Predicting the Risk of Posttraumatic Osteoarthritis After Primary Anterior Cruciate Ligament Reconstruction: A Machine Learning Time-to-Event Analysis

Machine learning survival models were used to reliably identify patients at risk of developing symptomatic PTOA, and these models consistently outperformed traditional Kaplan-Meier estimators. Strong predictors for the development of PTOA after ACLR included increased pain scores at injury and postoperative visit, older age at injury, total number of arthroscopic procedures, positive postoperative pivot-shift test, and secondary meniscal tear.