Topological Data Analysis of Coronary Plaques Demonstrates the Natural History of Coronary Atherosclerosis.
Document Type
Article
Publication Date
1-13-2021
Publication Title
JACC Cardiovasc Imaging
Abstract
OBJECTIVES: This study sought to identify distinct patient groups and their association with outcome based on the patient similarity network using quantitative coronary plaque characteristics from coronary computed tomography angiography (CTA).
BACKGROUND: Coronary CTA can noninvasively assess coronary plaques quantitatively.
METHODS: Patients who underwent 2 coronary CTAs at a minimum of 24 months' interval were analyzed (n = 1,264). A similarity Mapper network of patients was built by topological data analysis (TDA) based on the whole-heart quantitative coronary plaque analysis on coronary CTA to identify distinct patient groups and their association with outcome.
RESULTS: Three distinct patient groups were identified by TDA, and the patient similarity network by TDA showed a closed loop, demonstrating a continuous trend of coronary plaque progression. Group A had the least coronary plaque amount (median 12.4 mm
CONCLUSIONS: The TDA of quantitative whole-heart coronary plaque characteristics on coronary CTA identified distinct patient groups with different plaque dynamics and clinical outcomes. (Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging [PARADIGM]; NCT02803411).
Issue
Online ahead of print
Recommended Citation
Hwang D, Kim HJ, Lee SP, Lim S, Koo BK, Kim YJ, Kook W, Andreini D, Al-Mallah MH, Budoff MJ, Cademartiri F, Chinnaiyan K, Choi JH, Conte E, Marques H, de Araújo Gonçalves P, Gottlieb I, Hadamitzky M, Leipsic JA, Maffei E, Pontone G, Raff GL, Shin S, Lee BK, Chun EJ, Sung JM, Lee SE, Berman DS, Lin FY, Virmani R, Samady H, Stone PH, Narula J, Bax JJ, Shaw LJ, Min JK, Chang HJ. Topological Data Analysis of Coronary Plaques Demonstrates the Natural History of Coronary Atherosclerosis. JACC Cardiovasc Imaging. 2021 Jan 13:S1936-878X(20)31014-7. doi: 10.1016/j.jcmg.2020.11.009. Epub ahead of print. PMID: 33454260.
DOI
10.1016/j.jcmg.2020.11.009
ISSN
1876-7591
PubMed ID
33454260