Alternans and spiral breakup in a human ventricular tissue model

Successful cardiac resynchronization therapy reduces negative septal work in patient-specific models of dyssynchronous heart failure

In patients with dyssynchronous heart failure (DHF), cardiac conduction abnormalities cause the regional distribution of myocardial work to be non-homogeneous. Cardiac resynchronization therapy (CRT) using an implantable, programmed biventricular pacemaker/defibrillator, can improve the synchrony of contraction between the right and left ventricles in DHF, resulting in reduced morbidity and mortality and increased quality of life. Since regional work depends on wall stress, which cannot be measured in patients, we used computational methods to investigate regional work distributions and their changes after CRT. We used three-dimensional multi-scale patient-specific computational models parameterized by anatomic, functional, hemodynamic, and electrophysiological measurements in eight patients with heart failure and left bundle branch block (LBBB) who received CRT. To increase clinical translatability, we also explored whether streamlined computational methods provide accurate estimates of regional myocardial work. We found that CRT increased global myocardial work efficiency with significant improvements in non-responders. Reverse ventricular remodeling after CRT was greatest in patients with the highest heterogeneity of regional work at baseline, however the efficacy of CRT was not related to the decrease in overall work heterogeneity or to the reduction in late-activated regions of high myocardial work. Rather, decreases in early-activated regions of myocardium performing negative myocardial work following CRT best explained patient variations in reverse remodeling. These findings were also observed when regional myocardial work was estimated using ventricular pressure as a surrogate for myocardial stress and changes in endocardial surface area as a surrogate for strain. These new findings suggest that CRT promotes reverse ventricular remodeling in human dyssynchronous heart failure by increasing regional myocardial work in early-activated regions of the ventricles, where dyssynchrony is specifically associated with hypoperfusion, late systolic stretch, and altered metabolic activity and that measurement of these changes can be performed using streamlined approaches.

The Ithildin library for efficient numerical solution of anisotropic reaction-diffusion problems in excitable media

Ithildin is an open-source library and framework for efficient parallelized simulations of excitable media, written in the C++ programming language. It uses parallelization on multiple CPU processors via the message passing interface (MPI). We demonstrate the library's versatility through a series of simulations in the context of the monodomain description of cardiac electrophysiology, including the S1S2 protocol, spiral break-up, and spiral waves in ventricular geometry. Our work demonstrates the power of Ithildin as a tool for studying complex wave patterns in cardiac tissue and its potential to inform future experimental and theoretical studies. We publish our full code with this paper in the name of open science.

Stretch of the papillary insertion triggers reentrant arrhythmia: an in silico patient study

Background

The electrophysiological mechanism connecting mitral valve prolapse (MVP), premature ventricular complexes and life-threatening ventricular arrhythmia is unknown. A common hypothesis is that stretch activated channels (SACs) play a significant role. SACs can trigger depolarizations or shorten repolarization times in response to myocardial stretch. Through these mechanisms, pathological traction of the papillary muscle (PM), as has been observed in patients with MVP, may induce irregular electrical activity and result in reentrant arrhythmia.

Methods

Based on a patient with MVP and mitral annulus disjunction, we modeled the effect of excessive PM traction in a detailed medical image-derived ventricular model by activating SACs in the PM insertion region. By systematically varying the onset of SAC activation following sinus pacing, we identified vulnerability windows for reentry with 1 ms resolution. We explored how reentry was affected by the SAC reversal potential ( E SAC ) and the size of the region with simulated stretch (SAC region). Finally, the effect of global or focal fibrosis, modeled as reduction in tissue conductivity or mesh splitting (fibrotic microstructure), was investigated.

Results

In models with healthy tissue or fibrosis modeled solely as CV slowing, we observed two vulnerable periods of reentry: ForE SAC of -10 and -30 mV, SAC activated during the T-wave could cause depolarization of the SAC region which lead to reentry. ForE SAC of -40 and -70 mV, SAC activated during the QRS complex could result in early repolarization of the SAC region and subsequent reentry. In models with fibrotic microstructure in the SAC region, we observed micro-reentries and a larger variability in which times of SAC activation triggered reentry. In these models, 86% of reentries were triggered during the QRS complex or T-wave. We only observed reentry for sufficiently large SAC regions ( > = 8 mm radius in models with healthy tissue).

Conclusion

Stretch of the PM insertion region following sinus activation may initiate ventricular reentry in patients with MVP, with or without fibrosis. Depending on the SAC reversal potential and timing of stretch, reentry may be triggered by ectopy due to SAC-induced depolarizations or by early repolarization within the SAC region.

Transmural APD heterogeneity determines ventricular arrhythmogenesis in LQT8 syndrome: Insights from Bidomain computational modeling

Long QT Syndrome type 8 (LQT8) is a cardiac arrhythmic disorder associated with Timothy Syndrome, stemming from mutations in the CACNA1C gene, particularly the G406R mutation. While prior studies hint at CACNA1C mutations' role in ventricular arrhythmia genesis, the mechanisms, especially in G406R presence, are not fully understood. This computational study explores how the G406R mutation, causing increased transmural dispersion of repolarization, induces and sustains reentrant ventricular arrhythmias. Using three-dimensional numerical simulations on an idealized left-ventricular model, integrating the Bidomain equations with the ten Tusscher-Panfilov ionic model, we observe that G406R mutation with 11% and 50% heterozygosis significantly increases transmural dispersion of repolarization. During S1-S4 stimulation protocols, these gradients facilitate conduction blocks, triggering reentrant ventricular tachycardia. Sustained reentry pathways occur only with G406R mutation at 50% heterozygosis, while neglecting transmural heterogeneities of action potential duration prevents stable reentry, regardless of G406R mutation presence.

Characterization of hERG K+ channel inhibition by the new class III antiarrhythmic drug cavutilide

Cavutilide (niferidil, refralon) is a new class III antiarrhythmic drug which effectively terminates persistent atrial fibrillation (AF; 84.6% of patients, mean AF duration 3 months) and demonstrates low risk of torsade de pointes (1.7%). ERG channels of rapid delayed rectifier current(IKr) are the primary target of cavutilide, but the particular reasons of higher effectiveness and lower proarrhythmic risk in comparison with other class III IKr blockers are unclear. The inhibition of hERG channels expressed in CHO-K1 cells by cavutilide was studied using whole-cell patch-clamp. The present study demonstrates high sensitivity of IhERG expressed in CHO-K1 cells to cavutilide (IC50 = 12.8 nM). Similarly to methanesulfonanilide class III agents, but unlike amiodarone and related drugs, cavutilide does not bind to hERG channels in their resting state. However, in contrast to dofetilide, cavutilide binds not only to opened, but also to inactivated channels. Moreover, at positive constantly set membrane potential (+ 60 mV) inhibition of IhERG by 100 nM cavutilide develops faster than at 0 mV and, especially, - 30 mV (τ of inhibition was 78.8, 103, and 153 ms, respectively). Thereby, cavutilide produces IhERG inhibition only when the cell is depolarized. During the same period of time, cavutilide produces greater block of IhERG when the cell is depolarized with 2 Hz frequency, if compared to 0.2 Hz. We suggest that, during the limited time after injection, cavutilide produces stronger inhibition of IKr in fibrillating atrium than in non-fibrillating ventricle. This leads to beneficial combination of antiarrhythmic effectiveness and low proarrhythmicity of cavutilide.

Observable Atrial and Ventricular Fibrillation Episode Durations Are Conformant With a Power Law Based on System Size and Spatial Synchronization

BACKGROUND

Atrial fibrillation (AF) and ventricular fibrillation (VF) episodes exhibit varying durations, with some spontaneously ending quickly while others persist. A quantitative framework to explain episode durations remains elusive. We hypothesized that observable self-terminating AF and VF episode lengths, whereby durations are known, would conform with a power law based on the ratio of system size and correlation length ([Formula: see text].

METHODS

Using data from computer simulations (2-dimensional sheet and 3-dimensional left-atrial), human ischemic VF recordings (256-electrode sock, n=12 patients), and human AF recordings (64-electrode basket-catheter, n=9 patients; 16-electrode high definition-grid catheter, n=42 patients), conformance with a power law was assessed using the Akaike information criterion, Bayesian information criterion, coefficient of determination (R2, significance=P<0.05) and maximum likelihood estimation. We analyzed fibrillatory episode durations and [Formula: see text], computed by taking the ratio between system size ([Formula: see text], chamber/simulation size) and correlation length (xi, estimated from pairwise correlation coefficients over electrode/node distance).

