Psychological factors demonstrate the largest incremental predictive value in a multi-domain machine learning model for secondary injury risk after ACL reconstruction

BackgroundSecondary injury after anterior cruciate ligament (ACL) reconstruction, defined as ipsilateral graft rerupture or contralateral ACL rupture, remains a clinical challenge. Current prediction models predominantly fail to capture this multifactorial risk. In this study, we developed a multi-domain machine learning model to predict the risk of secondary injury.MethodsThis retrospective cohort study included 487 patients who underwent primary ACL reconstruction. Thirty predictor variables s