Machine Learning Predictive Model for Rotator Cuff Repair Failure

• Sponsor: La Tour Hospital (Other)
• Overall Status: Completed
• CT.gov ID: NCT06145815
• Collaborator: (none)
• Enrollment: 4,789
• Location: 1
• Actual Duration (Months): 14
• Patients Per Site Per Month: 342.2

Study Details

Study Description

Brief Summary

There is little overall evidence behind clinical practice guidelines for diagnosis and treatment of rotator cuff repair. The purpose of this study was to compare the performance of different machine learning models that use pre-operative data from an international and multicentric database to predict if a patient that underwent rotator cuff repair could achieve the minimal important change (MIC) for single assessment numeric evaluation (SANE) at one year follow-up.

Condition or Disease	Intervention/Treatment	Phase
Rotator Cuff Tears	Procedure: Arthroscopic rotator cuff repair

Study Design

• Study Type: Observational
• Actual Enrollment: 4789 participants
• Observational Model: Cohort
• Time Perspective: Retrospective
• Official Title: Predictive Model for Minimal Important Change After Rotator Cuff Repair Using Machine Learning Methods: A Pilot Study
• Actual Study Start Date: Sep 1, 2022
• Actual Primary Completion Date: Sep 1, 2022
• Actual Study Completion Date: Nov 1, 2023

Arms and Interventions

Arm	Intervention/Treatment
MIC RCR patients Patients who improved their SANE score beyond the minimal important change one year after arthroscopic rotator cuff repair	Procedure: Arthroscopic rotator cuff repair; Patients underwent an arthroscopic repair for rotator cuff lesions
No-MIC RCR patients Patients who did not improve their SANE score beyond the minimal important change one year after arthroscopic rotator cuff repair	Procedure: Arthroscopic rotator cuff repair; Patients underwent an arthroscopic repair for rotator cuff lesions

Outcome Measures

Primary Outcome Measures

1. SANE score [At 12 post-operative months]

   Single Assessment Numeric Evaluation (SANE). From 0 (worst) to 100 (best).

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years and Older
• Sexes Eligible for Study: All

Inclusion Criteria:

• Primarily treated for rotator cuff tears by partial or complete surgical repair with a planned arthroscopic procedure

• Reparable tears

• No language barrier hindering questionnaire completion or legal incompetence were not included

Exclusion Criteria:

• missing pre- or post-operative single-assessment numeric evaluation (SANE)

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	La Tour hospital	Meyrin	Geneva	Switzerland	1217

Sponsors and Collaborators

• La Tour Hospital

Investigators

• Principal Investigator: Alexandre Lädermann, MD, La Tour hospital, Meyrin (1217) Geneva, Switzerland

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Dr. Alexandre Lädermann, Orthopaedic Surgeon, La Tour Hospital
• ClinicalTrials.gov Identifier: NCT06145815
• Other Study ID Numbers: CCER #2020-02670
• First Posted: Nov 24, 2023
• Last Update Posted: Nov 29, 2023
• Last Verified: Nov 1, 2023

• Individual Participant Data (IPD) Sharing Statement: Undecided
• Plan to Share IPD: Undecided
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Keywords provided by Dr. Alexandre Lädermann, Orthopaedic Surgeon, La Tour Hospital: Artificial intelligence; Machine Learning; Minimal Important Change
• Additional relevant MeSH terms: Rotator Cuff Injuries

Study Results

No Results Posted as of Nov 29, 2023