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Journal of General Internal Medicine

, Volume 33, Issue 6, pp 898–905 | Cite as

Prediction of Future Chronic Opioid Use Among Hospitalized Patients

  • S. L. Calcaterra
  • S. Scarbro
  • M. L. Hull
  • A. D. Forber
  • I. A. Binswanger
  • K. L. Colborn
Original Research

Abstract

Background

Opioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of ≥ 90-day supply of opioids with < 30-day gap in supply over a 180-day period or receipt of ≥ 10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge.

Objective

The objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization.

Design

Retrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression.

Patients

Hospitalized patients at an urban, safety net hospital.

Main Measurements

Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others.

Key Results

Model prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden’s index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients.

Conclusions

Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.

KEY WORDS

hospital medicine statistical modeling prediction rules 

Notes

Funding Information

Funders: The authors would like to acknowledge the University of Colorado, Department of Medicine, Division of General Internal Medicine Small Grants Program for their generous grant which funded this project. Dr. Binswanger was supported by the National Institute On Drug Abuse of the National Institutes of Health under Award Numbers R34DA035952 and R01DA042059. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Prior Presentations: This work was presented at the National Society of General Internal Medicine Conference on April 21, 2017.

Compliance with Ethical Standards

This study was approved by the Colorado Multiple Institutional Review Board and adhered to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement on reporting predictive models.21

Conflict of Interest

The authors declare that they do not have a conflict of interest.

Supplementary material

11606_2018_4335_MOESM1_ESM.docx (15 kb)
Appendix 1 (DOCX 14 kb)
11606_2018_4335_MOESM2_ESM.docx (13 kb)
Appendix 2 (DOCX 13 kb)

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Copyright information

© Society of General Internal Medicine 2018

Authors and Affiliations

  • S. L. Calcaterra
    • 1
    • 2
  • S. Scarbro
    • 3
    • 4
  • M. L. Hull
    • 1
  • A. D. Forber
    • 5
  • I. A. Binswanger
    • 2
    • 6
  • K. L. Colborn
    • 5
  1. 1.Hospital MedicineDenver Health Medical CenterDenverUSA
  2. 2.Division of General Internal Medicine, Department of MedicineUniversity of Colorado School of MedicineAuroraUSA
  3. 3.University of Colorado Adult and Child Consortium for Health Outcomes Research and Delivery ScienceAuroraUSA
  4. 4.Rocky Mountain Prevention Research Center, Colorado School of Public Health, University of Colorado DenverAuroraUSA
  5. 5.Department of Biostatistics and InformaticsUniversity of Colorado Anschutz Medical CampusAuroraUSA
  6. 6.Institute for Health Research, Kaiser Permanente ColoradoDenverUSA

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