PMID- 33460708 OWN - NLM STAT- MEDLINE DCOM- 20210611 LR - 20210611 IS - 1526-3231 (Electronic) IS - 0749-8063 (Linking) VI - 37 IP - 5 DP - 2021 May TI - Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy. PG - 1488-1497 LID - S0749-8063(21)00016-5 [pii] LID - 10.1016/j.arthro.2021.01.005 [doi] AB - PURPOSE: To (1) develop and validate a machine learning algorithm to predict clinically significant functional improvements after hip arthroscopy for femoroacetabular impingement syndrome and to (2) develop a digital application capable of providing patients with individual risk profiles to determine their propensity to gain clinically significant improvements in function. METHODS: A retrospective review of consecutive hip arthroscopy patients who underwent cam/pincer correction, labral preservation, and capsular closure between January 2012 and 2017 from 1 large academic and 3 community hospitals operated on by a single high-volume hip arthroscopist was performed. The primary outcome was the minimal clinically important difference (MCID) for the Hip Outcome Score (HOS)-Activities of Daily Living (ADL) at 2 years postoperatively, which was calculated using a distribution-based method. A total of 21 demographic, radiographic, and patient-reported outcome measures were considered as potential covariates. An 80:20 random split was used to create training and testing sets from the patient cohort. Five supervised machine learning algorithms were developed using 3 iterations of 10-fold cross-validation on the training set and assessed by discrimination, calibration, Brier score, and decision curve analysis on an independent testing set of patients. RESULTS: A total of 818 patients with a median (interquartile range) age of 32.0 (22.0-42.0) and 69.2% female were included, of whom 74.3% achieved the MCID for the HOS-ADL. The best-performing algorithm was the stochastic gradient boosting model (c-statistic = 0.84, calibration intercept = 0.20, calibration slope = 0.83, and Brier score = 0.13). Of the initial 21 candidate variables, the 8 most important features for predicting the MCID for the HOS-ADL included in model training were body mass index, age, preoperative HOS-ADL score, preoperative pain level, sex, Tonnis grade, symptom duration, and drug allergies. The algorithm was subsequently transformed into a digital application using local explanations to provide customized risk assessment: https://orthoapps.shinyapps.io/HPRG_ADL/. CONCLUSIONS: The stochastic boosting gradient model conferred excellent predictive ability for propensity to gain clinically significant improvements in function after hip arthroscopy. An open-access digital application was created, which may augment shared decision-making and allow for preoperative risk stratification. External validation of this model is warranted to confirm the performance of these algorithms, as the generalizability is currently unknown. LEVEL OF EVIDENCE: IV, Case series. CI - Copyright (c) 2021 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved. FAU - Kunze, Kyle N AU - Kunze KN AD - Department of Orthopedic Surgery, Division of Sports Medicine, Hospital for Special Surgery, New York, New York, U.S.A.. Electronic address: Nho.research@rushortho.com. FAU - Polce, Evan M AU - Polce EM AD - Section of Young Adult Hip Surgery, Division of Sports Medicine, Department of Orthopedic Surgery, Rush Medical College of Rush University, Rush University Medical Center, Chicago, Illinois. FAU - Nwachukwu, Benedict U AU - Nwachukwu BU AD - Department of Orthopedic Surgery, Division of Sports Medicine, Hospital for Special Surgery, New York, New York, U.S.A. FAU - Chahla, Jorge AU - Chahla J AD - Section of Young Adult Hip Surgery, Division of Sports Medicine, Department of Orthopedic Surgery, Rush Medical College of Rush University, Rush University Medical Center, Chicago, Illinois. FAU - Nho, Shane J AU - Nho SJ AD - Section of Young Adult Hip Surgery, Division of Sports Medicine, Department of Orthopedic Surgery, Rush Medical College of Rush University, Rush University Medical Center, Chicago, Illinois. LA - eng PT - Journal Article PT - Validation Study DEP - 20210116 PL - United States TA - Arthroscopy JT - Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association JID - 8506498 SB - IM CIN - Arthroscopy. 2021 May;37(5):1498-1502. PMID: 33896503 MH - Activities of Daily Living MH - Adult MH - *Algorithms MH - *Arthroscopy MH - Calibration MH - Cohort Studies MH - Female MH - Hip Joint/*physiopathology/*surgery MH - Humans MH - Male MH - Patient Reported Outcome Measures MH - ROC Curve MH - *Recovery of Function MH - Retrospective Studies MH - Risk Factors MH - *Supervised Machine Learning MH - Treatment Outcome MH - Young Adult EDAT- 2021/01/19 06:00 MHDA- 2021/06/12 06:00 CRDT- 2021/01/18 20:10 PHST- 2020/06/03 00:00 [received] PHST- 2020/12/30 00:00 [revised] PHST- 2021/01/03 00:00 [accepted] PHST- 2021/01/19 06:00 [pubmed] PHST- 2021/06/12 06:00 [medline] PHST- 2021/01/18 20:10 [entrez] AID - S0749-8063(21)00016-5 [pii] AID - 10.1016/j.arthro.2021.01.005 [doi] PST - ppublish SO - Arthroscopy. 2021 May;37(5):1488-1497. doi: 10.1016/j.arthro.2021.01.005. Epub 2021 Jan 16.