In-silico study of the cardiac arrhythmogenic potential of biomaterial injection therapy

Abstract

Biomaterial injection is a novel therapy to treat ischemic heart failure (HF) that has shown to reduce remodeling and restore cardiac function in recent preclinical studies. While the effect of biomaterial injection in reducing mechanical wall stress has been recently demonstrated, the influence of biomaterials on the electrical behavior of treated hearts has not been elucidated. In this work, we developed computational models of swine hearts to study the electrophysiological vulnerability associated with biomaterial injection therapy. The propagation of action potentials on realistic biventricular geometries was simulated by numerically solving the monodomain electrophysiology equations on anatomically-detailed models of normal, HF untreated, and HF treated hearts. Heart geometries were constructed from high-resolution magnetic resonance images (MRI) where the healthy, peri-infarcted, infarcted and gel regions were identified, and the orientation of cardiac fibers was informed from diffusion-tensor MRI. Regional restitution properties in each case were evaluated by constructing a probability density function of the action potential duration (APD) at different cycle lengths. A comparative analysis of the ventricular fibrillation (VF) dynamics for every heart was carried out by measuring the number of filaments formed after wave braking. Our results suggest that biomaterial injection therapy does not affect the regional dispersion of repolarization when comparing untreated and treated failing hearts. Further, we found that the treated failing heart is more prone to sustain VF than the normal heart, and is at least as susceptible to sustained VF as the untreated failing heart. Moreover, we show that the main features of VF dynamics in a treated failing heart are not affected by the level of electrical conductivity of the biogel injectates. This work represents a novel proof-of-concept study demonstrating the feasibility of computer simulations of the heart in understanding the arrhythmic behavior in novel therapies for HF.

Figure 1

Spatial distribution of activation time (AT) and action-potential duration (APD) as measured after the fifth stimulus delivered at the apex. (Top row) computational model showing the biventricular geometry and cutting planes, with stimulation sites indicated with a star. (Middle row) AT maps for the selected cutting planes. (Bottom row) APD maps for the selected cutting planes. Infarcted zones (IZ) and bio-gel injections (GEL) are indicated with arrows.

Figure 2

Restitution curve of the normal (NC), untreated (HFC) and treated (HFI) heart for the epicardial (EPI), left ventricle (LV), right ventricle (RV) and left ventricle mid-myocardium (LVMM) regions. Gray zone ratio (GZR)is reported for the HFI and HFC hearts. In general, higher dispersion of APD is found in the LVMM region.

Figure 3

Empirical probability density functions of regional APD at CL$=370\text{ms}$. Median values are plotted with vertical lines. All three models result in similar distributions for the EPI and LVMM, while the NC case markedly differs from the HFC and HFI cases in the LV and RV regions.

Figure 4

Temporal evolution of ventricular fibrillation in the HFI heart. Electrically-active regions ($V_m>-75.0\mathrm{mV}$) are depicted according to the color scalebar. IZ is depicted in grey, and biomaterial injections are depicted in dark green. The S1 stimulation site is the septum LV endocardium, while the S2 stimulus is delivered at the posterior zone of the epicardium. Rotors rapidly increase in time, and constantly interact with regions where biomaterial injections are located.

Figure 5

Evolution of the number of filaments for all three hearts (solid lines). Dashed lines correspond to the time-averaged number of filaments after the first 2500 ms. The time-averaged number of filaments in the NC, HFC and HFI subjects was 27, 34 and 40, respectively.

Figure 6

(a) Time evolution of the number of filaments during VF simulations of the HFI model for varying levels of electrical conductivity of the gel injections, parameterized by the value of the c ratio. The average value after 2500 ms is depicted with dashed lines. There are not substantial differences between each case: the average number of filaments after 2500 ms found in these simulations was 40, 40, 38 and 38 for the cases of gel conductivity ratios of 0.0, 0.5, 1.0 and 1.5, respectively. (b) Pseudo-ECG computed for each case.

Figure 7

Heart geometry and fiber directions: (a) normal control (NC) heart, (b) heart-failure heart (HFC), (c) heart-failure heart treated with biomaterial injections (HFI). The geometry was reconstructed from high-resolution magnetic-resonance images (MRI), and the fiber directions were obtained from diffusion-tensor MRI.

Figure 8

Detailed views of the heart-failure treated heart (HFI) in which biomaterial injection distribution is depicted in blue and infarcted regions are depicted in gray. Biomaterial injections are dispersed within the mid-myocardial left ventricle. Some injections are placed within the NZ region, and others are placed within the intersection of IZ and NZ regions.

Figure 9

Hearts ventricles sections showing the endocardium (Endo), mid-myocardium (Mid), and epicardium (Epi) regions in (a) the NC model, (b) the HFC model, and (c) the HFI model. These domains are defined using a regional segmentation technique based on Laplace interpolations. Each domain considers a specific cellular model for ionic transmembrane current. (d) Action potentials for the three type of cells considered in the construction of the heart models

Figure 10

Detail of the nodes selected to compute the APD restitution curve (black dots) during simulation time, thereby reducing computational costs during post-processing. Regional distribution of the APD restitution is computed using the information taken from this set of nodes, which are selected based on unrefined meshes.