Key result
Machine learning effectively predicts patient satisfaction after rotator cuff repair with a 0.87 F1-score.
Why the study?
Patient satisfaction is a crucial measure of surgical success, but the relationship between preoperative variables and satisfaction after rotator cuff repair surgeries needed exploration using machine learning.
Can machine learning models accurately predict patient satisfaction based on preoperative variables in patients undergoing rotator cuff repair surgeries?
Cohort (n=189)
Can machine learning models accurately predict patient satisfaction based on preoperative variables in patients undergoing rotator cuff repair surgeries?
Machine learning models, particularly XGBoost with feature selection, can effectively predict patient satisfaction following rotator cuff repair surgeries using preoperative variables.
May support preoperative risk stratification in rotator cuff repair; leaves open prospective validation before clinical use.
Orthopedic interventions, including rotator cuff repair surgeries, play a pivotal role in addressing musculoskeletal conditions and restoring patients’ functional abilities. While clinical outcomes serve as important benchmarks, patient satisfaction has emerged as a crucial measure of success in evaluating the effectiveness of these surgical procedures. This study delves into the intricate relationship between preoperative variables and patient satisfaction outcomes specifically in the context of rotator cuff repair surgeries. By employing an array of 14 distinct machine learning models, featuring the Multi-Layer Perceptron (MLP) as one of the primary models.we aim to explore the effects of preoperative variables on the outcomes of patient satisfaction.These research outcomes underline the significance of correcting class imbalances in healthcare data and employing accurate predictive modeling techniques to elevate patient-centered care. Our analysis is based on a comprehensive cohort of 189 patients with shoulder pathology, utilizing a combination of stratified sampling and the Borderline Synthetic Minority Oversampling Technique (BSMOTE) to ensure a balanced dataset, facilitating a comprehensive assessment of satisfaction factors. In summary, the baseline models Extra Trees and CatBoost exhibited comparable validation performance with closely aligned F1-scores for both target classes. However, on the test set, Random Forest outperformed XGBoost in terms of achieving higher F1-scores and precision. Turning to feature selection, FS_Extra Trees, FS_XGBoost, and FS_Neural Network excelled on the validation set, demonstrating the highest Kappa values and significant F1 scores. Remarkably, on the test set, the FS_XGBoost model maintained consistent performance with a Kappa coefficient of 0.54, showcasing remarkable F1-scores of 0.87 for the "Satisfied" class and 0.67 for the "Not Satisfied" class. The study’s outcomes demonstrate the substantial impact of machine learning in orthopedic surgery, signifying a significant advancement in data-informed patient care and the enhancement of outcomes in Rotator cuff repair surgeries (RCR).
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Taneja et al. (2024) conducted a cohort in Shoulder pathology requiring rotator cuff repair (n=189). Machine learning predictive modeling (e.g., FS_XGBoost) vs. Other machine learning models was evaluated on Patient satisfaction prediction performance (F1-score and Kappa coefficient). Machine learning models, specifically FS_XGBoost, effectively predicted patient satisfaction after rotator cuff repair, achieving a Kappa coefficient of 0.54 and an F1-score of 0.87 for satisfaction.
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