Ye Zipeng, Zhang Tianlun, Wu Chenliang, Qiao Yi, Su Wei, Chen Jiebo, Xie Guoming, Dong Shikui, Xu Junjie, Zhao Jinzhong
Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Am J Sports Med. 2022 Dec;50(14):3786-3795. doi: 10.1177/03635465221129870. Epub 2022 Oct 26.
Sports levels, baseline patient-reported outcome measures (PROMs), and surgical procedures are correlated with the outcomes of anterior cruciate ligament reconstruction (ACLR). Machine learning may be superior to conventional statistical methods in making repeatable and accurate predictions.
To identify the best-performing machine learning models for predicting the objective and subjective clinical outcomes of ACLR and to determine the most important predictors.
Case-control study; Level of evidence, 3.
A total of 432 patients who underwent anatomic double-bundle ACLR with hamstring tendon autograft between January 2010 and February 2019 were included in the machine learning analysis. A total of 15 predictive variables and 6 outcome variables were selected to validate the logistic regression, Gaussian naïve Bayes machine, random forest, Extreme Gradient Boosting (XGBoost), isotonically calibrated XGBoost, and sigmoid calibrated XGBoost models. For each clinical outcome, the best-performing model was determined using the area under the receiver operating characteristic curve (AUC), whereas the importance and direction of each predictive variable were demonstrated in a Shapley Additive Explanations summary plot.
The AUC and accuracy of the best-performing model, respectively, were 0.944 (excellent) and 98.6% for graft failure; 0.920 (excellent) and 91.4% for residual laxity; 0.930 (excellent) and 91.0% for failure to achieve the minimal clinically important difference (MCID) of the Lysholm score; 0.942 (excellent) and 95.1% for failure to achieve the MCID of the International Knee Documentation Committee (IKDC) score; 0.773 (fair) and 70.5% for return to preinjury sports; and 0.777 (fair) and 69.2% for return to pivoting sports. Medial meniscal resection, participation in competitive sports, and steep posterior tibial slope were top predictors of graft failure, whereas high-grade preoperative knee laxity, long follow-up period, and participation in competitive sports were top predictors of residual laxity. High preoperative Lysholm and IKDC scores were highly predictive of not achieving the MCIDs of PROMs. Young age, male sex, high preoperative IKDC score, and large graft diameter were important predictors of return to preinjury or pivoting sports.
Machine learning analysis can provide reliable predictions for the objective and subjective clinical outcomes (graft failure, residual laxity, PROMs, and return to sports) of ACLR. Patient-specific evaluation and decision making are recommended before and after surgery.
运动水平、患者报告的基线结局指标（PROMs）以及手术方式与前交叉韧带重建（ACLR）的结果相关。在进行可重复且准确的预测方面，机器学习可能优于传统统计方法。
确定用于预测ACLR客观和主观临床结果的最佳性能机器学习模型，并确定最重要的预测因素。
病例对照研究；证据等级，3级。
共有432例在2010年1月至2019年2月期间接受自体腘绳肌腱解剖双束ACLR的患者纳入机器学习分析。共选择15个预测变量和6个结局变量，以验证逻辑回归、高斯朴素贝叶斯机器、随机森林、极端梯度提升（XGBoost）、等张校准XGBoost和Sigmoid校准XGBoost模型。对于每个临床结局，使用受试者操作特征曲线下面积（AUC）确定最佳性能模型，而每个预测变量的重要性和方向在Shapley加性解释汇总图中展示。
最佳性能模型的AUC和准确率分别为：移植物失败时为0.944（优秀）和98.6%；残余松弛时为0.920（优秀）和9l.4%；未达到Lysholm评分最小临床重要差异（MCID）时为0.930（优秀）和91.0%；未达到国际膝关节文献委员会（IKDC）评分MCID时为0.942（优秀）和95.1%；恢复到伤前运动水平时为0.773（一般）和70.5%；恢复到旋转运动水平时为0.777（一般）和69.2%。内侧半月板切除术、参加竞技运动和胫骨后倾坡度大是移植物失败的主要预测因素，而术前膝关节高度松弛、随访期长和参加竞技运动是残余松弛的主要预测因素。术前Lysholm和IKDC评分高强烈预测未达到PROMs的MCID。年轻、男性、术前IKDC评分高和移植物直径大是恢复到伤前或旋转运动水平重要预测因素。
机器学习分析可为ACLR的客观和主观临床结果（移植物失败、残余松弛、PROMs和恢复运动）提供可靠预测。建议在手术前后进行针对患者个体的评估和决策。
