Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Caveolin-3 and Caveolae regulate ventricular repolarization.

RATIONALE: Flask-shaped invaginations of the cardiomyocyte sarcolemma called caveolae require the structural protein caveolin-3 (Cav-3) and host a variety of ion channels, transporters, and signaling molecules. Reduced Cav-3 expression has been reported in models of heart failure, and variants in CAV3 have been associated with the inherited long-QT arrhythmia syndrome. Yet, it remains unclear whether alterations in Cav-3 levels alone are sufficient to drive aberrant repolarization and increased arrhythmia risk. OBJECTIVE: To determine the impact of cardiac-specific Cav-3 ablation on the electrophysiological properties of the adult mouse heart. METHODS AND RESULTS: Cardiac-specific, inducible Cav3 homozygous knockout (Cav-3KO) mice demonstrated a marked reduction in Cav-3 expression by Western blot and loss of caveolae by electron microscopy. However, there was no change in macroscopic cardiac structure or contractile function. The QTc interval was increased in Cav-3KO mice, and there was an increased propensity for ventricular arrhythmias. Ventricular myocytes isolated from Cav-3KO mice exhibited a prolonged action potential duration (APD) that was due to reductions in outward potassium currents (Ito, Iss) and changes in inward currents including slowed inactivation of ICa,L and increased INa,L. Mathematical modeling demonstrated that the changes in the studied ionic currents were adequate to explain the prolongation of the mouse ventricular action potential. Results from human iPSC-derived cardiomyocytes showed that shRNA knockdown of Cav-3 similarly prolonged APD. CONCLUSION: We demonstrate that Cav-3 and caveolae regulate cardiac repolarization and arrhythmia risk via the integrated modulation of multiple ionic currents.

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias.

Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning-based workflow for reconstructing personalized computational electrophysiological heart models to guide patient-specific treatment of VT. Contrast-enhanced computed tomography (CE-CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across five cohorts from three different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE-CT was developed, trained and evaluated. From both CNN-based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed and rapid pacing was used to induce VTs. CNN-based and expert segmentations were more concordant in the middle myocardium than in the heart's base or apex. Wavefront propagation during pacing was similar between CNN-based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co-localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning-based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly-derived heart models. Hence, a user-independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy. KEY POINTS: Personalized electrophysiological heart modelling can aid in patient-specific ventricular tachycardia (VT) risk stratification and VT ablation targeting. Current state-of-the-art, image-based heart models for VT prediction require expert-dependent, manual interactions that may not be accessible across clinical settings. In this study, we develop an automated, deep learning-based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert-generated heart models. The number and location of VTs was similar between heart models generated from the deep learning-based workflow and expert-generated heart models. These results demonstrate the feasibility of using an automated computational heart modelling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centres.

Mechanisms of Sinoatrial Node Dysfunction in Heart Failure With Preserved Ejection Fraction.

BACKGROUND: The ability to increase heart rate during exercise and other stressors is a key homeostatic feature of the sinoatrial node (SAN). When the physiological heart rate response is blunted, chronotropic incompetence limits exercise capacity, a common problem in patients with heart failure with preserved ejection fraction (HFpEF). Despite its clinical relevance, the mechanisms of chronotropic incompetence remain unknown. METHODS: Dahl salt-sensitive rats fed a high-salt diet and C57Bl6 mice fed a high-fat diet and an inhibitor of constitutive nitric oxide synthase (Nω-nitro-L-arginine methyl ester [L-NAME]; 2-hit) were used as models of HFpEF. Myocardial infarction was created to induce HF with reduced ejection fraction. Rats and mice fed with a normal diet or those that had a sham surgery served as respective controls. A comprehensive characterization of SAN function and chronotropic response was conducted by in vivo, ex vivo, and single-cell electrophysiologic studies. RNA sequencing of SAN was performed to identify transcriptomic changes. Computational modeling of biophysically-detailed human HFpEF SAN was created. RESULTS: Rats with phenotypically-verified HFpEF exhibited limited chronotropic response associated with intrinsic SAN dysfunction, including impaired ß-adrenergic responsiveness and an alternating leading pacemaker within the SAN. Prolonged SAN recovery time and reduced SAN sensitivity to isoproterenol were confirmed in the 2-hit mouse model. Adenosine challenge unmasked conduction blocks within the SAN, which were associated with structural remodeling. Chronotropic incompetence and SAN dysfunction were also found in rats with HF with reduced ejection fraction. Single-cell studies and transcriptomic profiling revealed HFpEF-related alterations in both the "membrane clock" (ion channels) and the "Ca2+ clock" (spontaneous Ca2+ release events). The physiologic impairments were reproduced in silico by empirically-constrained quantitative modeling of human SAN function. CONCLUSIONS: Chronotropic incompetence and SAN dysfunction were seen in both models of HF. We identified that intrinsic abnormalities of SAN structure and function underlie the chronotropic response in HFpEF.

