Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY.
Division of Sports Medicine, Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois.
J Bone Joint Surg Am. 2021 Jun 16;103(12):1055-1062. doi: 10.2106/JBJS.20.01640.
Despite previous reports of improvements for athletes following hip arthroscopy for femoroacetabular impingement syndrome (FAIS), many do not achieve clinically relevant outcomes. The purpose of this study was to develop machine learning algorithms capable of providing patient-specific predictions of which athletes will derive clinically relevant improvement in sports-specific function after undergoing hip arthroscopy for FAIS.
A registry was queried for patients who had participated in a formal sports program or athletic activities before undergoing primary hip arthroscopy between January 2012 and February 2018. The primary outcome was achieving the minimal clinically important difference (MCID) in the Hip Outcome Score-Sports Subscale (HOS-SS) at a minimum of 2 years postoperatively. Recursive feature selection was used to identify the combination of variables, from an initial pool of 26 features, that optimized model performance. Six machine learning algorithms (stochastic gradient boosting, random forest, adaptive gradient boosting, neural network, support vector machine, and elastic-net penalized logistic regression [ENPLR]) were trained using 10-fold cross-validation 3 times and applied to an independent testing set of patients. Models were evaluated using discrimination, decision-curve analysis, calibration, and the Brier score.
A total of 1,118 athletes were included, and 76.9% of them achieved the MCID for the HOS-SS. A combination of 6 variables optimized algorithm performance, and specific cutoffs were found to decrease the likelihood of achieving the MCID: preoperative HOS-SS score of ≥58.3, Tönnis grade of 1, alpha angle of ≥67.1°, body mass index (BMI) of >26.6 kg/m2, Tönnis angle of >9.7°, and age of >40 years. The ENPLR model demonstrated the best performance (c-statistic: 0.77, calibration intercept: 0.07, calibration slope: 1.22, and Brier score: 0.14). This model was transformed into an online application as an educational tool to demonstrate machine learning capabilities.
The ENPLR machine learning algorithm demonstrated the best performance for predicting clinically relevant sports-specific improvement in athletes who underwent hip arthroscopy for FAIS. In our population, older athletes with more degenerative changes, high preoperative HOS-SS scores, abnormal acetabular inclination, and an alpha angle of ≥67.1° achieved the MCID less frequently. Following external validation, the online application of this model may allow enhanced shared decision-making.
尽管先前有研究表明髋关节镜治疗股骨髋臼撞击综合征（FAIS）后运动员的情况有所改善，但许多患者并未达到临床相关的效果。本研究旨在开发机器学习算法，以能够针对每位患者提供特定的预测，从而判断运动员在接受髋关节镜治疗 FAIS 后，其运动专项功能是否会有临床相关的改善。
从 2012 年 1 月至 2018 年 2 月期间接受初次髋关节镜手术的患者中，通过注册表查询在手术前参加过正式运动项目或运动活动的患者。主要结局是在术后至少 2 年时，髋关节功能评分-运动亚量表（HOS-SS）达到最小临床重要差异（MCID）。使用递归特征选择从最初的 26 个特征池中确定变量组合，以优化模型性能。使用 10 折交叉验证 3 次训练了 6 种机器学习算法（随机梯度提升、随机森林、自适应梯度提升、神经网络、支持向量机和弹性网络惩罚逻辑回归[ENPLR]），并将其应用于一组独立的患者测试集。使用判别能力、决策曲线分析、校准和 Brier 评分评估模型。
共纳入 1118 名运动员，其中 76.9%的患者达到了 HOS-SS 的 MCID。有 6 个变量的组合优化了算法性能，且发现特定的截断值可降低达到 MCID 的可能性：术前 HOS-SS 评分≥58.3、Tönnis 分级为 1、α角≥67.1°、体重指数（BMI）>26.6kg/m2、Tönnis 角>9.7°和年龄>40 岁。ENPLR 模型的性能最佳（C 统计量：0.77，校准截距：0.07，校准斜率：1.22，Brier 评分：0.14）。该模型转化为在线应用程序，用作教育工具以展示机器学习能力。
ENPLR 机器学习算法在预测髋关节镜治疗 FAIS 后运动员的运动专项功能是否有临床相关改善方面表现最佳。在我们的人群中，年龄较大、退行性改变较多、术前 HOS-SS 评分较高、髋臼倾斜异常和α角≥67.1°的运动员更难以达到 MCID。经过外部验证，该模型的在线应用可能会增强共享决策。
