Terry L. Horton, M.D., FACP, FASAM

Chief, Christiana Care's Division of Addiction Medicine; Medical Director, Project Engage; Associate Service Line Leader, Behavioral Health

Publications

Implementation of a clinical pathway to screen and treat medical inpatients for opioid withdrawal
Implementation of a clinical pathway to screen and treat medical inpatients for opioid withdrawal
Williams KD, Wilson BL, Jurkovitz CT, Melson JA, Reitz JA, Pal CK, Hausman SP, Booker E, Lang LJ, Horton TL

Implement Res Pract ;3():

PMID: 35846074

Abstract

Background: Opioid-related inpatient hospital stays are increasing at alarming rates. Unidentified and poorly treated opioid withdrawal may be associated with inpatients leaving against medical advice and increased health care utilization. To address these concerns, we developed and implemented a clinical pathway to screen and treat medical service inpatients for opioid withdrawal.

Methods: The pathway process included a two-item universal screening instrument to identify opioid withdrawal risk (Opioid Withdrawal Risk Assessment [OWRA]), use of the validated Clinical Opiate Withdrawal Scale (COWS) to monitor opioid withdrawal symptoms and severity, and a 72-h buprenorphine/naloxone-based treatment protocol. Implementation outcomes including adoption, fidelity, and sustainability of this new pathway model were measured. To assess if there were changes in nursing staff acceptability, appropriateness, and adoption of the new pathway process, a cross-sectional survey was administered to pilot four hospital medical units before and after pathway implementation.

Results: Between 2016 and 2018, 72.4% (77,483/107,071) of admitted patients received the OWRA screening tool. Of those, 3.0% (2,347/77,483) were identified at risk for opioid withdrawal. Of those 2,347 patients, 2,178 (92.8%) were assessed with the COWS and 29.6% (645/2,178) were found to be in active withdrawal. A total of 49.5% (319/645) patients were treated with buprenorphine/naloxone. Fifty-seven percent (83/145) of nurses completed both the pre- and post-pathway implementation surveys. Analysis of the pre/post survey data revealed that nurse respondents were more confident in their ability to determine which patients were at risk for withdrawal ( = .01) and identify patients currently experiencing withdrawal ( < .01). However, they cited difficulty working with the patient population and coordinating care with physicians.

Conclusions: Our study demonstrates a process for successfully implementing and sustaining a clinical pathway to screen and treat medical service inpatients for opioid withdrawal. Standardizing care delivery for patients in opioid withdrawal can also improve nursing confidence when working with this complex population.

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Alcohol Withdrawal Rates in Hospitalized Patients During the COVID-19 Pandemic
Alcohol Withdrawal Rates in Hospitalized Patients During the COVID-19 Pandemic
Sharma RA, Subedi K, Gbadebo BM, Wilson B, Jurkovitz C, Horton T

JAMA Netw Open 2021 03;4(3):e210422

PMID: 33656526

Abstract

No abstract available.
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Alcohol Withdrawal Rates in Hospitalized Patients During the COVID-19 Pandemic
Alcohol Withdrawal Rates in Hospitalized Patients During the COVID-19 Pandemic
Sharma RA, Subedi K, Gbadebo BM, Wilson B, Jurkovitz C, Horton T

JAMA Netw Open 2021 03;4(3):e210422

PMID: 33656526

Abstract

No abstract available.
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Alcohol Withdrawal Rates in Hospitalized Patients During the COVID-19 Pandemic
Alcohol Withdrawal Rates in Hospitalized Patients During the COVID-19 Pandemic
Sharma RA, Subedi K, Gbadebo BM, Wilson B, Jurkovitz C, Horton T

JAMA Netw Open 2021 03;4(3):e210422

PMID: 33656526

Abstract

No abstract available.
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Using the AUDIT-PC to predict alcohol withdrawal in hospitalized patients
Using the AUDIT-PC to predict alcohol withdrawal in hospitalized patients
Pecoraro A, Ewen E, Horton T, Mooney R, Kolm P, McGraw P, Woody G

J Gen Intern Med 2014 Jan;29(1):34-40

PMID: 23959745

Abstract

BACKGROUND: Alcohol withdrawal syndrome (AWS) occurs when alcohol-dependent individuals abruptly reduce or stop drinking. Hospitalized alcohol-dependent patients are at risk. Hospitals need a validated screening tool to assess withdrawal risk, but no validated tools are currently available.
OBJECTIVE: To examine the admission Alcohol Use Disorders Identification Test-(Piccinelli) Consumption (AUDIT-PC) ability to predict the subsequent development of AWS among hospitalized medical-surgical patients admitted to a non-intensive care setting.
DESIGN: Retrospective case–control study of patients discharged from the hospital with a diagnosis of AWS. All patients with AWS were classified as presenting with AWS or developing AWS later during admission. Patients admitted to an intensive care setting and those missing AUDIT-PC scores were excluded from analysis. A hierarchical (by hospital unit) logistic regression was performed and receiver-operating characteristics were examined on those developing AWS after admission and randomly selected controls. Because those diagnosing AWS were not blinded to the AUDIT-PC scores, a sensitivity analysis was performed.
PARTICIPANTS: The study cohort included all patients age ≥18 years admitted to any medical or surgical units in a single health care system from 6 October 2009 to 7 October 2010.
KEY RESULTS: After exclusions, 414 patients were identified with AWS. The 223 (53.9 %) who developed AWS after admission were compared to 466 randomly selected controls without AWS. An AUDIT-PC score ≥4 at admission provides 91.0 % sensitivity and 89.7 % specificity (AUC=0.95; 95 % CI, 0.94–0.97) for AWS, and maximizes the correct classification while resulting in 17 false positives for every true positive identified. Performance remained excellent on sensitivity analysis (AUC=0.92; 95 % CI, 0.90–0.93). Increasing AUDIT-PC scores were associated with an increased risk of AWS (OR=1.68, 95 % CI 1.55–1.82, p<0.001).
CONCLUSIONS: The admission AUDIT-PC score is an excellent discriminator of AWS and could be an important component of future clinical prediction rules. Calibration and further validation on a large prospectivecohort is indicated.
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