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
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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.
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DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- 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
- Date Crossref
- 07/10/2023
- Éditeur
- Wiley
- Type
- journal-article
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St George’s University Hospitals NHS Foundation Trust pays non établi dans la noticeÉtablissement de santé
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St George's Hospital pays non établi dans la noticeÉtablissement de santé
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Turing Institute pays non établi dans la noticeStructure de recherche
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University of Cambridge pays non établi dans la noticeUniversité ou école supérieure
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The Alan Turing Institute pays non établi dans la noticeStructure de recherche
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Northumbria Healthcare NHS Foundation Trust pays non établi dans la noticeÉtablissement de santé
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Newcastle University pays non établi dans la noticeUniversité ou école supérieure
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Cambridge University Hospitals NHS Foundation Trust pays non établi dans la noticeOrganisme public
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St. George’s University Hospital London United Kingdom pays non établi dans la noticeUniversité ou école supérieure
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Academic foundation doctor: St George's University Hospital pays non établi dans la noticeUniversité ou école supérieure
St George’s University Hospitals NHS Foundation Trust, St George's Hospital et Turing Institute, avec 7 autres affiliations.
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