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Differential progression of coronary atherosclerosis according to plaque composition: a cluster analysis of PARADIGM registry data

Authors
 Yeonyee E Yoon  ;  Lohendran Baskaran  ;  Benjamin C Lee  ;  Mohit Kumar Pandey  ;  Benjamin Goebel  ;  Sang-Eun Lee  ;  Ji Min Sung  ;  Daniele Andreini  ;  Mouaz H Al-Mallah  ;  Matthew J Budoff  ;  Filippo Cademartiri  ;  Kavitha Chinnaiyan  ;  Jung Hyun Choi  ;  Eun Ju Chun  ;  Edoardo Conte  ;  Ilan Gottlieb  ;  Martin Hadamitzky  ;  Yong Jin Kim  ;  Byoung Kwon Lee  ;  Jonathon A Leipsic  ;  Erica Maffei  ;  Hugo Marques  ;  Pedro de Araújo Gonçalves  ;  Gianluca Pontone  ;  Sanghoon Shin  ;  Jagat Narula  ;  Jeroen J Bax  ;  Fay Yu-Huei Lin  ;  Leslee Shaw  ;  Hyuk-Jae Chang 
Citation
 SCIENTIFIC REPORTS, Vol.11(1) : 17121, 2021-08 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2021-08
MeSH
Aged ; Cluster Analysis ; Coronary Angiography ; Coronary Artery Disease / diagnostic imaging* ; Coronary Artery Disease / pathology ; Female ; Humans ; Male ; Middle Aged ; Plaque, Atherosclerotic / classification ; Plaque, Atherosclerotic / diagnostic imaging* ; Plaque, Atherosclerotic / pathology ; Vascular Calcification / diagnostic imaging* ; Vascular Calcification / pathology
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.
Files in This Item:
T202104778.pdf Download
DOI
10.1038/s41598-021-96616-w
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Lee, Byoung Kwon(이병권) ORCID logo https://orcid.org/0000-0001-9259-2776
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/186878
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