Sadie Trammell Velasquez, MD

Hospital medicine has grown dramatically over the past 20 years.^1,2 A recent survey regarding hospitalists’ clinical roles showed an expansion to triaging emergency department (ED) medical admissions and transfers from outside hospitals.³ From the hospitalist perspective, triaging involves the evaluation of patients for potential admission.⁴ With scrutiny on ED metrics, such as wait times (https://www.medicare.gov/hospitalcompare/search.html), health system administrators have heightened expectations for efficient patient flow, which increasingly falls to hospitalists.^5-7

Despite the growth in hospitalists’ triagist activities, there has been little formal assessment of their role. We hypothesized that this role differs from inpatient care in significant ways.^6-8 We sought to describe the triagist role in adult academic inpatient medicine settings to understand the responsibilities and skill set required.

METHODS

Ten academic medical center (AMC) sites were recruited from Research Committee session attendees at the 2014 Society of Hospital Medicine national meeting and the 2014 Society of General Internal Medicine southern regional meeting. The AMCs were geographically diverse: three Western, two Midwestern, two Southern, one Northeastern, and two Southeastern. Site representatives were identified and completed a web-based questionnaire about their AMC (see Appendix 1 for the information collected). Clarifications regarding survey responses were performed via conference calls between the authors (STV, ESW) and site representatives.

Hospitalist Survey

In January 2018, surveys were sent to 583 physicians who worked as triagists. Participants received an anonymous 28-item RedCap survey by e-mail and were sent up to five reminder e-mails over six weeks (see Appendix 2 for the questions analyzed in this paper). Respondents were given the option to be entered in a gift card drawing.

Demographic information and individual workflow/practices were obtained. A 5-point Likert scale (strongly disagree – strongly agree) was used to assess hospitalists’ concurrence with current providers (eg, ED, clinic providers) regarding the management and whether patients must meet the utilization management (UM) criteria for admission. Time estimates used 5% increments and were categorized into four frequency categories based on the local modes provided in responses: Seldom (0%-10%), Occasional (15%-35%), Half-the-Time (40%-60%), and Frequently (65%-100%). Free text responses on effective/ineffective triagist qualities were elicited. Responses were included for analysis if at least 70% of questions were completed.

Data Analysis

Quantitative

Descriptive statistics were calculated for each variable. The Kruskal-Wallis test was used to evaluate differences across AMCs in the time spent on in-person evaluation and communication. Weighting, based on the ratio of hospitalists to survey respondents at each AMC, was used to calculate the average institutional percentages across the study sample.

 

Qualitative

Responses to open-ended questions were analyzed using thematic analysis.⁹ Three independent reviewers (STV, JC, ESW) read, analyzed, and grouped the responses by codes. Codes were then assessed for overlap and grouped into themes by one reviewer (STV). A table of themes with supporting quotes and the number of mentions was subsequently developed by all three reviewers. Similar themes were combined to create domains. The domains were reviewed by the steering committee members to create a consensus description (Appendix 3).

The University of Texas Health San Antonio’s Institutional Review Board and participating institutions approved the study as exempt.

RESULTS

Site Characteristics

Representatives from 10 AMCs reported data on a range of one to four hospitals for a total of 22 hospitals. The median reported that the number of medical patients admitted in a 24-hour period was 31-40 (range, 11-20 to >50). The median group size of hospitalists was 41-50 (range, 0-10 to >70).

The survey response rate was 40% (n = 235), ranging from 9%-70% between institutions. Self-identified female hospitalists accounted for 52% of respondents. Four percent were 25-29 years old, 66% were 30-39 years old, 24% were 40-49 years old, and 6% were ≥50 years old. The average clinical time spent as a triagist was 16%.

Description of Triagist Activities

The activities identified by the majority of respondents across all sites included transferring patients within the hospital (73%), and assessing/approving patient transfers from outside hospitals and clinics (82%). Internal transfer activities reported by >50% of respondents included allocating patients within the hospital or bed capacity coordination, assessing intensive care unit transfers, assigning ED admissions, and consulting other services. The ED accounted for an average of 55% of calls received. Respondents also reported being involved with the documentation related to these activities.

Similarities and Differences across AMCs

Two AMCs did not have a dedicated triagist; instead, physicians supervised residents and advanced practice providers. Among the eight sites with triagists, triaging was predominantly done by faculty physicians contacted via pagers. At seven of these sites, 100% of hospitalists worked as triagists. The triage service was covered by faculty physicians from 8-24 hours per day.

Bed boards and transfer centers staffed by registered nurses, nurse coordinators, house supervisors, or physicians were common support systems, though this infrastructure was organized differently across institutions. A UM review before admission was performed at three institutions 24 hours/day. The remaining institutions reviewed patients retrospectively.

Twenty-eight percent of hospitalists across all sites “Disagreed” or “Strongly disagreed” that a patient must meet UM criteria for admission. Forty-two percent had “Frequent” different opinions regarding patient management than the consulting provider.

Triagist and current provider communication practices varied widely across AMCs (Figure). There was significant variability in verbal communication (P = .02), with >70% of respondents at two AMCs reporting verbal communication at least half the time, but <30% reporting this frequency at two other AMCs. Respondents reported variable use of electronic communication (ie, notes/orders in the electronic health record) across AMCs (P < .0001). Half of the hospitalists use it “Seldom”, 20% use it “Occasionally”, and 23% use it “Frequently”.

The practice of evaluating patients in person also varied significantly across AMCs (P < .0001, Figure). Across hospitalists, only 28% see patients in person about “Half-the-Time” or more.

 

Differences within AMCs

Variability within AMCs was greatest for the rate of verbal communication practices, with a typical interquartile range (IQR) of 20% to 90% among the hospitalists within a given AMC and for the rate of electronic communication with a typical IQR of 0% to 50%. For other survey questions, the IQR was typically 15 to 20 percentage points.

Thematic Analysis

We received 207 and 203 responses (88% and 86%, respectively) to the open-ended questions “What qualities does an effective triagist have?’ and ‘What qualities make a triagist ineffective?” We identified 22 themes for effective and ineffective qualities, which were grouped into seven domains (Table). All themes had at least three mentions by respondents. The three most frequently mentioned themes, communication skills, efficiency, and systems knowledge, had greater than 60 mentions.

DISCUSSION

Our study of the triagist role at 10 AMCs describes critical triagist functions and identifies key findings across and within AMCs. Twenty-eight percent of hospitalists reported admitting patients even when the patient did not meet the admission criteria, consistent with previous research demonstrating the influence of factors other than clinical disease severity on triage decisions.¹⁰ However, preventable admissions remain a hospital-level quality metric.^11,12 Triagists must often balance each patient’s circumstances with the complexities of the system. Juggling the competing demands of the system while providing patient-centered care can be challenging and may explain why attending physicians are more frequently filling this role.¹³

Local context/culture is likely to play a role in the variation across sites; however, compensation for the time spent may also be a factor. If triage activities are not reimbursable, this could lead to less documentation and a lower likelihood that patients are evaluated in person.¹⁴ This reason may also explain why all hospitalists were required to serve as a triagist at most sites.

Currently, no consensus definition of the triagist role has been developed. Our results demonstrate that this role is heterogeneous and grounded in the local healthcare system practices. We propose the following working definition of the triagist: a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting. A triagist should be equipped with a skill set that includes not only clinical knowledge but also emphasizes systems knowledge, awareness of others’ goals, efficiency, an ability to communicate effectively, and the knowledge of UM. We recommend that medical directors of hospitalist programs focus their attention on locally specific, systems-based skills development when orienting new hospitalists. The financial aspects of cost should be considered and delineated as well.

Our analysis is limited in several respects. Participant AMCs were not randomly chosen, but do represent a broad array of facility types, group size, and geographic regions. The low response rates at some AMCs may result in an inaccurate representation of those sites. Data was not obtained on hospitalists that did not respond to the survey; therefore, nonresponse bias may affect outcomes. This research used self-report rather than direct observation, which could be subject to recall and social desirability bias. Finally, our results may not be generalizable to nonacademic institutions.

 

CONCLUSION

The hospitalist role as triagist at AMCs emphasizes communication, organizational skills, efficiency, systems-based practice, and UM knowledge. Although we found significant variation across and within AMCs, internal transfer activities were common across programs. Hospitalist programs should focus on systems-based skills development to prepare hospitalists for the role. The skill set necessary for triagist responsibilities also has implications for internal medicine resident education.⁴ With increasing emphasis on value and system effectiveness in care delivery, further studies of the triagist role should be undertaken.

