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Non-Invasive Prediction of Site-Specific Coronary Atherosclerotic Plaque Progression using Lipidomics, Blood Flow, and LDL Transport Modeling

Authors :
Antonis I. Sakellarios
Panagiota Tsompou
Vassiliki Kigka
Panagiotis Siogkas
Savvas Kyriakidis
Nikolaos Tachos
Georgia Karanasiou
Arthur Scholte
Alberto Clemente
Danilo Neglia
Oberdan Parodi
Juhani Knuuti
Lampros K. Michalis
Gualtiero Pelosi
Silvia Rocchiccioli
Dimitrios I. Fotiadis
Source :
Applied Sciences, Vol 11, Iss 5, p 1976 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Background: coronary computed tomography angiography (CCTA) is a first line non-invasive imaging modality for detection of coronary atherosclerosis. Computational modeling with lipidomics analysis can be used for prediction of coronary atherosclerotic plaque progression. Methods: 187 patients (480 vessels) with stable coronary artery disease (CAD) undergoing CCTA scan at baseline and after 6.2 ± 1.4 years were selected from the SMARTool clinical study cohort (Clinicaltrial.gov Identifiers NCT04448691) according to a computed tomography (CT) scan image quality suitable for three-dimensional (3D) reconstruction of coronary arteries and the absence of implanted coronary stents. Clinical and biohumoral data were collected, and plasma lipidomics analysis was performed. Blood flow and low-density lipoprotein (LDL) transport were modeled using patient-specific data to estimate endothelial shear stress (ESS) and LDL accumulation based on a previously developed methodology. Additionally, non-invasive Fractional Flow Reserve (FFR) was calculated (SmartFFR). Plaque progression was defined as significant change of at least two of the morphological metrics: lumen area, plaque area, plaque burden. Results: a multi-parametric predictive model, including traditional risk factors, plasma lipids, 3D imaging parameters, and computational data demonstrated 88% accuracy to predict site-specific plaque progression, outperforming current computational models. Conclusions: Low ESS and LDL accumulation, estimated by computational modeling of CCTA imaging, can be used to predict site-specific progression of coronary atherosclerotic plaques.

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.887844a1d32343c9bfc0ce0debfb3cc8
Document Type :
article
Full Text :
https://doi.org/10.3390/app11051976