A new parameter for prediction of intracranial aneurysm hemodynamics and risk of rupture Summary Quantifying the likelihood of aneurysm rupture is a significant clinical goal. However, this is a daunting task due to the many risk factos implicated in aneurysm rupture. Hemodynamic factors such as shear stress are among the most important risk factors, but their calculation requires sophisticated flow measurements/simulations. Consequently, only simple geometric parameters are used in practice to monitor aneurysms or guide interventions. Geometric parameters, however, do not account for the flow conditions, which are shown to drastically affect the hemodynamics inside aneurysms. Our innovation is finding a simple, non-dimensional parameter (called An number) based on basic fluid mechanics principles (defined by the time scale of fluid transport to vortex formation, which depends on both geometry and flow) that we hypothesize can predict the flow mode (cavity vs. vortex mode) in aneurysms. This hypothesis is formed based on our previous work on the effects of waveform on the hemodynamics of an aneurysm on a rabbit carotid artery. Our previous work shows that the flow mode drastically affects the shear stress on the aneurysm dome: The vortex mode has a lower average but higher oscillatory wall shear stress than the cavity mode. The low average and high oscillatory shear stress are believed to be detrimental for cardiovascular health, i.e., we hypothesize that the vortex mode is more prone to rupture than the cavity mode. The validity of our hypotheses, nevertheless, should be tested for different human aneurysm geometries and flow conditions. We propose to test the hypothesis that the An number can predict the flow mode (vortex vs. cavity) by carrying out image-based simulations on at least one ruptured and one unruptured aneurysm for each location and type of aneurysms from a patient database containing 119 patient-specific 3D geometries of saccular aneurysms (38 ruptured, 81 unruptured). The patient database is already available and provided by the co-Investigator of this proposal. Furthermore, we propose to test the hypothesis that the An>1 (vortex mode) increases the risk of rupture by calculating the An number for all 119 aneurysms in the patient database using just the geometry and location information (no simulation). The statistical mean and deviations of the An number for both the ruptured and unruptured groups will be calculated, and statistical tests will be performed to assess the statistical significance of the observed difference between the ruptured and unruptured groups. For An to be correlated with rupture, statistical significance (P<0.01) is needed. Furthermore, we propose to use multivariate statistical analysis to combine the An number with other geometric parameters. The expected outcome of this proposal, if successful, is a new parameter that can correlate better with the risk of rupture relative to purely geometric parameters, and is simple enough to be calculated easily in clinical practice. Monitoring this parameter would be a valuable tool for physicians, which could greatly improve the timely treatment of patients.
An estimated 12 million (1 in 25) people carry an unruptured aneurysm, out of which about 30,000 (8 to 10 per 100,000) rupture annually in the United States. Therefore, quantifying the likelihood of aneurysm rupture that can guide intervention is a significant clinical goal. Here we propose a new parameter that depends on both geometric features and parent artery flow, which is hypothesize to correlate better with the aneurysm rupture and can be monitored to provide better guidelines for intervention.
| Asgharzadeh, Hafez; Borazjani, Iman (2017) A Newton-Krylov method with an approximate analytical Jacobian for implicit solution of Navier-Stokes equations on staggered overset-curvilinear grids with immersed boundaries. J Comput Phys 331:227-256 |
| Asgharzadeh, Hafez; Borazjani, Iman (2016) Effects of Reynolds and Womersley Numbers on the Hemodynamics of Intracranial Aneurysms. Comput Math Methods Med 2016:7412926 |
