Natalia A. Trayanova

Sudden Cardiac Death Prediction and Prevention Report From a National Heart, Lung, and Blood Institute and Heart Rhythm Society Workshop

Despite the significant decline in coronary artery disease (CAD) mortality in the second half of the 20th century,1 sudden cardiac death (SCD) continues to claim 250 000 to 300 000 US lives annually.2 In North America and Europe the annual incidence of SCD ranges between 50 to 100 per 100 000 in the general population.3,–,6 Because of the absence of emergency medical response systems in most world regions, worldwide estimates are currently not available.7 However, even in the presence of advanced first responder systems for resuscitation of out-of-hospital cardiac arrest, the overall survival rate in a recent North American analysis was 4.6%.8 SCD can manifest as ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electric activity (PEA), or asystole. In a significant proportion of patients, SCD can present without warning or a recognized triggering mechanism. The mean age of those affected is in the mid 60s, and at least 40% of patients will suffer SCD before the age of 65.4 Consequently, enhancement of methodologies for prediction and prevention of SCD acquires a unique and critical importance for management of this significant public health issue. Prediction and prevention of SCD is an area of active investigation, but considerable challenges persist that limit the efficacy and cost-effectiveness of available methodologies.7,9,10 It was recognized early on that optimization of SCD risk stratification will require integration of multi-disciplinary efforts at the bench and bedside, with studies in the general population.11,–,13 This integration has yet to be effectively accomplished. There is also increasing awareness that more investigation needs to be directed toward identification of early predictors of SCD.14 Significant advancements have occurred for risk prediction in the inherited channelopathies15,–,17 and …

Computational techniques for solving the bidomain equations in three dimensions

The bidomain equations are the most complete description of cardiac electrical activity. Their numerical solution is, however, computationally demanding, especially in three dimensions, because of the fine temporal and spatial sampling required. This paper methodically examines computational performance when solving the bidomain equations. Several techniques to speed up this computation are examined in this paper. The first step was to recast the equations into a parabolic part and an elliptic part. The parabolic part was solved by either the finite-element method (FEM) or the interconnected cable model (ICCM). The elliptic equation was solved by FEM on a coarser grid than the parabolic problem and at a reduced frequency. The performance of iterative and direct linear equation system solvers was analyzed as well as the scalability and parallelizability of each method. Results indicate that the ICCM was twice as fast as the FEM for solving the parabolic problem, but when the total problem was considered, this resulted in only a 20% decrease in computation time. The elliptic problem could be solved on a coarser grid at one-quarter of the frequency at which the parabolic problem was solved and still maintain reasonable accuracy. Direct methods were faster than iterative methods by at least 50% when a good estimate of the extracellular potential was required. Parallelization over four processors was efficient only when the model comprised at least 500 000 nodes. Thus, it was possible to speed up solution of the bidomain equations by an order of magnitude with a slight decrease in accuracy.

Whole-Heart Modeling Applications to Cardiac Electrophysiology and Electromechanics

Recent developments in cardiac simulation have rendered the heart the most highly integrated example of a virtual organ. We are on the brink of a revolution in cardiac research, one in which computational modeling of proteins, cells, tissues, and the organ permit linking genomic and proteomic information to the integrated organ behavior, in the quest for a quantitative understanding of the functioning of the heart in health and disease. The goal of this review is to assess the existing state-of-the-art in whole-heart modeling and the plethora of its applications in cardiac research. General whole-heart modeling approaches are presented, and the applications of whole-heart models in cardiac electrophysiology and electromechanics research are reviewed. The article showcases the contributions that whole-heart modeling and simulation have made to our understanding of the functioning of the heart. A summary of the future developments envisioned for the field of cardiac simulation and modeling is also presented. Biophysically based computational modeling of the heart, applied to human heart physiology and the diagnosis and treatment of cardiac disease, has the potential to dramatically change 21st century cardiac research and the field of cardiology.

A Computational Model to Predict the Effects of Class I Anti-Arrhythmic Drugs on Ventricular Rhythms

