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

NCT07333560 | Not Yet Recruiting

• 1 other identifier: 641/2025/Oss/IOR
• Study Type: observational
• Target: 943
• Locations: 1 country
• Sites: 1

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.

Conditions

Artificial Intelligence (AI); Machine Learning; Joint Replacement; Predictive Model

Outcome Measures

Primary Outcomes (1)

• Area under the receiver operating characteristic curve (AUROC) for discrimination ability of the machine learning predictive model

  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.

  Through study completion, an average of 2 years

Secondary Outcomes (2)

• 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

Interventions

Predictive Model for Early Mobility Recovery and Length of Stay OTHER

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

For the retrospective phase, data will be extracted from existing clinical and research databases at IRCCS Istituto Ortopedico Rizzoli. Pre-existing data collected between 2020 and 2023 will be used to develop automated predictive models. For the prospective phase, patients admitted to IRCCS Istituto Ortopedico Rizzoli for elective hip or knee arthroplasty between March 2026 and December 2027 will be consecutively enrolled. Newly collected data will be used to externally validate the predictive model, which will be applied without any modification (locked model).

You may qualify if:

• Adults aged 18 years or older
• Patients underwent elective hip or knee arthroplasty.
• Patients for whom postoperative physiotherapy was initiated.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Istituto Ortopedico Rizzoli: lead

Study Sites (1)

SAITeR IRCCS Istituto Ortopedico Rizzoli

Bologna, 40100, Italy

Location

Related Publications (6)

• Ribbons K, Cochrane J, Johnson S, Wills A, Ditton E, Dewar D, Broadhead M, Chan I, Dixon M, Dunkley C, Harbury R, Jovanovic A, Leong A, Summersell P, Todhunter C, Verheul R, Pollack M, Walker R, Nilsson M. Biopsychosocial based machine learning models predict patient improvement after total knee arthroplasty. Sci Rep. 2025 Feb 10;15(1):4926. doi: 10.1038/s41598-025-88560-w.

  PMID: 39929870 RESULT

• de Hond AAH, Steyerberg EW, van Calster B. Interpreting area under the receiver operating characteristic curve. Lancet Digit Health. 2022 Dec;4(12):e853-e855. doi: 10.1016/S2589-7500(22)00188-1. Epub 2022 Oct 18. No abstract available.

  PMID: 36270955 RESULT

• Hamel MB, Toth M, Legedza A, Rosen MP. Joint replacement surgery in elderly patients with severe osteoarthritis of the hip or knee: decision making, postoperative recovery, and clinical outcomes. Arch Intern Med. 2008 Jul 14;168(13):1430-40. doi: 10.1001/archinte.168.13.1430.

  PMID: 18625924 RESULT

• Gandhi R, Wasserstein D, Razak F, Davey JR, Mahomed NN. BMI independently predicts younger age at hip and knee replacement. Obesity (Silver Spring). 2010 Dec;18(12):2362-6. doi: 10.1038/oby.2010.72. Epub 2010 Apr 8.

  PMID: 20379147 RESULT

• Corbacioglu SK, Aksel G. Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value. Turk J Emerg Med. 2023 Oct 3;23(4):195-198. doi: 10.4103/tjem.tjem_182_23. eCollection 2023 Oct-Dec.

  PMID: 38024184 RESULT

• Baklola M, Reda Elmahdi R, Ali S, Elshenawy M, Mohamed Mossad A, Al-Bawah N, Mohamed Mansour R. Artificial intelligence in disease diagnostics: a comprehensive narrative review of current advances, applications, and future challenges in healthcare. Ann Med Surg (Lond). 2025 May 26;87(7):4237-4245. doi: 10.1097/MS9.0000000000003423. eCollection 2025 Jul.

  PMID: 40851938 RESULT

MeSH Terms

Interventions

Length of Stay

Intervention Hierarchy (Ancestors)

Hospitalization; Patient Care; Therapeutics; Health Services; Health Care Facilities Workforce and Services

Study Officials

• Mattia Morri

  IRCCS Istotuto Ortopedico Rizzoli

  PRINCIPAL INVESTIGATOR

• Morri

  IRCCS Istotuto Ortopedico Rizzoli

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Mattia Morri

CONTACT

+390516366694; mattia.morri@ior.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 16, 2025
• First Posted: January 12, 2026
• Study Start: February 1, 2026
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: February 20, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)