A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients.

ABSTRACT:

Background

Predicting which individuals who are prescribed buprenorphine for opioid use disorder are most likely to experience an overdose can help target interventions to prevent relapse and subsequent consequences.

Methods

We used Maryland prescription drug monitoring data from 2015 to identify risk factors for nonfatal opioid overdoses that were identified in hospital discharge records in 2016. We developed a predictive risk model for prospective nonfatal opioid overdoses among buprenorphine patients (N = 25,487). We estimated a series of models that included demographics plus opioid, buprenorphine and benzodiazepine prescription variables. We applied logistic regression to generate performance measures.

Results

About 3.24% of the study cohort had ≥1 nonfatal opioi

SUBMITTER: Chang HY 

PROVIDER: S-EPMC6713520 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature