Linking statistical shape models and simulated function in the healthy adult human heart (vol 17, e1008851, 2021)

Author summaryThe heart adapts to physiological and pathological changes in loading. This can cause the heart to change size and shape. These changes can in turn have significant impact on cardiac function. However, it is not clear if large changes in function are caused by large changes in shape or can smaller changes also be important. Biophysical computational models of the heart provide a quantitative framework for mapping changes in anatomy to whole heart function. We created a publicly available healthy four-chamber heart virtual cohort from clinical images. Each patient's heart anatomy in the virtual cohort was described by the contribution of different components of heart shape. The shape components are ranked by the amount of shape variance that they explain. Simulations of cardiac electrical activation and mechanical pump function in hearts with shapes described by different combinations of shape components were performed. This allowed us to show that some shape components that explain a large amount of electrical and mechanical function variance only explain a small amount of anatomical variance. This highlights the need to have high fidelity anatomical models in cardiac simulations and demonstrates that subtle changes in cardiac anatomy can have a large impact on cardiac function.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.