Acknowledgments

The TRIAGIST Collaborative Group consists of: Maralyssa Bann, MD, Andrew White, MD (University of Washington); Jagriti Chadha, MD (University of Kentucky); Joel Boggan, MD (Duke University); Sherwin Hsu, MD (UCLA); Jeff Liao, MD (Harvard Medical School); Tabatha Matthias, DO (University of Nebraska Medical Center); Tresa McNeal, MD (Scott and White Texas A&M); Roxana Naderi, MD, Khooshbu Shah, MD (University of Colorado); David Schmit, MD (University of Texas Health San Antonio); Manivannan Veerasamy, MD (Michigan State University).

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.

Hospital medicine has grown dramatically over the past 20 years.^1,2 A recent survey regarding hospitalists’ clinical roles showed an expansion to triaging emergency department (ED) medical admissions and transfers from outside hospitals.³ From the hospitalist perspective, triaging involves the evaluation of patients for potential admission.⁴ With scrutiny on ED metrics, such as wait times (https://www.medicare.gov/hospitalcompare/search.html), health system administrators have heightened expectations for efficient patient flow, which increasingly falls to hospitalists.^5-7

Despite the growth in hospitalists’ triagist activities, there has been little formal assessment of their role. We hypothesized that this role differs from inpatient care in significant ways.^6-8 We sought to describe the triagist role in adult academic inpatient medicine settings to understand the responsibilities and skill set required.

METHODS

Ten academic medical center (AMC) sites were recruited from Research Committee session attendees at the 2014 Society of Hospital Medicine national meeting and the 2014 Society of General Internal Medicine southern regional meeting. The AMCs were geographically diverse: three Western, two Midwestern, two Southern, one Northeastern, and two Southeastern. Site representatives were identified and completed a web-based questionnaire about their AMC (see Appendix 1 for the information collected). Clarifications regarding survey responses were performed via conference calls between the authors (STV, ESW) and site representatives.

Hospitalist Survey

In January 2018, surveys were sent to 583 physicians who worked as triagists. Participants received an anonymous 28-item RedCap survey by e-mail and were sent up to five reminder e-mails over six weeks (see Appendix 2 for the questions analyzed in this paper). Respondents were given the option to be entered in a gift card drawing.

Demographic information and individual workflow/practices were obtained. A 5-point Likert scale (strongly disagree – strongly agree) was used to assess hospitalists’ concurrence with current providers (eg, ED, clinic providers) regarding the management and whether patients must meet the utilization management (UM) criteria for admission. Time estimates used 5% increments and were categorized into four frequency categories based on the local modes provided in responses: Seldom (0%-10%), Occasional (15%-35%), Half-the-Time (40%-60%), and Frequently (65%-100%). Free text responses on effective/ineffective triagist qualities were elicited. Responses were included for analysis if at least 70% of questions were completed.

Data Analysis

Quantitative

Descriptive statistics were calculated for each variable. The Kruskal-Wallis test was used to evaluate differences across AMCs in the time spent on in-person evaluation and communication. Weighting, based on the ratio of hospitalists to survey respondents at each AMC, was used to calculate the average institutional percentages across the study sample.

 

Qualitative

Responses to open-ended questions were analyzed using thematic analysis.⁹ Three independent reviewers (STV, JC, ESW) read, analyzed, and grouped the responses by codes. Codes were then assessed for overlap and grouped into themes by one reviewer (STV). A table of themes with supporting quotes and the number of mentions was subsequently developed by all three reviewers. Similar themes were combined to create domains. The domains were reviewed by the steering committee members to create a consensus description (Appendix 3).

The University of Texas Health San Antonio’s Institutional Review Board and participating institutions approved the study as exempt.

RESULTS

Site Characteristics

Representatives from 10 AMCs reported data on a range of one to four hospitals for a total of 22 hospitals. The median reported that the number of medical patients admitted in a 24-hour period was 31-40 (range, 11-20 to >50). The median group size of hospitalists was 41-50 (range, 0-10 to >70).

The survey response rate was 40% (n = 235), ranging from 9%-70% between institutions. Self-identified female hospitalists accounted for 52% of respondents. Four percent were 25-29 years old, 66% were 30-39 years old, 24% were 40-49 years old, and 6% were ≥50 years old. The average clinical time spent as a triagist was 16%.

Description of Triagist Activities

The activities identified by the majority of respondents across all sites included transferring patients within the hospital (73%), and assessing/approving patient transfers from outside hospitals and clinics (82%). Internal transfer activities reported by >50% of respondents included allocating patients within the hospital or bed capacity coordination, assessing intensive care unit transfers, assigning ED admissions, and consulting other services. The ED accounted for an average of 55% of calls received. Respondents also reported being involved with the documentation related to these activities.

Similarities and Differences across AMCs

Two AMCs did not have a dedicated triagist; instead, physicians supervised residents and advanced practice providers. Among the eight sites with triagists, triaging was predominantly done by faculty physicians contacted via pagers. At seven of these sites, 100% of hospitalists worked as triagists. The triage service was covered by faculty physicians from 8-24 hours per day.

Bed boards and transfer centers staffed by registered nurses, nurse coordinators, house supervisors, or physicians were common support systems, though this infrastructure was organized differently across institutions. A UM review before admission was performed at three institutions 24 hours/day. The remaining institutions reviewed patients retrospectively.

Twenty-eight percent of hospitalists across all sites “Disagreed” or “Strongly disagreed” that a patient must meet UM criteria for admission. Forty-two percent had “Frequent” different opinions regarding patient management than the consulting provider.

Triagist and current provider communication practices varied widely across AMCs (Figure). There was significant variability in verbal communication (P = .02), with >70% of respondents at two AMCs reporting verbal communication at least half the time, but <30% reporting this frequency at two other AMCs. Respondents reported variable use of electronic communication (ie, notes/orders in the electronic health record) across AMCs (P < .0001). Half of the hospitalists use it “Seldom”, 20% use it “Occasionally”, and 23% use it “Frequently”.

The practice of evaluating patients in person also varied significantly across AMCs (P < .0001, Figure). Across hospitalists, only 28% see patients in person about “Half-the-Time” or more.

 

Differences within AMCs

Variability within AMCs was greatest for the rate of verbal communication practices, with a typical interquartile range (IQR) of 20% to 90% among the hospitalists within a given AMC and for the rate of electronic communication with a typical IQR of 0% to 50%. For other survey questions, the IQR was typically 15 to 20 percentage points.

Thematic Analysis

We received 207 and 203 responses (88% and 86%, respectively) to the open-ended questions “What qualities does an effective triagist have?’ and ‘What qualities make a triagist ineffective?” We identified 22 themes for effective and ineffective qualities, which were grouped into seven domains (Table). All themes had at least three mentions by respondents. The three most frequently mentioned themes, communication skills, efficiency, and systems knowledge, had greater than 60 mentions.

DISCUSSION

Our study of the triagist role at 10 AMCs describes critical triagist functions and identifies key findings across and within AMCs. Twenty-eight percent of hospitalists reported admitting patients even when the patient did not meet the admission criteria, consistent with previous research demonstrating the influence of factors other than clinical disease severity on triage decisions.¹⁰ However, preventable admissions remain a hospital-level quality metric.^11,12 Triagists must often balance each patient’s circumstances with the complexities of the system. Juggling the competing demands of the system while providing patient-centered care can be challenging and may explain why attending physicians are more frequently filling this role.¹³

Local context/culture is likely to play a role in the variation across sites; however, compensation for the time spent may also be a factor. If triage activities are not reimbursable, this could lead to less documentation and a lower likelihood that patients are evaluated in person.¹⁴ This reason may also explain why all hospitalists were required to serve as a triagist at most sites.

Currently, no consensus definition of the triagist role has been developed. Our results demonstrate that this role is heterogeneous and grounded in the local healthcare system practices. We propose the following working definition of the triagist: a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting. A triagist should be equipped with a skill set that includes not only clinical knowledge but also emphasizes systems knowledge, awareness of others’ goals, efficiency, an ability to communicate effectively, and the knowledge of UM. We recommend that medical directors of hospitalist programs focus their attention on locally specific, systems-based skills development when orienting new hospitalists. The financial aspects of cost should be considered and delineated as well.

