{"doi":"10.1111/add.16587","title":"Predictability of buprenorphine‐naloxone treatment retention: A multi‐site analysis combining electronic health records and machine learning","abstract":"BACKGROUND AND AIMS: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors. DESIGN: This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data. SETTING AND CASES: Data were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stanford and 7957 NeuroBlu treatment encounters from 2008 to 2023 and from 2003 to 2023, respectively. MEASUREMENTS: Predict continuous prescription of buprenorphine-naloxone for at least 6 months, without a gap of more than 30 days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC-AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time-to-event analysis on a single site to estimate the duration of buprenorphine-naloxone treatment continuity evaluated by the C-index and a comparative evaluation against predictions made by three human clinical experts. FINDINGS: Attrition rates at 6 months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC-AUCs up to 75.8 (95% confidence interval [CI] = 73.6-78.0). Addiction medicine specialists' predictions show a ROC-AUC of 67.8 (95% CI = 50.4-85.2). Time-to-event analysis on Stanford data indicated a median treatment retention time of 65 days, with random survival forest model achieving an average C-index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence. CONCLUSIONS: US patients with opioid use disorder or opioid dependence treated with buprenorphine-naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts.","journal":"Addiction","year":2024,"id":456079,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9594,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":513828,"name":"Sajjad Fouladvand","orcid":"0000-0002-9869-1836","position":1,"is_corresponding":false},{"id":1077639,"name":"Steven Tate","orcid":"0000-0001-9544-594X","position":2,"is_corresponding":false},{"id":1280980,"name":"Min Min Chan","orcid":null,"position":3,"is_corresponding":false},{"id":1280981,"name":"Joannas Jie Lin Yeow","orcid":null,"position":4,"is_corresponding":false},{"id":1177567,"name":"Kira Griffiths","orcid":"0000-0001-6885-086X","position":5,"is_corresponding":false},{"id":162098,"name":"Iván López","orcid":null,"position":6,"is_corresponding":false},{"id":491039,"name":"Jeremiah W. Bertz","orcid":null,"position":7,"is_corresponding":false},{"id":233273,"name":"Adam S. Miner","orcid":"0000-0002-5125-4735","position":8,"is_corresponding":false},{"id":35255,"name":"Tina Hernandez‐Boussard","orcid":"0000-0001-6553-3455","position":9,"is_corresponding":false},{"id":1077640,"name":"Chwen‐Yuen Angie Chen","orcid":"0000-0002-7207-598X","position":10,"is_corresponding":false},{"id":1280528,"name":"Huiqiong Deng","orcid":"0000-0001-9822-2856","position":11,"is_corresponding":false},{"id":240672,"name":"Keith Humphreys","orcid":"0000-0003-0694-5761","position":12,"is_corresponding":false},{"id":1280529,"name":"Anna Lembke","orcid":"0000-0002-1352-487X","position":13,"is_corresponding":false},{"id":1280530,"name":"L. Alexander Vance","orcid":"0000-0002-3131-6177","position":14,"is_corresponding":false},{"id":227523,"name":"Jonathan H. Chen","orcid":"0000-0002-4387-8740","position":15,"is_corresponding":false},{"id":1280527,"name":"Fateme Nateghi Haredasht","orcid":"0000-0002-8874-8835","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-19T02:03:27.785212Z","pmid":"38923168","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}