB.

Acknowledgments

The authors acknowledge the support of Erin Fanning, BS, and Angel Florentin, BS, in providing data for analysis. SCK received funding from the Johns Hopkins Institute for Clinical and Translational Research (ICTR), which is funded in part by grant number KL2TR001077 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), and the NIH Roadmap for Medical Research. These contents are solely the responsibility of the authors and do not necessarily represent the official view of the Johns Hopkins ICTR, NCATS, or NIH. We also acknowledge support from the Centers for Disease Control and Prevention’s Prevention Epicenter Program Q8377 (collaborative agreement U54 CK000447 to SEC). SEC has received support for consulting from Novartis and Theravance, and her institution has received a grant from Pfizer Grants for Learning and Change/The Joint Commission. This work was supported by the NIH T32 HL116275 to NC. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Disclosure

No conflicts of interest have been reported by any author.

Reducing the treatment of asymptomatic bacteriuria (ASB), or isolation of bacteria from a urine specimen in a patient without urinary tract infection (UTI) symptoms, is a key goal of antibiotic stewardship programs.¹ Treatment of ASB has been associated with the emergence of resistant organisms and subsequent UTI risk among women with recurrent UTI.^2,3 The Infectious Diseases Society of America and the American Board of Internal Medicine Foundation’s Choosing Wisely campaign recommend against treating ASB, with the exception of pregnant patients and urogenital surgical patients.^1,4

Obtaining urinalyses and urine cultures (UC) in asymptomatic patients may contribute to the unnecessary treatment of ASB. In a study of hospitalized patients, 62% received urinalysis testing, even though 82% of these patients did not have UTI symptoms.⁵ Of the patients found to have ASB, 30% were given antibiotics.⁵ Therefore, interventions aimed at reducing urine testing may reduce ASB treatment.

Electronic passive clinical decision support (CDS) alerts and electronic education may be effective interventions to reduce urine testing.⁶ While CDS tools are recommended in antibiotic stewardship guidelines,⁷ they have led to only modest improvements in appropriate antibiotic prescribing and are typically bundled with time-intensive educational interventions.⁸ Furthermore, most in-hospital interventions to decrease ASB treatment have focused on intensive care units (ICUs).⁹ We hypothesized that CDS and electronic education would decrease (1) urinalysis and UC ordering and (2) antibiotic orders for urinalyses and UCs in hospitalized adult patients.

Population

We conducted a prospective time series analysis (preintervention: September 2014 to June 2015; postintervention: September 2015 to June 2016) at a large tertiary medical center. All hospitalized patients ≥18 years old were eligible except those admitted to services requiring specialized ASB management (eg, leukemia and lymphoma, solid organ transplant, and obstetrics).¹ The study was declared quality improvement by the Johns Hopkins Institutional Review Board.

Intervention

In August 2015, we implemented a multifaceted intervention that included provider education and passive electronic CDS (supplementary Appendix 1 and supplementary Appendix 2). Materials were disseminated through hospital-wide computer workstation screensavers and a 1-page e-mailed newsletter to department of medicine clinicians. The CDS tool included simple informational messages recommending against urine testing without symptoms and against treating ASB; these messages accompanied electronic health record (EHR; Allscripts Sunrise Clinical Manager, Chicago, IL) orders for urinalysis, UC, and antibiotics commonly used within our institution to treat UTI (cefazolin, cephalexin, ceftriaxone, trimethoprim-sulfamethoxazole, nitrofurantoin, and ciprofloxacin). The information was displayed automatically when orders for these tests and antibiotics were selected; provider acknowledgment was not required to proceed.

Data Collection

The services within our hospital are geographically located. We collected orders for urinalysis, UC, and the associated antibiotics for all units except those housing patients excluded from our study. As the CDS tool appeared only in the inpatient EHR, only postadmission orders were included, excluding emergency department orders. For admissions with multiple urinalyses, urinalysis orders placed ≥72 hours apart were eligible. Only antibiotics ordered for ≥24 hours were included, excluding on-call and 1-time antibiotic orders.

Our approach to data collection attempted to model a clinician’s decision-making pathway from (1) ordering a urinalysis, to (2) ordering a UC in response to a urinalysis result, to (3) ordering antibiotics in response to a urinalysis or UC result. We focused on order placement rather than results to prioritize avoiding testing in asymptomatic patients, as our institution does not require positive urinalyses for UC testing (reflex testing). Urinalyses resulted within 1 to 2 hours, allowing for clinicians to quickly order UCs after urinalysis result review. Urinalysis and UC orders per monthly admissions were defined as (1) urinalyses, (2) UCs, (3) simultaneous urinalysis and UC (within 1 hour of each other), and (4) UCs ordered 1 to 24 hours after urinalysis. We also analyzed the following antibiotic orders per monthly admissions: (1) simultaneous urinalysis and antibiotic orders, (2) antibiotics ordered 1 to 24 hours after urinalysis order, and (3) antibiotics ordered within 24 hours of the UC result.

Outcome Measures

All outcome measures were calculated as the change over time per total monthly admissions in the preintervention and postintervention periods. In addition to symptoms, urinalysis is a critical, measurable early step in determining the presence of ASB. Therefore, the primary outcome measure was the postintervention change in monthly urinalysis orders, and the secondary outcome measure was the postintervention change in monthly UC orders. Additional outcome measures included monthly postintervention changes in (1) UC ordered 1 to 24 hours after urinalyses, (2) urinalyses and antibiotics ordered simultaneously, (3) antibiotic orders within 1 to 24 hours of urinalyses, and (4) antibiotics ordered within 24 hours of UC result.

Statistical Analysis

Statistical analyses were performed by using Stata (version 14.2; StataCorp LLC, College Station, TX). An interrupted time series analysis was performed to compare the change in orders per 100 monthly admissions in preintervention and postintervention periods. To do this, we created 2 separate segmented linear regression models for each dependent variable, pre- and postintervention. Normality was assumed because of large numbers. Rate differences per 100 monthly admissions are also calculated as the total number of orders divided by the total number of admissions in postintervention and preintervention periods with Mantel-Haenszel estimators. Differences were considered statistically significant at P ≤ .05.

A multifaceted but simple bundle of CDS and provider education reduced UC testing but not urinalyses in a large tertiary care hospital. The bundle also reduced antibiotic ordering in response to urinalyses as well as antibiotic ordering in response to UC results.

Other in-hospital CDS tools to decrease ASB treatment have focused only on ICUs.^9,10 Our intervention was evaluated hospital-wide and included urinalyses and UCs. Our intervention was clinician directed and not laboratory directed, such as a positive urinalysis reflexing to a UC. Simultaneous urinalysis and UC testing may lead to ASB treatment, as clinicians treat the positive UC and ignore the negative urinalysis.^11,12 Therefore, we focused on UCs being sent in response to urinalyses.

We chose to focus on laboratory testing data instead of administrative diagnoses for UTI. The sensitivity of administrative data to determine similar conditions such as catheter-associated UTIs is low (0%).¹³

Our single-center study may not be generalizable to other settings. We did not include emergency department patients, as this location used a different EHR. In addition, given the 600,000 yearly hospital admissions, it was impractical to assess the appropriateness of each antibiotic-based documentation of symptoms. Instead of focusing on symptoms of ASB or UTI diagnoses, we focused on ordering urinalysis, UC, and antibiotics. In investigating the antibiotics most frequently used to treat UTI in our hospital, we may have both missed some patients who were treated with other antibiotics for ASB (eg, 4th generation cephalosporins, penicillins, carbapenems, etc) and captured patients receiving antibiotics for indications other than UTI (eg, pneumonia). In our focus on overall ordering practices across a hospital, we did not capture data on bladder catheterization status or the predominant organism seen in UC. At the time of the intervention, the laboratory did not have the resources for urinalysis testing reflexing to UC. However, our intervention did not prevent ordering simultaneous urinalysis and UC in symptomatic patients in general or urosepsis in particular. With only 12 total time points, the interrupted time series analysis may have been underpowered.¹⁴ We also do not know if the intervention’s effect would decay over time.

Although the intervention took very little staff time and resources, alert fatigue was a risk.¹⁵ We attempted to mitigate this alert fatigue by making the CDS passive (in the form of a brief informational message) with no provider action required. In conversations with providers in our institution, there has been dissatisfaction with alerts requiring action, as these are thought to be overly intrusive. We are also not clear on which element of the intervention bundle (ie, the CDS or the educational intervention) may have had more of an impact, as the elements of the intervention bundle were rolled out simultaneously. It is possible and even probable that both elements are needed to raise awareness of the problem. Also, as our EHR required all interventions to be rolled out hospital-wide simultaneously, we were unable to randomize certain floors or providers to the CDS portion of the intervention bundle. Other analyses including the type of hospital unit were beyond the scope of this brief report.

Our intervention bundle was associated with reduced UC orders and reduced antibiotics ordered after urinalyses. If a provider does not know there is bacteriuria, then the provider will not be tempted to order antibiotics. This easily implementable bundle may play an important role as an antimicrobial stewardship strategy for ASB.

Acknowledgments

The authors acknowledge the support of Erin Fanning, BS, and Angel Florentin, BS, in providing data for analysis. SCK received funding from the Johns Hopkins Institute for Clinical and Translational Research (ICTR), which is funded in part by grant number KL2TR001077 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), and the NIH Roadmap for Medical Research. These contents are solely the responsibility of the authors and do not necessarily represent the official view of the Johns Hopkins ICTR, NCATS, or NIH. We also acknowledge support from the Centers for Disease Control and Prevention’s Prevention Epicenter Program Q8377 (collaborative agreement U54 CK000447 to SEC). SEC has received support for consulting from Novartis and Theravance, and her institution has received a grant from Pfizer Grants for Learning and Change/The Joint Commission. This work was supported by the NIH T32 HL116275 to NC. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Disclosure

No conflicts of interest have been reported by any author.

