Proposal of a comprehensive clinical typology of alcohol withdrawal - A cluster analysis approach

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 useful to predict the course of alcohol withdrawal already at the first day of treatmenti.