RESULTS

In all computer models, the relationship between episode durations and [Formula: see text] was conformant with a power law (Aliev-Panfilov R2: 0.90, P<0.001; Courtemanche R2: 0.91, P<0.001; Luo-Rudy R2: 0.61, P<0.001). Observable clinical AF/VF durations were also conformant with a power law relationship (VF R2: 0.86, P<0.001; AF basket R2: 0.91, P<0.001; AF grid R2: 0.92, P<0.001). [Formula: see text] also differentiated between self-terminating and sustained episodes of AF and VF (P<0.001; all systems), as well as paroxysmal versus persistent AF (P<0.001). In comparison, other electrogram metrics showed no statistically significant differences (dominant frequency, Shannon Entropy, mean voltage, peak-peak voltage; P>0.05).

CONCLUSIONS

Observable fibrillation episode durations are conformant with a power law based on system size and correlation length.

Re-entry in models of cardiac ventricular tissue with scar represented as a Gaussian random field

Introduction: Fibrotic scar in the heart is known to act as a substrate for arrhythmias. Regions of fibrotic scar are associated with slowed or blocked conduction of the action potential, but the detailed mechanisms of arrhythmia formation are not well characterised and this can limit the effective diagnosis and treatment of scar in patients. The aim of this computational study was to evaluate different representations of fibrotic scar in models of 2D 10 × 10 cm ventricular tissue, where the region of scar was defined by sampling a Gaussian random field with an adjustable length scale of between 1.25 and 10.0 mm. Methods: Cellular electrophysiology was represented by the Ten Tusscher 2006 model for human ventricular cells. Fibrotic scar was represented as a spatially varying diffusion, with different models of the boundary between normal and fibrotic tissue. Dispersion of activation time and action potential duration (APD) dispersion was assessed in each sample by pacing at an S1 cycle length of 400 ms followed by a premature S2 beat with a coupling interval of 323 ms. Vulnerability to reentry was assessed with an aggressive pacing protocol. In all models, simulated fibrosis acted to delay activation, to increase the dispersion of APD, and to generate re-entry. Results: A higher incidence of re-entry was observed in models with simulated fibrotic scar at shorter length scale, but the type of model used to represent fibrotic scar had a much bigger influence on the incidence of reentry. Discussion: This study shows that in computational models of fibrotic scar the effects that lead to either block or propagation of the action potential are strongly influenced by the way that fibrotic scar is represented in the model, and so the results of computational studies involving fibrotic scar should be interpreted carefully.

Activation of PKC affects the ventricular restitution properties and arrhythmogenesis through L-type Ca+ current

OBJECTIVE

To investigate the role of protein kinase C (PKC) in action potential duration (APD) restitution and ventricular tachyarrhythmias (VAs).

METHODS AND RESULTS

Rabbits hearts were isolated and prepared for Langendorff perfusion technique. The stimuli-extra-stimulus (S1-S2) method and dynamic S1 pacing protocol were performed to construct APD restitution and to induce APD alternans or VA, respectively, at 10 sites throughout the ventricular chamber. Administration of phorbol-12-myristate-13-acetate (PMA) (100 nM) (n = 15) greatly steepened the restitution curves (Smax > 1) (p < .01) at each site compared to the control group (n = 15). Furthermore, treatment with PMA also induced larger spatial dispersions of Smax (p < .05) and decreased the thresholds of the VA and APD alternans (p < .01). However, perfused with the PKC inhibitor, bisindolylmaleimide (BIM) (500 nM) (n = 10), reversibly flattened the APD restitution curves at each site (Smax < 1), decreased the spatial dispersions of Smax, and increased the thresholds of APD alternans and VA. According to the results of patch-clamp, peak amplitude of L-type Ca2+ current was significantly increased by addition of PMA compared with control (CTL) group (p < .05). Antagonize this current with verapamil (n = 10) can fully inhibited the PMA induced increasing of Smax and inducibility of VA and alternans.

CONCLUSION

PKC activation increased the dispersion of APD restitution and thus led to occurrence of VA, which possibly related to the increased Ca2+ influx.

Development of in-silico drug cardiac toxicity evaluation system with consideration of inter-individual variability

Safety pharmacology examines the potential for new drugs to have unusual, rare side effects such as torsade de pointes (TdP). Recently, as a part of the Comprehensive in vitro Proarrhythmia Assay (CiPA) project, techniques for predicting the development of drug-induced TdP through computer simulations have been proposed and verified. However, CiPA assessment generally does not consider the effect of cardiac cell inter-individual variability, especially related to metabolic status. The study aimed to explore whether rare proarrhythmic effects may be linked to the inter-individual variability of cardiac cells and whether incorporating this variability into computational models could alter the prediction of drugs' TdP risks. This study evaluated the contribution of two biological characteristics to the proarrhythmic effects. The first was spermine concentration, which varies with metabolic status; the second was L-type calcium permeability that could occur due to mutations. Twenty-eight drugs were examined throughout this study, and qNet was analyzed as an essential feature. Even though there were some discrepancies of TdP risk predictions from the baseline model, we found that considering the inter-individual variability might change the TdP risk of drugs. Several drugs in the high-risk drugs group were predicted to affect as intermediate and low-risk drugs in some individuals and vice versa. Also, most intermediate-risk drugs were expected to act as low-risk drugs. When compared, the effects of inter-individual variability of L-type calcium were more significant than spermine in altering the TdP risk of compounds. These results emphasize the importance of considering inter-individual variability to assess drugs.

Digital twinning of cardiac electrophysiology for congenital heart disease

In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in paediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and using rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in paediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.

ForCEPSS-A framework for cardiac electrophysiology simulations standardization

BACKGROUND AND OBJECTIVE

Simulation of cardiac electrophysiology (CEP) is an important research tool that is increasingly being adopted in industrial and clinical applications. Typical workflows for CEP simulation consist of a sequence of processing stages starting with building an anatomical model and then calibrating its electrophysiological properties to match observable data. While the calibration stages are common and generalizable, most CEP studies re-implement these steps in complex and highly variable workflows. This lack of standardization renders the execution of computational CEP studies in an efficient, robust, and reproducible manner a significant challenge. Here, we propose ForCEPSS as an efficient and robust, yet flexible, software framework for standardizing CEP simulation studies.

METHODS AND RESULTS

Key processing stages of CEP simulation studies are identified and implemented in a standardized workflow that builds on openCARP1 Plank et al. (2021) and the Python-based carputils2 framework. Stages include (i) the definition and initialization of action potential phenotypes, (ii) the tissue scale calibration of conduction properties, (iii) the functional initialization to approximate a limit cycle corresponding to the dynamic reference state according to an experimental protocol, and, (iv) the execution of the CEP study where the electrophysiological response to a perturbation of the limit cycle is probed. As an exemplar application, we employ ForCEPSS to prepare a CEP study according to the Virtual Arrhythmia Risk Prediction protocol used for investigating the arrhythmogenic risk of developing infarct-related ventricular tachycardia (VT) in ischemic cardiomyopathy patients. We demonstrate that ForCEPSS enables a fully automated execution of all stages of this complex protocol.

CONCLUSION

ForCEPSS offers a novel comprehensive, standardized, and automated CEP simulation workflow. The high degree of automation accelerates the execution of CEP simulation studies, reduces errors, improves robustness, and makes CEP studies reproducible. Verification of simulation studies within the CEP modeling community is thus possible. As such, ForCEPSS makes an important contribution towards increasing transparency, standardization, and reproducibility of in silico CEP experiments.

Effects of cardiac growth on electrical dyssynchrony in the single ventricle patient

Single ventricle patients, including those with hypoplastic left heart syndrome (HLHS), typically undergo three palliative heart surgeries culminating in the Fontan procedure. HLHS is associated with high rates of morbidity and mortality, and many patients develop arrhythmias, electrical dyssynchrony, and eventually ventricular failure. However, the correlation between ventricular enlargement and electrical dysfunction in HLHS physiology remains poorly understood. Here we characterize the relationship between growth and electrophysiology in HLHS using computational modeling. We integrate a personalized finite element model, a volumetric growth model, and a personalized electrophysiology model to perform controlled in silico experiments. We show that right ventricle enlargement negatively affects QRS duration and interventricular dyssynchrony. Conversely, left ventricle enlargement can partially compensate for this dyssynchrony. These findings have potential implications on our understanding of the origins of electrical dyssynchrony and, ultimately, the treatment of HLHS patients.