Portal vein thrombosis (PVT) is thought to be rare in the general population and is most commonly found among patients with cirrhosis.^1-3 The risk of developing PVT in patients with cirrhosis has been correlated with the severity of hepatic impairment.^4,5 There is a lack of national-level data on the epidemiology of PVT and its related outcomes in the inpatient setting. The aim of our study was to describe the prevalence of PVT in hospitalized patients with cirrhosis in the United States. Using the National Inpatient Sample (NIS) database, we described the differences in hepatic decompensation, length of stay, in-hospital mortality, and total charges between patients with cirrhosis with PVT and those without.

This study was performed using the 2012 NIS to assess the relationship between PVT and cirrhosis-related outcomes. The NIS has been used reliably to make national estimates of healthcare utilization and estimate disease burden, charges, and outcomes.⁶ All admissions with either a primary or secondary discharge diagnosis of an International Classification of Diseases, 9th Revision–Clinical Modification (ICD-9-CM) code for PVT (452) and cirrhosis (571.2, 571.5, and 571.6) were identified from the NIS and correlated with age, gender, inpatient length of stay, in-hospital mortality, total charges, and commonly associated diagnoses. Complications of cirrhosis, such as hepatic encephalopathy (572.2), abdominal ascites (789.5), and gastrointestinal bleeding (456 and 456.2), were also identified. Data were assessed using IBM Statistical Package for the Social Sciences Statistics version 19.0 (Chicago, IL). Statistical significance was defined as a P value < .05.

There were 7,296,968 total unweighted admissions in the 2012 NIS, which included 113,766 (1.6%) inpatient admissions for cirrhosis, with 61,867 for nonalcoholic cirrhosis, 49,698 for alcoholic cirrhosis, and 2202 for biliary cirrhosis. The prevalence of PVT among all inpatient admissions was 0.07% (n = 5046) and 1.8% (n = 2046) in patients with cirrhosis (P < .001). On univariate analysis, patients who had a diagnosis of both cirrhosis and PVT had higher proportions of hepatic encephalopathy (22.5% vs 17.7%; P < .00001) as well as gastrointestinal bleeding (11.6% vs 5.7%; P < .00001) as compared with patients with cirrhosis without PVT (Figure).

We found that hospitalized patients with concurrent diagnoses of cirrhosis and PVT had longer hospital length of stay, higher mean hospital charges, and a higher proportion of cirrhosis-related complications. Our study represents the largest examination of hospitalized patients with cirrhosis and PVT to date and contributes to the evolving understanding of PVT in end-stage liver disease. The relationship between cirrhotic complications and PVT may be independent, but the 2 have similar underlying etiologic processes. Thus, given our findings, intervening to address the underlying factors leading to microvascular and/or PVT or mitigating the propagation of PVT in patients with cirrhosis may be beneficial to reducing morbidity and mortality in these patients. In addition, the prevalence of PVT in the overall hospitalized patient population in our study (0.07%) was similar to the 0.05% to 0.5% previously described in a US autopsy series, which should decrease the likelihood that PVT was missed in the cirrhotic population, which is more likely to have inpatient ultrasound imaging.² Our study is limited by its retrospective nature, dependency on ICD-9-CM codes for extracting data, and lack of clinical, physical exam, and laboratory results to allow for the calculation of a model for the end-stage liver disease and Child-Pugh score. Also, the study was not designed to evaluate causation, and it is possible that patients with more severe cirrhosis were more likely to be diagnosed with PVT. Further prospective studies directed not only toward the mechanism and treatment of both micro- and macrovascular thrombosis but also at examining the prevention of PVT and attendant benefits are greatly needed. 

Disclosure

The authors have nothing to disclose. The contents of this work do not represent the views of the Department of Veterans Affairs or the United States Government.

Portal vein thrombosis (PVT) is thought to be rare in the general population and is most commonly found among patients with cirrhosis.^1-3 The risk of developing PVT in patients with cirrhosis has been correlated with the severity of hepatic impairment.^4,5 There is a lack of national-level data on the epidemiology of PVT and its related outcomes in the inpatient setting. The aim of our study was to describe the prevalence of PVT in hospitalized patients with cirrhosis in the United States. Using the National Inpatient Sample (NIS) database, we described the differences in hepatic decompensation, length of stay, in-hospital mortality, and total charges between patients with cirrhosis with PVT and those without.

This study was performed using the 2012 NIS to assess the relationship between PVT and cirrhosis-related outcomes. The NIS has been used reliably to make national estimates of healthcare utilization and estimate disease burden, charges, and outcomes.⁶ All admissions with either a primary or secondary discharge diagnosis of an International Classification of Diseases, 9th Revision–Clinical Modification (ICD-9-CM) code for PVT (452) and cirrhosis (571.2, 571.5, and 571.6) were identified from the NIS and correlated with age, gender, inpatient length of stay, in-hospital mortality, total charges, and commonly associated diagnoses. Complications of cirrhosis, such as hepatic encephalopathy (572.2), abdominal ascites (789.5), and gastrointestinal bleeding (456 and 456.2), were also identified. Data were assessed using IBM Statistical Package for the Social Sciences Statistics version 19.0 (Chicago, IL). Statistical significance was defined as a P value < .05.

There were 7,296,968 total unweighted admissions in the 2012 NIS, which included 113,766 (1.6%) inpatient admissions for cirrhosis, with 61,867 for nonalcoholic cirrhosis, 49,698 for alcoholic cirrhosis, and 2202 for biliary cirrhosis. The prevalence of PVT among all inpatient admissions was 0.07% (n = 5046) and 1.8% (n = 2046) in patients with cirrhosis (P < .001). On univariate analysis, patients who had a diagnosis of both cirrhosis and PVT had higher proportions of hepatic encephalopathy (22.5% vs 17.7%; P < .00001) as well as gastrointestinal bleeding (11.6% vs 5.7%; P < .00001) as compared with patients with cirrhosis without PVT (Figure).

We found that hospitalized patients with concurrent diagnoses of cirrhosis and PVT had longer hospital length of stay, higher mean hospital charges, and a higher proportion of cirrhosis-related complications. Our study represents the largest examination of hospitalized patients with cirrhosis and PVT to date and contributes to the evolving understanding of PVT in end-stage liver disease. The relationship between cirrhotic complications and PVT may be independent, but the 2 have similar underlying etiologic processes. Thus, given our findings, intervening to address the underlying factors leading to microvascular and/or PVT or mitigating the propagation of PVT in patients with cirrhosis may be beneficial to reducing morbidity and mortality in these patients. In addition, the prevalence of PVT in the overall hospitalized patient population in our study (0.07%) was similar to the 0.05% to 0.5% previously described in a US autopsy series, which should decrease the likelihood that PVT was missed in the cirrhotic population, which is more likely to have inpatient ultrasound imaging.² Our study is limited by its retrospective nature, dependency on ICD-9-CM codes for extracting data, and lack of clinical, physical exam, and laboratory results to allow for the calculation of a model for the end-stage liver disease and Child-Pugh score. Also, the study was not designed to evaluate causation, and it is possible that patients with more severe cirrhosis were more likely to be diagnosed with PVT. Further prospective studies directed not only toward the mechanism and treatment of both micro- and macrovascular thrombosis but also at examining the prevention of PVT and attendant benefits are greatly needed. 

Disclosure

The authors have nothing to disclose. The contents of this work do not represent the views of the Department of Veterans Affairs or the United States Government.

Portal vein thrombosis (PVT) is thought to be rare in the general population and is most commonly found among patients with cirrhosis.^1-3 The risk of developing PVT in patients with cirrhosis has been correlated with the severity of hepatic impairment.^4,5 There is a lack of national-level data on the epidemiology of PVT and its related outcomes in the inpatient setting. The aim of our study was to describe the prevalence of PVT in hospitalized patients with cirrhosis in the United States. Using the National Inpatient Sample (NIS) database, we described the differences in hepatic decompensation, length of stay, in-hospital mortality, and total charges between patients with cirrhosis with PVT and those without.

This study was performed using the 2012 NIS to assess the relationship between PVT and cirrhosis-related outcomes. The NIS has been used reliably to make national estimates of healthcare utilization and estimate disease burden, charges, and outcomes.⁶ All admissions with either a primary or secondary discharge diagnosis of an International Classification of Diseases, 9th Revision–Clinical Modification (ICD-9-CM) code for PVT (452) and cirrhosis (571.2, 571.5, and 571.6) were identified from the NIS and correlated with age, gender, inpatient length of stay, in-hospital mortality, total charges, and commonly associated diagnoses. Complications of cirrhosis, such as hepatic encephalopathy (572.2), abdominal ascites (789.5), and gastrointestinal bleeding (456 and 456.2), were also identified. Data were assessed using IBM Statistical Package for the Social Sciences Statistics version 19.0 (Chicago, IL). Statistical significance was defined as a P value < .05.

There were 7,296,968 total unweighted admissions in the 2012 NIS, which included 113,766 (1.6%) inpatient admissions for cirrhosis, with 61,867 for nonalcoholic cirrhosis, 49,698 for alcoholic cirrhosis, and 2202 for biliary cirrhosis. The prevalence of PVT among all inpatient admissions was 0.07% (n = 5046) and 1.8% (n = 2046) in patients with cirrhosis (P < .001). On univariate analysis, patients who had a diagnosis of both cirrhosis and PVT had higher proportions of hepatic encephalopathy (22.5% vs 17.7%; P < .00001) as well as gastrointestinal bleeding (11.6% vs 5.7%; P < .00001) as compared with patients with cirrhosis without PVT (Figure).

