An Ensemble Machine Learning Method to Predict Unplanned Return to Operating Room Following Primary Total Shoulder Arthroplasty

Reoperation is the most significant compilation following any surgical procedure. Developing machine learning methods that can predict the need for reoperation will allow for improved shared surgical decision making, and patient-specific and preoperative optimization. Yet, no precise machine learning models have been published to perform well in predicting the need for a reoperation within 30-days following primary total shoulder arthroplasty (TSA). This study aimed to build, train, and evaluate an ensemble machine learning method that predicts return to the operating room following primary TSA.