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J Gen Intern Med. 2018 Jun;33(6):898-905. doi: 10.1007/s11606-018-4335-8. Epub 2018 Feb 5.

Prediction of Future Chronic Opioid Use Among Hospitalized Patients.

Author information

1
Hospital Medicine, Denver Health Medical Center, Denver, CO, USA. susan.calcaterra@ucdenver.edu.
2
Division of General Internal Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, USA. susan.calcaterra@ucdenver.edu.
3
University of Colorado Adult and Child Consortium for Health Outcomes Research and Delivery Science, Aurora, CO, USA.
4
Rocky Mountain Prevention Research Center, Colorado School of Public Health, University of Colorado Denver, Aurora, CO, USA.
5
Hospital Medicine, Denver Health Medical Center, Denver, CO, USA.
6
Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
7
Division of General Internal Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, USA.
8
Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA.

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.

KEYWORDS:

hospital medicine; prediction rules; statistical modeling

PMID:
29404943
PMCID:
PMC5975151
[Available on 2019-06-01]
DOI:
10.1007/s11606-018-4335-8

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