Department of Internal Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Department of Internal Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland; University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland; Center of Competence Multimorbidity, University of Zurich, Zurich, Switzerland.
Drug Alcohol Depend. 2019 Dec 1;205:107708. doi: 10.1016/j.drugalcdep.2019.107708. Epub 2019 Nov 2.
The Wetterling alcohol withdrawal syndrome (AWS) scale determines withdrawal severity and guides treatment. We investigated associations between maximum AWS scores and clinical outcomes.
This retrospective cohort study considered AWS assessments measured from 8/2015-8/2017. We used multivariable linear and logistic regression to analyze associations between the maximum score and increased length of stay (LOS) and in-hospital mortality, respectively. Firstly, we investigated the maximum score of all AWS assessments any time during the stay, secondly, the maximum measured only within the first 3 days of withdrawal.
A total of 2,464 hospital stays showed that, patients with "mild" (<6), "moderate" (6-9), and "severe" (>9) maximum scores had median LOS of 5.93, 9.35, 14.71 days, mortality was 1.7%, 4.8%, 8.0%, respectively. Regression showed that a higher maximum score was independently associated with increased LOS and mortality (both p < 0.001). Based on the maximum AWS score within the first 3 days, the median LOS was 6.18, 9.00, 12.89 days, mortality was 2.2%, 3.6%, 7.6%, respectively. A higher maximum score in the first 3 days was independently associated with increased LOS (p = 0.036) and mortality (p = 0.001). Severe maximum AWS scores within 3 days of withdrawal had an odds ratio of 2.53 (95% CI: 1.27, 4.82; p = 0.0060) for in-hospital death.
Maximum AWS scores associate independently with increased LOS and in-hospital mortality. This association is reproducible within the first 3 days of withdrawal. Development of such a 3-day tool could help clinicians assess the risk of worse clinical outcomes early on and adjust care accordingly.
Wetterling 酒精戒断综合征(AWS)量表可用于确定戒断严重程度并指导治疗。我们研究了最大 AWS 评分与临床结局之间的关系。
这是一项回顾性队列研究,纳入了 2015 年 8 月至 2017 年 8 月期间的 AWS 评估。我们使用多变量线性和逻辑回归分析了最大评分与住院时间延长(LOS)和院内死亡率增加之间的关系。首先,我们调查了住院期间任何时间的 AWS 评估的最大评分;其次,我们只调查了戒断的前 3 天内测量的最大评分。
共有 2464 个住院病例显示,“轻度”(<6)、“中度”(6-9)和“重度”(>9)最大评分患者的 LOS 中位数分别为 5.93、9.35 和 14.71 天,死亡率分别为 1.7%、4.8%和 8.0%。回归显示,较高的最大评分与 LOS 和死亡率增加独立相关(均 P<0.001)。基于前 3 天内的最大 AWS 评分,LOS 中位数分别为 6.18、9.00 和 12.89 天,死亡率分别为 2.2%、3.6%和 7.6%。前 3 天内的较高最大评分与 LOS 增加独立相关(P=0.036)和死亡率增加独立相关(P=0.001)。戒断后 3 天内严重的最大 AWS 评分与院内死亡的比值比为 2.53(95%CI:1.27,4.82;P=0.0060)。
最大 AWS 评分与 LOS 和院内死亡率增加独立相关。这种关联在前 3 天内是可重复的。开发这样一个 3 天工具可以帮助临床医生尽早评估更差的临床结局风险,并相应地调整治疗。