Our analysis is limited in several respects. Participant AMCs were not randomly chosen, but do represent a broad array of facility types, group size, and geographic regions. The low response rates at some AMCs may result in an inaccurate representation of those sites. Data was not obtained on hospitalists that did not respond to the survey; therefore, nonresponse bias may affect outcomes. This research used self-report rather than direct observation, which could be subject to recall and social desirability bias. Finally, our results may not be generalizable to nonacademic institutions.

 

CONCLUSION

The hospitalist role as triagist at AMCs emphasizes communication, organizational skills, efficiency, systems-based practice, and UM knowledge. Although we found significant variation across and within AMCs, internal transfer activities were common across programs. Hospitalist programs should focus on systems-based skills development to prepare hospitalists for the role. The skill set necessary for triagist responsibilities also has implications for internal medicine resident education.⁴ With increasing emphasis on value and system effectiveness in care delivery, further studies of the triagist role should be undertaken.

Acknowledgments

The TRIAGIST Collaborative Group consists of: Maralyssa Bann, MD, Andrew White, MD (University of Washington); Jagriti Chadha, MD (University of Kentucky); Joel Boggan, MD (Duke University); Sherwin Hsu, MD (UCLA); Jeff Liao, MD (Harvard Medical School); Tabatha Matthias, DO (University of Nebraska Medical Center); Tresa McNeal, MD (Scott and White Texas A&M); Roxana Naderi, MD, Khooshbu Shah, MD (University of Colorado); David Schmit, MD (University of Texas Health San Antonio); Manivannan Veerasamy, MD (Michigan State University).

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.

Hospital medicine has grown dramatically over the past 20 years.^1,2 A recent survey regarding hospitalists’ clinical roles showed an expansion to triaging emergency department (ED) medical admissions and transfers from outside hospitals.³ From the hospitalist perspective, triaging involves the evaluation of patients for potential admission.⁴ With scrutiny on ED metrics, such as wait times (https://www.medicare.gov/hospitalcompare/search.html), health system administrators have heightened expectations for efficient patient flow, which increasingly falls to hospitalists.^5-7

Despite the growth in hospitalists’ triagist activities, there has been little formal assessment of their role. We hypothesized that this role differs from inpatient care in significant ways.^6-8 We sought to describe the triagist role in adult academic inpatient medicine settings to understand the responsibilities and skill set required.

METHODS

Ten academic medical center (AMC) sites were recruited from Research Committee session attendees at the 2014 Society of Hospital Medicine national meeting and the 2014 Society of General Internal Medicine southern regional meeting. The AMCs were geographically diverse: three Western, two Midwestern, two Southern, one Northeastern, and two Southeastern. Site representatives were identified and completed a web-based questionnaire about their AMC (see Appendix 1 for the information collected). Clarifications regarding survey responses were performed via conference calls between the authors (STV, ESW) and site representatives.

Hospitalist Survey

In January 2018, surveys were sent to 583 physicians who worked as triagists. Participants received an anonymous 28-item RedCap survey by e-mail and were sent up to five reminder e-mails over six weeks (see Appendix 2 for the questions analyzed in this paper). Respondents were given the option to be entered in a gift card drawing.

Demographic information and individual workflow/practices were obtained. A 5-point Likert scale (strongly disagree – strongly agree) was used to assess hospitalists’ concurrence with current providers (eg, ED, clinic providers) regarding the management and whether patients must meet the utilization management (UM) criteria for admission. Time estimates used 5% increments and were categorized into four frequency categories based on the local modes provided in responses: Seldom (0%-10%), Occasional (15%-35%), Half-the-Time (40%-60%), and Frequently (65%-100%). Free text responses on effective/ineffective triagist qualities were elicited. Responses were included for analysis if at least 70% of questions were completed.

Data Analysis

Quantitative

Descriptive statistics were calculated for each variable. The Kruskal-Wallis test was used to evaluate differences across AMCs in the time spent on in-person evaluation and communication. Weighting, based on the ratio of hospitalists to survey respondents at each AMC, was used to calculate the average institutional percentages across the study sample.

 

Qualitative

Responses to open-ended questions were analyzed using thematic analysis.⁹ Three independent reviewers (STV, JC, ESW) read, analyzed, and grouped the responses by codes. Codes were then assessed for overlap and grouped into themes by one reviewer (STV). A table of themes with supporting quotes and the number of mentions was subsequently developed by all three reviewers. Similar themes were combined to create domains. The domains were reviewed by the steering committee members to create a consensus description (Appendix 3).

The University of Texas Health San Antonio’s Institutional Review Board and participating institutions approved the study as exempt.

RESULTS

Site Characteristics

Representatives from 10 AMCs reported data on a range of one to four hospitals for a total of 22 hospitals. The median reported that the number of medical patients admitted in a 24-hour period was 31-40 (range, 11-20 to >50). The median group size of hospitalists was 41-50 (range, 0-10 to >70).

The survey response rate was 40% (n = 235), ranging from 9%-70% between institutions. Self-identified female hospitalists accounted for 52% of respondents. Four percent were 25-29 years old, 66% were 30-39 years old, 24% were 40-49 years old, and 6% were ≥50 years old. The average clinical time spent as a triagist was 16%.

Description of Triagist Activities

The activities identified by the majority of respondents across all sites included transferring patients within the hospital (73%), and assessing/approving patient transfers from outside hospitals and clinics (82%). Internal transfer activities reported by >50% of respondents included allocating patients within the hospital or bed capacity coordination, assessing intensive care unit transfers, assigning ED admissions, and consulting other services. The ED accounted for an average of 55% of calls received. Respondents also reported being involved with the documentation related to these activities.

Similarities and Differences across AMCs

Two AMCs did not have a dedicated triagist; instead, physicians supervised residents and advanced practice providers. Among the eight sites with triagists, triaging was predominantly done by faculty physicians contacted via pagers. At seven of these sites, 100% of hospitalists worked as triagists. The triage service was covered by faculty physicians from 8-24 hours per day.

Bed boards and transfer centers staffed by registered nurses, nurse coordinators, house supervisors, or physicians were common support systems, though this infrastructure was organized differently across institutions. A UM review before admission was performed at three institutions 24 hours/day. The remaining institutions reviewed patients retrospectively.

Twenty-eight percent of hospitalists across all sites “Disagreed” or “Strongly disagreed” that a patient must meet UM criteria for admission. Forty-two percent had “Frequent” different opinions regarding patient management than the consulting provider.

Triagist and current provider communication practices varied widely across AMCs (Figure). There was significant variability in verbal communication (P = .02), with >70% of respondents at two AMCs reporting verbal communication at least half the time, but <30% reporting this frequency at two other AMCs. Respondents reported variable use of electronic communication (ie, notes/orders in the electronic health record) across AMCs (P < .0001). Half of the hospitalists use it “Seldom”, 20% use it “Occasionally”, and 23% use it “Frequently”.

The practice of evaluating patients in person also varied significantly across AMCs (P < .0001, Figure). Across hospitalists, only 28% see patients in person about “Half-the-Time” or more.

 

Differences within AMCs

Variability within AMCs was greatest for the rate of verbal communication practices, with a typical interquartile range (IQR) of 20% to 90% among the hospitalists within a given AMC and for the rate of electronic communication with a typical IQR of 0% to 50%. For other survey questions, the IQR was typically 15 to 20 percentage points.

Thematic Analysis

We received 207 and 203 responses (88% and 86%, respectively) to the open-ended questions “What qualities does an effective triagist have?’ and ‘What qualities make a triagist ineffective?” We identified 22 themes for effective and ineffective qualities, which were grouped into seven domains (Table). All themes had at least three mentions by respondents. The three most frequently mentioned themes, communication skills, efficiency, and systems knowledge, had greater than 60 mentions.

DISCUSSION

Our study of the triagist role at 10 AMCs describes critical triagist functions and identifies key findings across and within AMCs. Twenty-eight percent of hospitalists reported admitting patients even when the patient did not meet the admission criteria, consistent with previous research demonstrating the influence of factors other than clinical disease severity on triage decisions.¹⁰ However, preventable admissions remain a hospital-level quality metric.^11,12 Triagists must often balance each patient’s circumstances with the complexities of the system. Juggling the competing demands of the system while providing patient-centered care can be challenging and may explain why attending physicians are more frequently filling this role.¹³

Local context/culture is likely to play a role in the variation across sites; however, compensation for the time spent may also be a factor. If triage activities are not reimbursable, this could lead to less documentation and a lower likelihood that patients are evaluated in person.¹⁴ This reason may also explain why all hospitalists were required to serve as a triagist at most sites.

