The multi-level computational plaque growth model achieved 80% accuracy in predicting disease progression in coronary arteries over 6.1 years.
Observational (n=94)
Yes
Does a computational biomechanics model accurately simulate and predict atherosclerotic plaque growth in human coronary arteries compared to serial CTCA imaging?
A novel computational biomechanics model successfully simulated and predicted atherosclerotic plaque growth and disease progression with 80% accuracy when compared to serial CTCA imaging.
p-value: p=<0.0001
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.
Pleouras et al. (Thu,) conducted a observational in Atherosclerosis (n=94). Multi-level computational plaque growth model vs. Real follow-up arterial geometries based on CTCA measurements was evaluated on Disease progression, evaluated by simulated plaque area and lumen area change (p=<0.0001). The multi-level computational plaque growth model achieved 80% accuracy in predicting disease progression in coronary arteries over 6.1 years.