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

May 27, 2026 updated by: Istituto Ortopedico Rizzoli

Development and Pre-validated Multiple Variable Prediction Model Using Machine Learning for Early Functional Recovery After Joint Replacement Surgery.

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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).

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.

Study Type

Observational

Enrollment (Estimated)

943

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Italy
      • Bologna, Italy, 40100
        • Recruiting
        • SAITeR IRCCS Istituto Ortopedico Rizzoli
        • Contact:
      • Reggio Emilia, Italy

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

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).

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
Through study completion, an average of 2 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Calibration of the machine learning predictive model assessed by calibration plots
Time Frame: through study completion, an average of 2 years
Agreement between predicted probabilities and observed outcomes will be evaluated using calibration plots. Calibration will be visually assessed by plotting predicted versus observed event probabilities.
through study completion, an average of 2 years
Predictive performance of the machine learning model assessed by precision and F1-score
Time Frame: Through study completion, an average of 2 years
Predictive performance will be evaluated using precision and F1-score derived from the confusion matrix by comparing predicted class labels with observed outcomes. Precision reflects the proportion of correctly predicted positive cases among all predicted positives. The F1-score represents the harmonic mean of precision and recall. Higher values indicate better predictive performance.
Through study completion, an average of 2 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Istituto Ortopedico Rizzoli

Collaborators

Azienda U.S.L. - IRCCS di Reggio Emilia

Investigators

  • Principal Investigator: Mattia Morri, IRCCS Istotuto Ortopedico Rizzoli
  • Principal Investigator: Morri, IRCCS Istotuto Ortopedico Rizzoli

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 9, 2026

Primary Completion (Estimated)

December 1, 2027

Study Completion (Estimated)

December 1, 2027

Study Registration Dates

First Submitted

December 16, 2025

First Submitted That Met QC Criteria

January 2, 2026

First Posted (Actual)

January 12, 2026

Study Record Updates

Last Update Posted (Actual)

June 1, 2026

Last Update Submitted That Met QC Criteria

May 27, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 641/2025/Oss/IOR

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

The datasets generated and/or analyzed during the current study will be available from the Principal Investigator upon reasonable request.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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