使用下肢肌肉骨骼评估和患者报告结局开发并外部验证机器学习模型以分类ACL重建状态
简介
本研究基于863例受试者（659例ACLR术后、204例对照）的下肢肌骨评估与患者报告结局数据，训练并内部验证随机森林、梯度提升机及XGBoost模型，并在独立外部队列（174例）中验证，以区分ACLR术后与健康人群。XGBoost模型表现最佳（内部测试AUC=0.98，外部验证AUC=0.83），最具信息量的特征为KOOS-QoL子量表、90°/s峰值膝伸…
英文摘要
Recovery following anterior cruciate ligament reconstruction (ACLR) is multifaceted, encompassing strength, functional, and patient-reported outcome measures (PROMs). Determining which factors are most important for characterizing recovery and informing return-to-activity (RTA) decisions remains a clinical challenge. This study aimed to develop and externally validate a supervised machine learning model to classify individuals post-ACLR from healthy controls using comprehensive lower-extremity musculoskeletal assessments. Data from 863 participants (659 post-ACLR, 204 controls) at Site 1 were used to train and internally test Random Forest, Gradient Boosting Machine, and Extreme Gradient Boosting (XGBoost) models, with an 80/20 training-testing split and inner fivefold cross-validation for hyperparameter optimization. Model performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC), and external validation was performed on an independent Site 2 cohort (N = 174; 133 post-ACLR, 41 controls). The XGBoost model was selected for further analysis (internal test AUC = 0.98, Accuracy = 0.95), and maintained good discrimination on external validation (AUC = 0.83, Accuracy = 0.82). The most informative features included the Knee Osteoarthritis Outcome Score quality-of-life subscale, limb symmetry index for peak knee extension torque at 90°/s, and the International Knee Documentation Committee score, which were consistent across age and sex subgroups. These findings demonstrate that machine learning can effectively integrate strength, function, and PROM data to identify key indicators of ACLR status and support development of streamlined, data-driven protocols for post-ACLR recovery monitoring and RTA decision-making. STATEMENT OF CLINICAL SIGNIFICANCE: The Knee Osteoarthritis Outcome Score quality-of-life subscale, limb symmetry index for peak knee extension torque at 90°/s, and the International Knee Documentation Committee score most effectively distinguish individuals following ACLR from healthy controls. Clinicians may focus on these tests to determine ACLR recovery status as a feasible streamlined assessment battery or when results from a comprehensive testing battery are conflicting or inconclusive.