We found that hospitalized patients with concurrent diagnoses of cirrhosis and PVT had longer hospital length of stay, higher mean hospital charges, and a higher proportion of cirrhosis-related complications. Our study represents the largest examination of hospitalized patients with cirrhosis and PVT to date and contributes to the evolving understanding of PVT in end-stage liver disease. The relationship between cirrhotic complications and PVT may be independent, but the 2 have similar underlying etiologic processes. Thus, given our findings, intervening to address the underlying factors leading to microvascular and/or PVT or mitigating the propagation of PVT in patients with cirrhosis may be beneficial to reducing morbidity and mortality in these patients. In addition, the prevalence of PVT in the overall hospitalized patient population in our study (0.07%) was similar to the 0.05% to 0.5% previously described in a US autopsy series, which should decrease the likelihood that PVT was missed in the cirrhotic population, which is more likely to have inpatient ultrasound imaging.² Our study is limited by its retrospective nature, dependency on ICD-9-CM codes for extracting data, and lack of clinical, physical exam, and laboratory results to allow for the calculation of a model for the end-stage liver disease and Child-Pugh score. Also, the study was not designed to evaluate causation, and it is possible that patients with more severe cirrhosis were more likely to be diagnosed with PVT. Further prospective studies directed not only toward the mechanism and treatment of both micro- and macrovascular thrombosis but also at examining the prevention of PVT and attendant benefits are greatly needed. 

Disclosure

The authors have nothing to disclose. The contents of this work do not represent the views of the Department of Veterans Affairs or the United States Government.

Hospitalists face complex questions about how to evaluate and treat the large number of individuals who are admitted on long-term opioid therapy (LTOT, defined as lasting 3 months or longer) for chronic noncancer pain. A recent study at one Veterans Affairs hospital, found 26% of medical inpatients were on LTOT.¹ Over the last 2 decades, use of LTOT has risen substantially in the United States, including among middle-aged and older adults.² Concurrently, inpatient hospitalizations related to the overuse of prescription opioids, including overdose, dependence, abuse, and adverse drug events, have increased by 153%.³ Individuals on LTOT can also be hospitalized for exacerbations of the opioid-treated chronic pain condition or unrelated conditions. In addition to affecting rates of hospitalization, use of LTOT is associated with higher rates of in-hospital adverse events, longer hospital stays, and higher readmission rates.^1,4,5

Physicians find managing chronic pain to be stressful, are often concerned about misuse and addiction, and believe their training in opioid prescribing is inadequate.⁶ Hospitalists report confidence in assessing and prescribing opioids for acute pain but limited success and satisfaction with treating exacerbations of chronic pain.⁷ Although half of all hospitalized patients receive opioids,⁵ little information is available to guide the care of hospitalized medical patients on LTOT for chronic noncancer pain.^8,9

Our multispecialty team sought to synthesize guideline recommendations and primary literature relevant to the assessment of medical inpatients on LTOT to assist practitioners balance effective pain treatment and opioid risk reduction. This article addresses obtaining a comprehensive pain history, identifying misuse and opioid use disorders, assessing the risk of overdose and adverse drug events, gauging the risk of withdrawal, and based on such findings, appraise indications for opioid therapy. Other authors have recently published narrative reviews on the management of acute pain in hospitalized patients with opioid dependence and the inpatient management of opioid use disorder.^10,11

To identify primary literature, we searched PubMed, EMBASE, The Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Economic Evaluations Database, key meeting abstracts, and hand searches. To identify guidelines, we searched PubMed, National Guidelines Clearinghouse, specialty societies’ websites, the Centers for Disease Control and Prevention (CDC), the United Kingdom National Institute for Health and Care Excellence, the Canadian Medical Association, and the Australian Government National Health and Medical Research Council. Search terms related to opioids and chronic pain, which was last updated in October 2016.¹²

We selected English-language documents on opioids and chronic pain among adults, excluding pain in the setting of procedures, labor and delivery, life-limiting illness, or specific conditions. For primary literature, we considered intervention studies of any design that addressed pain management among hospitalized medical patients. We included guidelines and specialty society position statements published after January 1, 2009, that addressed pain in the hospital setting, acute pain in any setting, or chronic pain in the outpatient setting if published by a national body. Due to the paucity of documents specific to inpatient care, we used a narrative review format to synthesize information. Dual reviewers extracted guideline recommendations potentially relevant to medical inpatients on LTOT. We also summarize relevant assessment instruments, emphasizing very brief screening instruments, which may be more likely to be used by busy hospitalists.

Obtaining a Comprehensive Pain History

Hospitalists newly evaluating patients on LTOT often face a dual challenge: deciding if the patient has an immediate indication for additional opioids and if the current long-term opioid regimen should be altered or discontinued. In general, opioids are an accepted short-term treatment for moderate to severe acute pain but their role in chronic noncancer pain is controversial. Newly released guidelines by the CDC recommend initiating LTOT as a last resort, and the Departments of Veterans Affairs and Defense guidelines recommend against initiation of LTOT.^22,23

A key first step, therefore, is distinguishing between acute and chronic pain. Among patients on LTOT, pain can represent a new acute pain condition, an exacerbation of chronic pain, opioid-induced hyperalgesia, or opioid withdrawal. Acute pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in relation to such damage.²⁶ In contrast, chronic pain is a complex response that may not be related to actual or ongoing tissue damage, and is influenced by physiological, contextual, and psychological factors. Two acute pain guidelines and 1 chronic pain guideline recommend distinguishing acute and chronic pain,^9,16,21 3 chronic pain guidelines reinforce the importance of obtaining a pain history (including timing, intensity, frequency, onset, etc),^20,22,23 and 6 guidelines recommend ascertaining a history of prior pain-related treatments.^{9,13,14,16,20,22} Inquiring how the current pain compares with symptoms “on a good day,” what activities the patient can usually perform, and what the patient does outside the hospital to cope with pain can serve as entry into this conversation.

Identifying Misuse and Opioid Use Disorders

Nonmedical use of prescription opioids and opioid use disorders have more than doubled over the last decade.³⁵ Five guidelines, including 3 specific guidelines for acute pain or the hospital setting, recommend screening for opioid misuse.^{13,14,16,19,23} Many states mandate practitioners assess patients for substance use disorders before prescribing controlled substances.³⁶ Instruments to identify aberrant and risky use include the Current Opioid Misuse Measure,³⁷ Prescription Drug Use Questionnaire,³⁸ Addiction Behaviors Checklist,³⁹ Screening Tool for Abuse,⁴⁰ and the Self-Administered Single-Item Screening Question (Table 3).⁴¹ However, the evidence for these and other tools is limited and absent for the inpatient setting.^21,42

In addition to obtaining a history from the patient, 4 guidelines specific to hospital settings/acute pain and 4 chronic pain guidelines recommend practitioners access prescription drug monitoring programs (PDMPs).^{13-16,19,21-24} PDMPs exist in all states except Missouri, and about half of states mandate practitioners check the PDMP database in certain circumstances.³⁶ Studies examining the effects of PDMPs on prescribing are limited, but checking these databases can uncover concerning patterns including overlapping prescriptions or multiple prescribers.⁴³ PDMPs can also confirm reported medication doses, for which patient report may be less reliable.

Two hospital/acute pain guidelines and 5 chronic pain guidelines also recommend urine drug testing, although differing on when and whom to test, with some favoring universal screening.^11,20,23 Screening hospitalized patients may reveal substances not reported by patients, but medications administered in emergency departments can confound results. Furthermore, the commonly used immunoassay does not distinguish heroin from prescription opioids, nor detect hydrocodone, oxycodone, methadone, buprenorphine, or certain benzodiazepines. Chromatography/mass spectrometry assays can but are often not available from hospital laboratories. The differential for unexpected results includes substance use, self treatment of uncontrolled pain, diversion, or laboratory error.²⁰

If concerning opioid use is identified, 3 hospital setting/acute pain specific guidelines and the CDC guideline recommend sharing concerns with patients and assessing for a substance use disorder.^9,13,16,22 Determining whether patients have an opioid use disorder that meets the criteria in the Diagnostic and Statistical Manual, 5th Edition⁴⁴ can be challenging. Patients may minimize or deny symptoms or fear that the stigma of an opioid use disorder will lead to dismissive or subpar care. Additionally, substance use disorders are subject to federal confidentiality regulations, which can hamper acquisition of information from providers.⁴⁵ Thus, hospitalists may find specialty consultation helpful to confirm the diagnosis.

Assessing the Risk of Overdose and Adverse Drug Events

Oversedation, respiratory depression, and death can result from iatrogenic or self-administered opioid overdose in the hospital.5 Patient factors that increase this risk among outpatients include a prior history of overdose, preexisting substance use disorders, cognitive impairment, mood and personality disorders, chronic kidney disease, sleep apnea, obstructive lung disease, and recent abstinence from opioids.12 Medication factors include concomitant use of benzodiazepines and other central nervous system depressants, including alcohol; recent initiation of long-acting opioids; use of fentanyl patches, immediate-release fentanyl, or methadone; rapid titration; switching opioids without adequate dose reduction; pharmacokinetic drug–drug interactions; and, importantly, higher doses.12,22 Two guidelines specific to acute pain and hospital settings and 5 chronic pain guidelines recommend screening for use of benzodiazepines among patients on LTOT.13,14,16,18-20,22,21
The CDC guideline recommends careful assessment when doses exceed 50 mg of morphine equivalents per day and avoiding doses above 90 mg per day due to the heightened risk of overdose.²² In the hospital, 23% of patients receive doses at or above 100 mg of morphine equivalents per day,⁵ and concurrent use of central nervous system depressants is common. Changes in kidney and liver function during acute illness may impact opioid metabolism and contribute to overdose.

In addition to overdose, opioids are leading causes of adverse drug events during hospitalization.⁴⁶ Most studies have focused on surgical patients reporting common opioid-related events as nausea/vomiting, pruritus, rash, mental status changes, respiratory depression, ileus, and urinary retention.⁴⁷ Hospitalized patients may also exhibit chronic adverse effects due to LTOT. At least one-third of patients on LTOT eventually stop because of adverse effects, such as endocrinopathies, sleep disordered breathing, constipation, fractures, falls, and mental status changes.⁴⁸ Patients may lack awareness that their symptoms are attributable to opioids and are willing to reduce their opioid use once informed, especially when alternatives are offered to alleviate pain.

