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Machine Learning Predictive Model for Rotator Cuff Repair Failure

NCT ID: NCT06145815

Last Updated: 2023-11-29

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

4789 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-09-01

Study Completion Date

2023-11-01

Brief Summary

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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

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Conditions

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Rotator Cuff Tears

Keywords

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Artificial intelligence Machine Learning Minimal Important Change

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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MIC RCR patients

Patients who improved their SANE score beyond the minimal important change one year after arthroscopic rotator cuff repair

Arthroscopic rotator cuff repair

Intervention Type PROCEDURE

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

Arthroscopic rotator cuff repair

Intervention Type PROCEDURE

Patients underwent an arthroscopic repair for rotator cuff lesions

Interventions

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Arthroscopic rotator cuff repair

Patients underwent an arthroscopic repair for rotator cuff lesions

Intervention Type PROCEDURE

Eligibility Criteria

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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)
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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La Tour Hospital

OTHER

Sponsor Role lead

Responsible Party

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Dr. Alexandre Lädermann

Orthopaedic Surgeon

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Alexandre Lädermann, MD

Role: PRINCIPAL_INVESTIGATOR

La Tour hospital, Meyrin (1217) Geneva, Switzerland

Locations

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La Tour hospital

Meyrin, Canton of Geneva, Switzerland

Site Status

Countries

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Switzerland

Other Identifiers

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CCER #2020-02670

Identifier Type: -

Identifier Source: org_study_id