Automated Risk Stratification of Hip Osteoarthritis Development in Patients With Femoroacetabular Impingement Using an Unsupervised Clustering Algorithm: A Study From the Rochester Epidemiology Project

Abstract

Background: Studies 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 Design: Cohort study (prognosis); Level of evidence, 2. Methods: A 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. Results: The 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 ( P = .02), 2 and 4 ( P < .005), 2 and 5 ( P = .01), and 3 and 4 ( P < .005) in the train set and between clusters 2 and 5 ( P = .03) and 3 and 4 ( P = .01) in the test set. Cluster characteristics and prognosis was well reproduced in the test set. Conclusion: Using 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.

Phenotyping and prediction of paediatric abdominal pain: A machine learning approach

“…Abdominal pain is one of the most common symptoms among children and adolescents, with prevalence rates across the USA and Europe ranging from 0.3% to 19.0% [1][2][3]. In primary care, most children presenting with abdominal pain are diagnosed with functional abdominal pain (FAP), whereas organic causes account for only 5% to 10% of cases [4,5].…”

“…One such stratification method is a data-driven clustering by machine learning (ML) that can visualize and stratify complicated multidimensional datasets to identify disease clusters with pathophysiological implications [17][18][19][20]. Furthermore, ML based modelling is a powerful tool that may aid clinicians to effectively detect the risk of abdominal pain development in early childhood [21].…”

Unsupervised Machine Learning of the Combined Danish and Norwegian Knee Ligament Registers: Identification of 5 Distinct Patient Groups With Differing ACL Revision Rates

“…This process enables the interpretation and simplification of highly complex data through the identification of hidden structures and patterns. 7 Within orthopaedic research, unsupervised learning approaches have recently been used to stratify groups of patients according to their risk of hip osteoarthritis progression 17 and to identify subphenotypes of osteoarthritis based on blood-based biochemical markers. 2 These examples highlight how a novel approach to a common problem can provide insight into the factors associated with complex clinical conditions.…”