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Journal of General Internal Medicine

, Volume 29, Issue 1, pp 34–40 | Cite as

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

  • Anna Pecoraro
  • Edward EwenEmail author
  • Terry Horton
  • Ruth Mooney
  • Paul Kolm
  • Patty McGraw
  • George Woody
Original Research

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.

KEY WORDS

alcoholism addictive behavior screening risk assessment hospital medicine 

Notes

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.

Supplementary material

11606_2013_2551_MOESM1_ESM.pdf (256 kb)
ESM 1 (PDF 256 kb)

REFERENCES

  1. 1.
    Saitz R, Freedner N, Palfai TP, Horton NJ, Samet JH. The severity of unhealthy alcohol use in hospitalized medical patients. The spectrum is narrow. J Gen Intern Med. 2006;21(4):381–385.PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Sharkey J, Brennan D, Curran P. The pattern of alcohol consumption of a general hospital population in North Belfast. Alcohol Alcohol. 1996;31:279–285.PubMedCrossRefGoogle Scholar
  3. 3.
    Taylor CL, Passmore N, Kilbane P. Prospective study of alcohol-related admissions to an inner-city hospital. Lancet. 1986;2:265–268.PubMedCrossRefGoogle Scholar
  4. 4.
    Dolman JM, Hawkes ND. Combining The Audit Questionnaire And Biochemical Markers To Assess Alcohol Use And Risk Of Alcohol Withdrawal In Medical Inpatients. Alcohol Alcohol. 2005;40(6):515–519.PubMedCrossRefGoogle Scholar
  5. 5.
    Gupta M. Alcohol Withdrawal and Prolonged Hospital Stay in a Patient with Neuroimaging Abnormalities: A Case Report. Alcohol Alcohol. 2009;44(2):183–184.PubMedCrossRefGoogle Scholar
  6. 6.
    Cross GM, Hennessey PT. Principles and practice of detoxification. Prim Care. 1993;20(1):81–93.PubMedGoogle Scholar
  7. 7.
    DeBellis R, Smith BS, Choi S, Malloy M. Management of Delirium Tremens. J Intensive Care Med. 2005;20(3):164–173.PubMedCrossRefGoogle Scholar
  8. 8.
    Kaner EF, Beyer F, Dickinson HO, et al. Effectiveness of brief alcohol interventions in primary care populations. Cochrane Database Syst Rev. 2007;2, CD004148.PubMedGoogle Scholar
  9. 9.
    Saitz R. Candidate performance measures for screening for, assessing, and treating unhealthy substance use in hospitals: advocacy or evidence-based practice? Ann Intern Med. 2010;153(1):40–43.PubMedCrossRefGoogle Scholar
  10. 10.
    Babor TF, McRee BG, Kassebaum PA, Grimaldi PL, Ahmed K, Bray J. Screening, Brief Intervention, and Referral to Treatment (SBIRT): Toward a public health approach to the management of substance abuse. Subst Abus. 2007;28(3):7–30.PubMedCrossRefGoogle Scholar
  11. 11.
    Sullivan JT, Sykora K, Schneiderman J, Naranjo CA, Sellers EM. Assessment of alcohol withdrawal: the revised clinical institute withdrawal assessment for alcohol scale (CIWA-Ar). Br J Addict. 1989;84(11):1353–1357.PubMedCrossRefGoogle Scholar
  12. 12.
    Saunders JB, Aasland OG, Amundsen A, Grant M. Alcohol consumption and related problems among primary health care patients: WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption–I. Addiction. 1993;88(3):349–362.PubMedCrossRefGoogle Scholar
  13. 13.
    Reinert DF, Allen JP. The Alcohol Use Disorders Identification Test: An Update of Research Findings. Alcohol Clin Exp Res. 2007;31(2):185–199.PubMedCrossRefGoogle Scholar
  14. 14.
