Combining numerical and clinical methods to assess aortic valve hemodynamics during exercise

Area of Science:

• Cardiovascular physiology
• Computational fluid dynamics
• Biomedical engineering

Background:

• Understanding cardiovascular hemodynamics during physiological conditions like exercise is crucial.
• Computational simulations offer a powerful tool to investigate these complex dynamics.
• Patient-specific modeling enhances the accuracy of hemodynamic predictions.

Purpose of the Study:

• To investigate blood hemodynamic parameters at rest and during exercise using a numerical method.
• To develop and validate a computational model for a healthy subject's cardiovascular system.
• To assess the impact of varying heart rates on hemodynamics and valve function.

Main Methods:

• Developed a 2D computational model of the aortic sinus of Valsalva and aortic root using echo-Doppler data.
• Applied systolic ventricular and aortic pressures as boundary conditions, derived from clinical measurements.
• Performed fluid-structure interaction simulations using an arbitrary Lagrangian-Eulerian mesh.
• Incorporated echocardiographic data for ejection times to refine pressure waveform equations.

Main Results:

• Exercise simulation showed significant increases in peak vorticity (14.8%), peak shear rate (15.8%), and peak cell Reynolds number (20%).
• Peak leaflet tip velocity increased by 47%, and blood velocity through leaflets rose by 3%.
• Full leaflet opening time decreased by 11% during simulated exercise conditions.

Conclusions:

• Numerical methods integrated with clinical measurements provide accurate patient-specific hemodynamic estimates.
• The study demonstrates the utility of computational modeling in understanding cardiovascular responses to exercise.
• This approach aids in predicting hemodynamic changes across different heart rates.