Coronary plaque rupture is the most common cause of vessel thrombosis and acute coronary syndrome. The accurate early detection of plaques prone to rupture may allow prospective, preventative treatment; however, current diagnostic methods remain inadequate to detect these lesions. Established imaging features indicating vulnerability do not confer adequate specificity for symptomatic rupture. Similarly, even though experimental and computational studies have underscored the importance of endothelial shear stress in progressive atherosclerosis, the ability of shear stress to predict plaque progression remains incremental. This review examines recent advances in image-based computational modelling that have elucidated possible mechanisms of plaque progression and rupture, and potentially novel features of plaques most prone to symptomatic rupture. With further study and clinical validation, these markers and techniques may improve the specificity of future culprit plaque detection.
Keywords: Atherosclerosis; Coronary artery disease; Shear stress; Computational modelling; Plaque rupture.