A 4D flow MRI evaluation of the impact of shear-dependent fluid viscosity on in vitro Fontan circulation flow

Abstract

The Fontan procedure for univentricular heart defects creates a nonphysiologic circulation where systemic venous blood drains directly into the pulmonary arteries, leading to multiorgan dysfunction secondary to chronic low-shear nonpulsatile pulmonary blood flow and central venous hypertension. Although blood viscosity increases exponentially in this low-shear environment, the role of shear-dependent ("non-Newtonian") blood viscosity in this pathophysiology is unclear. We studied three-dimensional (3D)-printed Fontan models in an in vitro flow loop with a Philips 3-T magnetic resonance imaging (MRI) scanner. A 4D flow phase-contrast sequence was used to acquire a time-varying 3D velocity field for each experimental condition. On the basis of blood viscosity of a cohort of patients who had undergone the Fontan procedure, it was decided to use 0.04% xanthan gum as a non-Newtonian blood analog; 45% glycerol was used as a Newtonian control fluid. MRI data were analyzed using GTFlow and MATLAB software. The primary outcome, power loss, was significantly higher with the Newtonian fluid [14.8 (13.3, 16.4) vs. 8.1 (6.4, 9.8)%, medians with 95% confidence interval, P < 0.0001]. The Newtonian fluid also demonstrated marginally higher right pulmonary artery flow, marginally lower shear stress, and a trend toward higher caval flow mixing. Outcomes were modulated by Fontan model complexity, cardiac output, and caval flow ratio. Vortexes, helical flow, and stagnant flow were more prevalent with the non-Newtonian fluid. Our data demonstrate that shear-dependent viscosity significantly alters qualitative flow patterns, power loss, pulmonary flow distribution, shear stress, and caval flow mixing in synthetic models of the Fontan circulation. Potential clinical implications include effects on exercise capacity, ventilation-perfusion matching, risk of pulmonary arteriovenous malformations, and risk of thromboembolism.