Successful Cardiac Resynchronization Therapy Reduces Negative Septal Work in Patient-Specific Models of Dyssynchronous Heart Failure

In patients with dyssynchronous heart failure (DHF), cardiac conduction abnormalities cause the regional distribution of myocardial work to be non-homogeneous. Cardiac resynchronization therapy (CRT) using an implantable, programmed biventricular pacemaker/defibrillator, can improve the synchrony of contraction between the right and left ventricles in DHF, resulting in reduced morbidity and mortality and increased quality of life. Since regional work depends on wall stress, which cannot be measured in patients, we used computational methods to investigate regional work distributions and their changes after CRT. We used three-dimensional multi-scale patient-specific computational models parameterized by anatomic, functional, hemodynamic, and electrophysiological measurements in eight patients with heart failure and left bundle branch block (LBBB) who received CRT. To increase clinical translatability, we also explored whether streamlined computational methods provide accurate estimates of regional myocardial work. We found that CRT increased global myocardial work efficiency with significant improvements in non-responders. Reverse ventricular remodeling after CRT was greatest in patients with the highest heterogeneity of regional work at baseline, however the efficacy of CRT was not related to the decrease in overall work heterogeneity or to the reduction in late-activated regions of high myocardial work. Rather, decreases in early-activated regions of myocardium performing negative myocardial work following CRT best explained patient variations in reverse remodeling. These findings were also observed when regional myocardial work was estimated using ventricular pressure as a surrogate for myocardial stress and changes in endocardial surface area as a surrogate for strain. These new findings suggest that CRT promotes reverse ventricular remodeling in human dyssynchronous heart failure by increasing regional myocardial work in early-activated regions of the ventricles, where dyssynchrony is specifically associated with hypoperfusion, late systolic stretch, and altered metabolic activity and that measurement of these changes can be performed using streamlined approaches.

Strain-controlled electrophysiological wave propagation alters in silico scar-based substrate for ventricular tachycardia

Introduction: Assessing a patient's risk of scar-based ventricular tachycardia (VT) after myocardial infarction is a challenging task. It can take months to years after infarction for VT to occur. Also, if selected for ablation therapy, success rates are low. Methods: Computational ventricular models have been presented previously to support VT risk assessment and to provide ablation guidance. In this study, an extension to such virtual-heart models is proposed to phenomenologically incorporate tissue remodeling driven by mechanical load. Strain amplitudes in the heart muscle are obtained from simulations of mechanics and are used to adjust the electrical conductivity. Results: The mechanics-driven adaptation of electrophysiology resulted in a more heterogeneous distribution of propagation velocities than that of standard models, which adapt electrophysiology in the structural substrate from medical images only. Moreover, conduction slowing was not only present in such a structural substrate, but extended in the adjacent functional border zone with impaired mechanics. This enlarged the volumes with high repolarization time gradients (≥10 ms/mm). However, maximum gradient values were not significantly affected. The enlarged volumes were localized along the structural substrate border, which lengthened the line of conduction block. The prolonged reentry pathways together with conduction slowing in functional regions increased VT cycle time, such that VT was easier to induce, and the number of recommended ablation sites increased from 3 to 5 locations. Discussion: Sensitivity testing showed an accurate model of strain-dependency to be critical for low ranges of conductivity. The model extension with mechanics-driven tissue remodeling is a potential approach to capture the evolution of the functional substrate and may offer insight into the progression of VT risk over time.

In silico study of the mechanisms of hypoxia and contractile dysfunction during ischemia and reperfusion of hiPSC cardiomyocytes

Interconnected mechanisms of ischemia and reperfusion (IR) has increased the interest in IR in vitro experiments using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). We developed a whole-cell computational model of hiPSC-CMs including the electromechanics, a metabolite-sensitive sarcoplasmic reticulum Ca2+-ATPase (SERCA) and an oxygen dynamics formulation to investigate IR mechanisms. Moreover, we simulated the effect and action mechanism of levosimendan, which recently showed promising anti-arrhythmic effects in hiPSC-CMs in hypoxia. The model was validated using hiPSC-CM and in vitro animal data. The role of SERCA in causing relaxation dysfunction in IR was anticipated to be comparable to its function in sepsis-induced heart failure. Drug simulations showed that levosimendan counteracts the relaxation dysfunction by utilizing a particular Ca2+-sensitizing mechanism involving Ca2+-bound troponin C and Ca2+ flux to the myofilament, rather than inhibiting SERCA phosphorylation. The model demonstrates extensive characterization and promise for drug development, making it suitable for evaluating IR therapy strategies based on the changing levels of cardiac metabolites, oxygen and molecular pathways.

Fibrosis modeling choice affects morphology of ventricular arrhythmia in non-ischemic cardiomyopathy

Introduction: Patients with non-ischemic cardiomyopathy (NICM) are at risk for ventricular arrhythmias, but diagnosis and treatment planning remain a serious clinical challenge. Although computational modeling has provided valuable insight into arrhythmic mechanisms, the optimal method for simulating reentry in NICM patients with structural disease is unknown. Methods: Here, we compare the effects of fibrotic representation on both reentry initiation and reentry morphology in patient-specific cardiac models. We investigate models with heterogeneous networks of non-conducting structures (cleft models) and models where fibrosis is represented as a dense core with a surrounding border zone (non-cleft models). Using segmented cardiac magnetic resonance with late gadolinium enhancement (LGE) of five NICM patients, we created 185 3D ventricular electrophysiological models with different fibrotic representations (clefts, reduced conductivity and ionic remodeling). Results: Reentry was induced by electrical pacing in 647 out of 3,145 simulations. Both cleft and non-cleft models can give rise to double-loop reentries meandering through fibrotic regions (Type 1-reentry). When accounting for fibrotic volume, the initiation sites of these reentries are associated with high local fibrotic density (mean LGE in cleft models: p< 0.001, core volume in non-cleft models: p = 0.018, negative binomial regression). In non-cleft models, Type 1-reentries required slow conduction in core tissue (non-cleftsc models) as opposed to total conduction block. Incorporating ionic remodeling in fibrotic regions can give rise to single- or double-loop rotors close to healthy-fibrotic interfaces (Type 2-reentry). Increasing the cleft density or core-to-border zone ratio in cleft and non-cleftc models, respectively, leads to increased inducibility and a change in reentry morphology from Type 2 to Type 1. Conclusions: By demonstrating how fibrotic representation affects reentry morphology and location, our findings can aid model selection for simulating arrhythmogenesis in NICM.

Novel Gain-of-Function Mutation in the Kv11.1 Channel Found in the Patient with Brugada Syndrome and Mild QTc Shortening

Brugada syndrome (BrS) is an inherited disease characterized by right precordial ST-segment elevation in the right precordial leads on electrocardiograms (ECG), and high risk of life-threatening ventricular arrhythmia and sudden cardiac death (SCD). Mutations in the responsible genes have not been fully characterized in the BrS patients, except for the SCN5A gene. We identified a new genetic variant, c.1189C>T (p.R397C), in the KCNH2 gene in the asymptomatic male proband diagnosed with BrS and mild QTc shortening. We hypothesize that this variant could alter IKr-current and may be causative for the rare non-SCN5A-related form of BrS. To assess its pathogenicity, we performed patch-clamp analysis on IKr reconstituted with this KCNH2 mutation in the Chinese hamster ovary cells and compared the phenotype with the wild type. It appeared that the R397C mutation does not affect the IKr density, but facilitates activation, hampers inactivation of the hERG channels, and increases magnitude of the window current suggesting that the p.R397C is a gain-of-function mutation. In silico modeling demonstrated that this missense mutation potentially leads to the shortening of action potential in the heart.

Fibroblast mediated dynamics in diffusively uncoupled myocytes: a simulation study using 2-cell motifs

In healthy hearts myocytes are typically coupled to nearest neighbours through gap junctions. Under pathological conditions such as fibrosis, or in scar tissue, or across ablation lines myocytes can uncouple from their neighbours. Electrical conduction may still occur via fibroblasts that not only couple proximal myocytes but can also couple otherwise unconnected regions. We hypothesise that such coupling can alter conduction between myocytes via introduction of delays or by initiation of premature stimuli that can potentially result in reentry or conduction blocks. To test this hypothesis we have developed several 2-cell motifs and investigated the effect of fibroblast mediated electrical coupling between uncoupled myocytes. We have identified various regimes of myocyte behaviour that depend on the strength of gap-junctional conductance, connection topology, and parameters of the myocyte and fibroblast models. These motifs are useful in developing a mechanistic understanding of long-distance coupling on myocyte dynamics and enable the characterisation of interaction between different features such as myocyte and fibroblast properties, coupling strengths and pacing period. They are computationally inexpensive and allow for incorporation of spatial effects such as conduction velocity. They provide a framework for constructing scar tissue boundaries and enable linking of cellular level interactions with scar induced arrhythmia.

