Author(s):
Yoon, Yeonyee E. ; Baskaran, Lohendran ; Lee, Benjamin C. ; Pandey, Mohit Kumar ; Goebel, Benjamin ; Lee, Sang Eun ; Sung, Ji Min ; Andreini, Daniele ; Al-Mallah, Mouaz H. ; Budoff, Matthew J. ; Cademartiri, Filippo ; Chinnaiyan, Kavitha ; Choi, Jung Hyun ; Chun, Eun Ju ; Conte, Edoardo ; Gottlieb, Ilan ; Hadamitzky, Martin ; Kim, Yong Jin ; Lee, Byoung Kwon ; Leipsic, Jonathon A. ; Maffei, Erica ; Pinto Marques, Hugo ; de Araújo Gonçalves, Pedro ; Pontone, Gianluca ; Shin, Sanghoon ; Narula, Jagat ; Bax, Jeroen J. ; Lin, Fay Yu Huei ; Shaw, Leslee ; Chang, Hyuk Jae
Date: 2021
Persistent ID: http://hdl.handle.net/10362/124961
Origin: Repositório Institucional da UNL
Subject(s): General
Description
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.
