Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern

Clinicopathological Bird's-Eye View of Left Atrial Myocardial Fibrosis in 121 Patients With Persistent Atrial Fibrillation: Developing Architecture and Main Cellular Players

BACKGROUND

Scientific research on atrial fibrosis in atrial fibrillation (AF) has mainly focused on quantitative or molecular features. The purpose of this study was to perform a clinicoarchitectural/structural investigation of fibrosis to provide one key to understanding the electrophysiological/clinical aspects of AF.

METHODS

We characterized the fibrosis (amount, architecture, cellular components, and ultrastructure) in left atrial biopsies from 121 patients with persistent/long-lasting persistent AF (group 1; 59 males; 60±11 years; 91 mitral disease-related AF, 30 nonmitral disease-related AF) and from 39 patients in sinus rhythm with mitral valve regurgitation (group 2; 32 males; 59±12 years). Ten autopsy hearts served as controls.

RESULTS

Qualitatively, the fibrosis exhibited the same characteristics in all cases and displayed particular architectural scenarios (which we arbitrarily subdivided into 4 stages) ranging from isolated foci to confluent sclerotic areas. The percentage of fibrosis was larger and at a more advanced stage in group 1 versus group 2 and, within group 1, in patients with rheumatic disease versus nonrheumatic cases. In patients with AF with mitral disease and no rheumatic disease, the percentage of fibrosis and the fibrosis stages correlated with both left atrial volume index and AF duration. The fibrotic areas mainly consisted of type I collagen with only a minor cellular component (especially fibroblasts/myofibroblasts; average value range 69-150 cells/mm², depending on the areas in AF biopsies). A few fibrocytes-circulating and bone marrow-derived mesenchymal cells-were also detectable. The fibrosis-entrapped cardiomyocytes showed sarcolemmal damage and connexin 43 redistribution/internalization.

CONCLUSIONS

Atrial fibrosis is an evolving and inhomogeneous histological/architectural change that progresses through different stages ranging from isolated foci to confluent sclerotic zones which-seemingly-constrain impulse conduction across restricted regions of electrotonically coupled cardiomyocytes. The fibrotic areas mainly consist of type I collagen extracellular matrix and, only to a lesser extent, mesenchymal cells.

Specific Electrogram Characteristics Identify the Extra-Pulmonary Vein Arrhythmogenic Sources of Persistent Atrial Fibrillation - Characterization of the Arrhythmogenic Electrogram Patterns During Atrial Fibrillation and Sinus Rhythm

Identification of atrial sites that perpetuate atrial fibrillation (AF), and ablation thereof terminates AF, is challenging. We hypothesized that specific electrogram (EGM) characteristics identify AF-termination sites (AFTS). Twenty-one patients in whom low-voltage-guided ablation after pulmonary vein isolation terminated clinical persistent AF were included. Patients were included if short RF-delivery for <8sec at a given atrial site was associated with acute termination of clinical persistent AF. EGM-characteristics at 21 AFTS, 105 targeted sites without termination and 105 non-targeted control sites were analyzed. Alteration of EGM-characteristics by local fibrosis was evaluated in a three-dimensional high resolution (100 µm)-computational AF model. AFTS demonstrated lower EGM-voltage, higher EGM-cycle-length-coverage, shorter AF-cycle-length and higher pattern consistency than control sites (0.49 ± 0.39 mV vs. 0.83 ± 0.76 mV, p < 0.0001; 79 ± 16% vs. 59 ± 22%, p = 0.0022; 173 ± 49 ms vs. 198 ± 34 ms, p = 0.047; 80% vs. 30%, p < 0.01). Among targeted sites, AFTS had higher EGM-cycle-length coverage, shorter local AF-cycle-length and higher pattern consistency than targeted sites without AF-termination (79 ± 16% vs. 63 ± 23%, p = 0.02; 173 ± 49 ms vs. 210 ± 44 ms, p = 0.002; 80% vs. 40%, p = 0.01). Low voltage (0.52 ± 0.3 mV) fractionated EGMs (79 ± 24 ms) with delayed components in sinus rhythm (‘atrial late potentials’, respectively ‘ALP’) were observed at 71% of AFTS. EGMs recorded from fibrotic areas in computational models demonstrated comparable EGM-characteristics both in simulated AF and sinus rhythm. AFTS may therefore be identified by locally consistent, fractionated low-voltage EGMs with high cycle-length-coverage and rapid activity in AF, with low-voltage, fractionated EGMs with delayed components/ ‘atrial late potentials’ (ALP) persisting in sinus rhythm.