Simulating impaired left ventricular-arterial coupling in aging and disease: a systematic review

Aortic stenosis, hypertension, and left ventricular hypertrophy often coexist in the elderly, causing a detrimental mismatch in coupling between the heart and vasculature known as ventricular-vascular (VA) coupling. Impaired left VA coupling, a critical aspect of cardiovascular dysfunction in aging and disease, poses significant challenges for optimal cardiovascular performance. This systematic review aims to assess the impact of simulating and studying this coupling through computational models. By conducting a comprehensive analysis of 34 relevant articles obtained from esteemed databases such as Web of Science, Scopus, and PubMed until July 14, 2022, we explore various modeling techniques and simulation approaches employed to unravel the complex mechanisms underlying this impairment. Our review highlights the essential role of computational models in providing detailed insights beyond clinical observations, enabling a deeper understanding of the cardiovascular system. By elucidating the existing models of the heart (3D, 2D, and 0D), cardiac valves, and blood vessels (3D, 1D, and 0D), as well as discussing mechanical boundary conditions, model parameterization and validation, coupling approaches, computer resources and diverse applications, we establish a comprehensive overview of the field. The descriptions as well as the pros and cons on the choices of different dimensionality in heart, valve, and circulation are provided. Crucially, we emphasize the significance of evaluating heart-vessel interaction in pathological conditions and propose future research directions, such as the development of fully coupled personalized multidimensional models, integration of deep learning techniques, and comprehensive assessment of confounding effects on biomarkers.

Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity

Large-cohort studies using cardiovascular imaging and diagnostic datasets have assessed cardiac anatomy, function, and outcomes, but typically do not reveal underlying biological mechanisms. Cardiac digital twins (CDTs) provide personalized physics- and physiology-constrained in-silico representations, enabling inference of multi-scale properties tied to these mechanisms. We constructed 3464 anatomically-accurate CDTs using cardiac magnetic resonance images from UK biobank and personalised their myocardial conduction velocities (CVs) from electrocardiograms (ECG), through an automated framework. We found well-known sex-specific differences in QRS duration were fully explained by myocardial anatomy, as CV remained consistent across sexes. Conversely, significant associations of CV with ageing and increased BMI suggest myocardial tissue remodelling. Novel associations were observed with left ventricular ejection fraction and mental-health phenotypes, through a phenome-wide association study, and CV was also linked with adverse clinical outcomes. Our study highlights the utility of population-based CDTs in assessing intersubject variability and uncovering strong links with mental health.

Branched Latent Neural Maps

We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional input-output maps encoding complex physical processes. A BLNM is defined by a simple and compact feedforward partially-connected neural network that structurally disentangles inputs with different intrinsic roles, such as the time variable from model parameters of a differential equation, while transferring them into a generic field of interest. BLNMs leverage latent outputs to enhance the learned dynamics and break the curse of dimensionality by showing excellent in-distribution generalization properties with small training datasets and short training times on a single processor. Indeed, their in-distribution generalization error remains comparable regardless of the adopted discretization during the testing phase. Moreover, the partial connections, in place of a fully-connected structure, significantly reduce the number of tunable parameters. We show the capabilities of BLNMs in a challenging test case involving biophysically detailed electrophysiology simulations in a biventricular cardiac model of a pediatric patient with hypoplastic left heart syndrome. The model includes a 1D Purkinje network for fast conduction and a 3D heart-torso geometry. Specifically, we trained BLNMs on 150 in silico generated 12-lead electrocardiograms (ECGs) while spanning 7 model parameters, covering cell-scale, organ-level and electrical dyssynchrony. Although the 12-lead ECGs manifest very fast dynamics with sharp gradients, after automatic hyperparameter tuning the optimal BLNM, trained in less than 3 hours on a single CPU, retains just 7 hidden layers and 19 neurons per layer. The resulting mean square error is on the order of 10 - 4 on an independent test dataset comprised of 50 additional electrophysiology simulations. In the online phase, the BLNM allows for 5000x faster real-time simulations of cardiac electrophysiology on a single core standard computer and can be employed to solve inverse problems via global optimization in a few seconds of computational time. This paper provides a novel computational tool to build reliable and efficient reduced-order models for digital twinning in engineering applications. The Julia implementation is publicly available under MIT License at https://github.com/StanfordCBCL/BLNM.jl.

The electrical restitution of the non-propagated cardiac ventricular action potential

Sudden changes in pacing cycle length are frequently associated with repolarization abnormalities initiating cardiac arrhythmias, and physiologists have long been interested in measuring the likelihood of these events before their manifestation. A marker of repolarization stability has been found in the electrical restitution (ER), the response of the ventricular action potential duration to a pre- or post-mature stimulation, graphically represented by the so-called ER curve. According to the restitution hypothesis (ERH), the slope of this curve provides a quantitative discrimination between stable repolarization and proneness to arrhythmias. ER has been studied at the body surface, whole organ, and tissue level, and ERH has soon become a key reference point in theoretical, clinical, and pharmacological studies concerning arrhythmia development, and, despite criticisms, it is still widely adopted. The ionic mechanism of ER and cellular applications of ERH are covered in the present review. The main criticism on ERH concerns its dependence from the way ER is measured. Over the years, in fact, several different experimental protocols have been established to measure ER, which are also described in this article. In reviewing the state-of-the art on cardiac cellular ER, I have introduced a notation specifying protocols and graphical representations, with the aim of unifying a sometime confusing nomenclature, and providing a physiological tool, better defined in its scope and limitations, to meet the growing expectations of clinical and pharmacological research.

Evaluating pro-arrhythmogenic effects of the T634S-hERG mutation: insights from a simulation study

A mutation to serine of a conserved threonine (T634S) in the hERG K+ channel S6 pore region has been identified as a variant of uncertain significance, showing a loss-of-function effect. However, its potential consequences for ventricular excitation and arrhythmogenesis have not been reported. This study evaluated possible functional effects of the T634S-hERG mutation on ventricular excitation and arrhythmogenesis by using multi-scale computer models of the human ventricle. A Markov chain model of the rapid delayed rectifier potassium current (IKr) was reconstructed for wild-type and T634S-hERG mutant conditions and incorporated into the ten Tusscher et al. models of human ventricles at cell and tissue (1D, 2D and 3D) levels. Possible functional impacts of the T634S-hERG mutation were evaluated by its effects on action potential durations (APDs) and their rate-dependence (APDr) at the cell level; and on the QT interval of pseudo-ECGs, tissue vulnerability to unidirectional conduction block (VW), spiral wave dynamics and repolarization dispersion at the tissue level. It was found that the T634S-hERG mutation prolonged cellular APDs, steepened APDr, prolonged the QT interval, increased VW, destablized re-entry and augmented repolarization dispersion across the ventricle. Collectively, these results imply potential pro-arrhythmic effects of the T634S-hERG mutation, consistent with LQT2.

Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning

Modelling complex systems, like the human heart, has made great progress over the last decades. Patient-specific models, called 'digital twins', can aid in diagnosing arrhythmias and personalizing treatments. However, building highly accurate predictive heart models requires a delicate balance between mathematical complexity, parameterization from measurements and validation of predictions. Cardiac electrophysiology (EP) models range from complex biophysical models to simplified phenomenological models. Complex models are accurate but computationally intensive and challenging to parameterize, while simplified models are computationally efficient but less realistic. In this paper, we propose a hybrid approach by leveraging deep learning to complete a simplified cardiac model from data. Our novel framework has two components, decomposing the dynamics into a physics based and a data-driven term. This construction allows our framework to learn from data of different complexity, while simultaneously estimating model parameters. First, using in silico data, we demonstrate that this framework can reproduce the complex dynamics of cardiac transmembrane potential even in the presence of noise in the data. Second, using ex vivo optical data of action potentials (APs), we demonstrate that our framework can identify key physical parameters for anatomical zones with different electrical properties, as well as to reproduce the AP wave characteristics obtained from various pacing locations. Our physics-based data-driven approach may improve cardiac EP modelling by providing a robust biophysical tool for predictions.

