Development and Pre-validated Multiple Variable Prediction Model Using Machine Learning for Early Functional Recovery After Joint Replacement Surgery.
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Istituto Ortopedico Rizzoli
- Enrollment
- 943
- Locations
- 2
- Primary Endpoint
- Area under the receiver operating characteristic curve (AUROC) for discrimination ability of the machine learning predictive model
Study Overview
Brief 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.
Detailed Description
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).
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Adults aged 18 years or older
- •Patients underwent elective hip or knee arthroplasty.
- •Patients for whom postoperative physiotherapy was initiated.
Exclusion Criteria
- •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.
Outcomes
Primary Outcomes
Area under the receiver operating characteristic curve (AUROC) for discrimination ability of the machine learning predictive model
Time Frame: Through study completion, an average of 2 years
The discrimination ability of the machine learning predictive model will be assessed using the area under the receiver operating characteristic curve (AUROC). AUROC summarizes the trade-off between sensitivity and specificity across all possible classification thresholds. AUROC values range from 0.5 (no discrimination) to 1.0 (perfect discrimination). Higher values indicate better model performance. Values above 0.8 will be considered indicative of good discriminatory performance.
Secondary Outcomes
- Calibration of the machine learning predictive model assessed by calibration plots(through study completion, an average of 2 years)
- Predictive performance of the machine learning model assessed by precision and F1-score(Through study completion, an average of 2 years)