Frontiers in rehabilitation sciences · 2026
Development and validation of machine learning models for predicting rehabilitation non-response after knee arthroplasty.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hubel Niclas J, Prüfer Ferdinand, Bily Walter, Schneider David, Holzner Bernhard, Fischer Michael J, Grote Vincent
Аннотация
INTRODUCTION: 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.
METHODS: Registry data from four Austrian rehabilitation centres (N = 5,463; 2018-2024), were used to predict non-response in a global (EQ-5D, n = 4,032), and an indication-specific patient-reported outcome measure (WOMAC, n = 4,213) and a performance test (Timed Up and Go Test - TUG, n = 3,354). Outcomes were dichotomized applying a distribution-based MCID and predicted using automated ML ensemble algorithms. Model performance was assessed in independent test data.
RESULTS: 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.
DISCUSSION: 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.
METHODS: Registry data from four Austrian rehabilitation centres (N = 5,463; 2018-2024), were used to predict non-response in a global (EQ-5D, n = 4,032), and an indication-specific patient-reported outcome measure (WOMAC, n = 4,213) and a performance test (Timed Up and Go Test - TUG, n = 3,354). Outcomes were dichotomized applying a distribution-based MCID and predicted using automated ML ensemble algorithms. Model performance was assessed in independent test data.
RESULTS: 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.
DISCUSSION: 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.