Machine Learning in Arrhythmia and Electrophysiology.

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.

Wavefront directionality and decremental stimuli synergistically improve identification of ventricular tachycardia substrate: insights from personalized computational heart models.

AIMS: Multiple wavefront pacing (MWP) and decremental pacing (DP) are two electroanatomic mapping (EAM) strategies that have emerged to better characterize the ventricular tachycardia (VT) substrate. The aim of this study was to assess how well MWP, DP, and their combination improve identification of electrophysiological abnormalities on EAM that reflect infarct remodelling and critical VT sites. METHODS AND RESULTS: Forty-eight personalized computational heart models were reconstructed using images from post-infarct patients undergoing VT ablation. Paced rhythms were simulated by delivering an initial (S1) and an extra-stimulus (S2) from one of 100 locations throughout each heart model. For each pacing, unipolar signals were computed along the myocardial surface to simulate substrate EAM. Six EAM features were extracted and compared with the infarct remodelling and critical VT sites. Concordance of S1 EAM features between different maps was lower in hearts with smaller amounts of remodelling. Incorporating S1 EAM features from multiple maps greatly improved the detection of remodelling, especially in hearts with less remodelling. Adding S2 EAM features from multiple maps decreased the number of maps required to achieve the same detection accuracy. S1 EAM features from multiple maps poorly identified critical VT sites. However, combining S1 and S2 EAM features from multiple maps paced near VT circuits greatly improved identification of critical VT sites. CONCLUSION: Electroanatomic mapping with MWP is more advantageous for characterization of substrate in hearts with less remodelling. During substrate EAM, MWP and DP should be combined and delivered from locations proximal to a suspected VT circuit to optimize identification of the critical VT site.

Computational modeling of aberrant electrical activity following remuscularization with intramyocardially injected pluripotent stem cell-derived cardiomyocytes.

Acute engraftment arrhythmias (EAs) remain a serious complication of remuscularization therapy. Preliminary evidence suggests that a focal source underlies these EAs stemming from the automaticity of immature pluripotent stem cell-derived cardiomyocytes (PSC-CMs) in nascent myocardial grafts. How these EAs arise though during early engraftment remains unclear. In a series of in silico experiments, we probed the origin of EAs-exploring aspects of altered impulse formation and altered impulse propagation within nascent PSC-CM grafts and at the host-graft interface. To account for poor gap junctional coupling during early PSC-CM engraftment, the voltage dependence of gap junctions and the possibility of ephaptic coupling were incorporated. Inspired by cardiac development, we also studied the contributions of another feature of immature PSC-CMs, circumferential sodium channel (NaCh) distribution in PSC-CMs. Ectopic propagations emerged from nascent grafts of immature PSC-CMs at a rate of <96 bpm. Source-sink effects dictated this rate and contributed to intermittent capture between host and graft. Moreover, ectopic beats emerged from dynamically changing sites along the host-graft interface. The latter arose in part because circumferential NaCh distribution in PSC-CMs contributed to preferential conduction slowing and block of electrical impulses from host to graft myocardium. We conclude that additional mechanisms, in addition to focal ones, contribute to EAs and recognize that their relative contributions are dynamic across the engraftment process.

Arrhythmia in hypertrophic cardiomyopathy: Risk prediction using contrast enhanced MRI, T1 mapping, and personalized virtual heart technology.

