TY - JOUR
T1 - Automated Risk Stratification of Hip Osteoarthritis Development in Patients With Femoroacetabular Impingement Using an Unsupervised Clustering Algorithm
T2 - A Study From the Rochester Epidemiology Project
AU - Ko, Sunho
AU - Pareek, Ayoosh
AU - Jo, Changwung
AU - Han, Hyuk Soo
AU - Lee, Myung Chul
AU - Krych, Aaron J.
AU - Ro, Du Hyun
N1 - Publisher Copyright:
© The Author(s) 2021.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - FAI
KW - UMAP
KW - clustering
KW - femoroacetabular impingement
KW - machine learning
KW - osteoarthritis
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U2 - 10.1177/23259671211050613
DO - 10.1177/23259671211050613
M3 - Article
AN - SCOPUS:85118478883
SN - 2325-9671
VL - 9
JO - Orthopaedic Journal of Sports Medicine
JF - Orthopaedic Journal of Sports Medicine
IS - 11
ER -