Currently, no consensus definition of the triagist role has been developed. Our results demonstrate that this role is heterogeneous and grounded in the local healthcare system practices. We propose the following working definition of the triagist: a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting. A triagist should be equipped with a skill set that includes not only clinical knowledge but also emphasizes systems knowledge, awareness of others’ goals, efficiency, an ability to communicate effectively, and the knowledge of UM. We recommend that medical directors of hospitalist programs focus their attention on locally specific, systems-based skills development when orienting new hospitalists. The financial aspects of cost should be considered and delineated as well.

Our analysis is limited in several respects. Participant AMCs were not randomly chosen, but do represent a broad array of facility types, group size, and geographic regions. The low response rates at some AMCs may result in an inaccurate representation of those sites. Data was not obtained on hospitalists that did not respond to the survey; therefore, nonresponse bias may affect outcomes. This research used self-report rather than direct observation, which could be subject to recall and social desirability bias. Finally, our results may not be generalizable to nonacademic institutions.

 

CONCLUSION

The hospitalist role as triagist at AMCs emphasizes communication, organizational skills, efficiency, systems-based practice, and UM knowledge. Although we found significant variation across and within AMCs, internal transfer activities were common across programs. Hospitalist programs should focus on systems-based skills development to prepare hospitalists for the role. The skill set necessary for triagist responsibilities also has implications for internal medicine resident education.⁴ With increasing emphasis on value and system effectiveness in care delivery, further studies of the triagist role should be undertaken.

Acknowledgments

The TRIAGIST Collaborative Group consists of: Maralyssa Bann, MD, Andrew White, MD (University of Washington); Jagriti Chadha, MD (University of Kentucky); Joel Boggan, MD (Duke University); Sherwin Hsu, MD (UCLA); Jeff Liao, MD (Harvard Medical School); Tabatha Matthias, DO (University of Nebraska Medical Center); Tresa McNeal, MD (Scott and White Texas A&M); Roxana Naderi, MD, Khooshbu Shah, MD (University of Colorado); David Schmit, MD (University of Texas Health San Antonio); Manivannan Veerasamy, MD (Michigan State University).

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.

Background: Acute pulmonary embolism is a common cause of morbidity and mortality in older adults, and IVC filters have historically and frequently been used to prevent subsequent PE. Almost one in six elderly Medicare fee-for-service (FFS) beneficiaries with PE currently receives an IVC filter.

Study design: Retrospective, matched cohort study.

Setting: United States inpatients during 2011-2014.

Synopsis: Of 214,579 Medicare FFS patients aged 65 years or older who were hospitalized for acute PE, 13.4% received an IVC filter. Mortality was higher in those receiving an IVC filter (11.6%), compared with those who did not receive an IVC filter (9.3%), with an adjusted odds ratio of 30-day mortality of 1.02 (95% CI, 0.98-1.06). One-year mortality rates were 20.5% in the IVC filter group and 13.4% in the group with no IVC filter, with an adjusted OR of 1.35 (95% CI, 1.31-1.40).

In the 76,198 Medicare FFS patients hospitalized with acute PE in the matched cohort group, 18.2% received an IVC filter. The IVC-filter group had higher odds for 30-day mortality, compared with the no–IVC filter group (OR, 2.19; 95% CI, 2.06-2.33).

Bottom line: In patients aged 65 years or older, use caution when considering IVC filter placement for prevention of subsequent PE. Future studies across patient subgroups are needed to analyze the safety and value of IVC filters.

Citation: Bikdeli B et al. Association of inferior vena cava filter use with mortality rates in older adults with acute pulmonary embolism. JAMA Intern Med. 2019;179(2):263-5.

Dr. Trammell Velasquez is an associate professor of medicine in the division of general and hospital medicine at UT Health San Antonio and a hospitalist at South Texas Veterans Health Care System.

Background: Acute pulmonary embolism is a common cause of morbidity and mortality in older adults, and IVC filters have historically and frequently been used to prevent subsequent PE. Almost one in six elderly Medicare fee-for-service (FFS) beneficiaries with PE currently receives an IVC filter.

Study design: Retrospective, matched cohort study.

Setting: United States inpatients during 2011-2014.

Synopsis: Of 214,579 Medicare FFS patients aged 65 years or older who were hospitalized for acute PE, 13.4% received an IVC filter. Mortality was higher in those receiving an IVC filter (11.6%), compared with those who did not receive an IVC filter (9.3%), with an adjusted odds ratio of 30-day mortality of 1.02 (95% CI, 0.98-1.06). One-year mortality rates were 20.5% in the IVC filter group and 13.4% in the group with no IVC filter, with an adjusted OR of 1.35 (95% CI, 1.31-1.40).

In the 76,198 Medicare FFS patients hospitalized with acute PE in the matched cohort group, 18.2% received an IVC filter. The IVC-filter group had higher odds for 30-day mortality, compared with the no–IVC filter group (OR, 2.19; 95% CI, 2.06-2.33).

Bottom line: In patients aged 65 years or older, use caution when considering IVC filter placement for prevention of subsequent PE. Future studies across patient subgroups are needed to analyze the safety and value of IVC filters.

Citation: Bikdeli B et al. Association of inferior vena cava filter use with mortality rates in older adults with acute pulmonary embolism. JAMA Intern Med. 2019;179(2):263-5.

Dr. Trammell Velasquez is an associate professor of medicine in the division of general and hospital medicine at UT Health San Antonio and a hospitalist at South Texas Veterans Health Care System.

Background: Acute pulmonary embolism is a common cause of morbidity and mortality in older adults, and IVC filters have historically and frequently been used to prevent subsequent PE. Almost one in six elderly Medicare fee-for-service (FFS) beneficiaries with PE currently receives an IVC filter.

Study design: Retrospective, matched cohort study.

Setting: United States inpatients during 2011-2014.

Synopsis: Of 214,579 Medicare FFS patients aged 65 years or older who were hospitalized for acute PE, 13.4% received an IVC filter. Mortality was higher in those receiving an IVC filter (11.6%), compared with those who did not receive an IVC filter (9.3%), with an adjusted odds ratio of 30-day mortality of 1.02 (95% CI, 0.98-1.06). One-year mortality rates were 20.5% in the IVC filter group and 13.4% in the group with no IVC filter, with an adjusted OR of 1.35 (95% CI, 1.31-1.40).

In the 76,198 Medicare FFS patients hospitalized with acute PE in the matched cohort group, 18.2% received an IVC filter. The IVC-filter group had higher odds for 30-day mortality, compared with the no–IVC filter group (OR, 2.19; 95% CI, 2.06-2.33).

Bottom line: In patients aged 65 years or older, use caution when considering IVC filter placement for prevention of subsequent PE. Future studies across patient subgroups are needed to analyze the safety and value of IVC filters.

Citation: Bikdeli B et al. Association of inferior vena cava filter use with mortality rates in older adults with acute pulmonary embolism. JAMA Intern Med. 2019;179(2):263-5.

Dr. Trammell Velasquez is an associate professor of medicine in the division of general and hospital medicine at UT Health San Antonio and a hospitalist at South Texas Veterans Health Care System.

Background: Active cancer places patients at increased risk for VTE. Ambulatory patients can be risk stratified using the validated Khorana score to assess risk for VTE, a complication resulting in significant morbidity, mortality, and health care costs.

Study design: Randomized, placebo-­controlled, double-blind clinical trial.

Setting: Ambulatory; Canada.

Synopsis: A total of 1,809 patients were assessed for eligibility, 1,235 were excluded, and 574 with a Khorana score of 2 or higher were randomized to apixaban 2.5 mg twice daily or placebo. Treatment or placebo was given within 24 hours after the initiation of chemotherapy and continued for 180 days. The primary efficacy outcome – first episode of major VTE within 180 days of randomization – occurred in 4.2% of the apixaban group and in 10.2% of the placebo group (hazard ratio, 0.41; 95% confidence interval, 0.26-0.65; P less than .001). Major bleeding in the modified intention-to-treat analysis occurred in 3.5% in the apixaban group and 1.8% in the placebo group (HR, 2.00; 95% CI, 1.01-3.95; P = .046).

There was no significant difference in overall survival, with 87% of deaths were related to cancer or cancer progression.

Bottom line: VTE is significantly lower with the use of apixaban, compared with placebo, in intermediate- to high-risk ambulatory patients with active cancer who are initiating chemotherapy.

