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Area of Science:

  • Addiction Medicine
  • Clinical Prediction Models
  • Public Health

Background:

  • Predicting return to opioid use in early treatment is crucial for effective intervention.
  • Current models lack the ability to identify high-risk patients at the initial stages of treatment for opioid use disorder (OUD).

Purpose of the Study:

  • To develop an individual-level prediction tool for assessing the risk of return to opioid use.
  • To identify key predictors of relapse in the early phase of OUD treatment.

Main Methods:

  • A decision analytical model was created using harmonized individual-level data from three pragmatic randomized clinical trials within the National Institute on Drug Abuse Clinical Trials Network (CTN).
  • The model incorporated data from 2199 adult participants receiving methadone, buprenorphine, or extended-release naltrexone.
  • Predictive models were developed for return to use, defined as four consecutive weeks of missing or positive urine drug screens (UDS) for nonprescribed opioids by week 12.

Main Results:

  • An initial model with four predictors (heroin use days, morphine- and cocaine-positive UDS, heroin injection) showed moderate predictive performance (AUROC, 0.67).
  • Incorporating UDS results from the first three treatment weeks significantly improved prediction (AUROC, 0.82).
  • A simplified risk score (CTN-0094 OUD Return-to-Use Risk Score) effectively stratified patients, with risks ranging from 13% to 85% based on early UDS results.

Conclusions:

  • The developed prediction model serves as a universal risk measure for return to opioid use by week 3 of treatment.
  • Interventions aimed at preventing relapse should prioritize the critical early treatment period.
  • The CTN-0094 OUD Return-to-Use Risk Score offers a practical tool for clinical risk stratification and targeted interventions.