Hemodynamics of thrombus formation in intracranial aneurysms: An in silico observational study

Abstract

How prevalent is spontaneous thrombosis in a population containing all sizes of intracranial aneurysms? How can we calibrate computational models of thrombosis based on published data? How does spontaneous thrombosis differ in normo- and hypertensive subjects? We address the first question through a thorough analysis of published datasets that provide spontaneous thrombosis rates across different aneurysm characteristics. This analysis provides data for a subgroup of the general population of aneurysms, namely, those of large and giant size (>10 mm). Based on these observed spontaneous thrombosis rates, our computational modeling platform enables the first in silico observational study of spontaneous thrombosis prevalence across a broader set of aneurysm phenotypes. We generate 109 virtual patients and use a novel approach to calibrate two trigger thresholds: residence time and shear rate, thus addressing the second question. We then address the third question by utilizing this calibrated model to provide new insight into the effects of hypertension on spontaneous thrombosis. We demonstrate how a mechanistic thrombosis model calibrated on an intracranial aneurysm cohort can help estimate spontaneous thrombosis prevalence in a broader aneurysm population. This study is enabled through a fully automatic multi-scale modeling pipeline. We use the clinical spontaneous thrombosis data as an indirect population-level validation of a complex computational modeling framework. Furthermore, our framework allows exploration of the influence of hypertension in spontaneous thrombosis. This lays the foundation for in silico clinical trials of cerebrovascular devices in high-risk populations, e.g., assessing the performance of flow diverters in aneurysms for hypertensive patients.

Model blood for simulating red thrombus formation owing to stagnant blood flow using hypercoagulable skim milk solution

“…Recent developments in the understanding of haemodynamics and computational fluid dynamics (CFD) have revealed a strong correlation between the blood flow field and site of thrombus formation (Morales et al, 2011;Tanemura et al, 2013;Otani et al, 2016;Gester et al, 2016); however, these CFD studies did not consider a model of thrombus formation and rheological changes of blood owing to flow stagnation. Certain studies (Aida and Shimano, 2013;Bodnár et al, 2014;Manning et al, 2021) have attempted to model the thrombus formation process mathematically and solve it simultaneously using CFD analysis (Menichini and Xu, 2016;Sarrami-Foroushani et al, 2019;Marsh et al, 2020;Liu et al, 2023). These mathematical models included, for example, platelets deposition to the blood vessel surface and platelets activation due to flow stagnation, production of activating substances by platelets, and increase in blood viscosity due to thrombus formation.…”