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Thrombosis Initiation Examined Through a Flexible 3D non-Newtonian Computational Framework
ISTH Academy. Lynch S. Jul 9, 2019; 264423; PB1232
Sabrina R. Lynch
Sabrina R. Lynch
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PB1232

Thrombosis Initiation Examined through a Flexible 3D non-Newtonian Computational Framework

S.R. Lynch1, N. Nama2, C.J. Arthurs3, Z. Xu4, O. Sahni4, J.A. Diaz2, C.A. Figueroa1,2,3
1University of Michigan, Department of Biomedical Engineering, Ann Arbor, United States, 2University of Michigan, Department of Surgery, Ann Arbor, United States, 3King's College London, Department of Biomedical Engineering, London, United Kingdom, 4Rensselaer Polytechnic Institute, Department of Mechanical, Aerospace and Nuclear Engineering, Troy, United States

Main Topic: Coagulation and Anticoagulation
Category: Regulation of Coagulation

Background: Computational modeling has led to advances in thrombosis research but unresolved challenges still exist, such as the spatiotemporal effects complex 3D hemodynamics have on the biochemical processes of thrombosis initiation. While blood viscosity is known to exhibit a nonlinear behavior, a Newtonian assumption is often employed in computational analyses. In pathological geometries, such as aneurysms, the Newtonian assumption fails, and nonlinear viscous effects become crucial for an accurate description of hemodynamics.
Aims: To develop a computational framework to investigate how flow characteristics affect thrombosis initiation in vivo.
Methods: A patient-specific thoracic aortic aneurysm geometric model was built from computed tomography angiography image data using the validated software package CRIMSON (www.crimson.software). Blood was modeled as both a Newtonian and non-Newtonian fluid.
To model thrombus formation, a staggered finite element approach was implemented for solving the reaction-advection-diffusion (RAD) equations. The reaction model used describes thrombin generation via four key players: thrombin, prothrombin, activated platelets, and resting platelets.
Results:


[Figure 1: Newtonian and non-Newtonian (Carreau-Yasuda) results in an aortic aneurysm. ]




[Figure 2: Scalar model of thrombin formation in a patient-specific aortic aneurysm. ]



The Newtonian and non-Newtonian models produced different in-plane flow patterns (Fig.1B), with higher near-wall velocities and gradients observed for the Newtonian model. Wall shear stress was observed to differ both qualitatively and quantitatively between the models (Fig.2C).
Fig.2A illustrates the proposed RAD python interface for thrombosis modeling. Prothrombin degradation (Fig.2B,E) and thrombin generation (Fig.2C,F) are shown indicating that thrombin is formed due to interaction between prothrombin and activated platelets and propagated through the domain.
Conclusions: We developed a computational framework of thrombosis initiation involving non-Newtonian hemodynamics in an imaged-based 3D cardiovascular model. We applied this framework to investigate a patient-specific model of an aortic aneurysm. Future investigations will expand the staggered finite element strategy for RAD to include an arbitrary number of scalars using a python interface. Future work will also employ animal models to capture parameters of in vivo venous thrombosis to better inform our computational model.

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