Gauging the Risk of Withdrawal

Sudden discontinuation of LTOT by patients, practitioners, or intercurrent events can have unanticipated and undesirable consequences. Withdrawal is not only distressing for patients; it can be dangerous because patients may resort to illicit use, diversion of opioids, or masking opioid withdrawal with other substances such as alcohol. The anxiety and distress associated with withdrawal, or anticipatory fear about withdrawal, can undermine therapeutic alliance and interfere with processes of care. Reviewed guidelines did not offer recommendations regarding withdrawal risk or specific strategies for avoidance. There is no specific prior dose threshold or degree of reduction in opioids that puts patients at risk for withdrawal, in part due to patients’ beliefs, expectations, and differences in response to opioid formulations. Symptoms of opioid withdrawal have been compared to a severe case of influenza, including stomach cramps, nausea and vomiting, diarrhea, tremor and muscle twitching, sweating, restlessness, yawning, tachycardia, anxiety and irritability, bone and joint aches, runny nose, tearing, and piloerection.⁴⁹ The Clinical Opiate Withdrawal Scale (COWS)⁴⁹ and the Clinical Institute Narcotic Assessment⁵¹ are clinician-administered tools to assess opioid withdrawal similar to the Clinical Institute Withdrawal Assessment of Alcohol Scale, Revised,⁵² to monitor for withdrawal in the inpatient setting.

Synthesizing and Appraising the Indications for Opioid Therapy

For medical inpatients who report adequate pain control and functional outcomes on current doses of LTOT, without evidence of misuse, the pragmatic approach is to continue the treatment plan established by the outpatient clinician rather than escalating or tapering the dose. If opioids are prescribed at discharge, 3 hospital setting/acute pain guidelines and the CDC guideline recommend prescribing the lowest effective dose of immediate release opioids for 3 to 7 days.^13,15,16,22

When patients exhibit evidence of an opioid use disorder, have a history of serious overdose, or are experiencing intolerable opioid-related adverse events, the hospitalist may conclude the harms of LTOT outweigh the benefits. For these patients, opioid treatment in the hospital can be aimed at preventing withdrawal, avoiding the perpetuation of inappropriate opioid use, managing other acute medical conditions, and communicating with outpatient prescribers. For patients with misuse, discontinuing opioids is potentially harmful and may be perceived as punitive. Hospitalists should consider consulting addiction or mental health specialists to assist with formulating a plan of care. However, such specialists may not be available in smaller or rural hospitals and referral at discharge can be challenging.⁵³

Beginning to taper opioids during the hospitalization can be appropriate when patients are motivated and can transition to an outpatient provider who will supervise the taper. In ambulatory settings, tapers of 10% to 30% every 2 to 5 days are generally well tolerated.⁵⁴ If patients started tapering opioids under supervision of an outpatient provider prior to hospitalization; ideally, the taper can be continued during hospitalization with close coordination with the outpatient clinician.

Unfortunately, many patients on LTOT are admitted with new sources of acute pain and or exacerbations of chronic pain, and some have concomitant substance use disorders; we plan to address the management of these complex situations in future work.

Despite the frequency with which patients on LTOT are hospitalized for nonsurgical stays and the challenges inherent in evaluating pain and assessing the possibility of substance use disorders, no formal guidelines or empirical research studies pertain to this population. Guidelines in this review were developed for hospital settings and acute pain in the absence of LTOT, and for outpatient care of patients on LTOT. We also included a nonsystematic synthesis of literature that varied in relevance to medical inpatients on LTOT.

Although inpatient assessment and treatment of patients with LTOT remains an underresearched area, we were able to extract and synthesize recommendations from 14 guideline statements and apply these to the assessment of patients with LTOT in the inpatient setting. Hospitalists frequently encounter patients on LTOT for chronic nonmalignant pain and are faced with complex decisions about the effectiveness and safety of LTOT; appropriate patient assessment is fundamental to making these decisions. Key guideline recommendations relevant to inpatient assessment include assessing both pain and functional status, differentiating acute from chronic pain, ascertaining preadmission pain treatment history, obtaining a psychosocial history, screening for mental health issues such as depression and anxiety, screening for substance use disorders, checking state prescription drug monitoring databases, ordering urine drug immunoassays, detecting use of sedative-hypnotics, identifying medical conditions associated with increased risk of overdose and adverse events, and appraising the potential benefits and harms of opioid therapy. Although approaches to assessing medical inpatients on LTOT can be extrapolated from outpatient guidelines, observational studies, and small studies in surgical populations, more work is needed to address these critical topics for inpatients on LTOT.

Disclosure

Dr. Herzig was funded by grant number K23AG042459 from the National Institute on Aging. The funding organization had no involvement in any aspect of the study, including design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. All other authors have no relevant conflicts of interest with the work.

Hospitalists face complex questions about how to evaluate and treat the large number of individuals who are admitted on long-term opioid therapy (LTOT, defined as lasting 3 months or longer) for chronic noncancer pain. A recent study at one Veterans Affairs hospital, found 26% of medical inpatients were on LTOT.¹ Over the last 2 decades, use of LTOT has risen substantially in the United States, including among middle-aged and older adults.² Concurrently, inpatient hospitalizations related to the overuse of prescription opioids, including overdose, dependence, abuse, and adverse drug events, have increased by 153%.³ Individuals on LTOT can also be hospitalized for exacerbations of the opioid-treated chronic pain condition or unrelated conditions. In addition to affecting rates of hospitalization, use of LTOT is associated with higher rates of in-hospital adverse events, longer hospital stays, and higher readmission rates.^1,4,5

Physicians find managing chronic pain to be stressful, are often concerned about misuse and addiction, and believe their training in opioid prescribing is inadequate.⁶ Hospitalists report confidence in assessing and prescribing opioids for acute pain but limited success and satisfaction with treating exacerbations of chronic pain.⁷ Although half of all hospitalized patients receive opioids,⁵ little information is available to guide the care of hospitalized medical patients on LTOT for chronic noncancer pain.^8,9

Our multispecialty team sought to synthesize guideline recommendations and primary literature relevant to the assessment of medical inpatients on LTOT to assist practitioners balance effective pain treatment and opioid risk reduction. This article addresses obtaining a comprehensive pain history, identifying misuse and opioid use disorders, assessing the risk of overdose and adverse drug events, gauging the risk of withdrawal, and based on such findings, appraise indications for opioid therapy. Other authors have recently published narrative reviews on the management of acute pain in hospitalized patients with opioid dependence and the inpatient management of opioid use disorder.^10,11

To identify primary literature, we searched PubMed, EMBASE, The Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Economic Evaluations Database, key meeting abstracts, and hand searches. To identify guidelines, we searched PubMed, National Guidelines Clearinghouse, specialty societies’ websites, the Centers for Disease Control and Prevention (CDC), the United Kingdom National Institute for Health and Care Excellence, the Canadian Medical Association, and the Australian Government National Health and Medical Research Council. Search terms related to opioids and chronic pain, which was last updated in October 2016.¹²

We selected English-language documents on opioids and chronic pain among adults, excluding pain in the setting of procedures, labor and delivery, life-limiting illness, or specific conditions. For primary literature, we considered intervention studies of any design that addressed pain management among hospitalized medical patients. We included guidelines and specialty society position statements published after January 1, 2009, that addressed pain in the hospital setting, acute pain in any setting, or chronic pain in the outpatient setting if published by a national body. Due to the paucity of documents specific to inpatient care, we used a narrative review format to synthesize information. Dual reviewers extracted guideline recommendations potentially relevant to medical inpatients on LTOT. We also summarize relevant assessment instruments, emphasizing very brief screening instruments, which may be more likely to be used by busy hospitalists.

Obtaining a Comprehensive Pain History

Hospitalists newly evaluating patients on LTOT often face a dual challenge: deciding if the patient has an immediate indication for additional opioids and if the current long-term opioid regimen should be altered or discontinued. In general, opioids are an accepted short-term treatment for moderate to severe acute pain but their role in chronic noncancer pain is controversial. Newly released guidelines by the CDC recommend initiating LTOT as a last resort, and the Departments of Veterans Affairs and Defense guidelines recommend against initiation of LTOT.^22,23

A key first step, therefore, is distinguishing between acute and chronic pain. Among patients on LTOT, pain can represent a new acute pain condition, an exacerbation of chronic pain, opioid-induced hyperalgesia, or opioid withdrawal. Acute pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in relation to such damage.²⁶ In contrast, chronic pain is a complex response that may not be related to actual or ongoing tissue damage, and is influenced by physiological, contextual, and psychological factors. Two acute pain guidelines and 1 chronic pain guideline recommend distinguishing acute and chronic pain,^9,16,21 3 chronic pain guidelines reinforce the importance of obtaining a pain history (including timing, intensity, frequency, onset, etc),^20,22,23 and 6 guidelines recommend ascertaining a history of prior pain-related treatments.^{9,13,14,16,20,22} Inquiring how the current pain compares with symptoms “on a good day,” what activities the patient can usually perform, and what the patient does outside the hospital to cope with pain can serve as entry into this conversation.

Identifying Misuse and Opioid Use Disorders

Nonmedical use of prescription opioids and opioid use disorders have more than doubled over the last decade.³⁵ Five guidelines, including 3 specific guidelines for acute pain or the hospital setting, recommend screening for opioid misuse.^{13,14,16,19,23} Many states mandate practitioners assess patients for substance use disorders before prescribing controlled substances.³⁶ Instruments to identify aberrant and risky use include the Current Opioid Misuse Measure,³⁷ Prescription Drug Use Questionnaire,³⁸ Addiction Behaviors Checklist,³⁹ Screening Tool for Abuse,⁴⁰ and the Self-Administered Single-Item Screening Question (Table 3).⁴¹ However, the evidence for these and other tools is limited and absent for the inpatient setting.^21,42

In addition to obtaining a history from the patient, 4 guidelines specific to hospital settings/acute pain and 4 chronic pain guidelines recommend practitioners access prescription drug monitoring programs (PDMPs).^{13-16,19,21-24} PDMPs exist in all states except Missouri, and about half of states mandate practitioners check the PDMP database in certain circumstances.³⁶ Studies examining the effects of PDMPs on prescribing are limited, but checking these databases can uncover concerning patterns including overlapping prescriptions or multiple prescribers.⁴³ PDMPs can also confirm reported medication doses, for which patient report may be less reliable.

