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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 t -test, p = 0.72). SHAP analysis of both the random forest and logistic regression models identified the TSK score and PHQ-9 as the most influential individual predictors; bootstrap resampling indicated moderate stability of these rankings. Domain ablation confirmed that the psychological domain provided the largest incremental predictive contribution, with its removal producing the greatest performance decrement (AUC decline: 0.031, 95% CI: 0.007–0.058), whereas the removal of the MRI or gait domains did not reduce model performance. All models demonstrated high negative predictive values (0.89–0.94) but limited precision (0.21–0.26), indicating that most patients flagged as high risk would not sustain a secondary injury. Decision curve analysis indicated a net clinical benefit in the 0.05–0.15 threshold range, supporting a low-threshold screening rather than a diagnostic application. 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.
Xiong et al. (Fri,) studied this question.