Machine Learning Predictive Model for Rotator Cuff Repair Failure
Completed
- Conditions
- Rotator Cuff Tears
- Interventions
- Procedure: Arthroscopic rotator cuff repair
- Registration Number
- NCT06145815
- Lead Sponsor
- La Tour Hospital
- 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 4789
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)
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description MIC RCR patients Arthroscopic rotator cuff repair Patients who improved their SANE score beyond the minimal important change one year after arthroscopic rotator cuff repair No-MIC RCR patients Arthroscopic rotator cuff repair Patients who did not improve their SANE score beyond the minimal important change one year after arthroscopic rotator cuff repair
- Primary Outcome Measures
Name Time Method SANE score At 12 post-operative months Single Assessment Numeric Evaluation (SANE). From 0 (worst) to 100 (best).
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
La Tour hospital
🇨ðŸ‡Meyrin, Geneva, Switzerland