Machine Learning Predictive Model for Rotator Cuff Repair Failure

Completed

• Conditions: Rotator Cuff Tears

• 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

Study Timeline

• Study Start: 2022-09-01
• Primary Completion: 2022-09-01
• Status Verified: 2023-11
• Study Completion: 2023-11-01
• Study First Submitted: 2023-11-17
• Study First Submitted QC: 2023-11-22
• Study First Posted: 2023-11-24
• Last Update Submitted: 2023-11-24
• Last Update Posted: 2023-11-29

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

Primary Outcome Measures

Name	Time	Method
SANE score	At 12 post-operative months	Single Assessment Numeric Evaluation (SANE). From 0 (worst) to 100 (best).

Trial Locations

Locations (1)

La Tour hospital; 🇨🇭 Meyrin, Geneva, Switzerland