Two hospital/acute pain guidelines and 5 chronic pain guidelines also recommend urine drug testing, although differing on when and whom to test, with some favoring universal screening.^11,20,23 Screening hospitalized patients may reveal substances not reported by patients, but medications administered in emergency departments can confound results. Furthermore, the commonly used immunoassay does not distinguish heroin from prescription opioids, nor detect hydrocodone, oxycodone, methadone, buprenorphine, or certain benzodiazepines. Chromatography/mass spectrometry assays can but are often not available from hospital laboratories. The differential for unexpected results includes substance use, self treatment of uncontrolled pain, diversion, or laboratory error.²⁰

If concerning opioid use is identified, 3 hospital setting/acute pain specific guidelines and the CDC guideline recommend sharing concerns with patients and assessing for a substance use disorder.^9,13,16,22 Determining whether patients have an opioid use disorder that meets the criteria in the Diagnostic and Statistical Manual, 5th Edition⁴⁴ can be challenging. Patients may minimize or deny symptoms or fear that the stigma of an opioid use disorder will lead to dismissive or subpar care. Additionally, substance use disorders are subject to federal confidentiality regulations, which can hamper acquisition of information from providers.⁴⁵ Thus, hospitalists may find specialty consultation helpful to confirm the diagnosis.

Assessing the Risk of Overdose and Adverse Drug Events

Oversedation, respiratory depression, and death can result from iatrogenic or self-administered opioid overdose in the hospital.5 Patient factors that increase this risk among outpatients include a prior history of overdose, preexisting substance use disorders, cognitive impairment, mood and personality disorders, chronic kidney disease, sleep apnea, obstructive lung disease, and recent abstinence from opioids.12 Medication factors include concomitant use of benzodiazepines and other central nervous system depressants, including alcohol; recent initiation of long-acting opioids; use of fentanyl patches, immediate-release fentanyl, or methadone; rapid titration; switching opioids without adequate dose reduction; pharmacokinetic drug–drug interactions; and, importantly, higher doses.12,22 Two guidelines specific to acute pain and hospital settings and 5 chronic pain guidelines recommend screening for use of benzodiazepines among patients on LTOT.13,14,16,18-20,22,21
The CDC guideline recommends careful assessment when doses exceed 50 mg of morphine equivalents per day and avoiding doses above 90 mg per day due to the heightened risk of overdose.²² In the hospital, 23% of patients receive doses at or above 100 mg of morphine equivalents per day,⁵ and concurrent use of central nervous system depressants is common. Changes in kidney and liver function during acute illness may impact opioid metabolism and contribute to overdose.

In addition to overdose, opioids are leading causes of adverse drug events during hospitalization.⁴⁶ Most studies have focused on surgical patients reporting common opioid-related events as nausea/vomiting, pruritus, rash, mental status changes, respiratory depression, ileus, and urinary retention.⁴⁷ Hospitalized patients may also exhibit chronic adverse effects due to LTOT. At least one-third of patients on LTOT eventually stop because of adverse effects, such as endocrinopathies, sleep disordered breathing, constipation, fractures, falls, and mental status changes.⁴⁸ Patients may lack awareness that their symptoms are attributable to opioids and are willing to reduce their opioid use once informed, especially when alternatives are offered to alleviate pain.

Gauging the Risk of Withdrawal

Sudden discontinuation of LTOT by patients, practitioners, or intercurrent events can have unanticipated and undesirable consequences. Withdrawal is not only distressing for patients; it can be dangerous because patients may resort to illicit use, diversion of opioids, or masking opioid withdrawal with other substances such as alcohol. The anxiety and distress associated with withdrawal, or anticipatory fear about withdrawal, can undermine therapeutic alliance and interfere with processes of care. Reviewed guidelines did not offer recommendations regarding withdrawal risk or specific strategies for avoidance. There is no specific prior dose threshold or degree of reduction in opioids that puts patients at risk for withdrawal, in part due to patients’ beliefs, expectations, and differences in response to opioid formulations. Symptoms of opioid withdrawal have been compared to a severe case of influenza, including stomach cramps, nausea and vomiting, diarrhea, tremor and muscle twitching, sweating, restlessness, yawning, tachycardia, anxiety and irritability, bone and joint aches, runny nose, tearing, and piloerection.⁴⁹ The Clinical Opiate Withdrawal Scale (COWS)⁴⁹ and the Clinical Institute Narcotic Assessment⁵¹ are clinician-administered tools to assess opioid withdrawal similar to the Clinical Institute Withdrawal Assessment of Alcohol Scale, Revised,⁵² to monitor for withdrawal in the inpatient setting.

Synthesizing and Appraising the Indications for Opioid Therapy

For medical inpatients who report adequate pain control and functional outcomes on current doses of LTOT, without evidence of misuse, the pragmatic approach is to continue the treatment plan established by the outpatient clinician rather than escalating or tapering the dose. If opioids are prescribed at discharge, 3 hospital setting/acute pain guidelines and the CDC guideline recommend prescribing the lowest effective dose of immediate release opioids for 3 to 7 days.^13,15,16,22

When patients exhibit evidence of an opioid use disorder, have a history of serious overdose, or are experiencing intolerable opioid-related adverse events, the hospitalist may conclude the harms of LTOT outweigh the benefits. For these patients, opioid treatment in the hospital can be aimed at preventing withdrawal, avoiding the perpetuation of inappropriate opioid use, managing other acute medical conditions, and communicating with outpatient prescribers. For patients with misuse, discontinuing opioids is potentially harmful and may be perceived as punitive. Hospitalists should consider consulting addiction or mental health specialists to assist with formulating a plan of care. However, such specialists may not be available in smaller or rural hospitals and referral at discharge can be challenging.⁵³

Beginning to taper opioids during the hospitalization can be appropriate when patients are motivated and can transition to an outpatient provider who will supervise the taper. In ambulatory settings, tapers of 10% to 30% every 2 to 5 days are generally well tolerated.⁵⁴ If patients started tapering opioids under supervision of an outpatient provider prior to hospitalization; ideally, the taper can be continued during hospitalization with close coordination with the outpatient clinician.

Unfortunately, many patients on LTOT are admitted with new sources of acute pain and or exacerbations of chronic pain, and some have concomitant substance use disorders; we plan to address the management of these complex situations in future work.

Despite the frequency with which patients on LTOT are hospitalized for nonsurgical stays and the challenges inherent in evaluating pain and assessing the possibility of substance use disorders, no formal guidelines or empirical research studies pertain to this population. Guidelines in this review were developed for hospital settings and acute pain in the absence of LTOT, and for outpatient care of patients on LTOT. We also included a nonsystematic synthesis of literature that varied in relevance to medical inpatients on LTOT.

Although inpatient assessment and treatment of patients with LTOT remains an underresearched area, we were able to extract and synthesize recommendations from 14 guideline statements and apply these to the assessment of patients with LTOT in the inpatient setting. Hospitalists frequently encounter patients on LTOT for chronic nonmalignant pain and are faced with complex decisions about the effectiveness and safety of LTOT; appropriate patient assessment is fundamental to making these decisions. Key guideline recommendations relevant to inpatient assessment include assessing both pain and functional status, differentiating acute from chronic pain, ascertaining preadmission pain treatment history, obtaining a psychosocial history, screening for mental health issues such as depression and anxiety, screening for substance use disorders, checking state prescription drug monitoring databases, ordering urine drug immunoassays, detecting use of sedative-hypnotics, identifying medical conditions associated with increased risk of overdose and adverse events, and appraising the potential benefits and harms of opioid therapy. Although approaches to assessing medical inpatients on LTOT can be extrapolated from outpatient guidelines, observational studies, and small studies in surgical populations, more work is needed to address these critical topics for inpatients on LTOT.

Disclosure

Dr. Herzig was funded by grant number K23AG042459 from the National Institute on Aging. The funding organization had no involvement in any aspect of the study, including design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. All other authors have no relevant conflicts of interest with the work.

Hospitalists face complex questions about how to evaluate and treat the large number of individuals who are admitted on long-term opioid therapy (LTOT, defined as lasting 3 months or longer) for chronic noncancer pain. A recent study at one Veterans Affairs hospital, found 26% of medical inpatients were on LTOT.¹ Over the last 2 decades, use of LTOT has risen substantially in the United States, including among middle-aged and older adults.² Concurrently, inpatient hospitalizations related to the overuse of prescription opioids, including overdose, dependence, abuse, and adverse drug events, have increased by 153%.³ Individuals on LTOT can also be hospitalized for exacerbations of the opioid-treated chronic pain condition or unrelated conditions. In addition to affecting rates of hospitalization, use of LTOT is associated with higher rates of in-hospital adverse events, longer hospital stays, and higher readmission rates.^1,4,5

Physicians find managing chronic pain to be stressful, are often concerned about misuse and addiction, and believe their training in opioid prescribing is inadequate.⁶ Hospitalists report confidence in assessing and prescribing opioids for acute pain but limited success and satisfaction with treating exacerbations of chronic pain.⁷ Although half of all hospitalized patients receive opioids,⁵ little information is available to guide the care of hospitalized medical patients on LTOT for chronic noncancer pain.^8,9

Our multispecialty team sought to synthesize guideline recommendations and primary literature relevant to the assessment of medical inpatients on LTOT to assist practitioners balance effective pain treatment and opioid risk reduction. This article addresses obtaining a comprehensive pain history, identifying misuse and opioid use disorders, assessing the risk of overdose and adverse drug events, gauging the risk of withdrawal, and based on such findings, appraise indications for opioid therapy. Other authors have recently published narrative reviews on the management of acute pain in hospitalized patients with opioid dependence and the inpatient management of opioid use disorder.^10,11

To identify primary literature, we searched PubMed, EMBASE, The Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Economic Evaluations Database, key meeting abstracts, and hand searches. To identify guidelines, we searched PubMed, National Guidelines Clearinghouse, specialty societies’ websites, the Centers for Disease Control and Prevention (CDC), the United Kingdom National Institute for Health and Care Excellence, the Canadian Medical Association, and the Australian Government National Health and Medical Research Council. Search terms related to opioids and chronic pain, which was last updated in October 2016.¹²

We selected English-language documents on opioids and chronic pain among adults, excluding pain in the setting of procedures, labor and delivery, life-limiting illness, or specific conditions. For primary literature, we considered intervention studies of any design that addressed pain management among hospitalized medical patients. We included guidelines and specialty society position statements published after January 1, 2009, that addressed pain in the hospital setting, acute pain in any setting, or chronic pain in the outpatient setting if published by a national body. Due to the paucity of documents specific to inpatient care, we used a narrative review format to synthesize information. Dual reviewers extracted guideline recommendations potentially relevant to medical inpatients on LTOT. We also summarize relevant assessment instruments, emphasizing very brief screening instruments, which may be more likely to be used by busy hospitalists.