BACKGROUND: Hypertrophic cardiomyopathy (HCM), a disease with myocardial fibrosis manifestation, is a common cause of sudden cardiac death (SCD) due to ventricular arrhythmias (VA). Current clinical risk stratification criteria are inadequate in identifying patients who are at risk for VA and in need of an implantable cardioverter defibrillator (ICD) for primary prevention. OBJECTIVE: We aimed to develop a risk prediction approach based on imaging biomarkers from the combination of late gadolinium contrast-enhanced (LGE) MRI and T1 mapping. We then aimed to compare the prediction to a virtual heart computational risk assessment approach based on LGE-T1 virtual heart models. METHODS: The methodology involved combining short-axis LGE-MRI with post-contrast T1 maps to define personalized thresholds for diffuse and dense fibrosis. The combined LGE-T1 maps were used to evaluate imaging biomarkers for VA risk prediction. The risk prediction capability of the biomarkers was compared with that of the LGE-T1 virtual heart arrhythmia inducibility simulation. VA risk prediction performance from both approaches was compared to clinical outcome (presence of clinical VA). RESULTS: Image-based biomarkers, including hypertrophy, signal intensity heterogeneity, and fibrotic border complexity, could not discriminate high vs low VA risk. LGE-T1 virtual heart technology outperformed all the image-based biomarker metrics and was statistically significant in predicting VA risk in HCM. CONCLUSIONS: We combined two MR imaging techniques to analyze imaging biomarkers in HCM. Raw and processed image-based biomarkers cannot discriminate patients with VA from those without VA. Hybrid LGE-T1 virtual heart models could correctly predict VA risk for this cohort and may improve SCD risk stratification to better identify HCM patients for primary preventative ICD implantation.

Artificial intelligence in the diagnosis and management of arrhythmias.

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care.

Characterizing the arrhythmogenic substrate in personalized models of atrial fibrillation: sensitivity to mesh resolution and pacing protocol in AF models.

AIMS: Computationally guided persistent atrial fibrillation (PsAF) ablation has emerged as an alternative to conventional treatment planning. To make this approach scalable, computational cost and the time required to conduct simulations must be minimized while maintaining predictive accuracy. Here, we assess the sensitivity of the process to finite-element mesh resolution. We also compare methods for pacing site distribution used to evaluate inducibility arrhythmia sustained by re-entrant drivers (RDs). METHODS AND RESULTS: Simulations were conducted in low- and high-resolution models (average edge lengths: 400/350 µm) reconstructed from PsAF patients' late gadolinium enhancement magnetic resonance imaging scans. Pacing was simulated from 80 sites to assess RD inducibility. When pacing from the same site led to different outcomes in low-/high-resolution models, we characterized divergence dynamics by analysing dissimilarity index over time. Pacing site selection schemes prioritizing even spatial distribution and proximity to fibrotic tissue were evaluated. There were no RD sites observed in low-resolution models but not high-resolution models, or vice versa. Dissimilarity index analysis suggested that differences in simulation outcome arising from differences in discretization were the result of isolated conduction block incidents in one model but not the other; this never led to RD sites unique to one mesh resolution. Pacing site selection based on fibrosis proximity led to the best observed trade-off between number of stimulation locations and predictive accuracy. CONCLUSION: Simulations conducted in meshes with 400 µm average edge length and ∼40 pacing sites proximal to fibrosis are sufficient to reveal the most comprehensive possible list of RD sites, given feasibility constraints.

Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification.

Deep learning (DL) has achieved promising performance in detecting common abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic redundancy exists in the 12-lead ECG, which could impose a systematic overfitting on DL, causing poor generalization. We, therefore, hypothesized that finding an optimal lead subset of the 12-lead ECG to eliminate the redundancy would help improve the generalizability of DL-based models. In this study, we developed and evaluated a DL-based model that has a feature extraction stage, an ECG-lead subset selection stage and a decision-making stage to automatically interpret multiple common ECG abnormality types. The data analysed in this study consisted of 6877 12-lead ECG recordings from CPSC 2018 (labelled as normal rhythm or eight types of ECG abnormalities, split into training (approx. 80%), validation (approx. 10%) and test (approx. 10%) sets) and 3998 12-lead ECG recordings from PhysioNet/CinC 2020 (labelled as normal rhythm or four types of ECG abnormalities, used as external text set). The ECG-lead subset selection module was introduced within the proposed model to efficiently constrain model complexity. It detected an optimal 4-lead ECG subset consisting of leads II, aVR, V1 and V4. The proposed model using the optimal 4-lead subset significantly outperformed the model using the complete 12-lead ECG on the validation set and on the external test dataset. The results demonstrated that our proposed model successfully identified an optimal subset of 12-lead ECG; the resulting 4-lead ECG subset improves the generalizability of the DL model in ECG abnormality interpretation. This study provides an outlook on what channels are necessary to keep and which ones may be ignored when considering an automated detection system for cardiac ECG abnormalities. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Feasibility study shows concordance between image-based virtual-heart ablation targets and predicted ECG-based arrhythmia exit-sites.