Two- and three-dimensional models of cardiac excitability based on sodium channel kinetics can predict the adverse effects of class I anti-arrhythmic drugs. Crowdsourcing the Heart for Drug Screening The old way: Consult a specialist to answer your question. The new way: Consult a crowd of generalists who in the aggregate can come up with a better answer. The old way—testing drugs on single cardiac cells in vitro—has not worked well for screening out potential anti-arrhythmia agents that can occasionally block conduction in the heart or exacerbate arrhythmia, serious problems that cause sudden death in treated patients. Instead, Moreno et al. have called on the crowd by building a model of heart tissue that includes many cardiac cells and their interactions. When anti-arrhythmia drugs are “applied” to the model’s beating heart tissue—but not when they are applied to the single cardiac cells that make up the model—the drugs that cause side effects, and the concentrations at which they do so, are revealed, results that the authors were able to validate experimentally. The model starts with the detailed kinetics of the heart’s sodium channels, first in the context of a single cell, then in two- and three-dimensional cardiac tissue. The authors compared the action of lidocaine, a class 1B anti-arrhythmic drug not known to cause conduction block, and flecainide, a prototypical class 1C drug that carries a warning from the Food and Drug Administration. In the modeled analyses of single cardiac cells, both drugs slowed excitability at concentrations that matched those used in patients, but the cells retained the ability to generate action potentials. But when the model incorporated coupled groups of cells, the behavior of the drugs diverged. Lidocaine lowered excitability without causing block, but at the higher concentrations (used clinically), flecainide caused serious conduction block when heart rates reached 160 beats per minute. Experiments in rabbit heart confirmed the results of the model. In scaled-up, 500 by 500 groups of cells, the authors’ model could also successfully predict the tendency of flecainide, but not lidocaine, to make the heart extra sensitive to heartbeats occurring too early or too late, an effect that causes even more severe arrhythmias in patients when they take anti-arrhythmia drugs. Again, experiments in rabbit hearts replicated the model’s predictions, as did simulations of anatomically accurate human hearts derived from magnetic resonance imaging images. The ability of this sophisticated model of living cardiac tissue to replicate the clinical adverse effects of lidocaine and flecainide is promising, but it will be necessary to validate its performance with other drugs to understand how to deploy it most effectively. Ideally, such models will be useful for screening out potential arrhythmic drugs that promote conduction block or exacerbate arrhythmias. Such a view of how drugs affect the collective activity of cardiac cells should help in these situations in which the cure proves more deadly than the disease. A long-sought, and thus far elusive, goal has been to develop drugs to manage diseases of excitability. One such disease that affects millions each year is cardiac arrhythmia, which occurs when electrical impulses in the heart become disordered, sometimes causing sudden death. Pharmacological management of cardiac arrhythmia has failed because it is not possible to predict how drugs that target cardiac ion channels, and have intrinsically complex dynamic interactions with ion channels, will alter the emergent electrical behavior generated in the heart. Here, we applied a computational model, which was informed and validated by experimental data, that defined key measurable parameters necessary to simulate the interaction kinetics of the anti-arrhythmic drugs flecainide and lidocaine with cardiac sodium channels. We then used the model to predict the effects of these drugs on normal human ventricular cellular and tissue electrical activity in the setting of a common arrhythmia trigger, spontaneous ventricular ectopy. The model forecasts the clinically relevant concentrations at which flecainide and lidocaine exacerbate, rather than ameliorate, arrhythmia. Experiments in rabbit hearts and simulations in human ventricles based on magnetic resonance images validated the model predictions. This computational framework initiates the first steps toward development of a virtual drug-screening system that models drug-channel interactions and predicts the effects of drugs on emergent electrical activity in the heart.

The role of cardiac tissue structure in defibrillation

The purpose of this paper is to investigate the relationship between cardiac tissue structure, applied electric field, and the transmembrane potential induced in the process of defibrillation. It outlines a general understanding of the structural mechanisms that contribute to the outcome of a defibrillation shock. Electric shocks defibrillate by changing the transmembrane potential throughout the myocardium. In this process first and foremost the shock current must access the bulk of myocardial mass. The exogenous current traverses the myocardium along convoluted intracellular and extracellular pathways channeled by the tissue structure. Since individual fibers follow curved pathways in the heart, and the fiber direction rotates across the ventricular wall, the applied current perpetually engages in redistribution between the intra- and extracellular domains. This redistribution results in changes in transmembrane potential (membrane polarization): regions of membrane hyper- and depolarization of extent larger than a single cell are induced in the myocardium by the defibrillation shock. Tissue inhomogeneities also contribute to local membrane polarization in the myocardium which is superimposed over the large-scale polarization associated with the fibrous organization of the myocardium. The paper presents simulation results that illustrate various mechanisms by which cardiac tissue structure assists the changes in transmembrane potential throughout the myocardium. (c) 1998 American Institute of Physics.

Differences Between Left and Right Ventricular Chamber Geometry Affect Cardiac Vulnerability to Electric Shocks

Although effects of shock strength and waveform on cardiac vulnerability to electric shocks have been extensively documented, the contribution of ventricular anatomy to shock-induced polarization and postshock propagation and thus, to shock outcome, has never been quantified; this is caused by lack of experimental methodology capable of mapping 3-D electrical activity. The goal of this study was to use optical imaging experiments and 3-D bidomain simulations to investigate the role of structural differences between left and right ventricles in vulnerability to electric shocks in rabbit hearts. The ventricles were paced apically, and uniform-field, truncated-exponential, monophasic shocks of reversed polarity were applied over a range of coupling intervals (CIs) in experiment and model. Experiments and simulations revealed that reversing the direction of externally-applied field (RV− or LV− shocks) alters the shape of the vulnerability area (VA), the 2-D grid encompassing episodes of arrhythmia induction. For RV− shocks, VA was nearly rectangular indicating little dependence of postshock arrhythmogenesis on CI. For LV− shocks, the probability of arrhythmia induction was higher for longer than for shorter CIs. The 3-D simulations demonstrated that these effects stem from the fact that reversal of field direction results in relocation of the main postshock excitable area from LV wall (RV− shocks) to septum (LV− shocks). Furthermore, the effect of septal (but not LV) excitable area in postshock propagation was found to strongly depend on preshock state. Knowledge regarding the location of the main postshock excitable area within the 3-D ventricular volume could be important for improving defibrillation efficacy.

