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Using the AUDIT-PC to Predict Alcohol Withdrawal in Hospitalized Patients

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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.

DESIGN

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.

PARTICIPANTS

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.

KEY 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 prospective cohort is indicated.

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Acknowledgements

Funding Sources

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).

Prior Presentation

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.

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Authors and Affiliations

  1. Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Anna Pecoraro PsyD & George Woody MD

  2. NIDA Clinical Trials Network, Delaware Valley Node, Philadelphia, PA, USA

    Anna Pecoraro PsyD, Terry Horton MD & George Woody MD

  3. Department of Medicine,, Christiana Care Health System, Newark, DE, USA

    Edward Ewen MD, Terry Horton MD & Patty McGraw MS RN

  4. Department of Nursing,, Christiana Care Health System, Newark, DE, USA

    Ruth Mooney PhD, MN, BSN

  5. Center for Outcomes Research,, Christiana Care Health System, Newark, DE, USA

    Paul Kolm PhD

Authors
  1. Anna Pecoraro PsyD
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  2. Edward Ewen MD
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  3. Terry Horton MD
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  4. Ruth Mooney PhD, MN, BSN
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  5. Paul Kolm PhD
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  6. Patty McGraw MS RN
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  7. George Woody MD
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Corresponding author

Correspondence to Edward Ewen MD.

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Pecoraro, A., Ewen, E., Horton, T. et al. Using the AUDIT-PC to Predict Alcohol Withdrawal in Hospitalized Patients. J GEN INTERN MED 29, 34–40 (2014). https://doi.org/10.1007/s11606-013-2551-9

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  • DOI: https://doi.org/10.1007/s11606-013-2551-9

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