Modeling isovolumetric phases in cardiac flows by an Augmented Resistive Immersed Implicit Surface method

A major challenge in the computational fluid dynamics modeling of the heart function is the simulation of isovolumetric phases when the hemodynamics problem is driven by a prescribed boundary displacement. During such phases, both atrioventricular and semilunar valves are closed: consequently, the ventricular pressure may not be uniquely defined, and spurious oscillations may arise in numerical simulations. These oscillations can strongly affect valve dynamics models driven by the blood flow, making unlikely to recovering physiological dynamics. Hence, prescribed opening and closing times are usually employed, or the isovolumetric phases are neglected altogether. In this article, we propose a suitable modification of the Resistive Immersed Implicit Surface (RIIS) method (Fedele et al., Biomech Model Mechanobiol 2017, 16, 1779-1803) by introducing a reaction term to correctly capture the pressure transients during isovolumetric phases. The method, that we call Augmented RIIS (ARIIS) method, extends the previously proposed ARIS method (This et al., Int J Numer Methods Biomed Eng 2020, 36, e3223) to the case of a mesh which is not body-fitted to the valves. We test the proposed method on two different benchmark problems, including a new simplified problem that retains all the characteristics of a heart cycle. We apply the ARIIS method to a fluid dynamics simulation of a realistic left heart geometry, and we show that ARIIS allows to correctly simulate isovolumetric phases, differently from standard RIIS method. Finally, we demonstrate that by the new method the cardiac valves can open and close without prescribing any opening/closing times.

Mathematical modeling of cardio-oncology: Modeling the systemic effects of cancer therapeutics on the cardiovascular system

Cardiotoxicity is a common side-effect of many cancer therapeutics; however, to-date there has been very little push to understand the mechanisms underlying this group of pathologies. This has led to the emergence of cardio-oncology, a field of medicine focused on understanding the effects of cancer and its treatment on the human heart. Here, we describe how mechanistic modeling approaches have been applied to study open questions in the cardiovascular system and how these approaches are being increasingly applied to advance knowledge of the underlying effects of cancer treatments on the human heart. A variety of mechanistic, mathematical modeling techniques have been applied to explore the link between common cancer treatments, such as chemotherapy, radiation, targeted therapy, and immunotherapy, and cardiotoxicity, nevertheless there is limited coverage in the different types of cardiac dysfunction that may be associated with these treatments. Moreover, cardiac modeling has a rich heritage of mathematical modeling and is well suited for the further development of novel approaches for understanding the cardiotoxicities associated with cancer therapeutics. There are many opportunities to combine mechanistic, bottom-up approaches with data-driven, top-down approaches to improve personalized, precision oncology to better understand, and ultimately mitigate, cardiac dysfunction in cancer patients.

Optogenetically mediated large volume suppression and synchronized excitation of human ventricular cardiomyocytes

A major challenge in cardiac optogenetics is to have minimally invasive large volume excitation and suppression for effective cardioversion and treatment of tachycardia. It is important to study the effect of light attenuation on the electrical activity of cells in in vivo cardiac optogenetic experiments. In this computational study, we present a detailed analysis of the effect of light attenuation in different channelrhodopsins (ChRs)-expressing human ventricular cardiomyocytes. The study shows that sustained illumination from the myocardium surface used for suppression, simultaneously results in spurious excitation in deeper tissue regions. Tissue depths of suppressed and excited regions have been determined for different opsin expression levels. It is shown that increasing the expression level by 5-fold enhances the depth of suppressed tissue from 2.24 to 3.73 mm with ChR2(H134R) (ChR2 with a single point mutation at position H134), 3.78 to 5.12 mm with GtACR1 (anion-conducting ChR from cryptophyte algae Guillardia theta) and 6.63 to 9.31 mm with ChRmine (a marine opsin gene from Tiarina fusus). Light attenuation also results in desynchrony in action potentials in different tissue regions under pulsed illumination. It is further shown that gradient-opsin expression not only enables suppression up to the same level of tissue depth but also enables synchronized excitation under pulsed illumination. The study is important for the effective treatment of tachycardia and cardiac pacing and for extending the scale of cardiac optogenetics.

Effect of strain-dependent conduction slowing on the re-entry formation and maintenance in cardiac muscle: 2D computer simulation

The effect of mechano-electrical feedback on re-entry formation and maintenance was studied using a model of myocardial electromechanics that accounts for two components of myocardial conductivity and delayed strain-dependent changes in membrane capacitance that causes a conduction slowing. Two scenarios were simulated in 2D numerical experiments: (i) propagation of an excitation-contraction wave beyond the edge of a nonconductive nonexcitable obstacle; (ii) circulation of a re-entry wave around a nonconductive nonexcitable obstacle. The simulations demonstrated that the delayed strain-dependent deceleration of the conduction waves promotes the detachment of the excitation-contraction waves from the sharp edge of an elongated obstacle and modulates the re-entry waves rotating around a compact obstacle. The data show that the mechano-electrical feedback, together with an increase in the stimulation frequency and an increase in the excitation threshold, is an arrhythmogenic factor that must be taken into account when analyzing the possibility of the re-entry formation.

Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

BACKGROUND

Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community.

RESULTS

This work introduces [Formula: see text]-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. [Formula: see text]-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, [Formula: see text]-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within [Formula: see text]-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying [Formula: see text]-ep, along with comprehensive implementation details and instructions for users. [Formula: see text]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of [Formula: see text]-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files.

CONCLUSIONS

[Formula: see text]-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. [Formula: see text]-ep represents a valuable tool for conducting in silico patient-specific simulations.

Differences in ventricular wall composition may explain inter-patient variability in the ECG response to variations in serum potassium and calcium

Objective: Chronic kidney disease patients have a decreased ability to maintain normal electrolyte concentrations in their blood, which increases the risk for ventricular arrhythmias and sudden cardiac death. Non-invasive monitoring of serum potassium and calcium concentration, [K+] and [Ca2+], can help to prevent arrhythmias in these patients. Electrocardiogram (ECG) markers that significantly correlate with [K+] and [Ca2+] have been proposed, but these relations are highly variable between patients. We hypothesized that inter-individual differences in cell type distribution across the ventricular wall can help to explain this variability. Methods: A population of human heart-torso models were built with different proportions of endocardial, midmyocardial and epicardial cells. Propagation of ventricular electrical activity was described by a reaction-diffusion model, with modified Ten Tusscher-Panfilov dynamics. [K+] and [Ca2+] were varied individually and in combination. Twelve-lead ECGs were simulated and the width, amplitude and morphological variability of T waves and QRS complexes were quantified. Results were compared to measurements from 29 end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). Results: Both simulations and patients data showed that most of the analyzed T wave and QRS complex markers correlated strongly with [K+] (absolute median Pearson correlation coefficients, r, ranging from 0.68 to 0.98) and [Ca2+] (ranging from 0.70 to 0.98). The same sign and similar magnitude of median r was observed in the simulations and the patients. Different cell type distributions in the ventricular wall led to variability in ECG markers that was accentuated at high [K+] and low [Ca2+], in agreement with the larger variability between patients measured at the onset of HD. The simulated ECG variability explained part of the measured inter-patient variability. Conclusion: Changes in ECG markers were similarly related to [K+] and [Ca2+] variations in our models and in the ESRD patients. The high inter-patient ECG variability may be explained by variations in cell type distribution across the ventricular wall, with high sensitivity to variations in the proportion of epicardial cells. Significance: Differences in ventricular wall composition help to explain inter-patient variability in ECG response to [K+] and [Ca2+]. This finding can be used to improve serum electrolyte monitoring in ESRD patients.

Theoretical prediction of early afterdepolarization-evoked triggered activity formation initiating ventricular reentrant arrhythmias

BACKGROUND AND OBJECTIVE

Excessive prolongation of QT interval on ECGs in patients with congenital/acquired long QT syndrome and heart failure is a sign suggesting the development of early afterdepolarization (EAD), an abnormal repolarization in the action potential of ventricular cardiomyocytes. The development of EAD has been believed to be a trigger for fatal tachyarrhythmia, which can be a risk for sudden cardiac death. The role of EAD in triggering ventricular tachycardia (VT) remains unclear. The aim of this study was to elucidate the mechanism of EAD-induced triggered activity formation that leads to the VT such as Torsades de Pointes.

METHODS

We investigated the relationship between EAD and tachyarrhythmia initiation by constructing homogeneous myocardial sheet models consisting of the mid-myocardial cell version of a human ventricular myocyte model and performing simulations of excitation propagation.

RESULTS

A solitary island-like (clustering) occurrence of EADs in the homogeneous myocardial sheet could induce a focal excitation wave. However, reentrant excitation, an entity of tachyarrhythmia, was not able to be triggered regardless of the EAD cluster size when the focal excitation wave formed a repolarization potential difference boundary consisting of only a convex surface. The discontinuous distribution of multiple EAD clusters in the ventricular tissue formed a specific repolarization heterogeneity due to the repolarization potential difference, the shape of which depended on EAD cluster size and placed intervals. We found that the triggered activity was formed in such a manner that the repolarization potential difference boundary included a concave surface.

