title: Using the AUDIT-PC to predict alcohol withdrawal in hospitalized patients.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors: Anna
authors: A
authors: Pecoraro
authors: Edward
authors: E
authors: Ewen
authors: Terry
authors: T
authors: Horton
authors: Ruth
authors: R
authors: Mooney
authors: Paul
authors: P
authors: Kolm
authors: Patty
authors: P
authors: McGraw
authors: George
authors: G
authors: Woody
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D064829:Alcohol Abstinence; D000437:Alcoholism / Q000175:diagnosis*; D016022:Case-Control Studies; D000431:Ethanol / Q000009:adverse effects*; D005260:Female; D006760:Hospitalization; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D011569:Psychiatric Status Rating Scales; D011594:Psychometrics; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D013375:Substance Withdrawal Syndrome / Q000175:diagnosis*; D055815:Young Adult
chemical_list: D000431:Ethanol
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