Parallel modeling of pain and depression in prediction of relapse during buprenorphine and naloxone treatment: A finite mixture model.

Drug and Alcohol Dependence
Noel A VestSarah Tragesser

Abstract

Relapse is common in treatment for opioid use disorders (OUDs). Pain and depression often co-occur during OUD treatment, yet little is known about how they influence relapse among patients with a primary diagnosis of prescription opioid use disorder (POUD). Advanced statistical analyses that can simultaneously model these two conditions may lead to targeted clinical interventions. The objective of this study was to utilize a discrete survival analysis with a growth mixture model to test time to prescription opioid relapse, predicted by parallel growth trajectories of depression and pain, in a clinical sample of patients in buprenorphine/naloxone treatment. The latent class analysis characterized heterogeneity with data collected from the National Institute of Drug Abuse Clinical Trials Network project (CTN-0030). Results suggested that a 4-class solution was the most parsimonious based on global fit indices and clinical relevance. The 4 classes identified were: 1) low relapse, 2) high depression and moderate pain, 3) high pain, and 4) high relapse. Odds ratios for time-to-first use indicated no statistically significant difference in time to relapse between the high pain and the high depression classes, but all other classes di...Continue Reading

Citations

Feb 23, 2021·Substance Abuse : Official Publication of the Association for Medical Education and Research in Substance Abuse·Patricia Cavazos-RehgLaura J Bierut
May 7, 2021·Pain Medicine : the Official Journal of the American Academy of Pain Medicine·Mohammed M FarajMark K Greenwald

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