INTRODUCTION: We recently developed two noninvasive methodologies to help guide VT ablation: population-derived automated VT exit localization (PAVEL) and virtual-heart arrhythmia ablation targeting (VAAT). We hypothesized that while very different in their nature, limitations, and type of ablation targets (substrate-based vs. clinical VT), the image-based VAAT and the ECG-based PAVEL technologies would be spatially concordant in their predictions. OBJECTIVE: The objective is to test this hypothesis in ischemic cardiomyopathy patients in a retrospective feasibility study. METHODS: Four post-infarct patients who underwent LV VT ablation and had pre-procedural LGE-CMRs were enrolled. Virtual hearts with patient-specific scar and border zone identified potential VTs and ablation targets. Patient-specific PAVEL based on a population-derived statistical method localized VT exit sites onto a patient-specific 238-triangle LV endocardial surface. RESULTS: Ten induced VTs were analyzed and 9-exit sites were localized by PAVEL onto the patient-specific LV endocardial surface. All nine predicted VT exit sites were in the scar border zone defined by voltage mapping and spatially correlated with successful clinical lesions. There were 2.3 ± 1.9 VTs per patient in the models. All five VAAT lesions fell within regions ablated clinically. VAAT targets correlated well with 6 PAVEL-predicted VT exit sites. The distance between the center of the predicted VT-exit-site triangle and nearest corresponding VAAT ablation lesion was 10.7 ± 7.3 mm. CONCLUSIONS: VAAT targets are concordant with the patient-specific PAVEL-predicted VT exit sites. These findings support investigation into combining these two complementary technologies as a noninvasive, clinical tool for targeting clinically induced VTs and regions likely to harbor potential VTs.

Characterizing Conduction Channels in Postinfarction Patients Using a Personalized Virtual Heart.

Patients with myocardial infarction have an abundance of conduction channels (CC); however, only a small subset of these CCs sustain ventricular tachycardia (VT). Identifying these critical CCs (CCCs) in the clinic so that they can be targeted by ablation remains a significant challenge. The objective of this study is to use a personalized virtual-heart approach to conduct a three-dimensional (3D) assessment of CCCs sustaining VTs of different morphologies in these patients, to investigate their 3D structural features, and to determine the optimal ablation strategy for each VT. To achieve these goals, ventricular models were constructed from contrast enhanced magnetic resonance imagings of six postinfarction patients. Rapid pacing induced VTs in each model. CCCs that sustained different VT morphologies were identified. CCCs' 3D structure and type and the resulting rotational electrical activity were examined. Ablation was performed at the optimal part of each CCC, aiming to terminate each VT with a minimal lesion size. Predicted ablation locations were compared to clinical. Analyzing the simulation results, we found that the observed VTs in each patient model were sustained by a limited number (2.7 ± 1.2) of CCCs. Further, we identified three types of CCCs sustaining VTs: I-type and T-type channels, with all channel branches bounded by scar, and functional reentry channels, which were fully or partially bounded by conduction block surfaces. The different types of CCCs accounted for 43.8, 18.8, and 37.4% of all CCCs, respectively. The mean narrowest width of CCCs or a branch of CCC was 9.7 ± 3.6 mm. Ablation of the narrowest part of each CCC was sufficient to terminate VT. Our results demonstrate that a personalized virtual-heart approach can determine the possible VT morphologies in each patient and identify the CCCs that sustain reentry. The approach can aid clinicians in identifying accurately the optimal VT ablation targets in postinfarction patients.

New insights on the cardiac safety factor: Unraveling the relationship between conduction velocity and robustness of propagation.

