{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T06:09:41Z","timestamp":1789711781830,"version":"4.0.1"},"reference-count":75,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund","doi-asserted-by":"publisher","award":["888360"],"award-info":[{"award-number":["888360"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Rehabil. Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Structured rehabilitation after total knee arthroplasty (TKA) effectively improves health-related quality of life (HRQoL) and function, yet some patients fail to achieve minimal clinically important differences (MCIDs). Machine-learning (ML) based prediction models offer one way to identify those patients. We aim to develop and externally validate ML models to predict MCID-based non-response at discharge.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Registry data from four Austrian rehabilitation centres (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u20095,463; 2018\u20132024), were used to predict non-response in a global (EQ-5D,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20094,032), and an indication-specific patient-reported outcome measure (WOMAC,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20094,213) and a performance test (Timed Up and Go Test - TUG,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20093,354). Outcomes were dichotomized applying a distribution-based MCID and predicted using automated ML ensemble algorithms. Model performance was assessed in independent test data.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Models predicted non-response with good discrimination (AUCs: TUG 0.771, WOMAC 0.704, EQ-5D 0.770); and varying calibration (TUG: acceptable, WOMAC: good, EQ-5D: poor). Admission values were the most important predictors, with better values resulting in a higher likelihood of not achieving a MCID.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>ML can predict MCID-based non-response after TKA rehabilitation using routinely collected admission data. Baseline status is a dominant driver, highlighting ceiling effects and the potential for bias, as well as the need for more precise outcome definitions and the search for suitable, independent predictors. Clinicians could use ML-based risk estimates to identify likely non-responders early, support personalized expectation management, and tailor therapy intensity or content. Translating these tools to practice will require prospective validation and integration into clinical workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fresc.2026.1930836","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:38:15Z","timestamp":1789709895000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of machine learning models for predicting rehabilitation non-response after knee arthroplasty"],"prefix":"10.3389","volume":"7","author":[{"given":"Niclas J.","family":"Hubel","sequence":"first","affiliation":[{"name":"Austrian Center for Medical Innovation and Technology","place":["Wiener Neustadt, Austria"]},{"name":"Health Outcomes Research Unit, University Hospital of Psychiatry II, Medical University of Innsbruck","place":["Innsbruck, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ferdinand","family":"Pr\u00fcfer","sequence":"additional","affiliation":[{"name":"Department of Orthopedics and Traumatology, Medical University of Graz","place":["Graz, Austria"]},{"name":"Ludwig Boltzmann Institute for Rehabilitation Research","place":["Vienna, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter","family":"Bily","sequence":"additional","affiliation":[{"name":"Ludwig Boltzmann Institute for Rehabilitation Research","place":["Vienna, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Schneider","sequence":"additional","affiliation":[{"name":"Vitrea Rehabilitation Center Kitzb\u00fchel","place":["Kitzb\u00fchel, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernhard","family":"Holzner","sequence":"additional","affiliation":[{"name":"Health Outcomes Research Unit, University Hospital of Psychiatry II, Medical University of Innsbruck","place":["Innsbruck, Austria"]},{"name":"Evaluation Software Development GmbH \u2013 ESD","place":["Innsbruck, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Fischer","sequence":"additional","affiliation":[{"name":"Ludwig Boltzmann Institute for Rehabilitation Research","place":["Vienna, Austria"]},{"name":"Vitrea Rehabilitation Center Kitzb\u00fchel","place":["Kitzb\u00fchel, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Grote","sequence":"additional","affiliation":[{"name":"Department of Orthopedics and Traumatology, Medical University of Graz","place":["Graz, Austria"]},{"name":"Ludwig Boltzmann Institute for Rehabilitation Research","place":["Vienna, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1016\/S0140-6736(11)60752-6","article-title":"Knee replacement","volume":"379","author":"Carr","year":"2012","journal-title":"Lancet"},{"key":"B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-662-55918-5_1","article-title":"Introduction to the indications and procedures","volume-title":"White Pap Jt Replace","author":"Seidlitz","year":"2018"},{"key":"B3","doi-asserted-by":"publisher","first-page":"j1131","DOI":"10.1136\/bmj.j1131","article-title":"Impact of total knee replacement practice: cost effectiveness analysis of data from the osteoarthritis initiative","volume":"356","author":"Ferket","year":"2017","journal-title":"Br Med J"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.1016\/S0140-6736(18)32344-4","article-title":"Knee replacement","volume":"392","author":"Price","year":"2018","journal-title":"Lancet"},{"key":"B5","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1186\/s12891-018-2159-7","article-title":"Functions, disabilities and perceived health in the first year after total knee arthroplasty; 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