Obtaining a Comprehensive Pain History

Hospitalists newly evaluating patients on LTOT often face a dual challenge: deciding if the patient has an immediate indication for additional opioids and if the current long-term opioid regimen should be altered or discontinued. In general, opioids are an accepted short-term treatment for moderate to severe acute pain but their role in chronic noncancer pain is controversial. Newly released guidelines by the CDC recommend initiating LTOT as a last resort, and the Departments of Veterans Affairs and Defense guidelines recommend against initiation of LTOT.^22,23

A key first step, therefore, is distinguishing between acute and chronic pain. Among patients on LTOT, pain can represent a new acute pain condition, an exacerbation of chronic pain, opioid-induced hyperalgesia, or opioid withdrawal. Acute pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in relation to such damage.²⁶ In contrast, chronic pain is a complex response that may not be related to actual or ongoing tissue damage, and is influenced by physiological, contextual, and psychological factors. Two acute pain guidelines and 1 chronic pain guideline recommend distinguishing acute and chronic pain,^9,16,21 3 chronic pain guidelines reinforce the importance of obtaining a pain history (including timing, intensity, frequency, onset, etc),^20,22,23 and 6 guidelines recommend ascertaining a history of prior pain-related treatments.^{9,13,14,16,20,22} Inquiring how the current pain compares with symptoms “on a good day,” what activities the patient can usually perform, and what the patient does outside the hospital to cope with pain can serve as entry into this conversation.

Identifying Misuse and Opioid Use Disorders

Nonmedical use of prescription opioids and opioid use disorders have more than doubled over the last decade.³⁵ Five guidelines, including 3 specific guidelines for acute pain or the hospital setting, recommend screening for opioid misuse.^{13,14,16,19,23} Many states mandate practitioners assess patients for substance use disorders before prescribing controlled substances.³⁶ Instruments to identify aberrant and risky use include the Current Opioid Misuse Measure,³⁷ Prescription Drug Use Questionnaire,³⁸ Addiction Behaviors Checklist,³⁹ Screening Tool for Abuse,⁴⁰ and the Self-Administered Single-Item Screening Question (Table 3).⁴¹ However, the evidence for these and other tools is limited and absent for the inpatient setting.^21,42

In addition to obtaining a history from the patient, 4 guidelines specific to hospital settings/acute pain and 4 chronic pain guidelines recommend practitioners access prescription drug monitoring programs (PDMPs).^{13-16,19,21-24} PDMPs exist in all states except Missouri, and about half of states mandate practitioners check the PDMP database in certain circumstances.³⁶ Studies examining the effects of PDMPs on prescribing are limited, but checking these databases can uncover concerning patterns including overlapping prescriptions or multiple prescribers.⁴³ PDMPs can also confirm reported medication doses, for which patient report may be less reliable.

Two hospital/acute pain guidelines and 5 chronic pain guidelines also recommend urine drug testing, although differing on when and whom to test, with some favoring universal screening.^11,20,23 Screening hospitalized patients may reveal substances not reported by patients, but medications administered in emergency departments can confound results. Furthermore, the commonly used immunoassay does not distinguish heroin from prescription opioids, nor detect hydrocodone, oxycodone, methadone, buprenorphine, or certain benzodiazepines. Chromatography/mass spectrometry assays can but are often not available from hospital laboratories. The differential for unexpected results includes substance use, self treatment of uncontrolled pain, diversion, or laboratory error.²⁰

If concerning opioid use is identified, 3 hospital setting/acute pain specific guidelines and the CDC guideline recommend sharing concerns with patients and assessing for a substance use disorder.^9,13,16,22 Determining whether patients have an opioid use disorder that meets the criteria in the Diagnostic and Statistical Manual, 5th Edition⁴⁴ can be challenging. Patients may minimize or deny symptoms or fear that the stigma of an opioid use disorder will lead to dismissive or subpar care. Additionally, substance use disorders are subject to federal confidentiality regulations, which can hamper acquisition of information from providers.⁴⁵ Thus, hospitalists may find specialty consultation helpful to confirm the diagnosis.

Assessing the Risk of Overdose and Adverse Drug Events

Oversedation, respiratory depression, and death can result from iatrogenic or self-administered opioid overdose in the hospital.5 Patient factors that increase this risk among outpatients include a prior history of overdose, preexisting substance use disorders, cognitive impairment, mood and personality disorders, chronic kidney disease, sleep apnea, obstructive lung disease, and recent abstinence from opioids.12 Medication factors include concomitant use of benzodiazepines and other central nervous system depressants, including alcohol; recent initiation of long-acting opioids; use of fentanyl patches, immediate-release fentanyl, or methadone; rapid titration; switching opioids without adequate dose reduction; pharmacokinetic drug–drug interactions; and, importantly, higher doses.12,22 Two guidelines specific to acute pain and hospital settings and 5 chronic pain guidelines recommend screening for use of benzodiazepines among patients on LTOT.13,14,16,18-20,22,21
The CDC guideline recommends careful assessment when doses exceed 50 mg of morphine equivalents per day and avoiding doses above 90 mg per day due to the heightened risk of overdose.²² In the hospital, 23% of patients receive doses at or above 100 mg of morphine equivalents per day,⁵ and concurrent use of central nervous system depressants is common. Changes in kidney and liver function during acute illness may impact opioid metabolism and contribute to overdose.

In addition to overdose, opioids are leading causes of adverse drug events during hospitalization.⁴⁶ Most studies have focused on surgical patients reporting common opioid-related events as nausea/vomiting, pruritus, rash, mental status changes, respiratory depression, ileus, and urinary retention.⁴⁷ Hospitalized patients may also exhibit chronic adverse effects due to LTOT. At least one-third of patients on LTOT eventually stop because of adverse effects, such as endocrinopathies, sleep disordered breathing, constipation, fractures, falls, and mental status changes.⁴⁸ Patients may lack awareness that their symptoms are attributable to opioids and are willing to reduce their opioid use once informed, especially when alternatives are offered to alleviate pain.

Gauging the Risk of Withdrawal

Sudden discontinuation of LTOT by patients, practitioners, or intercurrent events can have unanticipated and undesirable consequences. Withdrawal is not only distressing for patients; it can be dangerous because patients may resort to illicit use, diversion of opioids, or masking opioid withdrawal with other substances such as alcohol. The anxiety and distress associated with withdrawal, or anticipatory fear about withdrawal, can undermine therapeutic alliance and interfere with processes of care. Reviewed guidelines did not offer recommendations regarding withdrawal risk or specific strategies for avoidance. There is no specific prior dose threshold or degree of reduction in opioids that puts patients at risk for withdrawal, in part due to patients’ beliefs, expectations, and differences in response to opioid formulations. Symptoms of opioid withdrawal have been compared to a severe case of influenza, including stomach cramps, nausea and vomiting, diarrhea, tremor and muscle twitching, sweating, restlessness, yawning, tachycardia, anxiety and irritability, bone and joint aches, runny nose, tearing, and piloerection.⁴⁹ The Clinical Opiate Withdrawal Scale (COWS)⁴⁹ and the Clinical Institute Narcotic Assessment⁵¹ are clinician-administered tools to assess opioid withdrawal similar to the Clinical Institute Withdrawal Assessment of Alcohol Scale, Revised,⁵² to monitor for withdrawal in the inpatient setting.

Synthesizing and Appraising the Indications for Opioid Therapy

For medical inpatients who report adequate pain control and functional outcomes on current doses of LTOT, without evidence of misuse, the pragmatic approach is to continue the treatment plan established by the outpatient clinician rather than escalating or tapering the dose. If opioids are prescribed at discharge, 3 hospital setting/acute pain guidelines and the CDC guideline recommend prescribing the lowest effective dose of immediate release opioids for 3 to 7 days.^13,15,16,22

When patients exhibit evidence of an opioid use disorder, have a history of serious overdose, or are experiencing intolerable opioid-related adverse events, the hospitalist may conclude the harms of LTOT outweigh the benefits. For these patients, opioid treatment in the hospital can be aimed at preventing withdrawal, avoiding the perpetuation of inappropriate opioid use, managing other acute medical conditions, and communicating with outpatient prescribers. For patients with misuse, discontinuing opioids is potentially harmful and may be perceived as punitive. Hospitalists should consider consulting addiction or mental health specialists to assist with formulating a plan of care. However, such specialists may not be available in smaller or rural hospitals and referral at discharge can be challenging.⁵³

Beginning to taper opioids during the hospitalization can be appropriate when patients are motivated and can transition to an outpatient provider who will supervise the taper. In ambulatory settings, tapers of 10% to 30% every 2 to 5 days are generally well tolerated.⁵⁴ If patients started tapering opioids under supervision of an outpatient provider prior to hospitalization; ideally, the taper can be continued during hospitalization with close coordination with the outpatient clinician.

Unfortunately, many patients on LTOT are admitted with new sources of acute pain and or exacerbations of chronic pain, and some have concomitant substance use disorders; we plan to address the management of these complex situations in future work.

Despite the frequency with which patients on LTOT are hospitalized for nonsurgical stays and the challenges inherent in evaluating pain and assessing the possibility of substance use disorders, no formal guidelines or empirical research studies pertain to this population. Guidelines in this review were developed for hospital settings and acute pain in the absence of LTOT, and for outpatient care of patients on LTOT. We also included a nonsystematic synthesis of literature that varied in relevance to medical inpatients on LTOT.