Cardiac conduction disturbances are linked with arrhythmia development. The concept of safety factor (SF) has been derived to describe the robustness of conduction, but the usefulness of this metric has been constrained by several limitations. For example, due to the difficulty of measuring the necessary input variables, SF calculations have only been applied to synthetic data. Moreover, quantitative validation of SF is lacking; specifically, the practical meaning of particular SF values is unclear, aside from the fact that propagation failure (i.e., conduction block) is characterized by SF < 1. This study aims to resolve these limitations for our previously published SF formulation and explore its relationship to relevant electrophysiological properties of cardiac tissue. First, HL-1 cardiomyocyte monolayers were grown on multi-electrode arrays and the robustness of propagation was estimated using extracellular potential recordings. SF values reconstructed purely from experimental data were largely between 1 and 5 (up to 89.1% of sites characterized). This range is consistent with values derived from synthetic data, proving that the formulation is sound and its applicability is not limited to analysis of computational models. Second, for simulations conducted in 1-, 2-, and 3-dimensional tissue blocks, we calculated true SF values at locations surrounding the site of current injection for sub- and supra-threshold stimuli and found that they differed from values estimated by our SF formulation by <10%. Finally, we examined SF dynamics under conditions relevant to arrhythmia development in order to provide physiological insight. Our analysis shows that reduced conduction velocity (Θ) caused by impaired intrinsic cell-scale excitability (e.g., due to sodium current a loss-of-function mutation) is associated with less robust conduction (i.e., lower SF); however, intriguingly, Θ variability resulting from modulation of tissue scale conductivity has no effect on SF. These findings are supported by analytic derivation of the relevant relationships from first principles. We conclude that our SF formulation, which can be applied to both experimental and synthetic data, produces values that vary linearly with the excess charge needed for propagation. SF calculations can provide insights helpful in understanding the initiation and perpetuation of cardiac arrhythmia.

Early somatic mosaicism is a rare cause of long-QT syndrome.

Somatic mosaicism, the occurrence and propagation of genetic variation in cell lineages after fertilization, is increasingly recognized to play a causal role in a variety of human diseases. We investigated the case of life-threatening arrhythmia in a 10-day-old infant with long QT syndrome (LQTS). Rapid genome sequencing suggested a variant in the sodium channel NaV1.5 encoded by SCN5A, NM_000335:c.5284G > T predicting p.(V1762L), but read depth was insufficient to be diagnostic. Exome sequencing of the trio confirmed read ratios inconsistent with Mendelian inheritance only in the proband. Genotyping of single circulating leukocytes demonstrated the mutation in the genomes of 8% of patient cells, and RNA sequencing of cardiac tissue from the infant confirmed the expression of the mutant allele at mosaic ratios. Heterologous expression of the mutant channel revealed significantly delayed sodium current with a dominant negative effect. To investigate the mechanism by which mosaicism might cause arrhythmia, we built a finite element simulation model incorporating Purkinje fiber activation. This model confirmed the pathogenic consequences of cardiac cellular mosaicism and, under the presenting conditions of this case, recapitulated 2:1 AV block and arrhythmia. To investigate the extent to which mosaicism might explain undiagnosed arrhythmia, we studied 7,500 affected probands undergoing commercial gene-panel testing. Four individuals with pathogenic variants arising from early somatic mutation events were found. Here we establish cardiac mosaicism as a causal mechanism for LQTS and present methods by which the general phenomenon, likely to be relevant for all genetic diseases, can be detected through single-cell analysis and next-generation sequencing.

Computational Identification of Ventricular Arrhythmia Risk in Pediatric Myocarditis.

Children with myocarditis have increased risk of ventricular tachycardia (VT) due to myocardial inflammation and remodeling. There is currently no accepted method for VT risk stratification in this population. We hypothesized that personalized models developed from cardiac late gadolinium enhancement magnetic resonance imaging (LGE-MRI) could determine VT risk in patients with myocarditis using a previously-validated protocol. Personalized three-dimensional computational cardiac models were reconstructed from LGE-MRI scans of 12 patients diagnosed with myocarditis. Four patients with clinical VT and eight patients without VT were included in this retrospective analysis. In each model, we incorporated a personalized spatial distribution of fibrosis and myocardial fiber orientations. Then, VT inducibility was assessed in each model by pacing rapidly from 26 sites distributed throughout both ventricles. Sustained reentrant VT was induced from multiple pacing sites in all patients with clinical VT. In the eight patients without clinical VT, we were unable to induce sustained reentry in our simulations using rapid ventricular pacing. Application of our non-invasive approach in children with myocarditis has the potential to correctly identify those at risk for developing VT.

Termination of re-entrant atrial tachycardia via optogenetic stimulation with optimized spatial targeting: insights from computational models.