Understanding the transition from paroxysmal to persistent atrial fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhytmia, characterized by the chaotic motion of electrical wavefronts in the atria. In clinical practice, AF is classified under two primary categories: paroxysmal AF, short intermittent episodes separated by periods of normal electrical activity; and persistent AF, longer uninterrupted episodes of chaotic electrical activity. However, the precise reasons why AF in a given patient is paroxysmal or persistent is poorly understood. Recently, we have introduced the percolation-based Christensen-Manani-Peters (CMP) model of AF which naturally exhibits both paroxysmal and persistent AF, but precisely how these differences emerge in the model is unclear. In this paper, we dissect the CMP model to identify the cause of these different AF classifications. Starting from a mean-field model where we describe AF as a simple birth-death process, we add layers of complexity to the model and show that persistent AF arises from reentrant circuits which exhibit an asymmetry in their probability of activation relative to deactivation. As a result, different simulations generated at identical model parameters can exhibit fibrillatory episodes spanning several orders of magnitude from a few seconds to months. These findings demonstrate that diverse, complex fibrillatory dynamics can emerge from very simple dynamics in models of AF.

Utility of Cardiac MRI in Atrial Fibrillation Management

Advances in cardiac magnetic resonance (CMR) techniques and image acquisition have made it an excellent tool in the assessment of atrial myopathy. Remolding of the left atrium is the mainstay of atrial fibrillation (AF) development and its progression. CMR can detect phasic atrial volumes, atrial function, and atrial fibrosis using cine, and contrast-enhanced or non-contrast-enhanced images. These abilities make CMR a versatile and extraordinary tool in management of patients with AF including for risk stratification, ablation prognostication and planning, and assessment of stroke risk. We review the latest advancements in utility of CMR in management of patients with AF.

Sacubitril/valsartan attenuates atrial electrical and structural remodelling in a rabbit model of atrial fibrillation

Atrial structural and electrical remodelling play important roles in atrial fibrillation (AF). Sacubitril/valsartan attenuates cardiac remodelling in heart failure. However, the effect of sacubitril/valsartan on AF is unclear. The aim of this study was to evaluate the effect of sacubitril/valsartan on atrial electrical and structural remodelling in AF and investigate the underlying mechanism of action. Thirty-three rabbits were randomized into sham, RAP, and sac/val groups. HL-1 cells were subjected to control treatment or rapid pacing with or without LBQ657 and valsartan. Echocardiography, atrial electrophysiology, and histological examination were performed. The concentration of Ca²⁺ and expression levels of calcineurin, NFAT, p-NFAT, Cav1.2, collagen Ⅰ and Ⅲ, ANP, BNP, CNP, NT-proBNP, and ST2 in HL-1 cells, and I_caL in left atrial cells, were determined. We observed that compared to that in the sham group, the atrium and right ventricle were enlarged, myocardial fibrosis was markedly higher, AF inducibility was significantly elevated, and atrial effective refractory periods were shortened in the RAP group. These effects were significantly reversed by sacubitril/valsartan. Compared to that in the sham group, collagen Ⅰ and Ⅲ, NT-proBNP, ST2, calcineurin, and NFAT were significantly up-regulated, while p-NFAT and Ca_v1.2 were down-regulated in the RAP group, and sacubitril/valsartan inhibited these changes. Ca²⁺ concentration increased and I_CaL density decreased in in vivo and in vitro AF models, reversed by sacubitril/valsartan. Sacubitril/valsartan attenuates atrial electrical remodelling and ameliorates structure remodelling in AF. This study paves the way for the possibility of clinical use of sacubitril/valsartan in AF patients.

Ventricular arrhythmia risk prediction in repaired Tetralogy of Fallot using personalized computational cardiac models

BACKGROUND

Adults with repaired tetralogy of Fallot (rTOF) are at increased risk for ventricular tachycardia (VT) due to fibrotic remodeling of the myocardium. However, the current clinical guidelines for VT risk stratification and subsequent implantable cardioverter-defibrillator deployment for primary prevention of sudden cardiac death in rTOF remain inadequate.

