Simulation of aortopulmonary collateral flow in Fontan patients for use in prediction of interventional outcomes

Computational approaches for mechanobiology in cardiovascular development and diseases

The cardiovascular development in vertebrates evolves in response to genetic and mechanical cues. The dynamic interplay among mechanics, cell biology, and anatomy continually shapes the hydraulic networks, characterized by complex, non-linear changes in anatomical structure and blood flow dynamics. To better understand this interplay, a diverse set of molecular and computational tools has been used to comprehensively study cardiovascular mechanobiology. With the continual advancement of computational capacity and numerical techniques, cardiovascular simulation is increasingly vital in both basic science research for understanding developmental mechanisms and disease etiologies, as well as in clinical studies aimed at enhancing treatment outcomes. This review provides an overview of computational cardiovascular modeling. Beginning with the fundamental concepts of computational cardiovascular modeling, it navigates through the applications of computational modeling in investigating mechanobiology during cardiac development. Second, the article illustrates the utility of computational hemodynamic modeling in the context of treatment planning for congenital heart diseases. It then delves into the predictive potential of computational models for elucidating tissue growth and remodeling processes. In closing, we outline prevailing challenges and future prospects, underscoring the transformative impact of computational cardiovascular modeling in reshaping cardiovascular science and clinical practice.

In vivo hepatic flow distribution by computational fluid dynamics can predict pulmonary flow distribution in patients with Fontan circulation

In Fontan patients, a lung deprived of hepatic blood may develop pulmonary arterio-venous malformations (PAVMs) resulting in shunting, reduced pulmonary vascular resistance (PVR) and decreased oxygenation. To provide guidance for corrective invasive interventions, we aimed to non-invasively determine how the hepatic to pulmonary blood flow balance correlates with pulmonary flow, PVR, and with oxygen saturation. Magnetic resonance imaging (MRI) data from eighteen Fontan patients (eight females, age 3-14 years) was used to construct patient-specific computational fluid dynamics (CFD) models to calculate the hepatic to pulmonary blood flow. This was correlated with pulmonary vein flow, simulated PVR and oxygen saturation. Clinical applicability of the findings was demonstrated with an interventional patient case. The hepatic to pulmonary blood flow balance correlated with right/left pulmonary vein flow (R2 = 0.50), left/right simulated PVR (R2 = 0.47), and oxygen saturation at rest (R2 = 0.56). In the interventional patient, CFD predictions agreed with post-interventional MRI measurements and with regressions in the cohort. The balance of hepatic blood to the lungs has a continuous effect on PVR and oxygen saturation, even without PAVM diagnosis. MRI combined with CFD may help in planning of surgical and interventional designs affecting the hepatic to pulmonary blood flow balance in Fontan patients.

Computational Fluid Dynamics Support for Fontan Planning in Minutes, Not Hours: The Next Step in Clinical Pre-Interventional Simulations

Computational fluid dynamics (CFD) modeling may aid in planning of invasive interventions in Fontan patients. Clinical application of current CFD techniques is however limited by complexity and long computation times. Therefore, we validated a “lean” CFD method to magnetic resonance imaging (MRI) and an “established” CFD method, ultimately aiming to reduce complexity to enable predictive CFD during ongoing interventions. Fifteen Fontan patients underwent MRI for CFD modeling. The differences between lean and established approach, in hepatic and total flow percentage to the left pulmonary artery (%LPA), power loss and relative wall shear stress area were 1.5 ± 4.0%, -0.17 ± 1.1%, -0.055 ± 0.092 mW and 1.1 ± 1.4%. Compared with MRI, the lean and established method showed a bias in %LPA of -1.9 ± 3.4% and -1.8 ± 3.1%. Computation time was for the lean and established approach 3.0 ± 2.0 min and 7.0 ± 3.4 h, respectively. We conclude that the proposed lean method provides fast and reliable results for future CFD support during interventions.