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

Computational simulations have the potential to aid understanding of cardiovascular hemodynamics under physiological conditions, including exercise. Therefore, blood hemodynamic parameters during different heart rates, rest and exercise have been investigated, using a numerical method. A model was developed for a healthy subject. Using geometrical data acquired by echo-Doppler, a two-dimensional model of the chamber of aortic sinus valsalva and aortic root was created. Systolic ventricular and aortic pressures were applied as boundary conditions computationally. These pressures were the initial physical conditions applied to the model to predict valve deformation and changes in hemodynamics. They were the clinically measured brachial pressures plus differences between brachial, central and left ventricular pressures. Echocardiographic imaging was also used to acquire different ejection times, necessary for pressure waveform equations of blood flow during exercise. A fluid-structure interaction simulation was performed, using an arbitrary Lagrangian-Eulerian mesh. During exercise, peak vorticity increased by 14.8%, peak shear rate by 15.8%, peak cell Reynolds number by 20%, peak leaflet tip velocity increased by 47% and the blood velocity increased by 3% through the leaflets, whereas full opening time decreased by 11%. Our results show that numerical methods can be combined with clinical measurements to provide good estimates of patient-specific hemodynamics at different heart rates. </jats:p

Fluid-structure interaction modeling of aortic valve stenosis at different heart rates

Purpose: This paper proposes a model to measure the cardiac output and stroke volume at different aortic stenosis severities using a fluid–structure interaction (FSI) simulation at rest and during exercise. Methods: The geometry of the aortic valve is generated using echocardiographic imaging. An Arbitrary Lagrangian–Eulerian mesh was generated in order to perform the FSI simulations. Pressure loads on ventricular and aortic sides were applied as boundary conditions. Results: FSI modeling results for the increment rate of cardiac output and stroke volume to heart rate, were about 58.6% and –14%, respectively, at each different stenosis severity. The mean gradient of curves of cardiac output and stroke volume to stenosis severity were reduced by 57% and 48%, respectively, when stenosis severity varied from healthy to critical stenosis. Conclusions: Results of this paper confirm the promising potential of computational modeling capabilities for clinical diagnosis and measurements to predict stenosed aortic valve parameters including cardiac output and stroke volume at different heart rates

Estimation of maximum intraventricular pressure: a three-dimensional fluid-structure interaction model

BACKGROUND: The aim of this study was to propose a method to estimate the maximum pressure in the left ventricle (MPLV) for a healthy subject, based on cardiac outputs measured by echo-Doppler (non-invasive) and catheterization (invasive) techniques at rest and during exercise. METHODS: Blood flow through aortic valve was measured by Doppler flow echocardiography. Aortic valve geometry was calculated by echocardiographic imaging. A Fluid–structure Interaction (FSI) simulation was performed, using an Arbitrary Lagrangian–Eulerian (ALE) mesh. Boundary conditions were defined as pressure loads on ventricular and aortic sides during ejection phase. The FSI simulation was used to determine a numerical relationship between the cardiac output to aortic diastolic and left ventricular pressures. This relationship enabled the prediction of pressure loads from cardiac outputs measured by invasive and non-invasive clinical methods. RESULTS: Ventricular systolic pressure peak was calculated from cardiac output of Doppler, Fick oximetric and Thermodilution methods leading to a 22%, 18% and 24% increment throughout exercise, respectively. The mean gradients obtained from curves of ventricular systolic pressure based on Doppler, Fick oximetric and Thermodilution methods were 0.48, 0.41 and 0.56 mmHg/heart rate, respectively. Predicted Fick-MPLV differed by 4.7%, Thermodilution-MPLV by 30% and Doppler-MPLV by 12%, when compared to clinical reports. CONCLUSIONS: Preliminary results from one subject show results that are in the range of literature values. The method needs to be validated by further testing, including independent measurements of intraventricular pressure. Since flow depends on the pressure loads, measuring more accurate intraventricular pressures helps to understand the cardiac flow dynamics for better clinical diagnosis. Furthermore, the method is non-invasive, safe, cheap and more practical. As clinical Fick-measured values have been known to be more accurate, our Fick-based prediction could be the most applicable