Self-Powered Injection-Jet Fontan Circulation to Drop Caval Pressure in a Failing Fontan

  • Prather, Ray (The Heart Center, APH for Children)
  • Das, Arka (Embry-Riddle Aeronautical University)
  • Divo, Eduardo (Embry-Riddle Aeronautical University)
  • Hsia, Tain-Yen (The Heart Center, APH for Children)
  • Kassab, Alain (University of Central Florida)
  • DeCampli, William (The Heart Center, APH for Children)

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The Fontan circulation is a fragile system in which imperfections compromise quality of life. Elevated inferior vena cava (IVC) pressure plays a key role in “Fontan failure”. We hypothesize that an injection jet shunt (IJS) emerging from the aortic arch can entrain IVC flow leading to a clinically significant IVC pressure reduction (>3mmHg). We describe a tightly-coupled multi-scale lumped parameter/computational fluids dynamics model to examine this hypothesis. A synthetic 3D-CAD model of a 2-4 yo patient with a fenestrated total cavopulmonary connection (TCPC) was generated. The prescribed cardiac output is ~2.3L/min. Hemodynamics are modeled as unsteady, incompressible, turbulent and blood is assumed non-Newtonian. Turbulence is approximated using a large eddy simulation. The effects of the IJS implementation on IVC pressure and systemic oxygen saturation are calculated through a parametric sweep of several geometric design parameters such as TCPC morphology, shunt and fenestration diameter and location. A set of baseline simulations representing a failing Fontan with elevated IVC pressure (+17.8mmHg) is shown in Figure 1-A (cases 1-2 a). Fenestration enlargement to 7 mm (cases 1-2 b) results in a 3 mm Hg IVC pressure drop but also significant reduction in systemic oxygen saturation. Addition of an 2mm IJS to this model preserves the IVC pressure drop of 3.2 mm Hg and improves systemic oxygen saturation (cases 3-4). Our current models demonstrate the potential salutary effect of the IJS on the Fontan circulation. In-vitro simulations will also be considered to cross-validate the optimal outcome from the in-silico model.