Although inpatient assessment and treatment of patients with LTOT remains an underresearched area, we were able to extract and synthesize recommendations from 14 guideline statements and apply these to the assessment of patients with LTOT in the inpatient setting. Hospitalists frequently encounter patients on LTOT for chronic nonmalignant pain and are faced with complex decisions about the effectiveness and safety of LTOT; appropriate patient assessment is fundamental to making these decisions. Key guideline recommendations relevant to inpatient assessment include assessing both pain and functional status, differentiating acute from chronic pain, ascertaining preadmission pain treatment history, obtaining a psychosocial history, screening for mental health issues such as depression and anxiety, screening for substance use disorders, checking state prescription drug monitoring databases, ordering urine drug immunoassays, detecting use of sedative-hypnotics, identifying medical conditions associated with increased risk of overdose and adverse events, and appraising the potential benefits and harms of opioid therapy. Although approaches to assessing medical inpatients on LTOT can be extrapolated from outpatient guidelines, observational studies, and small studies in surgical populations, more work is needed to address these critical topics for inpatients on LTOT.

Disclosure

Dr. Herzig was funded by grant number K23AG042459 from the National Institute on Aging. The funding organization had no involvement in any aspect of the study, including design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. All other authors have no relevant conflicts of interest with the work.

Malaria is a major challenge in more than 100 countries. In 2015, nearly half the countries in the world had ongoing malaria transmission, according to Dr. Eileen Villasante, head of the Malaria Department at the Naval Medical Research Center (NMRC). But NMRC researchers may have found a new way to meet that challenge. They identified a novel highly protective malaria antigen, Plasmodium yoelii E140.

E140 is found in multiple stages of the life cycle of the malaria parasite, including sporozoites, liver stages, and blood stages, Villasante said. The researchers found that E140 induced up to 100% sterile protection, persisting for at least 3 months. They are now at work on a vaccine with the antigen.

Malaria is a major challenge in more than 100 countries. In 2015, nearly half the countries in the world had ongoing malaria transmission, according to Dr. Eileen Villasante, head of the Malaria Department at the Naval Medical Research Center (NMRC). But NMRC researchers may have found a new way to meet that challenge. They identified a novel highly protective malaria antigen, Plasmodium yoelii E140.

E140 is found in multiple stages of the life cycle of the malaria parasite, including sporozoites, liver stages, and blood stages, Villasante said. The researchers found that E140 induced up to 100% sterile protection, persisting for at least 3 months. They are now at work on a vaccine with the antigen.

Malaria is a major challenge in more than 100 countries. In 2015, nearly half the countries in the world had ongoing malaria transmission, according to Dr. Eileen Villasante, head of the Malaria Department at the Naval Medical Research Center (NMRC). But NMRC researchers may have found a new way to meet that challenge. They identified a novel highly protective malaria antigen, Plasmodium yoelii E140.

E140 is found in multiple stages of the life cycle of the malaria parasite, including sporozoites, liver stages, and blood stages, Villasante said. The researchers found that E140 induced up to 100% sterile protection, persisting for at least 3 months. They are now at work on a vaccine with the antigen.

UC San Diego Health

The enzyme ADAR1 may be a therapeutic target for multiple myeloma (MM), according to research published in Nature Communications.

Investigators found that high ADAR1 levels correlate with reduced survival rates in MM patients.

The team also discovered that blocking ADAR1 reduced regeneration of high-risk MM in serially transplantable patient-derived xenografts.

The investigators believe that JAK2 inhibitors could be used to dampen ADAR1 activity and ultimately prevent progression or relapse in patients with MM.

“Despite new therapies, it’s virtually inevitable that a patient with multiple myeloma will experience relapse of the disease at some point,” said study author Catriona Jamieson, MD, PhD, of University of California, San Diego, in La Jolla.

“That’s why it’s exciting that this discovery may allow us to detect the disease earlier and address the root cause.”

Dr Jamieson and her colleagues knew that ADAR1 is normally expressed during fetal development to help blood cells form. The enzyme edits the sequence of RNA and is known to promote cancer progression and resistance to therapy.

With previous work, Dr Jamieson’s team described ADAR1’s contributions to chronic myeloid leukemia (CML). The enzyme’s RNA-editing activity boosts leukemic stem cells, giving rise to CML, increasing disease recurrence, and allowing CML to resist treatment.

For their current study, Dr Jamieson and her colleagues investigated ADAR1’s role in MM, first analyzing a database of nearly 800 MM patient samples.

The investigators found that patients with low ADAR1 levels in their tumor cells survived longer than patients with high ADAR1 levels.

While more than 90% of patients with low ADAR1 levels survived longer than 2 years after their initial diagnosis, fewer than 70% of patients with high ADAR1 levels did the same.

The investigators also created a humanized mouse model of MM and found that silencing the ADAR1 gene reduced the engraftment of MM.

“This is a difficult disease to model in animals; there isn’t a single gene we can manipulate to mimic multiple myeloma,” said study author Leslie A. Crews, PhD, of University of California, San Diego.

“This study is important, in part because we now have a new xenograft model that will, for the first time, allow us to apply new biomarkers to better predict disease progression and test new therapeutics.”

To advance their findings from this study, the investigators are exploring ways to leverage ADAR1 to detect MM progression as early as possible.

They are also testing inhibitors of JAK2, a molecule that influences ADAR1 activity, for their ability to eliminate cancer stem cells in MM models.

“Several major advances in recent years have been good news for multiple myeloma patients, but those new drugs only target terminally differentiated cancer cells and, thus, can only reduce the bulk of the tumor,” Dr Jamieson said.

“They don’t get to the root cause of disease development, progression, and relapse—cancer stem cells—the way inhibiting ADAR1 does. I like to call our approach ‘precision regenerative medicine.’”

UC San Diego Health

The enzyme ADAR1 may be a therapeutic target for multiple myeloma (MM), according to research published in Nature Communications.

Investigators found that high ADAR1 levels correlate with reduced survival rates in MM patients.

The team also discovered that blocking ADAR1 reduced regeneration of high-risk MM in serially transplantable patient-derived xenografts.

The investigators believe that JAK2 inhibitors could be used to dampen ADAR1 activity and ultimately prevent progression or relapse in patients with MM.

“Despite new therapies, it’s virtually inevitable that a patient with multiple myeloma will experience relapse of the disease at some point,” said study author Catriona Jamieson, MD, PhD, of University of California, San Diego, in La Jolla.

“That’s why it’s exciting that this discovery may allow us to detect the disease earlier and address the root cause.”

Dr Jamieson and her colleagues knew that ADAR1 is normally expressed during fetal development to help blood cells form. The enzyme edits the sequence of RNA and is known to promote cancer progression and resistance to therapy.

With previous work, Dr Jamieson’s team described ADAR1’s contributions to chronic myeloid leukemia (CML). The enzyme’s RNA-editing activity boosts leukemic stem cells, giving rise to CML, increasing disease recurrence, and allowing CML to resist treatment.

For their current study, Dr Jamieson and her colleagues investigated ADAR1’s role in MM, first analyzing a database of nearly 800 MM patient samples.

The investigators found that patients with low ADAR1 levels in their tumor cells survived longer than patients with high ADAR1 levels.

While more than 90% of patients with low ADAR1 levels survived longer than 2 years after their initial diagnosis, fewer than 70% of patients with high ADAR1 levels did the same.

The investigators also created a humanized mouse model of MM and found that silencing the ADAR1 gene reduced the engraftment of MM.

“This is a difficult disease to model in animals; there isn’t a single gene we can manipulate to mimic multiple myeloma,” said study author Leslie A. Crews, PhD, of University of California, San Diego.

“This study is important, in part because we now have a new xenograft model that will, for the first time, allow us to apply new biomarkers to better predict disease progression and test new therapeutics.”

To advance their findings from this study, the investigators are exploring ways to leverage ADAR1 to detect MM progression as early as possible.

They are also testing inhibitors of JAK2, a molecule that influences ADAR1 activity, for their ability to eliminate cancer stem cells in MM models.

“Several major advances in recent years have been good news for multiple myeloma patients, but those new drugs only target terminally differentiated cancer cells and, thus, can only reduce the bulk of the tumor,” Dr Jamieson said.

“They don’t get to the root cause of disease development, progression, and relapse—cancer stem cells—the way inhibiting ADAR1 does. I like to call our approach ‘precision regenerative medicine.’”

UC San Diego Health

The enzyme ADAR1 may be a therapeutic target for multiple myeloma (MM), according to research published in Nature Communications.

Investigators found that high ADAR1 levels correlate with reduced survival rates in MM patients.

The team also discovered that blocking ADAR1 reduced regeneration of high-risk MM in serially transplantable patient-derived xenografts.

The investigators believe that JAK2 inhibitors could be used to dampen ADAR1 activity and ultimately prevent progression or relapse in patients with MM.

“Despite new therapies, it’s virtually inevitable that a patient with multiple myeloma will experience relapse of the disease at some point,” said study author Catriona Jamieson, MD, PhD, of University of California, San Diego, in La Jolla.

“That’s why it’s exciting that this discovery may allow us to detect the disease earlier and address the root cause.”

Dr Jamieson and her colleagues knew that ADAR1 is normally expressed during fetal development to help blood cells form. The enzyme edits the sequence of RNA and is known to promote cancer progression and resistance to therapy.

With previous work, Dr Jamieson’s team described ADAR1’s contributions to chronic myeloid leukemia (CML). The enzyme’s RNA-editing activity boosts leukemic stem cells, giving rise to CML, increasing disease recurrence, and allowing CML to resist treatment.

For their current study, Dr Jamieson and her colleagues investigated ADAR1’s role in MM, first analyzing a database of nearly 800 MM patient samples.

The investigators found that patients with low ADAR1 levels in their tumor cells survived longer than patients with high ADAR1 levels.

