Simulations for Improved Risk Stratification in Patients with Kawasaki Disease

A.L. Marsden, D. Sengupta, A. Kahn and J.C. Burns
University of California, San Diego, US

Keywords: nano fluidics, biomedical simulation

Summary:

Kawasaki disease (KD) is the leading cause of acquired heart disease in children, and can result in life-threatening coronary artery aneurysms in up to 25% of patients. These aneurysms put patients at risk of blood clot formation, such that clinicians must decide which patients are in need of treatment with anticoagulation medication, and / or surgical or percutaneous intervention. Currently, clinical decisions are typically made using anatomy alone, with diameter >8mm as the cutoff for anticoagulation therapy. We postulate that hemodynamic data derived from simulations can better predict risk of thrombosis than diameter alone. We will present simulation methods used for multiscale modeling of coronary flow, which is challenging to model due to heart contraction. We will then present preliminary data that illustrates how simulations may be used to develop a new clinical index for risk stratification. Our ultimate goal is to improve patient care by better selecting for anticoagulant and other therapies for patients with Kawasaki disease. Finally we will show examples of the application of optimization, uncertainty quantification and numerical coupling methods to a range of cardiovascular surgeries and devices and discuss the expanding role of simulations in the treatment of cardiovascular disease.