Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes

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.Disclosure: The authors indicated that no external funding was received for any aspect of this work. On the Disclosure of Potential Conflicts of Interest forms, which are provided with the online version of the article, one or more of the authors checked "yes" to indicate that the author had a relevant financial relationship in the biomedical arena outside the submitted work (http://links.lww.com/JBJS/G392).

Association Between Preoperative Patient Factors and Clinically Meaningful Outcomes After Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Machine Learning Analysis

“…Although there remains a paucity of literature studying the achievement rates and predictors of clinically significant outcomes for the IHOT-12 score, previous studies have reported that presence of anxiety/depression, 7,15,23 back pain, 24,46 chronic symptom duration, 20,21 and higher preoperative outcome scores negatively influence other clinically significant outcomes after hip arthroscopy. 9,22…”

“…Ramkumar et al 37,38 have demonstrated that ML is an appropriate and accurate method for clinically meaningful outcome prediction after osteochondral allograft in the knee using data sets as small as 153 patients. Kunze et al 22 reported that ML provided reliable and accurate predictions in determining whether athletes who underwent hip arthroscopy would experience clinically meaningful improvements in sports-specific function. However, little remains known about the association between demographics, preoperative PROMs, clinical examination data, and routine imaging in predicting clinically meaningful outcomes for more contemporary outcome measures such as the IHOT 12-Item Questionnaire (IHOT-12).…”

Personalized Medicine Using Predictive Analytics: A Machine Learning-Based Prognostic Model for Patients Undergoing Hip Arthroscopy

“…Several recent studies have also demonstrated the applicability of machine learning in prognostication of functional outcomes after hip arthroscopy using outcome scores such as the mHHS, Hip Outcome Score-Activities of Daily Living Subscale, and Hip Outcome Score-Sport Specific Subscale. [9][10][11]18 This study relied on the TRIPOD statement as a tool to provide high-quality evidence and improve the quality of reporting. 5 The TRIPOD statement includes recommendations for reporting that improve the transparency of the methodology and help with the generalization of the results.…”

“…Several recent studies have also demonstrated the applicability of machine learning in prognostication of functional outcomes after hip arthroscopy using outcome scores such as the mHHS, Hip Outcome Score–Activities of Daily Living Subscale, and Hip Outcome Score–Sport Specific Subscale. 9 -11,18…”

Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction

“…Furthermore, machine learning has demonstrated validity in predicting clinically meaningful outcome improvement after common orthopaedic procedures. 15 – 17 , 23 This allows for risk prediction at the individual patient level, overcoming the limitations of current sports medicine literature. The purpose of the current study was to develop machine learning algorithms to predict achievement of the MCID on the IKDC score at a minimum 2-year follow-up after ACLR.…”