PMID- 37816399 OWN - NLM STAT- MEDLINE DCOM- 20240319 LR - 20240319 IS - 1526-3231 (Electronic) IS - 0749-8063 (Linking) VI - 40 IP - 4 DP - 2024 Apr TI - Development of Machine-Learning Algorithms to Predict Attainment of Minimal Clinically Important Difference After Hip Arthroscopy for Femoroacetabular Impingement Yield Fair Performance and Limited Clinical Utility. PG - 1153-1163.e2 LID - S0749-8063(23)00798-3 [pii] LID - 10.1016/j.arthro.2023.09.023 [doi] AB - PURPOSE: To determine whether machine learning (ML) techniques developed using registry data could predict which patients will achieve minimum clinically important difference (MCID) on the International Hip Outcome Tool 12 (iHOT-12) patient-reported outcome measures (PROMs) after arthroscopic management of femoroacetabular impingement syndrome (FAIS). And secondly to determine which preoperative factors contribute to the predictive power of these models. METHODS: A retrospective cohort of patients was selected from the UK's Non-Arthroplasty Hip Registry. Inclusion criteria were a diagnosis of FAIS, management via an arthroscopic procedure, and a minimum follow-up of 6 months after index surgery from August 2012 to June 2021. Exclusion criteria were for non-arthroscopic procedures and patients without FAIS. ML models were developed to predict MCID attainment. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). RESULTS: In total, 1,917 patients were included. The random forest, logistic regression, neural network, support vector machine, and gradient boosting models had AUROC 0.75 (0.68-0.81), 0.69 (0.63-0.76), 0.69 (0.63-0.76), 0.70 (0.64-0.77), and 0.70 (0.64-0.77), respectively. Demographic factors and disease features did not confer a high predictive performance. Baseline PROM scores alone provided comparable predictive performance to the whole dataset models. Both EuroQoL 5-Dimension 5-Level and iHOT-12 baseline scores and iHOT-12 baseline scores alone provided AUROC of 0.74 (0.68-0.80) and 0.72 (0.65-0.78), respectively, with random forest models. CONCLUSIONS: ML models were able to predict with fair accuracy attainment of MCID on the iHOT-12 at 6-month postoperative assessment. The most successful models used all patient variables, all baseline PROMs, and baseline iHOT-12 responses. These models are not sufficiently accurate to warrant routine use in the clinic currently. LEVEL OF EVIDENCE: Level III, retrospective cohort design; prognostic study. CI - Copyright (c) 2023 The Author(s). Published by Elsevier Inc. All rights reserved. FAU - Pettit, Matthew H AU - Pettit MH AD - St. George's University Hospital, London, United Kingdom. FAU - Hickman, Sebastian H M AU - Hickman SHM AD - The Alan Turing Institute, London, United Kingdom; Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom. FAU - Malviya, Ajay AU - Malviya A AD - Newcastle University, Newcastle upon Tyne, United Kingdom. FAU - Khanduja, Viskas AU - Khanduja V AD - Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom. Electronic address: vk279@cam.ac.uk. LA - eng PT - Journal Article DEP - 20231008 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 MH - Humans MH - *Femoracetabular Impingement/surgery MH - Retrospective Studies MH - Arthroscopy MH - Minimal Clinically Important Difference MH - Treatment Outcome MH - Activities of Daily Living MH - Hip Joint/surgery MH - Machine Learning MH - Follow-Up Studies MH - Patient Reported Outcome Measures COIS- Disclosure The authors report the following potential conflicts of interest or sources of funding: A.M. reports grants, personal fees, and other from Pfizer, and grants from Schuelke and Vyfor, outside the submitted work. V.K. reports personal fees from Smith & Nephew and Arthrex, outside the submitted work. In addition, V.K. has a patent, Pressure Sensor - ArtioSense pending to ArtioSense. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Full ICMJE author disclosure forms are available for this article online, as supplementary material. EDAT- 2023/10/11 00:42 MHDA- 2024/03/19 06:43 CRDT- 2023/10/10 19:16 PHST- 2023/04/24 00:00 [received] PHST- 2023/09/05 00:00 [revised] PHST- 2023/09/13 00:00 [accepted] PHST- 2024/03/19 06:43 [medline] PHST- 2023/10/11 00:42 [pubmed] PHST- 2023/10/10 19:16 [entrez] AID - S0749-8063(23)00798-3 [pii] AID - 10.1016/j.arthro.2023.09.023 [doi] PST - ppublish SO - Arthroscopy. 2024 Apr;40(4):1153-1163.e2. doi: 10.1016/j.arthro.2023.09.023. Epub 2023 Oct 8.