Using the AUDIT-PC to predict alcohol withdrawal in hospitalized patients.

Abstract

Alcohol withdrawal syndrome (AWS) occurs when alcohol-dependent individuals abruptly reduce or stop drinking. Hospitalized alcohol-dependent patients are at risk. Hospitals need a validated screening tool to assess withdrawal risk, but no validated tools are currently available. To examine the admission Alcohol Use Disorders Identification Test-(Piccinelli) Consumption (AUDIT-PC) ability to predict the subsequent development of AWS among hospitalized medical-surgical patients admitted to a non-intensive care setting. Retrospective case-control study of patients discharged from the hospital with a diagnosis of AWS. All patients with AWS were classified as presenting with AWS or developing AWS later during admission. Patients admitted to an intensive care setting and those missing AUDIT-PC scores were excluded from analysis. A hierarchical (by hospital unit) logistic regression was performed and receiver-operating characteristics were examined on those developing AWS after admission and randomly selected controls. Because those diagnosing AWS were not blinded to the AUDIT-PC scores, a sensitivity analysis was performed. The study cohort included all patients age ≥18 years admitted to any medical or surgical units in a single health care system from 6 October 2009 to 7 October 2010. After exclusions, 414 patients were identified with AWS. The 223 (53.9 %) who developed AWS after admission were compared to 466 randomly selected controls without AWS. An AUDIT-PC score ≥4 at admission provides 91.0 % sensitivity and 89.7 % specificity (AUC=0.95; 95 % CI, 0.94-0.97) for AWS, and maximizes the correct classification while resulting in 17 false positives for every true positive identified. Performance remained excellent on sensitivity analysis (AUC=0.92; 95 % CI, 0.90-0.93). Increasing AUDIT-PC scores were associated with an increased risk of AWS (OR=1.68, 95 % CI 1.55-1.82, p<0.001). The admission AUDIT-PC score is an excellent discriminator of AWS and could be an important component of future clinical prediction rules. Calibration and further validation on a large prospectivecohort is indicated.