Citation: Carrier M et al. Apixaban to prevent venous thromboembolism in patients with cancer. N Engl J Med. 2018 Dec 4. doi: 10.1056/NEJMoa1814468.
 

Dr. Trammell Velasquez is an associate professor of medicine in the division of general and hospital medicine at UT Health San Antonio and a hospitalist at South Texas Veterans Health Care System.

Background: Active cancer places patients at increased risk for VTE. Ambulatory patients can be risk stratified using the validated Khorana score to assess risk for VTE, a complication resulting in significant morbidity, mortality, and health care costs.

Study design: Randomized, placebo-­controlled, double-blind clinical trial.

Setting: Ambulatory; Canada.

Synopsis: A total of 1,809 patients were assessed for eligibility, 1,235 were excluded, and 574 with a Khorana score of 2 or higher were randomized to apixaban 2.5 mg twice daily or placebo. Treatment or placebo was given within 24 hours after the initiation of chemotherapy and continued for 180 days. The primary efficacy outcome – first episode of major VTE within 180 days of randomization – occurred in 4.2% of the apixaban group and in 10.2% of the placebo group (hazard ratio, 0.41; 95% confidence interval, 0.26-0.65; P less than .001). Major bleeding in the modified intention-to-treat analysis occurred in 3.5% in the apixaban group and 1.8% in the placebo group (HR, 2.00; 95% CI, 1.01-3.95; P = .046).

There was no significant difference in overall survival, with 87% of deaths were related to cancer or cancer progression.

Bottom line: VTE is significantly lower with the use of apixaban, compared with placebo, in intermediate- to high-risk ambulatory patients with active cancer who are initiating chemotherapy.

Citation: Carrier M et al. Apixaban to prevent venous thromboembolism in patients with cancer. N Engl J Med. 2018 Dec 4. doi: 10.1056/NEJMoa1814468.
 

Dr. Trammell Velasquez is an associate professor of medicine in the division of general and hospital medicine at UT Health San Antonio and a hospitalist at South Texas Veterans Health Care System.

Background: Active cancer places patients at increased risk for VTE. Ambulatory patients can be risk stratified using the validated Khorana score to assess risk for VTE, a complication resulting in significant morbidity, mortality, and health care costs.

Study design: Randomized, placebo-­controlled, double-blind clinical trial.

Setting: Ambulatory; Canada.

Synopsis: A total of 1,809 patients were assessed for eligibility, 1,235 were excluded, and 574 with a Khorana score of 2 or higher were randomized to apixaban 2.5 mg twice daily or placebo. Treatment or placebo was given within 24 hours after the initiation of chemotherapy and continued for 180 days. The primary efficacy outcome – first episode of major VTE within 180 days of randomization – occurred in 4.2% of the apixaban group and in 10.2% of the placebo group (hazard ratio, 0.41; 95% confidence interval, 0.26-0.65; P less than .001). Major bleeding in the modified intention-to-treat analysis occurred in 3.5% in the apixaban group and 1.8% in the placebo group (HR, 2.00; 95% CI, 1.01-3.95; P = .046).

There was no significant difference in overall survival, with 87% of deaths were related to cancer or cancer progression.

Bottom line: VTE is significantly lower with the use of apixaban, compared with placebo, in intermediate- to high-risk ambulatory patients with active cancer who are initiating chemotherapy.

Citation: Carrier M et al. Apixaban to prevent venous thromboembolism in patients with cancer. N Engl J Med. 2018 Dec 4. doi: 10.1056/NEJMoa1814468.
 

Dr. Trammell Velasquez is an associate professor of medicine in the division of general and hospital medicine at UT Health San Antonio and a hospitalist at South Texas Veterans Health Care System.

Case

A 42-year-old man with a history of posttraumatic stress disorder (PTSD), hypertension, and alcohol use disorder (AUD) presents to the ED requesting alcohol detoxification. He has had six admissions in the last six months for alcohol detoxification. Two years ago, the patient had a documented alcohol withdrawal seizure. His last drink was eight hours ago, and he currently drinks a liter of vodka a day. On exam, his pulse rate is 126 bpm, and his blood pressure is 162/91 mm Hg. He appears anxious and has bilateral hand tremors. His serum ethanol level is 388.6 mg/dL.

Overview

DSM-5 integrated alcohol abuse and alcohol dependence that were previously classified in DSM-IV into AUDs with mild, moderate, and severe subclassifications. AUDs are the most serious substance abuse problem in the U.S. In the general population, the lifetime prevalence of alcohol abuse is 17.8% and of alcohol dependence is 12.5%.1–3 One study estimates that 24% of adult patients brought to the ED by ambulance suffer from alcoholism, and approximately 10% to 32% of hospitalized medical patients have an AUD.4–8 Patients who stop drinking will develop alcohol withdrawal as early as six hours after their last drink (see Figure 1). The majority of patients at risk of alcohol withdrawal syndrome (AWS) will develop only minor uncomplicated symptoms, but up to 20% will develop symptoms associated with complicated AWS, including withdrawal seizures and delirium tremens (DT).9 It is not entirely clear why some individuals suffer from more severe withdrawal symptoms than others, but genetic predisposition may play a role.10

DT is a syndrome characterized by agitation, disorientation, hallucinations, and autonomic instability (tachycardia, hypertension, hyperthermia, and diaphoresis) in the setting of acute reduction or abstinence from alcohol and is associated with a mortality rate as high as 20%.11 Complicated AWS is associated with increased in-hospital morbidity and mortality, longer lengths of stay, inflated costs of care, increased burden and frustration of nursing and medical staff, and worse cognitive functioning.9 In 80% of cases, the symptoms of uncomplicated alcohol withdrawal do not require aggressive medical intervention and usually disappear within two to seven days of the last drink.12 Physicians making triage decisions for patients who present to the ED in need of detoxification face a difficult dilemma concerning inpatient versus outpatient treatment.

Review of the Data

The literature on both inpatient and outpatient management and treatment of AWS is well-described. Currently, there are no guidelines or consensus on whether to admit patients with alcohol abuse syndromes to the hospital when the request for detoxification is made. Admission should be considered for all patients experiencing alcohol withdrawal who present to the ED.13 Patients with mild AWS may be discharged if they do not require admission for an additional medical condition, but patients experiencing moderate to severe withdrawal require admission for monitoring and treatment. Many physicians use a simple assessment of past history of DT and pulse rate, which may be easily evaluated in clinical settings, to readily identify patients who are at high risk of developing DT during an alcohol dependence period.14

Since 1978, the Clinical Institute Withdrawal Assessment for Alcohol (CIWA) has been consistently used for both monitoring patients with alcohol withdrawal and for making an initial assessment. CIWA-Ar was developed as a revised scale and is frequently used to monitor the severity of ongoing alcohol withdrawal and the response to treatment for the clinical care of patients in alcohol withdrawal (see Figure 2). CIWA-Ar was not developed to identify patients at risk for AWS but is frequently used to determine if patients require admission to the hospital for detoxification.15 Patients with CIWA-Ar scores > 15 require inpatient detoxification. Patients with scores between 8 and 15 should be admitted if they have a history of prior seizures or DT but could otherwise be considered for outpatient detoxification. Patients with scores < 8, which are considered mild alcohol withdrawal, can likely be safely treated as outpatients unless they have a history of DT or alcohol withdrawal seizures.16 Because symptoms of severe alcohol withdrawal are often not present for more than six hours after the patient’s last drink, or often longer, CIWA-Ar is limited and does not identify patients who are otherwise at high risk for complicated withdrawal. A protocol was developed incorporating the patient’s history of alcohol withdrawal seizure, DT, and the CIWA to evaluate the outcome of outpatient versus inpatient detoxification.16

 

 

The most promising tool to screen patients for AWS was developed recently by researchers at Stanford University in Stanford, Calif., using an extensive systematic literature search to identify evidence-based clinical factors associated with the development of AWS.15 The Prediction of Alcohol Withdrawal Severity Scale (PAWSS) was subsequently constructed from 10 items correlating with complicated AWS (see Figure 3). When using a PAWSS score cutoff of ≥ 4, the predictive value of identifying a patient who is at risk for complicated withdrawal is significantly increased to 93.1%. This tool has only been used in medically ill patients but could be extrapolated for use in patients who present to an acute-care setting requesting inpatient detoxification.

