Preoperative prediction of residual rotational instability after ACL reconstruction using a machine learning model.

Abstract

Residual rotational instability persists in 15%-30% of patients after anterior cruciate ligament (ACL) reconstruction and is associated with subjective instability, reduced return-to-sport rates and increased graft failure risk. Accurate preoperative prediction of residual pivot-shift could improve surgical planning and guide selective anterolateral reinforcement. This study aimed to develop and validate a machine-learning model to predict postoperative rotational instability (pivot-shift ≥2) using routinely available clinical and magnetic resonance imaging (MRI)-derived variables. A multicenter retrospective cohort of patients undergoing primary ACL reconstruction was screened (<i>n</i> = 312), of whom 246 met inclusion criteria and were analysed, including 79 patients with postoperative pivot-shift ≥2. Variables included demographic factors, clinical laxity, posterior tibial slope, lateral meniscal extrusion, graft type and anterolateral reinforcement. Three algorithms-Random Forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO) logistic regression-were trained (70%) and internally validated (30%) using five-fold cross-validation. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration and decision-curve analysis. External validation was performed in an independent cohort (<i>n</i> = 60). XGBoost showed the best discriminative performance (AUC 0.87; sensitivity 0.83; specificity 0.79), with consistent results in external validation (AUC 0.84). Posterior tibial slope and lateral meniscal extrusion were the strongest predictors. Decision-curve analysis demonstrated superior net clinical benefit compared with rule-based approaches using International Knee Documentation Committee (IKDC) or Lachman thresholds. A machine-learning model based on routine preoperative clinical and MRI variables accurately predicts residual mechanical pivot-shift after ACL reconstruction. This tool may support individualized surgical planning and selective indications for anterolateral reinforcement. Prospective preoperative evaluation of its impact on clinical decision-making is warranted. Level II, retrospective diagnostic-predictive study.