Artificial Intelligence (AI)FISIO_IANCT07333560
Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery
An observational study of Artificial Intelligence (AI) and Machine Learning, run by Istituto Ortopedico Rizzoli at 2 sites in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artificial Intelligence (AI), Machine Learning, Joint Replacement
- What is being tested?
- Predictive Model for Early Mobility Recovery and Length of Stay
- How many people
- 943 participants
- Where
- 2 sitesItaly
- Study dates
- 9 Mar 2026to Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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).
Who may be able to join
- Adults aged 18 years or older
- Patients underwent elective hip or knee arthroplasty.
- Patients for whom postoperative physiotherapy was initiated.
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Italy2 sites, 2 recruiting
- Recruiting
SAITeR IRCCS Istituto Ortopedico Rizzoli
Bologna, 40100
Mattia Morri, +390516366694, mattia.morri@ior.it
- Not yet recruiting
Azienda U.S.L. - IRCCS di Reggio Emilia
Reggio Emilia
Alessia Pecorari, alessia.pecorari@ausl.re.it
About this study
Official title: 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.
Read the 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.
- Being tested
- Predictive Model for Early Mobility Recovery and Length of Stay
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Predictive Model for Early Mobility Recovery and Length of StayOther
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, an average of 2 years
Secondary outcomes (2)
Calibration of the machine learning predictive model assessed by calibration plots
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.
Measured: through study completion, an average of 2 years
Predictive performance of the machine learning model assessed by precision and F1-score
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.
Measured: Through study completion, an average of 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jan 2026
- Study start9 Mar 2026
- Primary completionDec 2027 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Artificial Intelligence (AI)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 943 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Istituto Ortopedico Rizzoli
- Sponsor type
- University, hospital or other
- Collaborators
- Azienda USL Reggio Emilia - IRCCS
- Responsible party
- Sponsor
- Investigators
- Mattia Morri, Principal investigator, IRCCS Istotuto Ortopedico RizzoliMorri, Principal investigator, IRCCS Istotuto Ortopedico Rizzoli
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07333560
- Sponsor's protocol ID
- 641/2025/Oss/IOR
- Acronym
- FISIO_IA
- Status last verified
- May 2026
- First submitted
- 16 Dec 2025
- Last updated
- 1 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The datasets generated and/or analyzed during the current study will be available from the Principal Investigator upon reasonable request.
Publications
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.
PubMed 39929870de 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.
PubMed 36270955Hamel 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.
PubMed 18625924Gandhi 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.
PubMed 20379147Corbacioglu 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.
PubMed 38024184Baklola 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.
PubMed 40851938
Study at a glance
From the registry record, last updated by the sponsor on 1 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Pre-validated Multiple Variable Prediction Model Using Machine Learning for Early Functional Recovery After Joint Replacement Surgery.
- ClinicalTrials.gov ID
- NCT07333560
- Acronym / sponsor ID
- FISIO_IA / 641/2025/Oss/IOR
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 943 participants (estimated)
- Lead sponsor
- Istituto Ortopedico RizzoliUniversity, hospital or other
- Collaborators
- Azienda USL Reggio Emilia - IRCCS
- Study start
- 9 Mar 2026 (actual)
- Primary completion
- Dec 2027 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 12 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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).
Key inclusion criteria (3 listed)
- Adults aged 18 years or older
- Patients underwent elective hip or knee arthroplasty.
- Patients for whom postoperative physiotherapy was initiated.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Predictive Model for Early Mobility Recovery and Length of StayApplication 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. | Other | – |
Primary outcome
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.
2 secondary outcomes
Calibration of the machine learning predictive model assessed by calibration plots
Time frame: 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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Jun 2026.
- Results publication: PubMed 39929870Ribbons 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.
- Results publication: PubMed 36270955de 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.
- Results publication: PubMed 18625924Hamel 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.
- Results publication: PubMed 20379147Gandhi 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.
- Results publication: PubMed 38024184Corbacioglu 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.
- Results publication: PubMed 40851938Baklola 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.
- Individual participant dataSharing planned: No. The datasets generated and/or analyzed during the current study will be available from the Principal Investigator upon reasonable request.
Related trials for Artificial Intelligence (AI)
Recruiting
Longitudinal AI-Assisted Management of Indeterminate Pulmonary Nodules
Pulmonary Nodules, Lung Neoplasms
Recruiting
A Multicenter Randomized Trial of CT-Based AI Decision Support for Early Pneumonia Care
Pneumonia; Pulmonary Infection
Recruiting
Feasibility and Acceptability of an LLM-based Chatbot for Family Caregivers: Evaluation Study
Caregivers
Enrolling by invitationObservational study
Comparing an AI-driven Hearing Test With the Standard Hearing Test in Adults With Hearing Loss and Tinnitus
Sensorineural Hearing Loss, Tinnitus
Not yet recruiting
AI-Powered Sleep Hygiene Mentor for Adults Living With HIV
Sleep Wake Disorders, Sleep Initiation and Maintenance Disorders
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Mattia Morri principal investigator
IRCCS Istotuto Ortopedico Rizzoli
Morri principal investigator
IRCCS Istotuto Ortopedico Rizzoli
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence (AI) and Machine Learning, run by Istituto Ortopedico Rizzoli at 2 sites in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 1 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Mattia Morri (+390516366694, mattia.morri@ior.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07333560, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Jun 2026.