Past Event: Oden Institute Seminar

Patient-specific Computational Modeling for Thrombotic Risk Stratification in Coronary Artery Aneurysms Caused by Kawasaki Disease

Noelia Grande Gutiérrez, Research Assistant, PhD Candidate, Mechanical Engineering, Stanford University

Abstract

Computational methods are emerging as valuable tools to support clinical-decision making, risk assessment, and device design in cardiovascular medicine. In this talk, I will apply computational simulations to investigate the implications of coronary artery aneurysms hemodynamics on thrombus formation in children with Kawasaki disease, the most common cause of acquired heart disease in children. First, I will discuss novel image processing methods that use transluminal attenuation gradient (TAG) to extract functional information from CT scans. I demonstrate significantly abnormal TAG values in aneurysms caused by Kawasaki disease compared to normal coronary arteries. Second, I will present an image-based computational framework combining a deep understanding of coronary physiology with advanced numerical methods and high performance computing, to obtain fully resolved patient-specific hemodynamic data relevant to thrombotic risk stratification. Simulations are performed with finite element methods incorporating fluid structure interaction and closed loop lumped parameter models to represent vascular boundary conditions. The primary translational goal is to support clinical decisions about when and if a patient needs to start systemic anti-coagulation therapy. My results demonstrate that hemodynamic variables such as wall shear stress and residence time are significantly more predictive of thrombotic risk than the anatomical measurements currently used in clinical practice. Finally, I will discuss biochemical aspects of thrombus initiation and how these processes can be modeled using a continuum approach. I will present a new model based on scalar transport and incorporating velocity fields from patient-specific simulations to track activation and accumulation of platelets and other blood components critical to the coagulation cascade. This model provides a new approach to investigate thrombus initiation from a patient-specific perspective, which could help identify regions at higher risk of thrombosis as well as strategies for thrombosis prevention. Bio Noelia Grande Gutiérrez is a PhD Candidate in Mechanical Engineering at Stanford University. She obtained a M.S. in Engineering Sciences from the University of California, San Diego, a M.S in Biomedical Engineering from the University of Barcelona and her B.S. in Aerospace Engineering from the Technical University of Madrid. Her research interests lie at the intersection of computational engineering and cardiovascular medicine, and involve the development and application of multi-physics models that contribute to support clinical-decision making and provide novel insight into cardiovascular disease. For her doctoral research, she received the American Heart Association fellowship. Under the supervision of Dr. Alison Marsden, she has worked on applying patient-specific computational simulations to understand the role of hemodynamics on coronary artery aneurysms thrombosis.