Department of Internal Medicine, University of Nebraska Medical Center, 983332 Nebraska Medical Center Omaha, NE 68198-3332 United States.
Center for Collaboration on Research, Design and Analysis, College of Public Health, University of Nebraska Medical Center, 984355 Medical Center, Omaha, NE 68198-4355 United States.
Drug Alcohol Depend. 2020 Apr 1;209:107943. doi: 10.1016/j.drugalcdep.2020.107943. Epub 2020 Feb 27.
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.
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).
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.
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.
酒精戒断及其后果是大量因酒精使用障碍就诊于急诊部（ED）的患者共同关注的问题。虽然大多数出现酒精戒断症状的患者仅表现为轻度症状，但仍有一小部分患者会出现癫痫发作或震颤谵妄。本研究旨在开发一种工具，仅使用 ED 就诊期间通常可获得的客观标准来预测有酒精戒断风险的患者住院的需求。
我们在一家学术医疗中心进行了一项回顾性研究。我们的主要结局是严重酒精戒断综合征（SAWS），我们将其定义为震颤谵妄、癫痫发作或使用高剂量苯二氮䓬的复合症状。所有候选预测因素均从电子健康记录中提取。使用推导数据集构建逻辑回归模型，以创建酒精戒断分诊工具（AWTT）。
在 2038 名研究患者中，有 408 名（20.0%）患者出现 SAWS。我们确定了 8 个独立的 SAWS 预测因素。回归模型中的每个预测因素都被分配了一个分值。为每个预测因素的分值求和，得到 AWTT 评分。根据 SAWS 的敏感性为 90%和特异性为 47%，将 AWTT 评分≥3 定义为高风险。
我们能够识别出一组客观、及时、独立的 SAWS 预测因素。这些预测因素被用于创建一种新的临床预测规则，即 AWTT。