 

Patients presenting to the ED with alcohol withdrawal seizures have been shown to have an associated 35% risk of progression to DT when found to have a low platelet count, low blood pyridoxine, and a high blood level of homocysteine. In another retrospective cohort study in Hepatology, three clinical features were identified to be associated with an increased risk for DT: alcohol dependence, a prior history of DT, and a higher pulse rate at admission (> 100 bpm).14

Instructions for the assessment of the patient who requests detoxification are as follows:

1. A patient whose last drink of alcohol was more than five days ago and who shows no signs of withdrawal is unlikely to develop significant withdrawal symptoms and does not require inpatient detoxification.
2. Other medical and psychiatric conditions should be evaluated for admission including alcohol use disorder complications.
3. Calculate CIWA-Ar score:

   Scores < 8 may not need detoxification; consider calculating PAWSS score.

   Scores of 8 to 15 without symptoms of DT or seizures can be treated as an outpatient detoxification if no contraindication.

   Scores of ≥ 15 should be admitted to the hospital.

4. Calculate PAWSS score:

   Scores ≥ 4 suggest high risk for moderate to severe complicated AWS, and admission should be considered.

   Scores < 4 suggest lower risk for complicated AWS, and outpatient treatment should be considered if patients do not have a medical or surgical diagnosis requiring admission.

Back to the Case

At the time of his presentation, the patient was beginning to show signs of early withdrawal symptoms, including tremor and tachycardia, despite having an elevated blood alcohol level. This patient had a PAWSS score of 6, placing him at increased risk of complicated AWS, and a CIWA-Ar score of 13. He was subsequently admitted to the hospital, and symptom-triggered therapy for treatment of his alcohol withdrawal was used. The patient’s CIWA-Ar score peaked at 21 some 24 hours after his last drink. The patient otherwise had an uncomplicated four-day hospital course due to persistent nausea.

Bottom Line

Hospitalists unsure of which patients should be admitted for alcohol detoxification can use the PAWSS tool and an initial CIWA-Ar score to help determine a patient’s risk for developing complicated AWS. TH

---

Dr. Velasquez and Dr. Kornsawad are assistant professors and hospitalists at the University of Texas Health Science Center at San Antonio. Dr. Velasquez also serves as assistant professor and hospitalist at the South Texas Veterans Health Care System serving the San Antonio area.

References

1. Grant BF, Stinson FS, Dawson DA, et al. Prevalence and co-occurrence of substance use disorder and independent mood and anxiety disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2004;61(8):807-816.
2. Lieber CS. Medical disorders of alcoholism. N Engl J Med. 1995;333(16):1058-1065.
3. Hasin SD, Stinson SF, Ogburn E, Grant BF. Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64(7):830-842.
4. Whiteman PJ, Hoffman RS, Goldfrank LR. Alcoholism in the emergency department: an epidemiologic study. Acad Emerg Med. 2000;7(1):14-20.
5. Nielson SD, Storgarrd H, Moesgarrd F, Gluud C. Prevalence of alcohol problems among adult somatic in-patients of a Copenhagen hospital. Alcohol Alcohol. 1994;29(5):583-590.
6. Smothers BA, Yahr HT, Ruhl CE. Detection of alcohol use disorders in general hospital admissions in the United States. Arch Intern Med. 2004;164(7):749-756.
7. Dolman JM, Hawkes ND. Combining the audit questionnaire and biochemical markers to assess alcohol use and risk of alcohol withdrawal in medical inpatients. Alcohol Alcohol. 2005;40(6):515-519.
8. Doering-Silveira J, Fidalgo TM, Nascimento CL, et al. Assessing alcohol dependence in hospitalized patients. Int J Environ Res Public Health. 2014;11(6):5783-5791.
9. Maldonado JR, Sher Y, Das S, et al. Prospective validation study of the prediction of alcohol withdrawal severity scale (PAWSS) in medically ill inpatients: a new scale for the prediction of complicated alcohol withdrawal syndrome. Alcohol Alcohol. 2015;50(5):509-518.
10. Saitz R, O’Malley SS. Pharmacotherapies for alcohol abuse. Withdrawal and treatment. Med Clin North Am. 1997;81(4):881-907.
11. Turner RC, Lichstein PR, Pedan Jr JG, Busher JT, Waivers LE. Alcohol withdrawal syndromes: a review of pathophysiology, clinical presentation, and treatment. J Gen Intern Med. 1989;4(5):432-444.
12. Schuckit MA. Alcohol-use disorders. Lancet. 2009;373(9662):492-501.
13. Stehman CR, Mycyk MB. A rational approach to the treatment of alcohol withdrawal in the ED. Am J Emerg Med. 2013;31(4):734-742.
14. Lee JH, Jang MK, Lee JY, et al. Clinical predictors for delirium tremens in alcohol dependence. J Gastroenterol Hepatol. 2005;20(12):1833-1837.
15. Maldonado JR, Sher Y, Ashouri JF, et al. The “prediction of alcohol withdrawal severity scale” (PAWSS): systematic literature review and pilot study of a new scale for the prediction of complicated alcohol withdrawal syndrome. Alcohol. 2014;48(4):375-390.
16. Stephens JR, Liles AE, Dancel R, Gilchrist M, Kirsch J, DeWalt DA. Who needs inpatient detox? Development and implementation of a hospitalist protocol for the evaluation of patients for alcohol detoxification. J Gen Intern Med. 2014;29(4):587-593.

 

Case

A 42-year-old man with a history of posttraumatic stress disorder (PTSD), hypertension, and alcohol use disorder (AUD) presents to the ED requesting alcohol detoxification. He has had six admissions in the last six months for alcohol detoxification. Two years ago, the patient had a documented alcohol withdrawal seizure. His last drink was eight hours ago, and he currently drinks a liter of vodka a day. On exam, his pulse rate is 126 bpm, and his blood pressure is 162/91 mm Hg. He appears anxious and has bilateral hand tremors. His serum ethanol level is 388.6 mg/dL.

Overview

DSM-5 integrated alcohol abuse and alcohol dependence that were previously classified in DSM-IV into AUDs with mild, moderate, and severe subclassifications. AUDs are the most serious substance abuse problem in the U.S. In the general population, the lifetime prevalence of alcohol abuse is 17.8% and of alcohol dependence is 12.5%.1–3 One study estimates that 24% of adult patients brought to the ED by ambulance suffer from alcoholism, and approximately 10% to 32% of hospitalized medical patients have an AUD.4–8 Patients who stop drinking will develop alcohol withdrawal as early as six hours after their last drink (see Figure 1). The majority of patients at risk of alcohol withdrawal syndrome (AWS) will develop only minor uncomplicated symptoms, but up to 20% will develop symptoms associated with complicated AWS, including withdrawal seizures and delirium tremens (DT).9 It is not entirely clear why some individuals suffer from more severe withdrawal symptoms than others, but genetic predisposition may play a role.10

DT is a syndrome characterized by agitation, disorientation, hallucinations, and autonomic instability (tachycardia, hypertension, hyperthermia, and diaphoresis) in the setting of acute reduction or abstinence from alcohol and is associated with a mortality rate as high as 20%.11 Complicated AWS is associated with increased in-hospital morbidity and mortality, longer lengths of stay, inflated costs of care, increased burden and frustration of nursing and medical staff, and worse cognitive functioning.9 In 80% of cases, the symptoms of uncomplicated alcohol withdrawal do not require aggressive medical intervention and usually disappear within two to seven days of the last drink.12 Physicians making triage decisions for patients who present to the ED in need of detoxification face a difficult dilemma concerning inpatient versus outpatient treatment.

