Numerical study of hemodynamic flow in the aortic vessel of Williams syndrome patient with congenital heart disease

• Published in: Journal of biomechanics 2024-05, Vol.168, p.112124-112124, Article 112124
• Main Authors: Jack, Justin T., Jensen, Morten, Collins, R. Thomas, Chan, Frandics Pak, Millett, Paul C.
• Format: Article
• Language: English
• Subjects: Aorta; Aorta - diagnostic imaging; Aorta - physiopathology; Aortic stenosis; Blood flow; Blood Flow Velocity - physiology; Blood vessels; Computational fluid dynamics; Computer applications; Computer Simulation; Congenital diseases; Defects; Female; Flow distribution; Flow velocity; Heart Defects, Congenital - complications; Heart Defects, Congenital - diagnostic imaging; Heart Defects, Congenital - physiopathology; Heart diseases; Hemodynamics; Hemodynamics - physiology; Humans; Lumped parameter model; Magnetic resonance imaging; Male; Medical imaging; Models, Cardiovascular; Patients; Pediatrics; Physiology; Pressure drop; Supravalvular aortic stenosis; Wall shear stresses; Williams syndrome; Williams Syndrome - diagnostic imaging; Williams Syndrome - physiopathology
• ISSN: 0021-9290; 1873-2380
• DOI: 10.1016/j.jbiomech.2024.112124

Congenital arterial stenosis such as supravalvar aortic stenosis (SVAS) are highly prevalent in Williams syndrome (WS) and other arteriopathies pose a substantial health risk. Conventional tools for severity assessment, including clinical findings and pressure gradient estimations, often fall short due to their susceptibility to transient physiological changes and disease stage influences. Moreover, in the pediatric population, the severity of these and other congenital heart defects (CHDs) often restricts the applicability of invasive techniques for obtaining crucial physiological data. Conversely, evaluating CHDs and their progression requires a comprehensive understanding of intracardiac blood flow. Current imaging modalities, such as blood speckle imaging (BSI) and four-dimensional magnetic resonance imaging (4D MRI) face limitations in resolving flow data, especially in cases of elevated flow velocities. To address these challenges, we devised a computational framework employing zero-dimensional (0D) lumped parameter models coupled with patient-specific reconstructed geometries pre- and post-surgical intervention to execute computational fluid dynamic (CFD) simulations. This framework facilitates the analysis and visualization of intricate blood flow patterns, offering insights into geometry and flow dynamics alterations impacting cardiac function. In this study, we aim to assess the efficacy of surgical intervention in correcting an extreme aortic defect in a patient with WS, leading to reductions in wall shear stress (WSS), maximum velocity magnitude, pressure drop, and ultimately a decrease in cardiac workload.