Machine Learning Predictive Model for Rotator Cuff Repair Failure

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.

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Key information

Conditions

Rotator Cuff Tears Rotator Cuff Injuries Rupture Shoulder Injuries Tendon Injuries Wounds and Injuries

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

La Tour hospital

Meyrin, Canton of Geneva, 1217, Switzerland

Who can participate

Only the study team can determine whether someone qualifies for participation.

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)

Treatment and study plan

Arthroscopic Rotator Cuff Repair

Procedure

Patients underwent an arthroscopic repair for rotator cuff lesions

Primary outcomes

SANE score

Time frame: At 12 post-operative months

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

Sponsors and collaborators

Lead sponsor

La Tour Hospital

Other

Registry information

Official study title

Predictive Model for Minimal Important Change After Rotator Cuff Repair Using Machine Learning Methods: A Pilot Study

Important dates

Study start: 2022
Primary completion: 2022
Study completion: 2023
First posted: Nov 24, 2023
Registry last updated: Nov 29, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed