{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:53:01Z","timestamp":1792000381105,"version":"4.4.0"},"reference-count":44,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T00:00:00Z","timestamp":1777161600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T00:00:00Z","timestamp":1777161600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Pain"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Long\u2010term opioid therapy (LTOT) after hip fracture surgery is a common postoperative complication associated with adverse outcomes, yet tools to identify at\u2010risk patients among opioid\u2010na\u00efve older adults are lacking. This study aimed to develop and internally validate a parsimonious model to predict LTOT following hip fracture surgery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using Danish nationwide registries, we identified 26,057 opioid\u2010na\u00efve patients (\u2265\u200965\u2009years) undergoing hip fracture surgery (2010\u20132020) and analysed 29 predictors, covering demographics, comorbidities, medication, lifestyle, socioeconomics and surgical factors. LTOT was defined as redeeming \u2265\u20092 prescriptions between 31 and 365\u2009days of surgery. Models were developed using four machine learning methods: logistic regression, backwards elimination, elastic net penalised logistic regression and random forest. Model performance was assessed using area under the receiver operating characteristic curve (AUC), calibration slope, intercept, Brier score and decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LTOT was identified in 8095 (31.1%) patients. The backward elimination algorithm identified the best performing model, selecting 8 of 29 predictors and achieving an AUC of 0.68, calibration slope of 0.99, intercept of 0.02 and Brier score of 0.20. Predictors included age, marital status, preoperative non\u2010opioid pain medication, preoperative novel oral anticoagulants, fracture type, surgery delay, length of hospital stay and postoperative cumulated ambulation score at discharge.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A prediction model was developed and validated for use at discharge to identify patients at risk of LTOT 1\u2009year after hip fracture. The model may support risk stratification at discharge, but requires external validation and evaluation of clinical implementation before routine use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance Statement<\/jats:title>\n                    <jats:p>This study presents the first internally validated prediction model for long\u2010term opioid use in opioid\u2010na\u00efve older adults after hip fracture surgery. The model functions as a simple and interpretable risk stratification tool at discharge and has been incorporated in a freely available risk calculator. It addresses the lack of clinically applicable risk stratification tools in this frail population and highlights opportunities for more targeted postoperative pain management, although feasibility testing is required before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ejp.70280","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T05:32:09Z","timestamp":1777267929000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and Internal Validation of a Machine Learning Model for Predicting Long\u2010Term Opioid Therapy After Hip Fracture Surgery in Older, Opioid\u2010Na\u00efve Adults"],"prefix":"10.1002","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0131-4383","authenticated-orcid":false,"given":"Yasmina Maria","family":"Tudorache","sequence":"first","affiliation":[{"name":"Department of Clinical Epidemiology Aarhus 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