Computational simulations for aortic coarctation: representative results from a sampling of patients

Area of Science:

• Cardiovascular Physiology
• Biomedical Engineering
• Medical Imaging Analysis

Background:

• Coarctation of the aorta (CoA) treatments reduce blood pressure gradients but do not eliminate long-term morbidity.
• Altered hemodynamic and biomechanical indices are implicated in persistent morbidity after CoA repair.
• A deeper understanding of these indices under various conditions is crucial for improving patient outcomes.

Purpose of the Study:

• To introduce a computational fluid dynamics (CFD) technique for analyzing hemodynamic and biomechanical indices in coarctation of the aorta (CoA).
• To quantify the contribution of these indices to morbidity in CoA patients under resting and non-resting conditions.
• To evaluate the impact of CoA treatments on these indices.

Main Methods:

• Patient-specific CFD models were developed using imaging and blood pressure data from normal and CoA patients (native and post-operative).
• Simulations incorporated vessel deformation, vascular resistance, and compliance.
• Key indices quantified included cyclic strain, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI).

Main Results:

• CFD simulations accurately replicated resting blood pressure and flow data.
• Moderate native CoA patients exhibited the largest exercise-induced increases in systolic blood pressure and pressure gradients.
• CoA patients showed elevated cyclic strain near the coarctation, reduced strain post-treatment, and increased exposure to subnormal TAWSS or elevated OSI compared to normal individuals.
• Patterns of hemodynamic indices associated with atherosclerosis were accentuated in CoA patients.

Conclusions:

• CFD modeling provides a powerful tool for understanding complex hemodynamics in CoA patients.
• Altered biomechanical and hemodynamic indices persist after CoA treatment and contribute to long-term morbidity.
• This approach offers a foundation for future research into optimizing CoA treatment strategies and predicting patient risk.