A1 Refereed original research article in a scientific journal
Non-Invasive Prediction of Site-Specific Coronary Atherosclerotic Plaque Progression using Lipidomics, Blood Flow, and LDL Transport Modeling
Authors: Sakellarios Antonis I, Tsompou Panagiota, Kigka Vassiliki, Siogkas Panagiotis, Kyriakidis Savvas, Tachos Nikolaos, Karanasiou Georgia, Scholte Arthur, Clemente Alberto, Neglia Danilo, Parodi Oberdan, Knuuti Juhani, Michalis Lampros K, Pelosi Gualtiero, Rocchiccioli Silvia, Fotiadis Dimitrios I
Publisher: MDPI
Publication year: 2021
Journal: Applied Sciences
Journal name in source: APPLIED SCIENCES-BASEL
Journal acronym: APPL SCI-BASEL
Article number: ARTN 1976
Volume: 11
Issue: 5
Number of pages: 14
eISSN: 2076-3417
DOI: https://doi.org/10.3390/app11051976
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/54802282
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.
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