Quantifying the impact of mitral valve anatomy on clinical markers using surrogate models and sensitivity analysis

Blood flow studies within the left ventricle have proven to be promising for future clinical decision-making. However, accurate segmentation of heart valves, particularly the mitral valve, is still challenging. The mitral valve has a significant impact on local flow phenomena within the ventricle and assumptions about its anatomy and position introduce uncertainties that are not yet fully understood. The overall aim of this study is to quantify the impact of uncertainty in defining mitral valve anatomy and position on local and global hemodynamic markers, such as kinetic energy, energy loss, transventricular pressure gradient and locally resolved wall shear stresses. A combination of computational fluid dynamics moving mesh simulations of cardiac blood flow, reduced order modeling and variance-based global sensitivity analysis is used. The influence of annular displacement, angular orientation and long-axis radius is assessed using echocardiographic imaging data from an infant. A non-linear relationship between geometric uncertainties and flow biomarkers is uncovered, with mitral valve size and angle identified as the most important parameters. Uncertainty quantification of echocardiography measurements reveals a standard deviation between 5-32% for the different clinical markers. This study highlights the importance of robust surrogate models and global sensitivity analysis, as their choice can drastically affect both predicted clinical markers and identified key parameters most relevant for model personalization. The presented pipeline is summarized in the open source tool SASQUATCH - a framework for sensitivity analysis and uncertainty quantification in cardiac hemodynamics.