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Predicting Major Preoperative Risk Factors for Retears After Arthroscopic Rotator Cuff Repair Using Machine Learning
1Department of Orthopedic Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, Banpo-Daero 222, Secho-gu, Seoul 06591, Republic of Korea.
Journal of Clinical Medicine
|March 27, 2025
Summary
Machine learning models accurately predicted rotator cuff retears, identifying tear size, full-thickness tears, BMI, female sex, and pain scores as key risk factors after arthroscopic rotator cuff repair (ARCR). These findings enhance understanding of retear predictors.
Area of Science:
- Orthopedic Surgery
- Biomedical Engineering
- Data Science in Medicine
Background:
- Rotator cuff tears are common, and retears after surgical repair pose a significant challenge.
- Predicting retears is crucial for optimizing patient outcomes and surgical planning.
Purpose of the Study:
- To identify and rank risk factors for retears following arthroscopic rotator cuff repair (ARCR).
- To compare the predictive accuracy of machine learning models against traditional logistic regression.
Main Methods:
- Analysis of 788 primary ARCR cases with 27 preoperative variables.
- Application of Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), and logistic regression (LR) models.
- Model performance evaluated using Area Under the Curve (AUC) with 8:2 training/testing split and three-fold validation.
Main Results:
- The overall retear rate was 11.9%.
- RF (AUC=0.9790) and XGBoost (AUC=0.9785) were the top-performing models.
- Key risk factors identified: tear size (ML/AP dimensions), full-thickness tears, Body Mass Index (BMI), female sex, and Visual Analogue Scale (VAS) pain score.
Conclusions:
- Machine learning models significantly outperform traditional logistic regression in predicting rotator cuff retears.
- Tear size, full-thickness tears, BMI, female sex, and VAS pain score are the most influential risk factors for retears after ARCR.
