Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes. (16th June 2021)

• Record Type: Journal Article
• Authors: Kunze, Kyle N.; Polce, Evan M.; Clapp, Ian; Nwachukwu, Benedict U.; Chahla, Jorge; Nho, Shane J.
• Is Part Of: Journal of bone and joint surgery. Volume 103:Number 12(2021)
• Publication Date: 2021-06-16
• Subjects: Bones -- Surgery -- Periodicals; Joints -- Surgery -- Periodicals; Orthopedics -- Periodicals; Orthopedics; General Surgery; Bone Diseases; Joint Diseases; Bones -- Surgery; Joints -- Surgery; Orthopedics; Bot (anatomie); Gewrichten; Chirurgie (geneeskunde); Periodicals; Electronic journals; Periodicals; 617.47005
• DOI: 10.2106/JBJS.20.01640
• Languages: English
• ISSNs: 0021-9355

Abstract

Abstract : Background: 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. Methods: 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. Results: 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/m 2, 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. Conclusions: 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.