ArticleOrthopaedic journal of sports medicine2021
Automated Risk Stratification of Hip Osteoarthritis Development in Patients With Femoroacetabular Impingement Using an Unsupervised Clustering Algorithm: A Study From the Rochester Epidemiology Project.
Article in Orthopaedic journal of sports medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 5 citations in OpenAlex.
- Epidemiological Characteristics and Factors Affecting Baseline Hip Function in Chinese Patients Undergoing Arthroscopic Treatment for Femoroacetabular Impingement Syndrome.Orthopaedic journal of sports medicine · 2025Article
- An exploratory machine learning study on paediatric abdominal pain phenotyping and prediction.PloS one · 2025Article
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7 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundStudies evaluating the natural history of femoroacetabular impingement (FAI) are limited. PURPOSE: To stratify the risk of progression to osteoarthritis (OA) in patients with FAI using an unsupervised machine-learning algorithm, compare the characteristics of each subgroup, and validate the reproducibility of staging. STUDY
designCohort study (prognosis); Level of evidence, 2.
methodsA geographic database from the Rochester Epidemiology Project was used to identify patients with hip pain between 2000 and 2016. Medical charts were reviewed to obtain characteristic information, physical examination findings, and imaging details. The patient data were randomly split into 2 mutually exclusive sets: train set (70%) for model development and test set (30%) for validation. The data were transformed via Uniform Manifold Approximation and Projection and were clustered using Hierarchical Density-based Spatial Clustering of Applications with Noise.
resultsThe study included 1071 patients with a mean follow-up period of 24.7 ± 12.5 years. The patients were clustered into 5 subgroups based on train set results: patients in cluster 1 were in their early 20s (20.9 ± 9.6 years), female dominant (84%), with low body mass index (<19 ); patients in cluster 2 were in their early 20s (22.9 ± 6.7 years), female dominant (95%), and pincer-type FAI (100%) dominant; patients in cluster 3 were in their mid 20s (26.4 ± 9.7) and were mixed-type FAI dominant (92%); patients in cluster 4 were in their early 30s (32.7 ± 7.8), with high body mass index (≥29 ), and diabetes (17%); and patients in cluster 5 were in their early 30s (30.0 ± 9.1), with a higher percentage of males (43%) compared with the other clusters and with limited internal rotation (14%). Mean survival for clusters 1 to 5 was 17.9 ± 0.6, 18.7 ± 0.3, 17.1 ± 0.4, 15.0 ± 0.5, and 15.6 ± 0.5 years, respectively, in the train set. The survival difference was significant between clusters 1 and 4 (
conclusionUsing the clustering algorithm, it was possible to determine the prognosis for OA progression in patients with FAI in the presence of conflicting risk factors acting in combination.
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