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

Background: Secondary 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. Methods: This retrospective cohort study included 487 patients who underwent primary ACL reconstruction. Thirty predictor variables spanning demographic, magnetic resonance imaging (MRI), gait analysis, isokinetic strength, and psychological domains were collected at a standardized 6-month postoperative follow-up. Five machine learning algorithms were evaluated using a nested cross-validation scheme, with SHAP analysis and domain ablation applied for interpretability. Results: Sixty-four patients (13.1%) sustained secondary injuries (ipsilateral graft re-rupture or contralateral ACL rupture). Logistic regression achieved the best discriminative performance (AUC = 0.739, 95% CI: 0.672-0.806) and calibration (Brier score = 0.106), although no statistically significant difference was observed between logistic regression and random forest (corrected paired Conclusion: A multi-domain machine learning model identified patients at elevated secondary injury risk with acceptable discrimination and calibration. Kinesiophobia and depressive symptoms showed the largest incremental predictive contributions among the domains examined, suggesting that systematic psychological screening within postoperative rehabilitation warrants further investigation in prospective and externally validated studies.