Abstract
Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.
Highlights
Cardiac anatomy plays a crucial role in determining the function of the heart [1,2,3]
An example of the scaled Jacobian (SJ) values projected onto a mesh and as a histogram are shown in Fig 3A and 3B respectively
In S5 Text we provide the values of the SJ, edge lengths and number of elements, nodes and edges for the Computed Tomography (CT) and extreme3 cohorts
Summary
Cardiac anatomy plays a crucial role in determining the function of the heart [1,2,3]. In patient-specific cardiac models, the anatomy is often derived from Computed Tomography (CT), echocardiography or Magnetic Resonance Imaging (MRI) [9]. Differences in imaging modality will impact the accuracy of the anatomical model of a specific patient’s heart [10], adding an extra layer of uncertainty [11]. These different imaging modalities have known biases [12, 13], yet we do not know how specific changes in the observed cardiac anatomy will impact subsequent simulations of cardiac function
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