Using the AUDIT-PC to Predict Alcohol Withdrawal in Hospitalized Patients
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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 prospective cohort is indicated.
KEY WORDSalcoholism addictive behavior screening risk assessment hospital medicine
This work was not funded by any external sources. Drs. Pecoraro and Woody receive salary support through the National Institute on Drug Abuse (NIDA): Clinical Trials Network (CTN) U10 DA-13043 (Pecoraro and Woody) and KO5 DA-17009 (Woody).
Results were presented as a poster at the 35th annual SGIM meeting in Orlando, FL on 9 May 2012.
Conflicts of Interest
Drs. Ewen and Kolm have received research grant funding unrelated to this manuscript from: Bristol-Myers Squibb (Ewen and Kolm), Sanofi-Aventis (Ewen and Kolm), and Astra Zeneca (Kolm). Dr. Woody received a consulting payment from Alkermes for work on clinical guidelines regarding how to start opioid-addicted individuals on extended release-injectable naltrexone (XR-NTX), and Alkermes has provided XR-NTX for a study of amphetamine addiction treatment in Iceland on which Dr. Woody is a Co-Investigator. Dr. Woody is on the scientific advisory board of RADARS, a non-profit organization administered by Denver Health to study abuse liability of prescription drugs that is funded by contracts with pharmaceutical companies. Drs. Pecoraro, Horton, and Mooney, and Ms. McGraw declare they have no conflicts of interest.