ArticleDrug and alcohol dependence2019
A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients.
Article in Drug and alcohol dependence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence-based framework for identifying opioid use disorder in administrative data: A systematic review and methodological development study.Pain medicine (Malden, Mass.) · 2026Pooled it
- Development and validation of a risk-score model for opioid overdose using a national claims database.Scientific reports · 2022Trial
- Development and evaluation of machine learning algorithms for the prediction of opioid-related deaths among UK patients with non-cancer pain.PLOS digital health · 2026Article
- Article
- An uncontrolled pilot trial of a buprenorphine dispensing device to enhance outpatient treatment for opioid use disorder.Heroin addiction and related clinical problems · 2023Article
- Outcome class imbalance and rare events: An underappreciated complication for overdose risk prediction modeling.Addiction (Abingdon, England) · 2023Article
- Evaluation of the Effectiveness of Buprenorphine-Naloxone on Opioid Overdose and Death among Insured Patients with Opioid Use Disorder in the United States.Pharmacoepidemiology · 2022Article
- Sociodemographic and prescribing characteristics that impact long-term retention in buprenorphine treatment for opioid use disorder among a statewide population.Drug and alcohol dependence · 2022Article
- Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Assessing opioid overdose risk: a review of clinical prediction models utilizing patient-level data.Translational research : the journal of laboratory and clinical medicine · 2021Review
- Predictive Modeling of Opioid Overdose Using Linked Statewide Medical and Criminal Justice Data.JAMA psychiatry · 2020Article
- Detoxification, 12-step meeting attendance, and non-fatal opioid overdoses among a suburban/exurban population with opioid use disorder.Journal of addictive diseasesArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundPredicting 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.
methodsWe 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.
resultsAbout 3.24% of the study cohort had ≥1 nonfatal opioid overdoses. In the model with all predictors, odds of nonfatal overdoses among buprenorphine patients were higher among males (OR = 1.39, 95% CI:1.21-1.62) and those with more buprenorphine pharmacies (OR = 1.19, 95% CI:1.11-1.28), 1+ buprenorphine prescription paid by Medicaid (OR = 1.21, 95% CI:1.02-1.48), Medicare (OR = 1.93, 95% CI:1.63-2.43), or a commercial plan (OR = 1.98, 95% CI:1.30-2.89), 1+ opioid prescription paid by Medicare (OR = 1.30, 95% CI:1.03-1.68), and more benzodiazepine prescriptions (OR = 1.04, 95% CI:1.02-1.05). The odds were lower among those with longer days of buprenorphine (OR = 0.64, 95% CI:0.60-0.69) or opioid (OR = 0.79, 95% CI:0.65-0.95) supply. The model had moderate predictive ability (c-statistic = 0.69).
conclusionsSeveral modifiable risk factors, such as length of buprenorphine treatment, may be targets for interventions to improve clinical care and reduce harms. This model could be practically implemented with common prescription-related information and allow payers and clinical systems to better target overdose risk reduction interventions, such as naloxone distribution.
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