Predictors of response to full agonist opioids in enriched enrollment randomized withdrawal clinical trials: a participant-level data meta-analysis.

Abstract

This study aims to identify predictors of success in treating chronic pain patients with full agonist opioids by analyzing harmonized individual patient data from 5594 participants in 9 enriched enrollment randomized withdrawal clinical trials available in the Food and Drug Administration data repository. We analyzed both the participants' success with titration and continued success in the 84-day maintenance phases after randomization for those maintained on the drug. We used the full data set to assess participant demographics and subsets of data containing participant reported outcomes at baseline. Participants had an average age of 51, with 55% female participants and 66% non-Hispanic white. No clinically relevant differences were observed between participants who failed titration or those who continued on full agonists through the maintenance phase. Prediction models were developed using mixed effects logistic regression and generalized linear mixed models, with the study as a random effect to account for inter-study differences. Despite large numbers, the analysis did not reveal clinically useful prediction models for either the titration or maintenance phase; however, higher initial pain scores were modest predictors of poorer outcomes. No patient-reported outcome measures were predictive of responses to therapy. The study's limitations include its volunteer-based sample and the exclusion criteria, although excluding patients with opioid use disorder or serious psychological conditions are similar to those used in clinical care. As no strong predictive factors for successful treatment were identified, the decision to use opioids to treat chronic pain requires careful clinical judgment and close monitoring.