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

Autor: Stanley Xu, Ingrid A. Binswanger, Shane R. Mueller, Komal J. Narwaney, Jason M. Glanz, Susan L. Calcaterra, Kristin Breslin, Edward M. Gardner
Rok vydání: 2018
Předmět:
Male
Narcotic Antagonists
01 natural sciences
Cohort Studies
0302 clinical medicine
Risk Factors
Electronic Health Records
030212 general & internal medicine
Aged
80 and over
education.field_of_study
Naloxone
Chronic pain
Middle Aged
Prognosis
Analgesics
Opioid
Editorial
Cohort
Female
Chronic Pain
medicine.drug
Adult
medicine.medical_specialty
Colorado
Adolescent
Substance-Related Disorders
medicine.drug_class
Population
Risk Assessment
Drug Administration Schedule
Young Adult
03 medical and health sciences
Internal Medicine
medicine
Humans
0101 mathematics
education
Aged
Retrospective Studies
Models
Statistical
Primary Health Care
business.industry
010102 general mathematics
Retrospective cohort study
Opioid overdose
medicine.disease
Opioid
Emergency medicine
Drug Overdose
business
Opioid antagonist
Zdroj: Journal of General Internal Medicine. 33:1646-1653
ISSN: 1525-1497
0884-8734
Popis: 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. To develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone. Retrospective cohort. Derivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado. 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. 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. 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. 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.
Databáze: OpenAIRE
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