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J Gen Intern Med. 2018 Oct;33(10):1646-1653. doi: 10.1007/s11606-017-4288-3. Epub 2018 Jan 29.

Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy.

Author information

1
Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA. jason.m.glanz@kp.org.
2
Department of Epidemiology, Colorado School of Public Health, Denver, CO, USA. jason.m.glanz@kp.org.
3
Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA.
4
Denver Health and Hospital Authority, Denver, CO, USA.
5
Division of General Internal Medicine, University of Colorado School of Medicine, Denver, CO, USA.
6
Department of Epidemiology, Colorado School of Public Health, Denver, CO, USA.

Abstract

BACKGROUND:

Naloxone is a life-saving opioid antagonist. Chronic pain guidelines recommend that physicians co-prescribe naloxone to patients at high risk for opioid overdose. However, clinical tools to efficiently identify patients who could benefit from naloxone are lacking.

OBJECTIVE:

To develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone.

DESIGN:

Retrospective cohort.

SETTING:

Derivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado.

PARTICIPANTS:

We developed a predictive model in a cohort of 42,828 patients taking chronic opioid therapy and externally validated the model in 10,708 patients.

MAIN MEASURES:

Potential predictors and outcomes (nonfatal pharmaceutical and heroin overdoses) were extracted from electronic health records. Fatal overdose outcomes were identified from state vital records. To match the approximate shelf-life of naloxone, we used Cox proportional hazards regression to model the 2-year risk of overdose. Calibration and discrimination were assessed.

KEY RESULTS:

A five-variable predictive model showed good calibration and discrimination (bootstrap-corrected c-statistic = 0.73, 95% confidence interval [CI] 0.69-0.78) in the derivation site, with sensitivity of 66.1% and specificity of 66.6%. In the validation site, the model showed good discrimination (c-statistic = 0.75, 95% CI 0.70-0.80) and less than ideal calibration, with sensitivity and specificity of 82.2% and 49.5%, respectively.

CONCLUSIONS:

Among patients on chronic opioid therapy, the predictive model identified 66-82% of all subsequent opioid overdoses. This model is an efficient screening tool to identify patients who could benefit from naloxone to prevent overdose deaths. Population differences across the two sites limited calibration in the validation site.

KEYWORDS:

naloxone; opioids; overdose; predictive model; substance use disorder

PMID:
29380216
PMCID:
PMC6153224
[Available on 2019-10-01]
DOI:
10.1007/s11606-017-4288-3

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