CONCLUSIONS

The formation of triggered activity that led to tachyarrhythmia required not only the occurrence of EAD onset-mediated focal excitation wave but also a repolarization heterogeneity-based specific repolarization potential difference boundary shape formed within the tissue.

Composite spiral waves in discrete-time systems

Spiral waves are a type of typical pattern in open reaction-diffusion systems far from thermodynamic equilibrium. The study of spiral waves has attracted great interest because of its nonlinear characteristics and extensive applications. However, the study of spiral waves has been confined to continuous-time systems, while spiral waves in discrete-time systems have been rarely reported. In recent years, discrete-time models have been widely studied in ecology because of their appropriateness to systems with nonoverlapping generations and other factors. Therefore, spiral waves in discrete-time systems need to be studied. Here, we investigated a novel type of spiral wave called a composite spiral wave in a discrete-time predator-pest model, and we revealed the formation mechanism. To explain the observed phenomena, we defined and quantified a move state effect of multiperiod states caused by the coupling of adjacent stable multiperiod orbits, which is strictly consistent with the numerical results. The other move state effect is caused by an unstable focus, which is the state of the local points at the spiral center. The combined effect of these two influences can lead to rich dynamical behaviors of spiral waves, and the specific structure of the composite spiral waves is the result of the competition of the two effects in opposite directions. Our findings shed light on the dynamics of spiral waves in discrete-time systems, and they may guide the prediction and control of pests in deciduous forests.

The age of computational cardiology and future of long-term ablation target prediction for ventricular tachycardia

Ventricular arrhythmias, particularly ventricular tachycardia, are ubiquitously linked to 300,000 deaths annually. However, the current interventional procedure-the cardiac ablation-predict only short-term responses to treatment as the heart constantly remodels itself post-arrhythmia. To assist in the design of computational methods which focuses on long-term arrhythmia prediction, this review postulates three interdependent prospectives. The main objective is to propose computational methods for predicting long-term heart response to interventions in ventricular tachycardia Following a general discussion on the importance of devising simulations predicting long-term heart response to interventions, each of the following is discussed: (i) application of "metabolic sink theory" to elucidate the "re-entry" mechanism of ventricular tachycardia; (ii) application of "growth laws" to explain "mechanical load" translation in ventricular tachycardia; (iii) derivation of partial differential equations (PDE) to establish a pipeline to predict long-term clinical outcomes in ventricular tachycardia.

Non-invasive localization of the ventricular excitation origin without patient-specific geometries using deep learning

Cardiovascular diseases account for 17 million deaths per year worldwide. Of these, 25% are categorized as sudden cardiac death, which can be related to ventricular tachycardia (VT). This type of arrhythmia can be caused by focal activation sources outside the sinus node. Catheter ablation of these foci is a curative treatment in order to inactivate the abnormal triggering activity. However, the localization procedure is usually time-consuming and requires an invasive procedure in the catheter lab. To facilitate and expedite the treatment, we present two novel localization support techniques based on convolutional neural networks (CNNs) that address these clinical needs. In contrast to existing methods, our approaches were designed to be independent of the patient-specific geometry and directly applicable to surface ECG signals, while also delivering a binary transmural position. Moreover, one of the method's outputs can be interpreted as several ranked solutions. The CNNs were trained on a dataset containing only simulated data and evaluated both on simulated test data and clinical data. On a novel large and open simulated dataset, the median test error was below 3 mm. The median localization error on the unseen clinical data ranged from 32 mm to 41 mm without optimizing the pre-processing and CNN to the clinical data. Interpreting the output of one of the approaches as ranked solutions, the best median error of the top-3 solutions decreased to 20 mm on the clinical data. The transmural position was correctly detected in up to 82% of all clinical cases. These results demonstrate a proof of principle to utilize CNNs to localize the activation source without the intrinsic need for patient-specific geometrical information. Furthermore, providing multiple solutions can assist physicians in identifying the true activation source amongst more than one possible location. With further optimization to clinical data, these methods have high potential to accelerate clinical interventions, replace certain steps within these procedures and consequently reduce procedural risk and improve VT patient outcomes.

A comprehensive mathematical model for cardiac perfusion

The aim of this paper is to introduce a new mathematical model that simulates myocardial blood perfusion that accounts for multiscale and multiphysics features. Our model incorporates cardiac electrophysiology, active and passive mechanics, hemodynamics, valve modeling, and a multicompartment Darcy model of perfusion. We consider a fully coupled electromechanical model of the left heart that provides input for a fully coupled Navier-Stokes-Darcy Model for myocardial perfusion. The fluid dynamics problem is modeled in a left heart geometry that includes large epicardial coronaries, while the multicompartment Darcy model is set in a biventricular myocardium. Using a realistic and detailed cardiac geometry, our simulations demonstrate the biophysical fidelity of our model in describing cardiac perfusion. Specifically, we successfully validate the model reliability by comparing in-silico coronary flow rates and average myocardial blood flow with clinically established values ranges reported in relevant literature. Additionally, we investigate the impact of a regurgitant aortic valve on myocardial perfusion, and our results indicate a reduction in myocardial perfusion due to blood flow taken away by the left ventricle during diastole. To the best of our knowledge, our work represents the first instance where electromechanics, hemodynamics, and perfusion are integrated into a single computational framework.

Optogenetic Modulation of Arrhythmia Triggers: Proof-of-Concept from Computational Modeling

Introduction

Early afterdepolarizations (EADs) are secondary voltage depolarizations associated with reduced repolarization reserve (RRR) that can trigger lethal arrhythmias. Relating EADs to triggered activity is difficult to study, so the ability to suppress or provoke EADs would be experimentally useful. Here, we use computational simulations to assess the feasibility of subthreshold optogenetic stimulation modulating the propensity for EADs (cell-scale) and EAD-associated ectopic beats (organ-scale).

Methods

We modified a ventricular ionic model by reducing rapid delayed rectifier potassium (0.25-0.1 × baseline) and increasing L-type calcium (1.0-3.5 × baseline) currents to create RRR conditions with varying severity. We ran simulations in models of single cardiomyocytes and left ventricles from post-myocardial infarction patient MRI scans. Optogenetic stimulation was simulated using either ChR2 (depolarizing) or GtACR1 (repolarizing) opsins.

Results

In cell-scale simulations without illumination, EADs were seen for 164 of 416 RRR conditions. Subthreshold stimulation of GtACR1 reduced EAD incidence by up to 84.8% (25/416 RRR conditions; 0.1 μW/mm2); in contrast, subthreshold ChR2 excitation increased EAD incidence by up to 136.6% (388/416 RRR conditions; 50 μW/mm2). At the organ scale, we assumed simultaneous, uniform illumination of the epicardial and endocardial surfaces. GtACR1-mediated suppression (10-50 μW/mm2) and ChR2-mediated unmasking (50-100 μW/mm2) of EAD-associated ectopic beats were feasible in three distinct ventricular models.

Conclusions

Our findings suggest that optogenetics could be used to silence or provoke both EADs and EAD-associated ectopic beats. Validation in animal models could lead to exciting new experimental regimes and potentially to novel anti-arrhythmia treatments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-023-00781-z.

Predicting Ventricular Tachycardia Circuits in Patients with Arrhythmogenic Right Ventricular Cardiomyopathy using Genotype-specific Heart Digital Twins

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.

Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

Introduction: The 30-50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance D_PS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude. Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient. Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance D_PS was shorter in the responders. The max ML-score and D_PS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and D_PS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%). Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT.

Impact of electrode orientation, myocardial wall thickness, and myofiber direction on intracardiac electrograms: numerical modeling and analytical solutions

Intracardiac electrograms (iEGMs) are time traces of the electrical potential recorded close to the heart muscle. We calculate unipolar and bipolar iEGMs analytically for a myocardial slab with parallel myofibers and validate them against numerical bidomain simulations. The analytical solution obtained via the method of mirrors is an infinite series of arctangents. It goes beyond the solid angle theory and is in good agreement with the simulations, even though bath loading effects were not accounted for in the analytical calculation. At a large distance from the myocardium, iEGMs decay as 1/R (unipolar), 1/R ² (bipolar and parallel), and 1/R ³ (bipolar and perpendicular to the endocardium). At the endocardial surface, there is a mathematical branch cut. Here, we show how a thicker myocardium generates iEGMs with larger amplitudes and how anisotropy affects the iEGM width and amplitude. If only the leading-order term of our expansion is retained, it can be determined how the conductivities of the bath, torso, myocardium, and myofiber direction together determine the iEGM amplitude. Our results will be useful in the quantitative interpretation of iEGMs, the selection of thresholds to characterize viable tissues, and for future inferences of tissue parameters.

