Initial Insight to Effect of Exercise on Maximum Pressure in the Left Ventricle Using 2D Fluid-Structure Interaction Model

Aims: Study of maximum pressure in the left ventricle (MPLV) has already been a challenging aspect of clinical diagnosis. The aim of this study was to propose a model to estimate the MPLV for a healthy subject based on cardiac outputs measured by echo-Doppler (non-invasive) and catheterization (invasive) techniques at rest and during exercise.
Study Design and Methodology: Blood flow through the aortic valve was measured by Doppler flow echocardiography. The aortic valve geometry was then 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 modelling was applied to determine a numerical relationship between the cardiac output to left ventricular and aortic diastolic pressures. These relationships enable the prediction of pressure loads from cardiac outputs measured by invasive and non-invasive clinical methods.
Results: Peak ventricular systolic pressure calculated from cardiac output of Doppler method, Fick oximetric and Thermodilution methods led to a 82.1%, 95.6% and 147% increment throughout exercise, respectively. The mean slopes obtained from curves of ventricular systolic pressure based on Doppler, Fick oximetric and Thermodilution methods are 1.27, 1.85 and 2.65mmHg.min, respectively. Our predicted Fick-MPLV values were 8% to 19% lower, 17% to 25% lower for Thermodilution-MPLV and 57% to 73% lower for Doppler-MPLV values when compared to clinical reports.
Conclusion: Predicted results are in good agreement with values in the literature. The method, however, requires validation by additional experiments, comprising independent quantifications of MPLV. 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 noninvasive, safe, cheap and practical. As clinical Fick-measured values have been known to be more accurate, our Fick-based prediction could be the most applicable.