Derivation and validation of a multivariable model, the alcohol withdrawal triage tool (AWTT), for predicting severe alcohol withdrawal syndrome. (1st April 2020)

• Record Type: Journal Article
• Authors: Mahabir, C. Arun; Anderson, Matthew; Cimino, Jamie; Lyden, Elizabeth; Siahpush, Mohammad; Shiffermiller, Jason
• Is Part Of: Drug and alcohol dependence. Volume 209(2020)
• Publication Date: 2020-04-01
• Subjects: Alcohol-related disorders -- Alcohol withdrawal delirium -- Alcohol withdrawal seizures -- Patient admission -- Clinical decision rules; Drug abuse -- Periodicals; Alcoholism -- Periodicals; 616.86
• DOI: 10.1016/j.drugalcdep.2020.107943
• Languages: English
• ISSNs: 0376-8716

Abstract

Highlights: The Alcohol Withdrawal Triage Tool (AWTT) is a novel clinical prediction rule. Severe alcohol withdrawal syndrome (SAWS) developed in 20 % of patients. Eight variables predicted SAWS with sensitivity 90.5 % and specificity 47.2 %. Abstract: Background: Alcohol withdrawal and its consequences are a common concern for the large numbers of patients who present to emergency departments (EDs) with alcohol use disorders. While the majority of patients who go on to develop alcohol withdrawal experience only mild symptoms, a small proportion will experience seizures or delirium tremens. The aim of this study was to develop a tool to predict the need for hospital admission in patients at risk for alcohol withdrawal using only objective criteria that are typically available during the course of an ED visit. Methods: We conducted a retrospective study at an academic medical center. Our primary outcome was severe alcohol withdrawal syndrome (SAWS), which we defined as a composite of delirium tremens, seizure, or use of high benzodiazepine doses. All candidate predictors were abstracted from the electronic health record. A logistic regression model was constructed using the derivation dataset to create the alcohol withdrawal triage tool (AWTT). Results: Of the 2038 study patients, 408 20.0 %) developed SAWS. We identified eight independent predictors of SAWS. Each of the predictors in the regression model was assigned one point. Summing the points for each predictor generated the AWTT score. An AWTT score of 3 or greater was defined as high risk based on sensitivity of 90 % and specificity of 47 % for predicting SAWS. Conclusions: We were able to identify a set of objective, timely, independent predictors of SAWS. The predictors were used to create a novel clinical prediction rule, the AWTT.