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

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.

Error Propagation in the Simulation of Atherosclerotic Plaque Growth and the Prediction of Atherosclerotic Disease Progression

“…Using this model, the user can simulate a time-dependent wall thickening and the gradual lumen narrowing and blockage of blood flow. Briefly, the model was based on the modelling of blood flow in the lumen and the transport of low density lipoprotein (LDL) [ 5 ], high density lipoprotein (HDL) and monocytes in the lumen, employing convection-diffusion equations. The endothelial membrane acts as a barrier between the lumen and the arterial wall and we applied the Kedem–Katchalsky equations to define the path of molecular penetration in the wall.…”

“…In this section, we present the results of the propagated error in the prognostic model of plaque growth prediction. The results of this model were previously presented [ 5 ]. Briefly, this predictive model is based on the computational modelling of blood flow, LDL transport and SmartFFR calculation, and these results are combined with morphological characteristics and non-imaging data to build binary logistic models of prediction of plaque progression.…”

“…Traditional approaches are based on statistical models, while the most recent are based on machine learning algorithms. Moreover, the use of coronary imaging enables the 3D reconstruction of the coronary arteries and their utilization for the computational simulation of blood flow towards the estimation of endothelial shear stress (ESS), since it has been found that low ESS is an independent predictor of disease progression [ 4 , 5 , 6 ]. Furthermore, advanced computational models simulating the main mechanisms of atherosclerotic disease evolution and growth also exist [ 7 , 8 ].…”

New Trends in Biosciences

“…Computational modeling with lipidomics analysis can be used for the prediction of coronary atherosclerotic plaque progression. In the study proposed by Sakellarios et al, 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 [22].…”

Two-Phase Non-Newtonian Pulsatile Blood Flow Simulations in a Rigid and Flexible Patient-Specific Left Coronary Artery (LCA) Exhibiting Multi-Stenosis

“…More than 400 slices were used to create images in all directions of sagittal, coronal, and axial planes. The data were acquired in standard DICOM formats, and a realistic 3D left coronary artery model was reconstructed using CT volume data and image processing software MIMICS (Materialise HQ Technologielaan 15, Leuven, Belgium), also used in earlier studies [20,28,29].…”