{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:53:42Z","timestamp":1792000422475,"version":"4.4.0"},"reference-count":47,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"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":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Knee osteoarthritis (OA) affects approximately 10% of the general population. Of these, nearly 20% of patients undergo total knee arthroplasty (TKA). A proportion subsequently develops long\u2010term opioid use, yet comprehensive evaluations of perioperative risk factors remain limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 15,051 patients with knee OA who underwent TKA between 2002 and 2018 were included. Our primary analysis used a Least Absolute Shrinkage and Selection Operator (LASSO)\u2013Elastic Net logistic regression model with a 70\/30 training\u2013validation split to identify predictors of long\u2010term opioid use and to estimate model performance using the area under the receiver operating characteristic curve (AUC) and calibration. As supplementary analyses, hierarchical logistic regression models progressively incorporated demographic, preoperative, in\u2010hospital, and early post\u2010discharge variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Overall, 1029 patients were identified as long\u2010term opioid users. The LASSO\u2013Elastic Net model achieved an AUC of 0.8213 (95% CI: 0.8080\u20130.8347). The AUCs of the hierarchical logistic models were 0.7523 (95% CI: 0.7364\u20130.7681) for demographic and preoperative factors (Model 1), 0.7682 (95% CI: 0.7534\u20130.7829) with added hospitalisation factors (Model 2), and 0.8251 (95% CI: 0.8119\u20130.8383) after further including post\u2010discharge variables (Model 3).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study systematically identifies risk factors for long\u2010term opioid use across different phases of TKA care. A predictive model based on LASSO\u2013Elastic analysis demonstrated an AUC of 0.8213, highlighting its potential for early identification of high\u2010risk patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance Statement<\/jats:title>\n                    <jats:p>Using a population\u2010based analysis, this study identifies risk factors for prolonged opioid use following TKA, aiding clinicians in early recognition and targeted preventive strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ejp.70195","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T23:22:06Z","timestamp":1766704926000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Prediction Model for Factors Associated With Long\u2010Term Opioid Use Following Total Knee Arthroplasty: A Retrospective Population\u2010Based 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