Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data

Abstract

Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for prevention strategies. In this work, a novel computational model is developed, which is used for simulation of plaque growth to 94 realistic 3D reconstructed coronary arteries. This model considers several factors of the atherosclerotic process even mechanical factors such as the effect of endothelial shear stress, responsible for the initiation of atherosclerosis, and biological factors such as the accumulation of low and high density lipoproteins (LDL and HDL), monocytes, macrophages, cytokines, nitric oxide and formation of foams cells or proliferation of contractile and synthetic smooth muscle cells (SMCs). The model is validated using the serial imaging of CTCA comparing the simulated geometries with the real follow-up arteries. Additionally, we examine the predictive capability of the model to identify regions prone of disease progression. The results presented good correlation between the simulated lumen area (P < 0.0001), plaque area (P < 0.0001) and plaque burden (P < 0.0001) with the realistic ones. Finally, disease progression is achieved with 80% accuracy with many of the computational results being independent predictors.

Consistency in Geometry Among Coronary Atherosclerotic Plaques Extracted From Computed Tomography Angiography

“…As a result, an area with low values of wall shear stress (WSS), which is a hemodynamic parameter directly related to endothelial function and atherosclerosis, will appear in the distal segment of the plaque, accelerating the plaque growth (Liu et al, 2018;Arzani, 2020). In a study on 900 artery segments extracted from 94 patient-specific coronary arteries, Pleouras et al found that the plaque growth and degree of stenosis predicted using the baseline WSS values derived from computational simulation were in accordance with the follow-up clinical observation (Pleouras et al, 2020b). Therefore, the plaque geometry can influence the plaque growth by hemodynamic effects in pathological mechanisms, which contributes to the geometric consistency of different coronary plaques.…”

Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

“…Moreover, for a more exhaustive vision related to the modeling of vascular adaptation, the reader should be directed also to multiscale frameworks entirely based on continuum models (which are not the object of this review), implying that also the cell scale is represented through ODE/PDE systems. Examples can be found in models of atherosclerosis (e.g., Cilla et al (2014) , Di Tomaso et al (2015) , Thon et al (2018) and Pleouras et al (2020) ), ISR (e.g., Lally and Prendergast (2006) , Escuer et al (2019) and Maes et al (2021) ), vein graft remodeling (e.g., Budu-Grajdeanu et al (2008) and Casarin et al (2017) ) and other vascular applications (see Humphrey (2021) for an extensive review on constrained mixture models of tissue growth and remodeling). The difference of these works with those reviewed in Multiscale Agent-Based Modeling Frameworks of Vascular Pathophysiology mainly regarded the representation of the cell scale (through a ODE/PDE versus ABM approach), which thus determined the nature of the multiscale framework to be either hybrid (i.e., based on the combination of continuum models with an ABM) or fully-continuum.…”

“…Besides the works by Casarin et al (2017) and Maes et al (2021) , in which a set of ODEs was adopted to describe the temporal dynamics of tissue growth and remodeling, in all the other cited continuum-based studies PDE systems were implemented to capture the spatio-temporal evolution of the species of interest (e.g., growth factors, cells, ECM components, LDL), and thus the subsequent tissue remodeling, in response to fluid or mechanical stimuli. For example, in the patient-specific atherosclerosis model by Pleouras et al (2020) , CFD simulations were coupled with a PDE system describing mass transport of monocytes, LDL, and high-density lipoproteins, and inflammatory species’ dynamics in the arterial wall, ultimately leading to plaque growth over time ( Figure 11 ). The model predictions well replicated the in-vivo follow-ups in terms of plaque growth and lumen area reduction (accuracy of about 80%), thus supporting the potentialities of the proposed framework.…”

A Novel Approach to Generate a Virtual Population of Human Coronary Arteries for In Silico Clinical Trials of Stent Design

“…Our approach is aiming to the utilization of a plaque growth model (20,21) which is used for the simulation of coronary arterial disease progress in human data with the potential to predict atheromatic areas of risk. The development of this model requires the proper definition of the equations that define the biologic processes responsible for atherosclerosis, using the state-of-the-art models, but also the latest available in literature experimental data.…”

“…The employed model has been previously validated and it can be used to simulate the plaque growth and lumen narrowing of 3D reconstructed coronary arteries (20). For this purpose, a multi-level approach is implemented.…”