External Validation of a Machine Learning Algorithm for Predicting Clinically Meaningful Functional Improvement After Arthroscopic Hip Preservation Surgery

Individualized risk prediction has become possible with machine learning (ML), which may have important implications in enhancing clinical decision-making. To assess the external validity of an ML algorithm for predicting clinically meaningful improvement after hip arthroscopy. The performance of this algorithm in an independent patient population in the northeast region of the United States demonstrated superior discrimination and comparable calibration to that of the derivation cohort. The external validation of this algorithm suggests that it is a reliable method to predict the propensity for clinically meaningful improvement after hip arthroscopy and is an essential step forward toward introducing initial use in clinical practice.