PMID- 33877058 OWN - NLM STAT- MEDLINE DCOM- 20211112 LR - 20211112 IS - 1535-1386 (Electronic) IS - 0021-9355 (Linking) VI - 103 IP - 12 DP - 2021 Jun 16 TI - Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes. PG - 1055-1062 LID - 10.2106/JBJS.20.01640 [doi] AB - 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, Tonnis grade of 1, alpha angle of >/=67.1 degrees , body mass index (BMI) of >26.6 kg/m2, Tonnis angle of >9.7 degrees , 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 degrees achieved the MCID less frequently. Following external validation, the online application of this model may allow enhanced shared decision-making. CI - Copyright (c) 2021 by The Journal of Bone and Joint Surgery, Incorporated. FAU - Kunze, Kyle N AU - Kunze KN AUID- ORCID: 0000-0002-0363-3482 AD - Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY. FAU - Polce, Evan M AU - Polce EM AUID- ORCID: 0000-0001-9707-3609 AD - Division of Sports Medicine, Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois. FAU - Clapp, Ian AU - Clapp I AUID- ORCID: 0000-0001-8823-0932 AD - Division of Sports Medicine, Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois. FAU - Nwachukwu, Benedict U AU - Nwachukwu BU AUID- ORCID: 0000-0002-6170-7769 AD - Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY. FAU - Chahla, Jorge AU - Chahla J AUID- ORCID: 0000-0002-9194-1150 AD - Division of Sports Medicine, Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois. FAU - Nho, Shane J AU - Nho SJ AUID- ORCID: 0000-0002-2789-1531 AD - Division of Sports Medicine, Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois. LA - eng PT - Journal Article PL - United States TA - J Bone Joint Surg Am JT - The Journal of bone and joint surgery. American volume JID - 0014030 SB - IM MH - Adult MH - *Algorithms MH - *Arthroscopy MH - Female MH - Femoracetabular Impingement/physiopathology/*surgery MH - Humans MH - *Machine Learning MH - Male MH - Minimal Clinically Important Difference MH - Predictive Value of Tests MH - Recovery of Function MH - *Sports MH - Treatment Outcome MH - Young Adult COIS- 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). EDAT- 2021/04/21 06:00 MHDA- 2021/11/16 06:00 CRDT- 2021/04/20 12:15 PHST- 2021/04/21 06:00 [pubmed] PHST- 2021/11/16 06:00 [medline] PHST- 2021/04/20 12:15 [entrez] AID - 00004623-202106160-00003 [pii] AID - 10.2106/JBJS.20.01640 [doi] PST - ppublish SO - J Bone Joint Surg Am. 2021 Jun 16;103(12):1055-1062. doi: 10.2106/JBJS.20.01640.