KEY POINTS: Optogenetics has emerged as a potential alternative to electrotherapy for treating heart rhythm disorders, but its applicability for terminating atrial arrhythmias remains largely unexplored. We used computational models reconstructed from clinical MRI scans of fibrotic patient atria to explore the feasibility of optogenetic termination of atrial tachycardia (AT), comparing two different illumination strategies: distributed vs. targeted. We show that targeted optogenetic stimulation based on automated, non-invasive flow-network analysis of patient-specific re-entry morphology may be a reliable approach for identifying the optimal illumination target in each individual (i.e. the critical AT isthmus). The above-described approach yields very high success rates (up to 100%) and requires dramatically less input power than distributed illumination We conclude that simulations in patient-specific models show that targeted light pulses lasting longer than the AT cycle length can efficiently and reliably terminate AT if the human atria can be successfully light-sensitized via gene delivery of ChR2. ABSTRACT: Optogenetics has emerged as a potential alternative to electrotherapy for treating arrhythmia, but feasibility studies have been limited to ventricular defibrillation via epicardial light application. Here, we assess the efficacy of optogenetic atrial tachycardia (AT) termination in human hearts using a strategy that targets for illumination specific regions identified in an automated manner. In three patient-specific models reconstructed from late gadolinium-enhanced MRI scans, we simulated channelrhodopsin-2 (ChR2) expression via gene delivery. In all three models, we attempted to terminate re-entrant AT (induced via rapid pacing) via optogenetic stimulation. We compared two strategies: (1) distributed illumination of the endocardium by multi-optrode grids (number of optrodes, Nopt  = 64, 128, 256) and (2) targeted illumination of the critical isthmus, which was identified via analysis of simulated activation patterns using an algorithm based on flow networks. The illuminated area and input power were smaller for the targeted approach (19-57.8 mm2 ; 0.6-1.8 W) compared to the sparsest distributed arrays (Nopt  = 64; 124.9 ± 6.3 mm2 ; 3.9 ± 0.2 W). AT termination rates for distributed illumination were low, ranging from <5% for short pulses (1/10 ms long) to ∼20% for longer stimuli (100/1000 ms). When we attempted to terminate the same AT episodes with targeted illumination, outcomes were similar for short pulses (1/10 ms long: 0% success) but improved for longer stimuli (100 ms: 54% success; 1000 ms: 90% success). We conclude that simulations in patient-specific models show that light pulses lasting longer than the AT cycle length can efficiently and reliably terminate AT in atria light-sensitized via gene delivery. We show that targeted optogenetic stimulation based on analysis of AT morphology may be a reliable approach for defibrillation and requires less power than distributed illumination.

Increased thin filament activation enhances alternans in human chronic atrial fibrillation.

Action potential duration (APD) alternans (APD-ALT), defined as beat-to-beat oscillations in APD, has been proposed as an important clinical marker for chronic atrial fibrillation (cAF) risk when it occurs at pacing rates of 120-200 beats/min. Although the ionic mechanisms for occurrence of APD-ALT in human cAF at these clinically relevant rates have been investigated, little is known about the effects of myofilament protein kinetics on APD-ALT. Therefore, we used computer simulations of single cell function to explore whether remodeling in myofilament protein kinetics in human cAF alters the occurrence of APD-ALT and to uncover how these mechanisms are affected by sarcomere length and the degree of cAF-induced myofilament remodeling. Mechanistically based, bidirectionally coupled electromechanical models of human right and left atrial myocytes were constructed, incorporating both ionic and myofilament remodeling associated with cAF. By comparing results from our electromechanical model with those from the uncoupled ionic model, we found that intracellular Ca2+ concentration buffering of troponin C has a dampening effect on the magnitude of APD-ALT (APD-ANM) at slower rates (150 beats/min) due to the cooperativity between strongly bound cross-bridges and Ca2+-troponin C binding affinity. We also discovered that cAF-induced enhanced thin filament activation enhanced APD-ANM at these clinically relevant heart rates (150 beats/min). In addition, longer sarcomere lengths increased APD-ANM, suggesting that atrial stretch is an important modulator of APD-ALT. Together, these findings demonstrate that myofilament kinetics mechanisms play an important role in altering APD-ALT in human cAF. NEW & NOTEWORTHY Using a single cell simulation approach, we explored how myofilament protein kinetics alter the formation of alternans in action potential duration (APD) in human myocytes with chronic atrial fibrillation remodeling. We discovered that enhanced thin filament activation and longer sarcomere lengths increased the magnitude of APD alternans at clinically important pacing rates of 120-200 beats/min. Furthermore, we found that altered intracellular Ca2+ concentration buffering of troponin C has a dampening effect on the magnitude of APD alternans.