A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients. (1st August 2019)

• Record Type: Journal Article
• Authors: Chang, Hsien-Yen; Krawczyk, Noa; Schneider, Kristin E.; Ferris, Lindsey; Eisenberg, Matthew; Richards, Tom M.; Lyons, B. Casey; Jackson, Kate; Weiner, Jonathan P.; Saloner, Brendan
• Is Part Of: Drug and alcohol dependence. Volume 201(2019)
• Page Start: 127
• Page End: 133
• Publication Date: 2019-08-01
• Subjects: Prescription drug monitoring programs -- Opioid overdose -- Buprenorphine -- Opioid use disorder -- Opioid analgesics -- Predictive risk model; Drug abuse -- Periodicals; Alcoholism -- Periodicals; 616.86
• DOI: 10.1016/j.drugalcdep.2019.04.016
• Languages: English
• ISSNs: 0376-8716

Abstract

Highlights: Buprenorphine reduces opioid overdose risk, but many patients remain vulnerable. About 3.24% of the cohort had 1+ nonfatal overdose in the prospective year. Nonfatal overdoses were more likely among male and younger individuals. Longer buprenorphine supply contributed to lower odds of nonfatal overdose. We can predict nonfatal overdose among buprenorphine users with moderate accuracy. 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 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). Conclusions: Several 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.