Review of the Data

The literature on both inpatient and outpatient management and treatment of AWS is well-described. Currently, there are no guidelines or consensus on whether to admit patients with alcohol abuse syndromes to the hospital when the request for detoxification is made. Admission should be considered for all patients experiencing alcohol withdrawal who present to the ED.13 Patients with mild AWS may be discharged if they do not require admission for an additional medical condition, but patients experiencing moderate to severe withdrawal require admission for monitoring and treatment. Many physicians use a simple assessment of past history of DT and pulse rate, which may be easily evaluated in clinical settings, to readily identify patients who are at high risk of developing DT during an alcohol dependence period.14

Since 1978, the Clinical Institute Withdrawal Assessment for Alcohol (CIWA) has been consistently used for both monitoring patients with alcohol withdrawal and for making an initial assessment. CIWA-Ar was developed as a revised scale and is frequently used to monitor the severity of ongoing alcohol withdrawal and the response to treatment for the clinical care of patients in alcohol withdrawal (see Figure 2). CIWA-Ar was not developed to identify patients at risk for AWS but is frequently used to determine if patients require admission to the hospital for detoxification.15 Patients with CIWA-Ar scores > 15 require inpatient detoxification. Patients with scores between 8 and 15 should be admitted if they have a history of prior seizures or DT but could otherwise be considered for outpatient detoxification. Patients with scores < 8, which are considered mild alcohol withdrawal, can likely be safely treated as outpatients unless they have a history of DT or alcohol withdrawal seizures.16 Because symptoms of severe alcohol withdrawal are often not present for more than six hours after the patient’s last drink, or often longer, CIWA-Ar is limited and does not identify patients who are otherwise at high risk for complicated withdrawal. A protocol was developed incorporating the patient’s history of alcohol withdrawal seizure, DT, and the CIWA to evaluate the outcome of outpatient versus inpatient detoxification.16

 

 

The most promising tool to screen patients for AWS was developed recently by researchers at Stanford University in Stanford, Calif., using an extensive systematic literature search to identify evidence-based clinical factors associated with the development of AWS.15 The Prediction of Alcohol Withdrawal Severity Scale (PAWSS) was subsequently constructed from 10 items correlating with complicated AWS (see Figure 3). When using a PAWSS score cutoff of ≥ 4, the predictive value of identifying a patient who is at risk for complicated withdrawal is significantly increased to 93.1%. This tool has only been used in medically ill patients but could be extrapolated for use in patients who present to an acute-care setting requesting inpatient detoxification.

 

Patients presenting to the ED with alcohol withdrawal seizures have been shown to have an associated 35% risk of progression to DT when found to have a low platelet count, low blood pyridoxine, and a high blood level of homocysteine. In another retrospective cohort study in Hepatology, three clinical features were identified to be associated with an increased risk for DT: alcohol dependence, a prior history of DT, and a higher pulse rate at admission (> 100 bpm).14

Instructions for the assessment of the patient who requests detoxification are as follows:

1. A patient whose last drink of alcohol was more than five days ago and who shows no signs of withdrawal is unlikely to develop significant withdrawal symptoms and does not require inpatient detoxification.
2. Other medical and psychiatric conditions should be evaluated for admission including alcohol use disorder complications.
3. Calculate CIWA-Ar score:

   Scores < 8 may not need detoxification; consider calculating PAWSS score.

   Scores of 8 to 15 without symptoms of DT or seizures can be treated as an outpatient detoxification if no contraindication.

   Scores of ≥ 15 should be admitted to the hospital.

4. Calculate PAWSS score:

   Scores ≥ 4 suggest high risk for moderate to severe complicated AWS, and admission should be considered.

   Scores < 4 suggest lower risk for complicated AWS, and outpatient treatment should be considered if patients do not have a medical or surgical diagnosis requiring admission.

Back to the Case

At the time of his presentation, the patient was beginning to show signs of early withdrawal symptoms, including tremor and tachycardia, despite having an elevated blood alcohol level. This patient had a PAWSS score of 6, placing him at increased risk of complicated AWS, and a CIWA-Ar score of 13. He was subsequently admitted to the hospital, and symptom-triggered therapy for treatment of his alcohol withdrawal was used. The patient’s CIWA-Ar score peaked at 21 some 24 hours after his last drink. The patient otherwise had an uncomplicated four-day hospital course due to persistent nausea.

Bottom Line

Hospitalists unsure of which patients should be admitted for alcohol detoxification can use the PAWSS tool and an initial CIWA-Ar score to help determine a patient’s risk for developing complicated AWS. TH

---

Dr. Velasquez and Dr. Kornsawad are assistant professors and hospitalists at the University of Texas Health Science Center at San Antonio. Dr. Velasquez also serves as assistant professor and hospitalist at the South Texas Veterans Health Care System serving the San Antonio area.

References

1. Grant BF, Stinson FS, Dawson DA, et al. Prevalence and co-occurrence of substance use disorder and independent mood and anxiety disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2004;61(8):807-816.
2. Lieber CS. Medical disorders of alcoholism. N Engl J Med. 1995;333(16):1058-1065.
3. Hasin SD, Stinson SF, Ogburn E, Grant BF. Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64(7):830-842.
4. Whiteman PJ, Hoffman RS, Goldfrank LR. Alcoholism in the emergency department: an epidemiologic study. Acad Emerg Med. 2000;7(1):14-20.
5. Nielson SD, Storgarrd H, Moesgarrd F, Gluud C. Prevalence of alcohol problems among adult somatic in-patients of a Copenhagen hospital. Alcohol Alcohol. 1994;29(5):583-590.
6. Smothers BA, Yahr HT, Ruhl CE. Detection of alcohol use disorders in general hospital admissions in the United States. Arch Intern Med. 2004;164(7):749-756.
7. Dolman JM, Hawkes ND. Combining the audit questionnaire and biochemical markers to assess alcohol use and risk of alcohol withdrawal in medical inpatients. Alcohol Alcohol. 2005;40(6):515-519.
8. Doering-Silveira J, Fidalgo TM, Nascimento CL, et al. Assessing alcohol dependence in hospitalized patients. Int J Environ Res Public Health. 2014;11(6):5783-5791.
9. Maldonado JR, Sher Y, Das S, et al. Prospective validation study of the prediction of alcohol withdrawal severity scale (PAWSS) in medically ill inpatients: a new scale for the prediction of complicated alcohol withdrawal syndrome. Alcohol Alcohol. 2015;50(5):509-518.
10. Saitz R, O’Malley SS. Pharmacotherapies for alcohol abuse. Withdrawal and treatment. Med Clin North Am. 1997;81(4):881-907.
11. Turner RC, Lichstein PR, Pedan Jr JG, Busher JT, Waivers LE. Alcohol withdrawal syndromes: a review of pathophysiology, clinical presentation, and treatment. J Gen Intern Med. 1989;4(5):432-444.
12. Schuckit MA. Alcohol-use disorders. Lancet. 2009;373(9662):492-501.
13. Stehman CR, Mycyk MB. A rational approach to the treatment of alcohol withdrawal in the ED. Am J Emerg Med. 2013;31(4):734-742.
14. Lee JH, Jang MK, Lee JY, et al. Clinical predictors for delirium tremens in alcohol dependence. J Gastroenterol Hepatol. 2005;20(12):1833-1837.
15. Maldonado JR, Sher Y, Ashouri JF, et al. The “prediction of alcohol withdrawal severity scale” (PAWSS): systematic literature review and pilot study of a new scale for the prediction of complicated alcohol withdrawal syndrome. Alcohol. 2014;48(4):375-390.
16. Stephens JR, Liles AE, Dancel R, Gilchrist M, Kirsch J, DeWalt DA. Who needs inpatient detox? Development and implementation of a hospitalist protocol for the evaluation of patients for alcohol detoxification. J Gen Intern Med. 2014;29(4):587-593.

 

Case

A 42-year-old man with a history of posttraumatic stress disorder (PTSD), hypertension, and alcohol use disorder (AUD) presents to the ED requesting alcohol detoxification. He has had six admissions in the last six months for alcohol detoxification. Two years ago, the patient had a documented alcohol withdrawal seizure. His last drink was eight hours ago, and he currently drinks a liter of vodka a day. On exam, his pulse rate is 126 bpm, and his blood pressure is 162/91 mm Hg. He appears anxious and has bilateral hand tremors. His serum ethanol level is 388.6 mg/dL.

Overview

DSM-5 integrated alcohol abuse and alcohol dependence that were previously classified in DSM-IV into AUDs with mild, moderate, and severe subclassifications. AUDs are the most serious substance abuse problem in the U.S. In the general population, the lifetime prevalence of alcohol abuse is 17.8% and of alcohol dependence is 12.5%.1–3 One study estimates that 24% of adult patients brought to the ED by ambulance suffer from alcoholism, and approximately 10% to 32% of hospitalized medical patients have an AUD.4–8 Patients who stop drinking will develop alcohol withdrawal as early as six hours after their last drink (see Figure 1). The majority of patients at risk of alcohol withdrawal syndrome (AWS) will develop only minor uncomplicated symptoms, but up to 20% will develop symptoms associated with complicated AWS, including withdrawal seizures and delirium tremens (DT).9 It is not entirely clear why some individuals suffer from more severe withdrawal symptoms than others, but genetic predisposition may play a role.10

DT is a syndrome characterized by agitation, disorientation, hallucinations, and autonomic instability (tachycardia, hypertension, hyperthermia, and diaphoresis) in the setting of acute reduction or abstinence from alcohol and is associated with a mortality rate as high as 20%.11 Complicated AWS is associated with increased in-hospital morbidity and mortality, longer lengths of stay, inflated costs of care, increased burden and frustration of nursing and medical staff, and worse cognitive functioning.9 In 80% of cases, the symptoms of uncomplicated alcohol withdrawal do not require aggressive medical intervention and usually disappear within two to seven days of the last drink.12 Physicians making triage decisions for patients who present to the ED in need of detoxification face a difficult dilemma concerning inpatient versus outpatient treatment.