The response of a spherical heart to a uniform electric field: a bidomain analysis of cardiac stimulation

A mathematical model describing electrical stimulation of the heart is developed, in which a uniform electric field is applied to a spherical shell of cardiac tissue. The electrical properties of the tissue are characterized using the bidomain model. Analytical expressions for the induced transmembrane potential are derived for the cases of equal anisotropy ratios in the intracellular and interstitial (extracellular) spaces, and no transverse coupling between fibers. Numerical calculations of the transmembrane potential are also performed using realistic electrical conductivities. The model illustrates several mechanisms for polarization of the cell membrane, which can be divided into two categories, depending on if they polarize fibers at the heart surface only or if they polarize fibers both at the surface and within the bulk of the tissue. The latter mechanisms can be classified further according to whether they originate from continuous or discrete properties of cardiac tissue.<<ETX>>

From mitochondrial ion channels to arrhythmias in the heart: computational techniques to bridge the spatio-temporal scales

Computer simulations of electrical behaviour in the whole ventricles have become commonplace during the last few years. The goals of this article are (i) to review the techniques that are currently employed to model cardiac electrical activity in the heart, discussing the strengths and weaknesses of the various approaches, and (ii) to implement a novel modelling approach, based on physiological reasoning, that lifts some of the restrictions imposed by current state-of-the-art ionic models. To illustrate the latter approach, the present study uses a recently developed ionic model of the ventricular myocyte that incorporates an excitation–contraction coupling and mitochondrial energetics model. A paradigm to bridge the vastly disparate spatial and temporal scales, from subcellular processes to the entire organ, and from sub-microseconds to minutes, is presented. Achieving sufficient computational efficiency is the key to success in the quest to develop multiscale realistic models that are expected to lead to better understanding of the mechanisms of arrhythmia induction following failure at the organelle level, and ultimately to the development of novel therapeutic applications.

Image-based models of cardiac structure in health and disease.

Computational approaches to investigating the electromechanics of healthy and diseased hearts are becoming essential for the comprehensive understanding of cardiac function. In this article, we first present a brief review of existing image‐based computational models of cardiac structure. We then provide a detailed explanation of a processing pipeline which we have recently developed for constructing realistic computational models of the heart from high resolution structural and diffusion tensor (DT) magnetic resonance (MR) images acquired ex vivo. The presentation of the pipeline incorporates a review of the methodologies that can be used to reconstruct models of cardiac structure. In this pipeline, the structural image is segmented to reconstruct the ventricles, normal myocardium, and infarct. A finite element mesh is generated from the segmented structural image, and fiber orientations are assigned to the elements based on DTMR data. The methods were applied to construct seven different models of healthy and diseased hearts. These models contain millions of elements, with spatial resolutions in the order of hundreds of microns, providing unprecedented detail in the representation of cardiac structure for simulation studies. Copyright

Action potential dynamics explain arrhythmic vulnerability in human heart failure: a clinical and modeling study implicating abnormal calcium handling.

OBJECTIVES The purpose of this study was to determine whether abnormalities of calcium cycling explain ventricular action potential (AP) oscillations and cause electrocardiogram T-wave alternans (TWA). BACKGROUND Mechanisms explaining why heart failure patients are at risk for malignant ventricular arrhythmias (ventricular tachycardia [VT]/ventricular fibrillation [VF]) are unclear. We studied whether oscillations in human ventricular AP explain TWA and predict VT/VF, and used computer modeling to suggest potential cellular mechanisms. METHODS We studied 53 patients with left ventricular ejection fraction 28 +/- 8% and 18 control subjects. Monophasic APs were recorded in the right ventricle (n = 62) and/or left ventricle (n = 9) at 109 beats/min. RESULTS Alternans of AP amplitude, computed spectrally, had higher magnitude in study patients than in controls (p = 0.03), particularly in AP phase II (p = 0.02) rather than phase III (p = 0.10). The AP duration and activation restitution (n = 11 patients) were flat at 109 beats/min and did not explain TWA. In computer simulations, only reduced sarcoplasmic reticulum calcium uptake explained our results, causing calcium oscillations, AP amplitude alternans, and TWA that were all abolished by calcium clamping. On prospective follow-up for 949 +/- 553 days, 17 patients had VT/VF. The AP amplitude alternans predicted VT/VF (p = 0.04), and was 78% concordant with simultaneous TWA (p = 0.003). CONCLUSIONS Patients with systolic dysfunction show ventricular AP amplitude alternans that prospectively predicted VT/VF. Alternans in AP amplitude, but not variations in AP duration or conduction, explained TWA at < or =109 beats/min. In computer models, these findings were best explained by reduced sarcoplasmic reticulum calcium uptake. Thus, in heart failure patients, in vivo AP alternans may reflect cellular calcium abnormalities and provide a mechanistic link with VT/VF.