A prediction tool for plaque progression based on patient-specific multi-physical modeling

Abstract

Atherosclerotic plaque rupture is responsible for a majority of acute vascular syndromes and this study aims to develop a prediction tool for plaque progression and rupture. Based on the follow-up coronary intravascular ultrasound imaging data, we performed patient-specific multi-physical modeling study on four patients to obtain the evolutional processes of the microenvironment during plaque progression. Four main pathophysiological processes, i.e., lipid deposition, inflammatory response, migration and proliferation of smooth muscle cells (SMCs), and neovascularization were coupled based on the interactions demonstrated by experimental and clinical observations. A scoring table integrating the dynamic microenvironmental indicators with the classical risk index was proposed to differentiate their progression to stable and unstable plaques. The heterogeneity of plaque microenvironment for each patient was demonstrated by the growth curves of the main microenvironmental factors. The possible plaque developments were predicted by incorporating the systematic index with microenvironmental indicators. Five microenvironmental factors (LDL, ox-LDL, MCP-1, SMC, and foam cell) showed significant differences between stable and unstable group (p < 0.01). The inflammatory microenvironments (monocyte and macrophage) had negative correlations with the necrotic core (NC) expansion in the stable group, while very strong positive correlations in unstable group. The inflammatory microenvironment is strongly correlated to the NC expansion in unstable plaques, suggesting that the inflammatory factors may play an important role in the formation of a vulnerable plaque. This prediction tool will improve our understanding of the mechanism of plaque progression and provide a new strategy for early detection and prediction of high-risk plaques.

Understanding the Parameter Influence on Lesion Growth for a Mechanobiology Model of Atherosclerosis

“…With all of this information, knowing the influence of all the parameters, they could be adjusted to achieve more or less vulnerable atheroma plaque, according to the percentage volume of foam cells, synthetic smooth muscle cells, and collagen fibre [67,68]. The vulnerability of a plaque is dependent on multiple factors, such as its size or stresses caused by blood flow in it, but it is also dependent on its composition.…”

Image-based biomechanical modeling for coronary atherosclerotic plaque progression and vulnerability prediction

“…Pathologically, atherosclerotic plaque advanced along with accumulations of lipid and calcification compositions, as well as fibrosis intima thickening [8,9,39]. Based on this pathological insight, many morphological characteristics could be adopted as the measures for plaque progression, such as plaque area, plaque volume among others which has been slightly mentioned in the large cohort study before.…”

“…There are multiple modeling approaches to study plaque development and growth. Biologically and physically based mathematical models have been proposed to study the molecular or cellular activity during the plaque progression [8]. Overall, biomechanical modeling has been widely employed in CAD diagnosis, prevention, and treatment [34,38].…”

“…Therefore, it is of clinical importance to prospectively identify these vulnerable plaques and accurately predict future plaque progression and vulnerability behaviors by means of biomarkers and risk factors before they actually cause drastic or even fatal cardiovascular events [5,6]. Biomarkers involved in plaque development process such as oxidized low-density lipoproteins, macrophage, matrix metalloproteinases among others have been identified in clinical studies, and extensively investigated [6][7][8].…”

Circulating Biomarkers Reflecting Destabilization Mechanisms of Coronary Artery Plaques: Are We Looking for the Impossible?

“…In a feasibility study by Guo et al, the authors demonstrated that machine learning methods could be used to accurately predict changes in plaque vulnerability using morphological and biomechanical factors from multi-modality imagebased 3D fluid-structure interaction coronary model [176]. Interestingly, in a recent study by Pan et al, author used multi-physical model for identification of the spatial-temporal dynamics and progression in coronary atheroma microenvironment [177]. Using the multiphysical model, mathematical model based on previous work from the same group, authors integrated the dynamic microenvironmental indicators with the classical risk factors in order to differentiate plaque progression to either stable or unstable plaques [178,179].…”