{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T01:54:16Z","timestamp":1790128456172,"version":"4.0.1"},"reference-count":49,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T00:00:00Z","timestamp":1719187200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000936","name":"Gordon and Betty Moore Foundation","doi-asserted-by":"publisher","award":["12409"],"award-info":[{"award-number":["12409"]}],"id":[{"id":"10.13039\/100000936","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000936","name":"Gordon and Betty Moore Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000936","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Addiction"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and aims<\/jats:title>\n                    <jats:p>Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine\u2010naloxone, remains unpredictable. Our objective was to determine the predictability of 6\u2010month retention in buprenorphine\u2010naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>This retrospective observational study developed and validated machine learning\u2010based clinical risk prediction models using EHR data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting and cases<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Measurements<\/jats:title>\n                    <jats:p>Predict continuous prescription of buprenorphine\u2010naloxone for at least 6\u00a0months, without a gap of more than 30\u2009days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC\u2010AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time\u2010to\u2010event analysis on a single site to estimate the duration of buprenorphine\u2010naloxone treatment continuity evaluated by the C\u2010index and a comparative evaluation against predictions made by three human clinical experts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Attrition rates at 6\u00a0months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC\u2010AUCs up to 75.8 (95% confidence interval [CI]\u2009=\u200973.6\u201378.0). Addiction medicine specialists' predictions show a ROC\u2010AUC of 67.8 (95% CI\u2009=\u200950.4\u201385.2). Time\u2010to\u2010event analysis on Stanford data indicated a median treatment retention time of 65\u2009days, with random survival forest model achieving an average C\u2010index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>US patients with opioid use disorder or opioid dependence treated with buprenorphine\u2010naloxone prescriptions appear to have a high (\u223c60%) treatment attrition by 6\u00a0months. 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/add.16587","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T04:04:28Z","timestamp":1719201868000},"page":"1792-1802","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Predictability of buprenorphine\u2010naloxone treatment retention: A multi\u2010site analysis combining electronic health records and machine learning"],"prefix":"10.1111","volume":"119","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8874-8835","authenticated-orcid":false,"given":"Fateme","family":"Nateghi Haredasht","sequence":"first","affiliation":[{"name":"Stanford Center for Biomedical Informatics Research Stanford University  Stanford California USA"},{"name":"Division of Hospital Medicine Stanford University  Stanford California USA"},{"name":"Clinical Excellence Research Center Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sajjad","family":"Fouladvand","sequence":"additional","affiliation":[{"name":"Stanford Center for Biomedical Informatics Research Stanford University  Stanford California USA"},{"name":"Division of Hospital Medicine Stanford University  Stanford California USA"},{"name":"Clinical Excellence Research Center Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9544-594X","authenticated-orcid":false,"given":"Steven","family":"Tate","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min Min","family":"Chan","sequence":"additional","affiliation":[{"name":"Holmusk Technologies, Inc.  Singapore Singapore"},{"name":"Holmusk Technologies, Inc.  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joannas Jie Lin","family":"Yeow","sequence":"additional","affiliation":[{"name":"Holmusk Technologies, Inc.  Singapore Singapore"},{"name":"Holmusk Technologies, Inc.  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kira","family":"Griffiths","sequence":"additional","affiliation":[{"name":"Holmusk Technologies, Inc.  Singapore Singapore"},{"name":"Holmusk Technologies, Inc.  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