While more than 90% of patients with low ADAR1 levels survived longer than 2 years after their initial diagnosis, fewer than 70% of patients with high ADAR1 levels did the same.

The investigators also created a humanized mouse model of MM and found that silencing the ADAR1 gene reduced the engraftment of MM.

“This is a difficult disease to model in animals; there isn’t a single gene we can manipulate to mimic multiple myeloma,” said study author Leslie A. Crews, PhD, of University of California, San Diego.

“This study is important, in part because we now have a new xenograft model that will, for the first time, allow us to apply new biomarkers to better predict disease progression and test new therapeutics.”

To advance their findings from this study, the investigators are exploring ways to leverage ADAR1 to detect MM progression as early as possible.

They are also testing inhibitors of JAK2, a molecule that influences ADAR1 activity, for their ability to eliminate cancer stem cells in MM models.

“Several major advances in recent years have been good news for multiple myeloma patients, but those new drugs only target terminally differentiated cancer cells and, thus, can only reduce the bulk of the tumor,” Dr Jamieson said.

“They don’t get to the root cause of disease development, progression, and relapse—cancer stem cells—the way inhibiting ADAR1 does. I like to call our approach ‘precision regenerative medicine.’”

Photo courtesy of Novartis

Health Canada has approved use of the multi-targeted kinase inhibitor midostaurin (Rydapt™).

This makes midostaurin the first targeted therapy approved to treat FLT3-mutated acute myeloid leukemia (AML) in Canada.

Midostaurin is approved for use with standard cytarabine and daunorubicin induction and cytarabine consolidation for the treatment of adults with newly diagnosed FLT3-mutated AML.

Health Canada’s approval of midostaurin is based on results from the phase 3 RATIFY trial, which were published in NEJM in August.

In RATIFY, researchers compared midostaurin plus standard chemotherapy to placebo plus standard chemotherapy in 717 adults younger than age 60 who had FLT3-mutated AML.

The median overall survival was significantly longer in the midostaurin arm than the placebo arm—74.7 months and 25.6 months, respectively (hazard ratio=0.77, P=0.016).

And the median event-free survival was significantly longer in the midostaurin arm than the placebo arm—8.2 months and 3.0 months, respectively (hazard ratio=0.78, P=0.004).

The most frequent adverse events (AEs) in the midostaurin arm (occurring in at least 20% of patients) were febrile neutropenia, nausea, vomiting, mucositis, headache, musculoskeletal pain, petechiae, device-related infection, epistaxis, hyperglycemia, and upper respiratory tract infection.

The most frequent grade 3/4 AEs (occurring in at least 10% of patients) were febrile neutropenia, device-related infection, and mucositis.

Nine percent of patients in the midostaurin arm stopped treatment due to AEs, as did 6% in the placebo arm.

Photo courtesy of Novartis

Health Canada has approved use of the multi-targeted kinase inhibitor midostaurin (Rydapt™).

This makes midostaurin the first targeted therapy approved to treat FLT3-mutated acute myeloid leukemia (AML) in Canada.

Midostaurin is approved for use with standard cytarabine and daunorubicin induction and cytarabine consolidation for the treatment of adults with newly diagnosed FLT3-mutated AML.

Health Canada’s approval of midostaurin is based on results from the phase 3 RATIFY trial, which were published in NEJM in August.

In RATIFY, researchers compared midostaurin plus standard chemotherapy to placebo plus standard chemotherapy in 717 adults younger than age 60 who had FLT3-mutated AML.

The median overall survival was significantly longer in the midostaurin arm than the placebo arm—74.7 months and 25.6 months, respectively (hazard ratio=0.77, P=0.016).

And the median event-free survival was significantly longer in the midostaurin arm than the placebo arm—8.2 months and 3.0 months, respectively (hazard ratio=0.78, P=0.004).

The most frequent adverse events (AEs) in the midostaurin arm (occurring in at least 20% of patients) were febrile neutropenia, nausea, vomiting, mucositis, headache, musculoskeletal pain, petechiae, device-related infection, epistaxis, hyperglycemia, and upper respiratory tract infection.

The most frequent grade 3/4 AEs (occurring in at least 10% of patients) were febrile neutropenia, device-related infection, and mucositis.

Nine percent of patients in the midostaurin arm stopped treatment due to AEs, as did 6% in the placebo arm.

Photo courtesy of Novartis

Health Canada has approved use of the multi-targeted kinase inhibitor midostaurin (Rydapt™).

This makes midostaurin the first targeted therapy approved to treat FLT3-mutated acute myeloid leukemia (AML) in Canada.

Midostaurin is approved for use with standard cytarabine and daunorubicin induction and cytarabine consolidation for the treatment of adults with newly diagnosed FLT3-mutated AML.

Health Canada’s approval of midostaurin is based on results from the phase 3 RATIFY trial, which were published in NEJM in August.

In RATIFY, researchers compared midostaurin plus standard chemotherapy to placebo plus standard chemotherapy in 717 adults younger than age 60 who had FLT3-mutated AML.

The median overall survival was significantly longer in the midostaurin arm than the placebo arm—74.7 months and 25.6 months, respectively (hazard ratio=0.77, P=0.016).

And the median event-free survival was significantly longer in the midostaurin arm than the placebo arm—8.2 months and 3.0 months, respectively (hazard ratio=0.78, P=0.004).

The most frequent adverse events (AEs) in the midostaurin arm (occurring in at least 20% of patients) were febrile neutropenia, nausea, vomiting, mucositis, headache, musculoskeletal pain, petechiae, device-related infection, epistaxis, hyperglycemia, and upper respiratory tract infection.

The most frequent grade 3/4 AEs (occurring in at least 10% of patients) were febrile neutropenia, device-related infection, and mucositis.

Nine percent of patients in the midostaurin arm stopped treatment due to AEs, as did 6% in the placebo arm.

Photo by Bill Branson

Two companies have announced the US launch of a generic busulfan product, Myleran Injection.

Mylan NV and Aspen have partnered to develop Myleran (busulfan) Injection, 60 mg/10 mL (6 mg/mL) Single-dose Vial, a generic version of Otsuka Pharmaceutical’s Busulfex® Injection.

The US Food and Drug Administration approved Myleran Injection for use in combination with cyclophosphamide as a conditioning regimen prior to allogeneic hematopoietic stem cell transplant in patients with chronic myeloid leukemia.

Photo by Bill Branson

Two companies have announced the US launch of a generic busulfan product, Myleran Injection.

Mylan NV and Aspen have partnered to develop Myleran (busulfan) Injection, 60 mg/10 mL (6 mg/mL) Single-dose Vial, a generic version of Otsuka Pharmaceutical’s Busulfex® Injection.

The US Food and Drug Administration approved Myleran Injection for use in combination with cyclophosphamide as a conditioning regimen prior to allogeneic hematopoietic stem cell transplant in patients with chronic myeloid leukemia.

Photo by Bill Branson

Two companies have announced the US launch of a generic busulfan product, Myleran Injection.

Mylan NV and Aspen have partnered to develop Myleran (busulfan) Injection, 60 mg/10 mL (6 mg/mL) Single-dose Vial, a generic version of Otsuka Pharmaceutical’s Busulfex® Injection.

The US Food and Drug Administration approved Myleran Injection for use in combination with cyclophosphamide as a conditioning regimen prior to allogeneic hematopoietic stem cell transplant in patients with chronic myeloid leukemia.

ANSWER

This ECG shows evidence of sinus tachycardia with biatrial enlargement, right-axis deviation, right ventricular hypertrophy, and poor R-wave progression consistent with a septal MI.

Sinus tachycardia is signified by an atrial rate > 100 beats/min. The markedly notched P waves in leads I and II and biphasic P waves in lead V₁ suggest biatrial enlargement. Right-axis deviation is diagnosed based on the R axis of 119°, and right ventricular hypertrophy is indicated by the right-axis deviation, a QR pattern in lead V₁, and an R wave ≥ 5 mm in lead aVR. All of the above are findings seen with a history of pulmonary hypertension.

Poor R-wave progression in leads V₁ to V₄ suggests a septal MI of indeterminate age; however, there is no history of previous infarction.

The patient was diagnosed with a complex fracture of the left humerus and referred to orthopedics for repair. At his request, a pulmonary medicine consultation was ordered so that he could establish care in this facility.

ANSWER

This ECG shows evidence of sinus tachycardia with biatrial enlargement, right-axis deviation, right ventricular hypertrophy, and poor R-wave progression consistent with a septal MI.

Sinus tachycardia is signified by an atrial rate > 100 beats/min. The markedly notched P waves in leads I and II and biphasic P waves in lead V₁ suggest biatrial enlargement. Right-axis deviation is diagnosed based on the R axis of 119°, and right ventricular hypertrophy is indicated by the right-axis deviation, a QR pattern in lead V₁, and an R wave ≥ 5 mm in lead aVR. All of the above are findings seen with a history of pulmonary hypertension.

Poor R-wave progression in leads V₁ to V₄ suggests a septal MI of indeterminate age; however, there is no history of previous infarction.

The patient was diagnosed with a complex fracture of the left humerus and referred to orthopedics for repair. At his request, a pulmonary medicine consultation was ordered so that he could establish care in this facility.

ANSWER

This ECG shows evidence of sinus tachycardia with biatrial enlargement, right-axis deviation, right ventricular hypertrophy, and poor R-wave progression consistent with a septal MI.

Sinus tachycardia is signified by an atrial rate > 100 beats/min. The markedly notched P waves in leads I and II and biphasic P waves in lead V₁ suggest biatrial enlargement. Right-axis deviation is diagnosed based on the R axis of 119°, and right ventricular hypertrophy is indicated by the right-axis deviation, a QR pattern in lead V₁, and an R wave ≥ 5 mm in lead aVR. All of the above are findings seen with a history of pulmonary hypertension.

Poor R-wave progression in leads V₁ to V₄ suggests a septal MI of indeterminate age; however, there is no history of previous infarction.

The patient was diagnosed with a complex fracture of the left humerus and referred to orthopedics for repair. At his request, a pulmonary medicine consultation was ordered so that he could establish care in this facility.