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

Relapse is common in treatment for opioid use disorders (OUD). Pain and depression often co-occur during OUD treatment, but 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 NIDA Clinical Trials Network protocol CTN-0030 (Prescription Opioid Addiction Treatment Study, which compared buprenorphine/naloxone with standard medical management).

Results suggested a 4-class solution was the most conservative 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 differences in time to relapse between the high pain and the high depression classes, but all other classes differed significantly.

Conclusions: This is the first longitudinal study to characterize the influence of pain, depression, and relapse in patients receiving buprenorphine and naloxone treatment. These results emphasize the need to monitor the influence of pain and depression during stabilization on buprenorphine and naloxone. Future work may identify appropriate interventions that can be introduced to extend time-to-first prescription opioid use among patients.

Related protocols: CTN-0030