    Roux JP, Malte CA, Kivlahan DR. The alcohol use disorders identification test (AUDIT) predicts alcohol withdrawal symptoms durin ginpatient detoxification. J Addict Dis. 2002;4:81–91.CrossRefGoogle Scholar
  15. 15.
    Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Arch Intern Med. 1998;158(16):1789–1795.PubMedCrossRefGoogle Scholar
  16. 16.
    Aertgeerts B, Buntinx F, Ansoms S, Fevery J. Questionnaires are better than laboratory tests to screen for current alcohol abuse or dependence in a male inpatient population. Acta Clin Belg. 2002;57:241–249.PubMedGoogle Scholar
  17. 17.
    Gomez A, Conde A, Santana JM, Jorrin A. Diagnostic usefulness of brief versions of Alcohol Use Disorders Identification Test (AUDIT) for detecting hazardous drinkers in primary care settings. J Stud Alcohol. 2005;66:305–308.PubMedGoogle Scholar
  18. 18.
    Piccinelli M, Tessari E, Bortolomasi M, et al. Efficacy of the alcohol use disorders identification test as a screening tool for hazardous alcohol intake and related disorders in primary care: A validity study. Br Med J. 1997;314(7078):420–424.CrossRefGoogle Scholar
  19. 19.
    Reoux JP, Malte CA, Kivlahan DR, Saxon AJ. The alcohol use disorders identification test (AUDIT) predicts alcohol withdrawal symptoms durin ginpatient detoxification. J Addict Dis. 2002;4:81–91.CrossRefGoogle Scholar
  20. 20.
    Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–174.PubMedCrossRefGoogle Scholar
  21. 21.
    Metz CE. Basic principles of ROC analysis. Semin Nucl Med. 1978;8(4):283–298.PubMedCrossRefGoogle Scholar
  22. 22.
    Obuchowski NA. Receiver operating characteristic curves and their use in radiology. Radiology. 2003;229(1):3–8.PubMedCrossRefGoogle Scholar
  23. 23.
    Wan S, Zhang B. Smooth semiparametric receiver operating characteristic curves for continuous diagnostic tests. Stat Med. 2007;26(12):2565–2586.PubMedCrossRefGoogle Scholar
  24. 24.
    Alemayehu D, Zou KH. Applications of ROC Analysis in Medical Research: Recent Developments and Future Directions. Acad Radiol. 2012;19(12):1457–1464.PubMedCrossRefGoogle Scholar
  25. 25.
    Metz CE. Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. J Am Coll Radiol: JACR. 2006;3(6):413–422.PubMedCrossRefGoogle Scholar
  26. 26.
    Schisterman EF, Perkins NJ, Liu A, Bondell H. Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples. Epidemiology. 2005;16(1):73–81.PubMedCrossRefGoogle Scholar
  27. 27.
    Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32–35.PubMedCrossRefGoogle Scholar
  28. 28.
    Swets JA. The science of choosing the right decision threshold in high-stakes diagnostics. Am Psychol. 1992;47(4):522–532.PubMedCrossRefGoogle Scholar
  29. 29.
    Swets JA, Dawes RM, Monahan J. Better decisions through science. Sci Am. 2000;283(4):82–87.PubMedCrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2013

Authors and Affiliations

  • Anna Pecoraro
    • 1
    • 2
  • Edward Ewen
    • 3
    Email author
  • Terry Horton
    • 2
    • 3
  • Ruth Mooney
    • 4
  • Paul Kolm
    • 5
  • Patty McGraw
    • 3
  • George Woody
    • 1
    • 2
  1. 1.Perelman School of Medicine at the University of PennsylvaniaPhiladelphiaUSA
  2. 2.NIDA Clinical Trials Network, Delaware Valley NodePhiladelphiaUSA
  3. 3.Department of Medicine, Christiana Care Health SystemNewarkUSA
  4. 4.Department of Nursing, Christiana Care Health SystemNewarkUSA
  5. 5.Center for Outcomes Research, Christiana Care Health SystemNewarkUSA

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