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JOURNAL ARTICLE

Prospective Validation Study of the Prediction of Alcohol Withdrawal Severity Scale (PAWSS) in Medically Ill Inpatients: A New Scale for the Prediction of Complicated Alcohol Withdrawal Syndrome

José R Maldonado, Yelizaveta Sher, Smita Das, Kelsey Hills-Evans, Anna Frenklach, Sermsak Lolak, Rachel Talley, Eric Neri
Alcohol and Alcoholism: International Journal of the Medical Council on Alcoholism 2015, 50 (5): 509-18
25999438

AIMS: The prevalence of alcohol use disorders (AUDs) among hospitalized medically ill patients exceeds 40%. Most AUD patients experience uncomplicated alcohol withdrawal syndrome (AWS), requiring only supportive medical intervention, while complicated AWS occurs in up to 20% of cases (i.e. seizures, delirium tremens). We aimed to prospectively test and validate the Prediction of Alcohol Withdrawal Severity Scale (PAWSS), a new tool to identify patients at risk for developing complicated AWS, in medically ill hospitalized patients.

METHODS: We prospectively considered all subjects hospitalized to selected general medicine and surgery units over a 12-month period. Participants were assessed independently and blindly on a daily basis with PAWSS, Clinical Institute Withdrawal Assessment-Alcohol, Revised (CIWA-Ar) and clinical monitoring throughout their admission to determine the presence and severity of AWS.

RESULTS: Four hundred and three patients were enrolled in the study. Patients were grouped by PAWSS score: Group A (PAWSS < 4; considered at low risk for complicated AWS); Group B (PAWSS ≥ 4; considered at high risk for complicated AWS). The results of this study suggest that, using a PAWSS cutoff of 4, the tool's sensitivity for identifying complicated AWS is 93.1% (95%CI[77.2, 99.2%]), specificity is 99.5% (95%CI[98.1, 99.9%]), positive predictive value is 93.1% and negative predictive value is 99.5%; and has excellent inter-rater reliability with Lin's concordance coefficient of 0.963 (95% CI [0.936, 0.979]).

CONCLUSION: PAWSS has excellent psychometric characteristics and predictive value among medically ill hospitalized patients, helping clinicians identify those at risk for complicated AWS and allowing for prevention and timely treatment of complicated AWS.

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