Machine Learning Predictive Model for Rotator Cuff Repair Failure

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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06145815

Enrollment

4789

Registered

2023-11-24

Start date

2022-09-01

Completion date

2023-11-01

Last updated

2023-11-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Rotator Cuff Tears

Keywords

Artificial intelligence, Machine Learning, Minimal Important Change

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.

Interventions

PROCEDUREArthroscopic rotator cuff repair

Patients underwent an arthroscopic repair for rotator cuff lesions

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

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)

Design outcomes

Primary

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

Countries

Switzerland

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026