Abstract
Patient-specific phenotyping of coronary atherosclerosis would facilitate personalized risk assessment and preventive treatment. We explored whether unsupervised cluster analysis can categorize patients with coronary atherosclerosis according to their plaque composition, and determined how these differing plaque composition profiles impact plaque progression. Patients with coronary atherosclerotic plaque (n = 947; median age, 62 years; 59% male) were enrolled from a prospective multi-national registry of consecutive patients who underwent serial coronary computed tomography angiography (median inter-scan duration, 3.3 years). K-means clustering applied to the percent volume of each plaque component and identified 4 clusters of patients with distinct plaque composition. Cluster 1 (n = 52), which comprised mainly fibro-fatty plaque with a significant necrotic core (median, 55.7% and 16.0% of the total plaque volume, respectively), showed the least total plaque volume (PV) progression (+ 23.3 mm3), with necrotic core and fibro-fatty PV regression (− 5.7 mm3 and − 5.6 mm3, respectively). Cluster 2 (n = 219), which contained largely fibro-fatty (39.2%) and fibrous plaque (46.8%), showed fibro-fatty PV regression (− 2.4 mm3). Cluster 3 (n = 376), which comprised mostly fibrous (62.7%) and calcified plaque (23.6%), showed increasingly prominent calcified PV progression (+ 21.4 mm3). Cluster 4 (n = 300), which comprised mostly calcified plaque (58.7%), demonstrated the greatest total PV increase (+ 50.7mm3), predominantly increasing in calcified PV (+ 35.9 mm3). Multivariable analysis showed higher risk for plaque progression in Clusters 3 and 4, and higher risk for adverse cardiac events in Clusters 2, 3, and 4 compared to that in Cluster 1. Unsupervised clustering algorithms may uniquely characterize patient phenotypes with varied atherosclerotic plaque profiles, yielding distinct patterns of progressive disease and outcome.
Highlights
Patient-specific phenotyping of coronary atherosclerosis would facilitate personalized risk assessment and preventive treatment
When we visualized clusters in 3D space (Fig. 3), the separation of Cluster 1 was mainly driven by its higher %vol of the necrotic core, and Cluster 4 was separated from others due to its higher calcified plaque %vol Cluster 2 was separated from Clusters 3 and 4 by its higher %vol of fibro-fatty plaque, separation from Cluster 1 depended on its %vol of the necrotic core and fibrous plaque
We have provided 2D plots using radial visualization (RadViz) and t-distributed stochastic neighbour embedding (t-SNE) (Supplementary Fig. 3)
Summary
Patient-specific phenotyping of coronary atherosclerosis would facilitate personalized risk assessment and preventive treatment. We explored whether unsupervised cluster analysis can categorize patients with coronary atherosclerosis according to their plaque composition, and determined how these differing plaque composition profiles impact plaque progression. Unsupervised clustering algorithms may uniquely characterize patient phenotypes with varied atherosclerotic plaque profiles, yielding distinct patterns of progressive disease and outcome. Machine learning using unsupervised cluster analysis aims to group similar data points into clusters based on inherent similarities among them It enables the exploration of possible heterogeneity within a disease category that has historically been considered homogeneous. We hypothesized that unsupervised cluster analysis could categorize heterogeneous patients according to atherosclerotic plaque component proportions. We aimed to determine how these differences in atherosclerotic plaque components at baseline differentially impact plaque progression and composition change
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