Review of the Data

The literature on both inpatient and outpatient management and treatment of AWS is well-described. Currently, there are no guidelines or consensus on whether to admit patients with alcohol abuse syndromes to the hospital when the request for detoxification is made. Admission should be considered for all patients experiencing alcohol withdrawal who present to the ED.13 Patients with mild AWS may be discharged if they do not require admission for an additional medical condition, but patients experiencing moderate to severe withdrawal require admission for monitoring and treatment. Many physicians use a simple assessment of past history of DT and pulse rate, which may be easily evaluated in clinical settings, to readily identify patients who are at high risk of developing DT during an alcohol dependence period.14

Since 1978, the Clinical Institute Withdrawal Assessment for Alcohol (CIWA) has been consistently used for both monitoring patients with alcohol withdrawal and for making an initial assessment. CIWA-Ar was developed as a revised scale and is frequently used to monitor the severity of ongoing alcohol withdrawal and the response to treatment for the clinical care of patients in alcohol withdrawal (see Figure 2). CIWA-Ar was not developed to identify patients at risk for AWS but is frequently used to determine if patients require admission to the hospital for detoxification.15 Patients with CIWA-Ar scores > 15 require inpatient detoxification. Patients with scores between 8 and 15 should be admitted if they have a history of prior seizures or DT but could otherwise be considered for outpatient detoxification. Patients with scores < 8, which are considered mild alcohol withdrawal, can likely be safely treated as outpatients unless they have a history of DT or alcohol withdrawal seizures.16 Because symptoms of severe alcohol withdrawal are often not present for more than six hours after the patient’s last drink, or often longer, CIWA-Ar is limited and does not identify patients who are otherwise at high risk for complicated withdrawal. A protocol was developed incorporating the patient’s history of alcohol withdrawal seizure, DT, and the CIWA to evaluate the outcome of outpatient versus inpatient detoxification.16

 

 

The most promising tool to screen patients for AWS was developed recently by researchers at Stanford University in Stanford, Calif., using an extensive systematic literature search to identify evidence-based clinical factors associated with the development of AWS.15 The Prediction of Alcohol Withdrawal Severity Scale (PAWSS) was subsequently constructed from 10 items correlating with complicated AWS (see Figure 3). When using a PAWSS score cutoff of ≥ 4, the predictive value of identifying a patient who is at risk for complicated withdrawal is significantly increased to 93.1%. This tool has only been used in medically ill patients but could be extrapolated for use in patients who present to an acute-care setting requesting inpatient detoxification.

 

Patients presenting to the ED with alcohol withdrawal seizures have been shown to have an associated 35% risk of progression to DT when found to have a low platelet count, low blood pyridoxine, and a high blood level of homocysteine. In another retrospective cohort study in Hepatology, three clinical features were identified to be associated with an increased risk for DT: alcohol dependence, a prior history of DT, and a higher pulse rate at admission (> 100 bpm).14

Instructions for the assessment of the patient who requests detoxification are as follows:

1. A patient whose last drink of alcohol was more than five days ago and who shows no signs of withdrawal is unlikely to develop significant withdrawal symptoms and does not require inpatient detoxification.
2. Other medical and psychiatric conditions should be evaluated for admission including alcohol use disorder complications.
3. Calculate CIWA-Ar score:

   Scores < 8 may not need detoxification; consider calculating PAWSS score.

   Scores of 8 to 15 without symptoms of DT or seizures can be treated as an outpatient detoxification if no contraindication.

   Scores of ≥ 15 should be admitted to the hospital.

4. Calculate PAWSS score:

   Scores ≥ 4 suggest high risk for moderate to severe complicated AWS, and admission should be considered.

   Scores < 4 suggest lower risk for complicated AWS, and outpatient treatment should be considered if patients do not have a medical or surgical diagnosis requiring admission.

Back to the Case

At the time of his presentation, the patient was beginning to show signs of early withdrawal symptoms, including tremor and tachycardia, despite having an elevated blood alcohol level. This patient had a PAWSS score of 6, placing him at increased risk of complicated AWS, and a CIWA-Ar score of 13. He was subsequently admitted to the hospital, and symptom-triggered therapy for treatment of his alcohol withdrawal was used. The patient’s CIWA-Ar score peaked at 21 some 24 hours after his last drink. The patient otherwise had an uncomplicated four-day hospital course due to persistent nausea.

Bottom Line

Hospitalists unsure of which patients should be admitted for alcohol detoxification can use the PAWSS tool and an initial CIWA-Ar score to help determine a patient’s risk for developing complicated AWS. TH

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Dr. Velasquez and Dr. Kornsawad are assistant professors and hospitalists at the University of Texas Health Science Center at San Antonio. Dr. Velasquez also serves as assistant professor and hospitalist at the South Texas Veterans Health Care System serving the San Antonio area.

References

1. Grant BF, Stinson FS, Dawson DA, et al. Prevalence and co-occurrence of substance use disorder and independent mood and anxiety disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2004;61(8):807-816.
2. Lieber CS. Medical disorders of alcoholism. N Engl J Med. 1995;333(16):1058-1065.
3. Hasin SD, Stinson SF, Ogburn E, Grant BF. Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64(7):830-842.
4. Whiteman PJ, Hoffman RS, Goldfrank LR. Alcoholism in the emergency department: an epidemiologic study. Acad Emerg Med. 2000;7(1):14-20.
5. Nielson SD, Storgarrd H, Moesgarrd F, Gluud C. Prevalence of alcohol problems among adult somatic in-patients of a Copenhagen hospital. Alcohol Alcohol. 1994;29(5):583-590.
6. Smothers BA, Yahr HT, Ruhl CE. Detection of alcohol use disorders in general hospital admissions in the United States. Arch Intern Med. 2004;164(7):749-756.
7. Dolman JM, Hawkes ND. Combining the audit questionnaire and biochemical markers to assess alcohol use and risk of alcohol withdrawal in medical inpatients. Alcohol Alcohol. 2005;40(6):515-519.
8. Doering-Silveira J, Fidalgo TM, Nascimento CL, et al. Assessing alcohol dependence in hospitalized patients. Int J Environ Res Public Health. 2014;11(6):5783-5791.
9. Maldonado JR, Sher Y, Das S, et al. Prospective validation study of the prediction of alcohol withdrawal severity scale (PAWSS) in medically ill inpatients: a new scale for the prediction of complicated alcohol withdrawal syndrome. Alcohol Alcohol. 2015;50(5):509-518.
10. Saitz R, O’Malley SS. Pharmacotherapies for alcohol abuse. Withdrawal and treatment. Med Clin North Am. 1997;81(4):881-907.
11. Turner RC, Lichstein PR, Pedan Jr JG, Busher JT, Waivers LE. Alcohol withdrawal syndromes: a review of pathophysiology, clinical presentation, and treatment. J Gen Intern Med. 1989;4(5):432-444.
12. Schuckit MA. Alcohol-use disorders. Lancet. 2009;373(9662):492-501.
13. Stehman CR, Mycyk MB. A rational approach to the treatment of alcohol withdrawal in the ED. Am J Emerg Med. 2013;31(4):734-742.
14. Lee JH, Jang MK, Lee JY, et al. Clinical predictors for delirium tremens in alcohol dependence. J Gastroenterol Hepatol. 2005;20(12):1833-1837.
15. Maldonado JR, Sher Y, Ashouri JF, et al. The “prediction of alcohol withdrawal severity scale” (PAWSS): systematic literature review and pilot study of a new scale for the prediction of complicated alcohol withdrawal syndrome. Alcohol. 2014;48(4):375-390.
16. Stephens JR, Liles AE, Dancel R, Gilchrist M, Kirsch J, DeWalt DA. Who needs inpatient detox? Development and implementation of a hospitalist protocol for the evaluation of patients for alcohol detoxification. J Gen Intern Med. 2014;29(4):587-593.