Introduction/Objective: Alcohol Withdrawal Syndrome (AWS) occurs following the sudden cessation of prolonged alcohol intake and is driven by neuroadaptive changes affecting inhibitory and excitatory neurotransmission. AWS can be categorised into uncomplicated and complicated, with severity influenced by multiple biological and psychosocial factors. The objective of this study was to examine the association between the severity of Alcohol Withdrawal Syndrome (AWS) with consumption characteristics and psychometric predictors. Methods: A cross-sectional study was conducted for a period of 6 months on 87 patients in the psychiatry department of a tertiary care hospital, using a convenience sampling method. Demographic data, alcohol use patterns, and psychometric assessments, including the Alcohol Use Disorder Identification Test (AUDIT), Clinical Institute Withdrawal Assessment (CIWA), and the 18-item Brief Psychiatric Rating Scale (BPRS), were analyzed. Patients were categorized into uncomplicated and complicated AWS groups, and statistical comparisons were performed using SPSS 23. As the data was not normally distributed, non-parametric tests were used. Results: Among 87 AWS patients, 73.6% had uncomplicated and 26.4% had complicated withdrawal. Demographics, duration, and volume of alcohol intake, and mean AUDIT and CIWA scores showed descriptive patterns of difference between uncomplicated and complicated AWS. BPRS scores were higher in complicated AWS, with some domains strongly linked to it. Discussion: Psychiatric symptom burden rather than consumption characteristics emerged as the strongest predictor of complicated AWS. This finding complements but also challenges existing literature, suggesting that cultural, biological, and socioeconomic modifiers may influence AWS severity. Conclusion: Integrating the usage of tools like consumption metrics and psychiatric symptoms into routine assessment enables early identification of high-risk patients. Larger, multi-centre longitudinal studies are needed to validate predictive models and improve AWS management.
GUNNAM et al. (Thu,) studied this question.