Multivariate analysis of hemodynamic parameters on intracranial aneurysm initiation of the internal carotid artery

Abstract

Although fluctuating hemodynamic wall stressors are known to impact intracranial aneurysms (IA) initiation, specificity of those stressors has not been evaluated. In this study, using human IA data, we investigated: (1) specificity of stressors in regions with and without IA eventual IA formation; and (2) how combinations of multiple stressors could improve IA formation prediction. 3D computational vasculatures were constructed based on angiographic images of 18 subjects having multiple closely-spaced IAs in the internal carotid artery. Two models were created: Model A with all IAs computationally removed, Model B which kept keep one IA. Computational fluid dynamics (CFD) simulated flow within models. Based on simulated flow fields, wall shear stress and its gradient (WSS, WSSG), oscillatory shear index (OSI), gradient oscillatory number (GON), aneurysm formation index (AFI), and mean number of swirling flow vortices (MV) were analysed. Multivariate logistic regression determined the accuracy of different combinations of those above-mentioned stressors. Overall, we found that combining hemodynamic stressors improves IA formation prediction over individual indices. Both Model A and Model B's parsimonious model was MV+WSS+GON: AUROC 0.88 and 0.83, respectively. Future studies are planned to understand biological meanings induced by fluctuating stressors.

Keywords: Computational fluid dynamics; Hemodynamic stressors; Intracranial aneurysm; Multivariate predictive modeling.