Predictors of severe alcohol withdrawal syndrome: a systematic review and meta-analysis.

Background: Severity of alcohol withdrawal syndrome (AWS) is associated with hospital mortality and length of stay. However, as there is no consensus regarding how to predict the development of severe alcohol withdrawal syndrome (SAWS), we sought to determine independent predictors of SAWS.

Methods: We conducted a systematic review and meta-analysis of studies evaluating hospitalized patients with AWS versus SAWS-delirium tremens (DT) and/or seizures. Random-effects meta-analysis [PRISMA guidelines] was performed on common baseline variables and predictive effects for development of SAWS were calculated using RevMan v5.2. Funnel plots were constructed, and tests of heterogeneity were performed.

Results: Of 226 studies screened, 17 met criteria and 15 were included in the meta-analysis. The primary findings were that an incident occurrence of DT or alcohol withdrawal seizures was significantly predicted by history of a similar event (OR 2.58 for DT vs. no-DT, 95% CI 1.41, 4.7; OR 2.8 for seizure vs. no-seizure, 95% CI 1.09, 7.19). Both a lower initial platelet count and serum potassium level were predictive of an incident occurrence of DT (platelet count mean difference [MD] -45.64/mm(3) vs. no-DT, 95% CI -75.95, -15.33; potassium level MD -0.26 mEq/l vs. no-DT, 95% CI -0.45, -0.08), seizures, and SAWS. Higher initial alanine aminotransferase was seen in patients with SAWS (MD 20.97 U/l vs. no-SAWS, 95% CI 0.89, 41.05). Higher initial serum gamma-glutamyl transpeptidase was seen in patients with incident alcohol withdrawal seizures (MD 202.56 U/l vs. no-seizure, 95% CI 3.62, 401.5). Significant heterogeneity was observed, and there was evidence of publication bias. Notably, neither gender nor comorbid liver disease was predictive.

Conclusions: The course of prior episodes of AWS is the most reliable predictor of subsequent episodes. Thrombocytopenia and hypokalemia also correlate with SAWS. We propose further research into drinking patterns, gender, and medical comorbidities.