Predicting premature discontinuation of medication for opioid use disorder from electronic medical records.

Lopez I, Fouladvand S, Kollins S, Chen CA, Bertz J, Hernandez-Boussard T, Lembke A, Humphreys K, Miner AS, Chen JH. Predicting premature discontinuation of medication for opioid use disorder from electronic medical records. AMIA ... Annual Symposium proceedings. AMIA Symposium. 2024 Jan 11; 2023:1067-1076.

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• RCS 04-141 – Evaluating Treatment & Self-help Methods of Improving Functioning & Quality of Life Outcomes of Patients with Substance Abuse Disorders

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Abstract:

Medications such as buprenorphine-naloxone are among the most effective treatments for opioid use disorder, but limited retention in treatment limits long-term outcomes. In this study, we assess the feasibility of a machine learning model to predict retention vs. attrition in medication for opioid use disorder (MOUD) treatment using electronic medical record data including concepts extracted from clinical notes. A logistic regression classifier was trained on 374 MOUD treatments with 68% resulting in potential attrition. On a held-out test set of 157 events, the full model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% CI: 0.64-0.90) and AUROC of 0.74 (95% CI: 0.62-0.87) with a limited model using only structured EMR data. Risk prediction for opioid MOUD retention vs. attrition is feasible given electronic medical record data, even without necessarily incorporating concepts extracted from clinical notes.