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

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

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

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
Katso/Avaa
Publisher´s pdf (3.544Mb)
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MDPI
doi:10.3390/app11051976
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2021093048460
Tiivistelmä
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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