{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T12:04:14Z","timestamp":1790769854250,"version":"4.1.0"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":21,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute on Drug Abuse, National Institutes of Health","award":["R03DA054496"],"award-info":[{"award-number":["R03DA054496"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Prescription drug monitoring programs (PDMPs) offer a promising data source for the development of clinical decision support (CDS) systems to guide opioid prescribing, but most PDMP agencies lack capacity for in-house model development\u2014instead relying on third-party tools. This study aims to externally validate a PDMP-based model, developed using 2018 California PDMP records, to predict new long-term use among Kentucky opioid-na\u00efve patients and compare its performance to Kentucky-specific machine learning (ML) models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We identified opioid-na\u00efve Kentucky residents who filled an index pill opioid prescription between 2021 and 2023 (development cohort: 2021-2022; validation cohort: 2023). The California model was updated via logistic calibration and evaluated against 5 newly developed Kentucky ML models (least absolute shrinkage and selection operator [LASSO], elastic net, deep neural network, random forest, XGBoost) using discrimination and calibration metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 985\u2009317 opioid-na\u00efve patients, 33\u2009298 (3.4%) transitioned to new long-term opioid use. The updated California model achieved excellent discrimination (area under the receiver operating characteristic curve [AUROC]: 0.919 [0.915-0.923]) and was calibrated up to the highest risk levels (calibration intercept: 0.010, [\u22120.021, 0.041]; slope: 0.997, [0.979, 1.014]), with negative predictive values exceeding 0.99. Performance was comparable to Kentucky-specific ML models, including the highest performer XGBoost (AUROC: 0.930 [0.926-0.933]). All models struggled with low positive predictive values (0.15-0.19).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A California PDMP-derived prediction model transported successfully to Kentucky with minimal updating. Its strong comparative performance provides a feasible foundation for PDMP agencies to develop transparent, in-house CDS systems with potential clinical implications for safer and more individualized opioid prescribing.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag192","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T11:58:45Z","timestamp":1789473525000},"source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the validity and performance of a modifiable prediction model to predict new long-term opioid use in prescription drug monitoring programs"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5320-9444","authenticated-orcid":false,"given":"Iraklis Erik","family":"Tseregounis","sequence":"first","affiliation":[{"name":"Division of General Internal Medicine & Bioethics, Department of Internal Medicine, University of California, Davis , Sacramento, CA 95817,","place":["United States"]},{"name":"Center for Healthcare Policy & Research, University of California, Davis , Sacramento, CA 95817,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugene","family":"Shin","sequence":"additional","affiliation":[{"name":"Institute for Pharmaceutical Outcomes & Policy, Department of Pharmacy Practice and Science, College of Pharmacy, University of Kentucky , Lexington, KY 40536,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen G","family":"Henry","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine & Bioethics, Department of Internal Medicine, University of California, Davis , Sacramento, CA 95817,","place":["United States"]},{"name":"Center for Healthcare Policy & Research, University of California, Davis , Sacramento, CA 95817,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7214-0326","authenticated-orcid":false,"given":"Chris","family":"Delcher","sequence":"additional","affiliation":[{"name":"Institute for Pharmaceutical Outcomes & Policy, Department of Pharmacy Practice and Science, College of Pharmacy, University of Kentucky , Lexington, KY 40536,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"2026093007505266100_ooag192-B1","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1001\/jama.2016.1464","article-title":"CDC guideline for prescribing opioids for chronic pain\u2014United States, 2016","volume":"315","author":"Dowell","year":"2016","journal-title":"JAMA"},{"key":"2026093007505266100_ooag192-B2","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1056\/NEJMp2211040","article-title":"Prescribing opioids for pain\u2013the new CDC clinical practice guideline","volume":"387","author":"Dowell","year":"2022","journal-title":"N Engl J Med"},{"key":"2026093007505266100_ooag192-B3","author":"Medicare Program","year":"2018"},{"key":"2026093007505266100_ooag192-B4","author":"Express scripts to limit opioids; doctors concerned","year":"2017"},{"key":"2026093007505266100_ooag192-B5","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1093\/pm\/pny307","article-title":"Challenges with implementing the centers for disease control and prevention opioid guideline: a consensus panel report","volume":"20","author":"Kroenke","year":"2019","journal-title":"Pain Med"},{"key":"2026093007505266100_ooag192-B6","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.1056\/NEJMp1904190","article-title":"No shortcuts to safer opioid prescribing","volume":"380","author":"Dowell","year":"2019","journal-title":"N Engl J Med"},{"key":"2026093007505266100_ooag192-B7","author":"Australian Government Department of Health D and A. 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