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Machine Learning Predictive Model for Rotator Cuff Repair Failure

NCT06145815 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 4789

Last updated 2023-11-29

No results posted yet for this study

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.

Conditions

  • Rotator Cuff Tears

Interventions

PROCEDURE

Arthroscopic rotator cuff repair

Patients underwent an arthroscopic repair for rotator cuff lesions

Sponsors & Collaborators

  • La Tour Hospital

    lead OTHER

Principal Investigators

  • Alexandre Lädermann, MD · La Tour hospital, Meyrin (1217) Geneva, Switzerland

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-09-01
Primary Completion
2022-09-01
Completion
2023-11-01

Countries

  • Switzerland

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06145815 on ClinicalTrials.gov