doi:10.1093/alcalc/agh167, available online at www.alcalc.oupjournals.org PROPOSAL OF A COMPREHENSIVE CLINICAL TYPOLOGY OF ALCOHOL WITHDRAWAL—A CLUSTER ANALYSIS APPROACH

Abstract — Aims: To characterize the various courses of alcohol withdrawal. Methods: The Alcohol Withdrawal Scale (AWS) was applied to 217 alcohol-dependent patients every 4 h till the symptoms of withdrawal had passed (until each of four consecutive scores were <3). Patients were medicated by a standardized treatment scheme according to AWS-scores. Hierarchical cluster analysis and discriminant analysis were applied. Results: We found five clusters representing increasing severity of alcohol withdrawal. Each cluster is characterized by a combination of the two maximum subscores (vegetative and psychopathological subscore) and three additional psychopathological symptoms (anxiety, disorientation, and hallucination). In 18.4 % of the patients, relevant symptoms were not observed (cluster 1), 18.9 % developed mild or moderate vegetative symptoms only (cluster 2), and 40.6 % additional anxiety (cluster 3). In cluster 4 (11.1%) the most frequent psychopathological symptoms were disorientation and anxiety but no hallucinations, which could be observed only in cluster 5 (11.1%). Discriminant analysis using the maximum subscores at the first day of treatment as independent variables correctly predicted 89.9 % of the five clusters. Conclusions: Our findings support a model of alcohol withdrawal clustering along the two dimensions of vegetative and psychopathological severity. Furthermore, the AWS may be usefu