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

Area of Science:

• Cardiovascular Research
• Biomedical Engineering
• Computational Biology

Background:

• Atherosclerotic plaque rupture is a primary cause of acute vascular syndromes.
• Current prediction methods for plaque progression and rupture require enhancement.

Purpose of the Study:

• To develop a patient-specific prediction tool for atherosclerotic plaque progression and rupture.
• To integrate microenvironmental factors with classical risk indices for improved prediction.

Main Methods:

• Patient-specific multi-physical modeling using coronary intravascular ultrasound imaging data.
• Coupling of four key pathophysiological processes: lipid deposition, inflammation, smooth muscle cell (SMC) migration/proliferation, and neovascularization.
• Development of a scoring table integrating dynamic microenvironmental indicators with classical risk indices.

Main Results:

• Demonstrated heterogeneity of plaque microenvironment and its evolution during progression.
• Identified five microenvironmental factors (LDL, ox-LDL, MCP-1, SMC, foam cell) with significant differences between stable and unstable plaques (p < 0.01).
• Found strong positive correlation between inflammatory microenvironment and necrotic core expansion in unstable plaques.

Conclusions:

• The developed prediction tool enhances understanding of plaque progression mechanisms.
• Inflammatory factors play a crucial role in vulnerable plaque formation.
• This tool offers a new strategy for early detection and prediction of high-risk atherosclerotic plaques.