Graft-host coupling changes can lead to engraftment arrhythmia: a computational study

After myocardial infarction (MI), a significant portion of heart muscle is replaced with scar tissue, progressively leading to heart failure. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CM) offer a promising option for improving cardiac function after MI. However, hPSC-CM transplantation can lead to engraftment arrhythmia (EA). EA is a transient phenomenon arising shortly after transplantation then spontaneously resolving after a few weeks. The underlying mechanism of EA is unknown. We hypothesize that EA may be explained partially by time-varying, spatially heterogeneous, graft-host electrical coupling. Here, we created computational slice models derived from histological images that reflect different configuration of grafts in the infarcted ventricle. We ran simulations with varying degrees of connection imposed upon the graft-host perimeter to assess how heterogeneous electrical coupling affected EA with non-conductive scar, slow-conducting scar and scar replaced by host myocardium. We also quantified the effect of variation in intrinsic graft conductivity. Susceptibility to EA initially increased and subsequently decreased with increasing graft-host coupling, suggesting the waxing and waning of EA is regulated by progressive increases in graft-host coupling. Different spatial distributions of graft, host and scar yielded markedly different susceptibility curves. Computationally replacing non-conductive scar with host myocardium or slow-conducting scar, and increasing intrinsic graft conductivity both demonstrated potential means to blunt EA vulnerability. These data show how graft location, especially relative to scar, along with its dynamic electrical coupling to host, can influence EA burden; moreover, they offer a rational base for further studies aimed to define the optimal delivery of hPSC-CM injection. KEY POINTS: Human pluripotent stem cell-derived cardiomyocytes (hPSC-CM) hold great cardiac regenerative potential but can also cause engraftment arrhythmias (EA). Spatiotemporal evolution in the pattern of electrical coupling between injected hPSC-CMs and surrounding host myocardium may explain the dynamics of EA observed in large animal models. We conducted simulations in histology-derived 2D slice computational models to assess the effects of heterogeneous graft-host electrical coupling on EA propensity, with or without scar tissue. Our findings suggest spatiotemporally heterogeneous graft-host coupling can create an electrophysiological milieu that favours graft-initiated host excitation, a surrogate metric of EA susceptibility. Removing scar from our models reduced but did not abolish the propensity for this phenomenon. Conversely, reduced intra-graft electrical connectedness increased the incidence of graft-initiated host excitation. The computational framework created for this study can be used to generate new hypotheses, targeted delivery of hPSC-CMs.

The importance of mechanical conditions in the testing of excitation abnormalities in a population of electro-mechanical models of human ventricular cardiomyocytes

Background: Populations of in silico electrophysiological models of human cardiomyocytes represent natural variability in cell activity and are thoroughly calibrated and validated using experimental data from the human heart. The models have been shown to predict the effects of drugs and their pro-arrhythmic risks. However, excitation and contraction are known to be tightly coupled in the myocardium, with mechanical loads and stretching affecting both mechanics and excitation through mechanisms of mechano-calcium-electrical feedback. However, these couplings are not currently a focus of populations of cell models. Aim: We investigated the role of cardiomyocyte mechanical activity under different mechanical conditions in the generation, calibration, and validation of a population of electro-mechanical models of human cardiomyocytes. Methods: To generate a population, we assumed 11 input parameters of ionic currents and calcium dynamics in our recently developed TP + M model as varying within a wide range. A History matching algorithm was used to generate a non-implausible parameter space by calibrating the action potential and calcium transient biomarkers against experimental data and rejecting models with excitation abnormalities. The population was further calibrated using experimental data on human myocardial force characteristics and mechanical tests involving variations in preload and afterload. Models that passed the mechanical tests were validated with additional experimental data, including the effects of drugs with high or low pro-arrhythmic risk. Results: More than 10% of the models calibrated on electrophysiological data failed mechanical tests and were rejected from the population due to excitation abnormalities at reduced preload or afterload for cell contraction. The final population of accepted models yielded action potential, calcium transient, and force/shortening outputs consistent with experimental data. In agreement with experimental and clinical data, the models demonstrated a high frequency of excitation abnormalities in simulations of Dofetilide action on the ionic currents, in contrast to Verapamil. However, Verapamil showed a high frequency of failed contractions at high concentrations. Conclusion: Our results highlight the importance of considering mechanoelectric coupling in silico cardiomyocyte models. Mechanical tests allow a more thorough assessment of the effects of interventions on cardiac function, including drug testing.

Computational Study on Effect of KCNQ1 P535T Mutation in a Cardiac Ventricular Tissue

Heart diseases such as arrhythmia are the main causes of sudden death. Arrhythmias are typically caused by mutations in specific genes, damage in the cardiac tissue, or due to some chemical exposure. Arrhythmias caused due to mutation is called inherited arrhythmia. Induced arrhythmias are caused due to tissue damage or chemical exposure. Mutations in genes that encode ion channels of the cardiac cells usually result in (dysfunction) improper functioning of the channel. Improper functioning of the ion channel may lead to major changes in the action potential (AP) of the cardiac cells. This further leads to distorted electrical activity of the heart. Distorted electrical activity will affect the ECG that results in arrhythmia. KCNQ1 P535T mutation is one such gene mutation that encodes the potassium ion channel (KV7.1) of the cardiac ventricular tissue. Its clinical significance is not known. This study aims to perform a simulation study on P535T mutation in the KCNQ1 gene that encodes the potassium ion channel KV7.1 in the ventricular tissue grid. The effect of P535T mutation on transmural tissue grids for three genotypes (wild type, heterozygous, and homozygous) of cells are studied and the generated pseudo-ECGs are compared. Results show the delayed repolarization in the cells of ventricular tissue grid. Slower propagation of action potential in the transmural tissue grid is observed in the mutated (heterozygous and homozygous) genotypes. Longer QT interval is also observed in the pseudo-ECG of heterozygous and homozygous genotype tissue grids. From the pseudo-ECGs, it is observed that KCNQ1 P535T mutation leads to Long QT Syndrome (LQTS) which may result in life-threatening arrhythmias, such as Torsade de Pointes (TdP), Jervell and Lange-Nielsen syndrome (JLNS), and Romano-Ward syndrome (RWS).

Contribution of cytokine-mediated prolongation of QTc interval to the multi-hit theory of Torsade de Pointes

BACKGROUND

Torsade de pointes is a potentially lethal polymorphic ventricular tachyarrhythmia that can occur in the setting of long QT syndrome (LQTS). LQTS is multi-hit in nature and multiple factors combine their effects leading to increased arrhythmic risk. While hypokalemia and multiple medications are accounted for in LQTS, the arrhythmogenic role of systemic inflammation is increasingly recognized but often overlooked. We tested the hypothesis that the inflammatory cytokine interleukin(IL)-6 will significantly increase the incidence of arrhythmia when combined with other pro-arrhythmic conditions (hypokalemia and the psychotropic medication, quetiapine).

METHODS

Guinea pigs were injected intraperitoneally with IL-6/soluble IL-6 receptor and QT changes were measured in vivo. Subsequently, hearts were cannulated via Langendorff perfusion for ex vivo optical mapping measurements of action potential duration (APD90) and arrhythmia inducibility. Computer simulations (MATLAB) were performed to investigate IKr inhibition at varying IL-6 and quetiapine concentrations.

RESULTS

IL-6 prolonged QTc in vivo guinea pigs from 306.74 ± 7.19 ms to 332.60 ± 8.75 ms (n = 8, p = .0021). Optical mapping on isolated hearts demonstrated APD prolongation in IL-6- vs saline groups (3Hz APD90:179.67 ± 2.47 ms vs 153.5 ± 7.86 ms, p = .0357). When hypokalemia was introduced, the APD90 increased to 195.8 ± 5.02 ms[IL-6] and 174.57 ± 10.7 ms[saline] (p = .2797), and when quetiapine was added to hypokalemia to 207.67 ± 3.03 ms[IL-6] and 191.37 ± 9.49 ms[saline] (p = .2449). After the addition of hypokalemia ± quetiapine, arrhythmia was induced in 75% of IL-6-treated hearts (n = 8), while in none of the control hearts (n = 6). Computer simulations demonstrated spontaneous depolarizations at ∼83% aggregate IKr inhibition.

