The interplay of sex-specific electrophysiology and repolarization heterogeneity governs ventricular arrhythmia sustainability in women

Background

The role of sex-specific electrophysiology in the development of lethal ventricular arrhythmia is poorly understood. Since female ventricles have a longer action potential duration (APD) than male ventricles, we hypothesized that female hearts are more vulnerable to sustained ventricular arrhythmia in the presence of known arrhythmogenic substrates.

Purpose

To determine if prolonged APD in females leads to sustained ventricular arrhythmia in the presence of left ventricular (LV) hypertrophy, diffuse fibrosis, and repolarization heterogeneity.

Methods

Magnetic resonance imaging (MRI) data were used to develop computational biventricular models of two aortic stenosis patients (male, 54 years; female, 64 years) before (LV hypertrophy) and 3 months after (regressed LV hypertrophy) aortic valve replacement (AVR). These patients were chosen since they exhibit reduced LV mass, less fibrosis, and no ventricular arrhythmias after AVR. The models were assigned sex-specific cellular electrophysiology and an apicobasal repolarization gradient derived from human data. Simulations of apical pacing with a cycle length (CL) of 750 ms were performed while APD at 90% repolarization (APD90) and repolarization time (activation time + APD90) were calculated. Reentrant arrhythmia was induced by rapidly pacing the LV with a 2 mm wide apicobasal line electrode at CL≤265 ms. The simulations were repeated in the presence of diffuse myocardial fibrosis derived from the MRI patient data, and repeated again with an apicobasal repolarization gradient reduced by ≈35%. Ventricular mass was calculated as the product of model volume and myocardial density.

Results

In hypertrophic ventricles with default apicobasal repolarization gradient, rapid pacing led to reentry that lasted longer in the female (>25 s) than in the male (1.0 s) ventricles. The duration of reentry increased in the presence of fibrosis in the male ventricles (male: 6.6 s; female >25 s) and decreased in the female ventricles when the apicobasal repolarization gradient was reduced (Figure). Sustained reentry (>25 s) was not observed in the ventricles with regressed hypertrophy, regardless of the presence of fibrosis or repolarization gradient. Average APD90 was longer in the female ventricles both before (male: 267±35 ms; female: 318±34 ms) and after (male: 272±35 ms; female: 328±38 ms) AVR. The female heart had lower myocardial mass than the male heart both before (280 vs. 406 g) and after AVR (227 vs. 310 g).

Conclusion

Longer APD and steeper apicobasal repolarization gradients in hypertrophic female ventricles resulted in sustained arrhythmia, despite their lower mass when compared to males. The absence of sustained reentry in the post-AVR models indicates a potential electrophysiological benefit of AVR and encourages future clinical studies to quantify sex-specific differences in spatial repolarization heterogeneity.

Funding Acknowledgement

Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Union's Horizon 2020 research and innovation program under the ERA-NET co-fund action No. 680969 with ANR (ERA-CVD SICVALVES) and Agence Nationale de la Recherche

Image-Based Personalization of Cardiac Anatomy for Coupled Electromechanical Modeling

Computational models of cardiac electromechanics (EM) are increasingly being applied to clinical problems, with patient-specific models being generated from high fidelity imaging and used to simulate patient physiology, pathophysiology and response to treatment. Current structured meshes are limited in their ability to fully represent the detailed anatomical data available from clinical images and capture complex and varied anatomy with limited geometric accuracy. In this paper, we review the state of the art in image-based personalization of cardiac anatomy for biophysically detailed, strongly coupled EM modeling, and present our own tools for the automatic building of anatomically and structurally accurate patient-specific models. Our method relies on using high resolution unstructured meshes for discretizing both physics, electrophysiology and mechanics, in combination with efficient, strongly scalable solvers necessary to deal with the computational load imposed by the large number of degrees of freedom of these meshes. These tools permit automated anatomical model generation and strongly coupled EM simulations at an unprecedented level of anatomical and biophysical detail.

Image-Based Personalization of Cardiac Anatomy for Coupled Electromechanical Modeling

Computational models of cardiac electromechanics (EM) are increasingly being applied to clinical problems, with patient-specific models being generated from high fidelity imaging and used to simulate patient physiology, pathophysiology and response to treatment. Current structured meshes are limited in their ability to fully represent the detailed anatomical data available from clinical images and capture complex and varied anatomy with limited geometric accuracy. In this paper, we review the state of the art in image-based personalization of cardiac anatomy for biophysically detailed, strongly coupled EM modeling, and present our own tools for the automatic building of anatomically and structurally accurate patient-specific models. Our method relies on using high resolution unstructured meshes for discretizing both physics, electrophysiology and mechanics, in combination with efficient, strongly scalable solvers necessary to deal with the computational load imposed by the large number of degrees of freedom of these meshes. These tools permit automated anatomical model generation and strongly coupled EM simulations at an unprecedented level of anatomical and biophysical detail.

Solvers for the cardiac bidomain equations

The bidomain equations are widely used for the simulation of electrical activity in cardiac tissue. They are especially important for accurately modeling extracellular stimulation, as evidenced by their prediction of virtual electrode polarization before experimental verification. However, solution of the equations is computationally expensive due to the fine spatial and temporal discretization needed. This limits the size and duration of the problem which can be modeled. Regardless of the specific form into which they are cast, the computational bottleneck becomes the repeated solution of a large, linear system. The purpose of this review is to give an overview of the equations and the methods by which they have been solved. Of particular note are recent developments in multigrid methods, which have proven to be the most efficient.