Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery

This observational study aims to develop and pre-validate a machine learning algorithm to predict early mobility recovery and hospital length of stay in patients undergoing elective hip or knee arthroplasty. The study includes a retrospective phase (2020-2023) using existing clinical and physiotherapy data, and a prospective phase (March 2026-December 2027) to validate the model in routine clinical practice. Data Collection and Outcomes: Mobility recovery: assessed by the ability to ascend and descend three steps within the first four postoperative days, recorded in the physiotherapy diary and electronic health record. Length of stay: considered regular if discharged by the fifth postoperative day; longer stays are defined as prolonged. Predictors: Baseline demographics (age, sex, BMI, ASA score, preoperative hemoglobin) and clinical/perioperative characteristics (type of surgery and anesthesia, initiation of physiotherapy, pain level, urinary catheter use, orthostatic intolerance). Sample Size: 943 patients total (600 retrospective, 343 prospective), based on model development requirements and AUROC estimation. Data Analysis: The dataset will be split into training, validation, and test sets. Multiple supervised learning algorithms (e.g., logistic regression, random forest, gradient boosting) will be compared. Model performance will be evaluated using AUROC, sensitivity, specificity, precision, F1-score, and calibration. Missing data will be handled with imputation or native algorithm methods when supported. Model Validation: Prospective data will be used to assess model discrimination and calibration, and to identify potential temporal or clinical biases. Retraining may be performed using combined datasets to improve generalizability. Study Flow: Retrospective patients identified via hospital records; prospective patients identified on the first postoperative physiotherapy session, provided with study information, and consented. Predictive results are stored in a separate registry inaccessible to treating clinicians. Participating Centers: IRCCS Istituto Ortopedico Rizzoli, Bologna - patient enrollment. Complex Structure of Medical Physics, Arcispedale S. Maria Nuova - data analysis and AI modeling.