CONCLUSIONS

Our experimental observations strongly suggest that controlling inflammation, specifically IL-6, could be a viable and important route for reducing QT prolongation and arrhythmia incidence in the clinical setting.

lifex-fiber: an open tool for myofibers generation in cardiac computational models

BACKGROUND

Modeling the whole cardiac function involves the solution of several complex multi-physics and multi-scale models that are highly computationally demanding, which call for simpler yet accurate, high-performance computational tools. Despite the efforts made by several research groups, no software for whole-heart fully-coupled cardiac simulations in the scientific community has reached full maturity yet.

RESULTS

In this work we present [Formula: see text]-fiber, an innovative tool for the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, which are the essential building blocks for modeling the electrophysiological, mechanical and electromechanical cardiac function, from single-chamber to whole-heart simulations. [Formula: see text]-fiber is the first publicly released module for cardiac simulations based on [Formula: see text], an open-source, high-performance Finite Element solver for multi-physics, multi-scale and multi-domain problems developed in the framework of the iHEART project, which aims at making in silico experiments easily reproducible and accessible to a wide community of users, including those with a background in medicine or bio-engineering.

CONCLUSIONS

The tool presented in this document is intended to provide the scientific community with a computational tool that incorporates general state of the art models and solvers for simulating the cardiac function within a high-performance framework that exposes a user- and developer-friendly interface. This report comes with an extensive technical and mathematical documentation to welcome new users to the core structure of [Formula: see text]-fiber and to provide them with a possible approach to include the generated cardiac fibers into more sophisticated computational pipelines. In the near future, more modules will be successively published either as pre-compiled binaries for x86-64 Linux systems or as open source software.

Mechanoelectric effects in healthy cardiac function and under Left Bundle Branch Block pathology

Mechanoelectric feedback (MEF) in the heart operates through several mechanisms which serve to regulate cardiac function. Stretch activated channels (SACs) in the myocyte membrane open in response to cell lengthening, while tension generation depends on stretch, shortening velocity, and calcium concentration. How all of these mechanisms interact and their effect on cardiac output is still not fully understood. We sought to gauge the acute importance of the different MEF mechanisms on heart function. An electromechanical computer model of a dog heart was constructed, using a biventricular geometry of 500K tetrahedral elements. To describe cellular behavior, we used a detailed ionic model to which a SAC model and an active tension model, dependent on stretch and shortening velocity and with calcium sensitivity, were added. Ventricular inflow and outflow were connected to the CircAdapt model of cardiovascular circulation. Pressure-volume loops and activation times were used for model validation. Simulations showed that SACs did not affect acute mechanical response, although if their trigger level was decreased sufficiently, they could cause premature excitations. The stretch dependence of tension had a modest effect in reducing the maximum stretch, and stroke volume, while shortening velocity had a much bigger effect on both. MEF served to reduce the heterogeneity in stretch while increasing tension heterogeneity. In the context of left bundle branch block, a decreased SAC trigger level could restore cardiac output by reducing the maximal stretch when compared to cardiac resynchronization therapy. MEF is an important aspect of cardiac function and could potentially mitigate activation problems.

Fast and robust parameter estimation with uncertainty quantification for the cardiac function

BACKGROUND AND OBJECTIVES

Parameter estimation and uncertainty quantification are crucial in computational cardiology, as they enable the construction of digital twins that faithfully replicate the behavior of physical patients. Many model parameters regarding cardiac electromechanics and cardiovascular hemodynamics need to be robustly fitted by starting from a few, possibly non-invasive, noisy observations. Moreover, short execution times and a small amount of computational resources are required for the effective clinical translation.

METHODS

In the framework of Bayesian statistics, we combine Maximum a Posteriori estimation and Hamiltonian Monte Carlo to find an approximation of model parameters and their posterior distributions. Fast simulations and minimal memory requirements are achieved by using an accurate and geometry-specific Artificial Neural Network surrogate model for the cardiac function, matrix-free methods, automatic differentiation and automatic vectorization. Furthermore, we account for the surrogate modeling error and measurement error.

RESULTS

We perform three different in silico test cases, ranging from the ventricular function to the entire cardiocirculatory system, involving whole-heart mechanics, arterial and venous hemodynamics. By employing a single central processing unit on a standard laptop, we attain highly accurate estimations for all model parameters in short computational times. Furthermore, we obtain posterior distributions that contain the true values inside the 90% credibility regions.

CONCLUSIONS

Many model parameters regarding the entire cardiovascular system can be fastly and robustly identified with minimal hardware requirements. This can be achieved when a small amount of non-invasive data is available and when high levels of signal-to-noise ratio are present in the quantities of interest. With these features, our approach meets the requirements for clinical exploitation, while being compliant with Green Computing practices.

In silico analysis of the contribution of cardiomyocyte-fibroblast electromechanical interaction to the arrhythmia

Although fibroblasts are about 5–10 times smaller than cardiomyocytes, their number in the ventricle is about twice that of cardiomyocytes. The high density of fibroblasts in myocardial tissue leads to a noticeable effect of their electromechanical interaction with cardiomyocytes on the electrical and mechanical functions of the latter. Our work focuses on the analysis of the mechanisms of spontaneous electrical and mechanical activity of the fibroblast-coupled cardiomyocyte during its calcium overload, which occurs in a variety of pathologies, including acute ischemia. For this study, we developed a mathematical model of the electromechanical interaction between cardiomyocyte and fibroblasts and used it to simulate the impact of overloading cardiomyocytes. In contrast to modeling only the electrical interaction between cardiomyocyte and fibroblasts, the following new features emerge in simulations with the model that accounts for both electrical and mechanical coupling and mechano-electrical feedback loops in the interacting cells. First, the activity of mechanosensitive ion channels in the coupled fibroblasts depolarizes their resting potential. Second, this additional depolarization increases the resting potential of the coupled myocyte, thus augmenting its susceptibility to triggered activity. The triggered activity associated with the cardiomyocyte calcium overload manifests itself in the model either as early afterdepolarizations or as extrasystoles, i.e., extra action potentials and extra contractions. Analysis of the model simulations showed that mechanics contribute significantly to the proarrhythmic effects in the cardiomyocyte overloaded with calcium and coupled with fibroblasts, and that mechano-electrical feedback loops in both the cardiomyocyte and fibroblasts play a key role in this phenomenon.

A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart

We propose a mathematical and numerical model for the simulation of the heart function that couples cardiac electrophysiology, active and passive mechanics and hemodynamics, and includes reduced models for cardiac valves and the circulatory system. Our model accounts for the major feedback effects among the different processes that characterize the heart function, including electro-mechanical and mechano-electrical feedback as well as force-strain and force-velocity relationships. Moreover, it provides a three-dimensional representation of both the cardiac muscle and the hemodynamics, coupled in a fluid-structure interaction (FSI) model. By leveraging the multiphysics nature of the problem, we discretize it in time with a segregated electrophysiology-force generation-FSI approach, allowing for efficiency and flexibility in the numerical solution. We employ a monolithic approach for the numerical discretization of the FSI problem. We use finite elements for the spatial discretization of partial differential equations. We carry out a numerical simulation on a realistic human left heart model, obtaining results that are qualitatively and quantitatively in agreement with physiological ranges and medical images.

Cellular electrophysiological effects of botulinum toxin A on neonatal rat cardiomyocytes and on cardiomyocytes derived from human-induced pluripotent stem cells

Botulinum toxin A is a well-known neurotransmitter inhibitor with a wide range of applications in modern medicine. Recently, botulinum toxin A preparations have been used in clinical trials to suppress cardiac arrhythmias, especially in the postoperative period. Its antiarrhythmic action is associated with inhibition of the nervous system of the heart, but its direct effect on heart tissue remains unclear. Accordingly, we investigate the effect of botulinum toxin A on isolated cardiac cells and on layers of cardiac cells capable of conducting excitation. Cardiomyocytes of neonatal rat pups and human cardiomyocytes obtained through cell reprogramming were used. A patch-clamp study showed that botulinum toxin A inhibited fast sodium currents and L-type calcium currents in a dose-dependent manner, with no apparent effect on potassium currents. Optical mapping showed that in the presence of botulinum toxin A, the propagation of the excitation wave in the layer of cardiac cells slows down sharply, conduction at high concentrations becomes chaotic, but reentry waves do not form. The combination of botulinum toxin A with a preparation of chitosan showed a stronger inhibitory effect by an order of magnitude. Further, the inhibitory effect of botulinum toxin A is not permanent and disappears after 12 days of cell culture in a botulinum toxin A-free medium. The main conclusion of the work is that the antiarrhythmic effect of botulinum toxin A found in clinical studies is associated not only with depression of the nervous system but also with a direct effect on heart tissue. Moreover, the combination of botulinum toxin A and chitosan reduces the effective dose of botulinum toxin A.