Individual-Level Risk Prediction of Return to Use During Opioid Use Disorder Treatment

Author:

Luo Sean X.1,Feaster Daniel J.2,Liu Ying1,Balise Raymond R.2,Hu Mei-Chen1,Bouzoubaa Layla2,Odom Gabriel J.3,Brandt Laura4,Pan Yue2,Hser Yih-Ing5,VanVeldhuisen Paul6,Castillo Felipe1,Calderon Anna R.2,Rotrosen John7,Saxon Andrew J.8,Weiss Roger D.9,Wall Melanie1,Nunes Edward V.1

Affiliation:

1. Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York

2. Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, Florida

3. Department of Biostatistics, Stempel College of Public Health, Florida International University, Miami, Florida

4. Department of Psychology, City College of New York, New York

5. Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles

6. Emmes Company, LLC, Rockville, Maryland

7. Department of Psychiatry, NYU Grossman School of Medicine, New York University, New York, New York

8. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington

9. Department of Psychiatry, Harvard Medical School, Belmont, Massachusetts

Abstract

ImportanceNo existing model allows clinicians to predict whether patients might return to opioid use in the early stages of treatment for opioid use disorder.ObjectiveTo develop an individual-level prediction tool for risk of return to use in opioid use disorder.Design, Setting, and ParticipantsThis decision analytical model used predictive modeling with individual-level data harmonized in June 1, 2019, to October 1, 2022, from 3 multicenter, pragmatic, randomized clinical trials of at least 12 weeks’ duration within the National Institute on Drug Abuse Clinical Trials Network (CTN) performed between 2006 and 2016. The clinical trials covered a variety of treatment settings, including federally licensed treatment sites, physician practices, and inpatient treatment facilities. All 3 trials enrolled adult participants older than 18 years, with broad pragmatic inclusion and few exclusion criteria except for major medical and unstable psychiatric comorbidities.InterventionAll participants received 1 of 3 medications for opioid use disorder: methadone, buprenorphine, or extended-release naltrexone.Main Outcomes and MeasuresPredictive models were developed for return to use, which was defined as 4 consecutive weeks of urine drug screen (UDS) results either missing or positive for nonprescribed opioids by week 12 of treatment.ResultsThe overall sample included 2199 trial participants (mean [SD] age, 35.3 [10.7] years; 728 women [33.1%] and 1471 men [66.9%]). The final model based on 4 predictors at treatment entry (heroin use days, morphine- and cocaine-positive UDS results, and heroin injection in the past 30 days) yielded an area under the receiver operating characteristic curve (AUROC) of 0.67 (95% CI, 0.62-0.71). Adding UDS in the first 3 treatment weeks improved model performance (AUROC, 0.82; 95% CI, 0.78-0.85). A simplified score (CTN-0094 OUD Return-to-Use Risk Score) provided good clinical risk stratification wherein patients with weekly opioid-negative UDS results in the 3 weeks after treatment initiation had a 13% risk of return to use compared with 85% for those with 3 weeks of opioid-positive or missing UDS results (AUROC, 0.80; 95% CI, 0.76-0.84).Conclusions and RelevanceThe prediction model described in this study may be a universal risk measure for return to opioid use by treatment week 3. Interventions to prevent return to regular use should focus on this critical early treatment period.

Publisher

American Medical Association (AMA)

Subject

Psychiatry and Mental health

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