"SIMULATION MODELING OF CORONARY ARTERY DISEASE: A TOOL FOR CLINICAL DECISION SUPPORT"

Coronary atherosclerosis (ATS) is a degenerative-inflammatory artery pathology underlying the different clinical manifestations of coronary heart disease (CHD), from stable angina due to constrictive plaque growth obstructing artery lumen, to acute coronary syndrome (ACS), secondary to abrupt lumen occlusion by atherothrombosis at the site of a ruptured or eroded plaque. Major coronary adverse events (MACE) are known to be related to local factors, the so called "high risk plaque" characterized by large lipid-necrotic core with a thin fibrous cap, intraplaque hemorrhage, rupture, erosion, and to systemic, patient-specific, factors, contributing to the atherogenic genotype/phenotype of the so called "high risk patient", presenting with an abnormally activated thrombogenic and/or inflammatory state or increased plasma levels of atherogenic lipid species. The huge social and economic impact of CHD in western and developing countries is primarily due to the difficulty to identify and predict, in the clinical context, which "high risk plaque" in which "high risk patient" will cause, independently of stenosis severity, an acute coronary event such as myocardial infarction or sudden coronary death, which are often the first manifestations of CHD in a large proportion of otherwise asymptomatic subjects. Plaque burden, compared to stenosis, is recognized as a better predictor of ACS and coronary CT angiography (CCTA) is considered as the optimal non-invasive coronary imaging modality to assess and quantify plaque burden and to evaluate the functional significance of a stenosis, by computationally estimating fractional flow reserve. Moreover, molecular studies of CHD patients have mostly examined associations with clinical cardiovascular outcomes: associations with coronary ATS assessed by quantitative CCTA may provide insight into the pathophysiological role of several molecular species in plaque formation and growth, and elucidate their potential role as discriminative bi