Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery
Development and Pre-validated Multiple Variable Prediction Model Using Machine Learning for Early Functional Recovery After Joint Replacement Surgery.
Istituto Ortopedico Rizzoli
943 participants
Mar 9, 2026
OBSERVATIONAL
Conditions
Summary
The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is: Can a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery? Patients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.
Eligibility
Inclusion Criteria3
- Adults aged 18 years or older
- Patients underwent elective hip or knee arthroplasty.
- Patients for whom postoperative physiotherapy was initiated.
Exclusion Criteria3
- Patients who underwent surgery for oncologic disease, femoral fracture, or revision joint arthroplasty.
- Patients for whom postoperative physiotherapy was not provided due to postoperative complications
- clinical data are unavailable.
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Interventions
Application of a machine learning-based predictive algorithm to retrospectively and prospectively analyze clinical and perioperative data in patients undergoing hip or knee arthroplasty, without influencing clinical decision-making.
Locations(2)
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NCT07333560