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.

A real cardiovascular disease population was utilized to generate virtual patients with cardiovascular disease. To this purpose, data augmentation was performed to create virtual clinical data. Additionally, the imaging of the real population was utilized for 3D arterial reconstruction, which subsequently were used for atherosclerotic plaque growth simulation.Using this model, new arterial geometries were generated. At the final stage the virtual clinical data were combined with the virtual arterial geometries to produce a complete virtual population of atherosclerotic patients.

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Since atherosclerosis has been declared as the leading cause of mortality worldwide, the imminent need for the design and development of straightforward computational modeling workflows to improve the existing cardiovascular disease risk stratification models is more important than ever. Agent-based modelling (ABM) is a promising computational approach which can be utilized for decision making in various domains from the healthcare sector to industrial applications. In the present study, we propose a straightforward approach for atheromatic plaque progression in the coronary and peripheral arteries using specialized mathematical models and computational simulations which will enable the accurate prediction of the cardiovascular disease evolution. The model incorporates the realistic 3D geometry of the artery and is the first ABM implemented in C#. According to our results, the 3D ABM was able to simulate the Trans Endothelial Migration of Lymphocytes, Monocytes and Neutrophils, the artery wall cells, endothelium cells and plaque cells reducing the time step for each cycle from 40 seconds to 0.04 seconds per cycle.

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