A Network-based approach to investigate flow coherence alteration in coronary lesions at risk of myocardial infarction.

Many recent studies ascertained that cardiovascular diseases are in first place for cause of mortality worldwide. Atherosclerosis is a chronic and progressive pathology characterized by an initial deposit of lipids inside the inner layer (tunica intima) of an artery and it is the most prominent cardiovascular disease. As the fat accumulation progresses there is the creation of a plaque composed by a lipidic core and a thick fibrous cap. The growth of plaque causes an obstruction of the artery and consequently a drastic change in blood flow. If atherosclerosis affects coronary arteries, it leads to reduction in myocardial perfusion and may evolve in myocardial infarction (MI). It has been widely demonstrated that a local disturbed hemodynamic environment, especially in terms of altered wall shear stress (WSS) features, can trigger a response of the endothelium characterized by uncontrolled intimal thickening with subsequent luminal narrowing (stenosis). The development of computational fluid dynamics (CFD) in recent years has helped to evaluate hemodynamic descriptors that could not be quantified properly in vivo, such as the WSS. Moreover, very recent studies have proposed a new approach to synthetize and disentangle the four-dimensional hemodynamic complexity by modelling arterial flows as ¿social networks¿, using the Complex Networks (CNs) theory. The aim of this thesis is to apply a CNs strategy to investigate the link between WSS-based quantities and the patient-specific blood flow rate waveform and explore possible implications of this link in MI prediction and the existence of correlations with anatomical, functional, and hemodynamic descriptors. The investigated dataset is represented by 188 coronary lesions: 80 coronary lesions culprit of MI (FC) within 5 years and 108 non-culprit coronary lesions (NFC) within 5 years. The here-adopted CNs approach consists in building ¿one-to-all¿ networks based on the correlations between the reference blood flow rate waveform Q(t) at the inlet section of the coronary artery and the time-histories along the cardiac cycle of WSS-based descriptors (in detail, WSS vector magnitude (|WSS(t)|), axial (WSSax(t)), and secondary (WSSsc(t)) WSS components time-histories) obtained with CFD simulations previously performed on the investigated dataset. For each model, the correlations of the ¿one-to-all¿ network were then used to calculate the network metric called Average Weighted Curvilinear distance (AWCD), a measure that quantifies the anatomical length of persistence of correlation between the driving blood flow rate waveform and the WSS. Similar values were found for the length of persistence of the |WSS(t)| vs. Q(t) correlation and of the WSSax(t) vs. Q(t) correlation, when considering the whole study population (AWCD|WSS| median value 0.412, IQR 0.386¿0.433; AWCDWSSax median value 0.395, IQR 0.330¿0.421). More interestingly, statistically significant differences emerged when comparing the results in terms of AWCD|WSS| and AWCDWSSax between FC and NFC lesions. In particular, both AWCD|WSS| and AWCDWSSax values were higher in the NFC group (median of 0.406 in FC vs. 0.417 in NFC, p = 0.02, and 0.363 in FC vs. 0.406 in NFC, p < 0.001, respectively). The latter results suggest that the flow coherence of WSS-based near-wall coronary flow as imparted by the proximal blood flow rate waveform Q(t) expires within a shorter distance in the presence of coronary lesions culprit of future myocardial infarction.