OBJECTIVE

The purpose of this study was to determine the feasibility of using an rTOF-specific virtual-heart approach to identify patients stratified incorrectly as being at low VT risk by current clinical criteria.

METHODS

This multicenter retrospective pilot study included 7 adult rTOF patients who were considered low risk for VT based on clinical criteria. Patient-specific computational heart models were generated from late gadolinium enhanced magnetic resonance imaging (LGE-MRI), incorporating the individual distribution of rTOF fibrotic remodeling in both ventricles. Simulations of rapid pacing determined VT inducibility. Model creation and simulations were performed by operators blinded to clinical outcome.

RESULTS

Two patients in the study experienced clinical VT. The virtual hearts constructed from LGE-MRI scans of 7 rTOF patients correctly predicted reentrant VT in the models from VT-positive patients and no arrhythmia in those from VT-negative patients. There were no statistically significant differences in clinical criteria commonly used to assess VT risk, including QRS duration and age, between patients who did and those who did not experience clinical VT.

CONCLUSION

This study demonstrates the feasibility of image-based virtual-heart modeling in patients with congenital heart disease and structurally abnormal hearts. It highlights the potential of the methodology to improve VT risk stratification in patients with rTOF.

Epicardial Fibrosis Explains Increased Endo-Epicardial Dissociation and Epicardial Breakthroughs in Human Atrial Fibrillation

Background

Atrial fibrillation (AF) is accompanied by progressive epicardial fibrosis, dissociation of electrical activity between the epicardial layer and the endocardial bundle network, and transmural conduction (breakthroughs). However, causal relationships between these phenomena have not been demonstrated yet. Our goal was to test the hypothesis that epicardial fibrosis suffices to increase endo–epicardial dissociation (EED) and breakthroughs (BT) during AF.

Methods

We simulated the effect of fibrosis in the epicardial layer on EED and BT in a detailed, high-resolution, three-dimensional model of the human atria with realistic electrophysiology. The model results were compared with simultaneous endo–epicardial mapping in human atria. The model geometry, specifically built for this study, was based on MR images and histo-anatomical studies. Clinical data were obtained in four patients with longstanding persistent AF (persAF) and three patients without a history of AF.

Results

The AF cycle length (AFCL), conduction velocity (CV), and EED were comparable in the mapping studies and the simulations. EED increased from 24.1 ± 3.4 to 56.58 ± 6.2% (p < 0.05), and number of BTs per cycle from 0.89 ± 0.55 to 6.74 ± 2.11% (p < 0.05), in different degrees of fibrosis in the epicardial layer. In both mapping data and simulations, EED correlated with prevalence of BTs. Fibrosis also increased the number of fibrillation waves per cycle in the model.

Conclusion

A realistic 3D computer model of AF in which epicardial fibrosis was increased, in the absence of other pathological changes, showed increases in EED and epicardial BT comparable to those in longstanding persAF. Thus, epicardial fibrosis can explain both phenomena.

In situ procedure for high-efficiency computational modeling of atrial fibrillation reflecting personal anatomy, fiber orientation, fibrosis, and electrophysiology

We previously reported the feasibility and efficacy of a simulation-guided clinical catheter ablation of atrial fibrillation (AF) in an in-silico AF model. We developed a highly efficient realistic AF model reflecting the patient endocardial voltage and local conduction and tested its clinical feasibility. We acquired > 500 endocardial bipolar electrograms during right atrial pacing at the beginning of the AF ablation procedures. Based on the clinical bipolar electrograms, we generated simulated voltage maps by applying fibrosis and local activation maps adjusted for the fiber orientation. The software's accuracy (CUVIA2.5) was retrospectively tested in 17 patients and feasibility prospectively in 10 during clinical AF ablation. Results: We found excellent correlations between the clinical and simulated voltage maps (R = 0.933, p < 0.001) and clinical and virtual local conduction (R = 0.958, p < 0.001). The proportion of virtual local fibrosis was 15.4, 22.2, and 36.9% in the paroxysmal AF, persistent AF, and post-pulmonary vein isolation (PVI) states, respectively. The reconstructed virtual bipolar electrogram exhibited a relatively good similarities of morphology to the local clinical bipolar electrogram (R = 0.60 ± 0.08, p < 0.001). Feasibility testing revealed an in situ procedural computing time from the clinical data acquisition to wave-dynamics analyses of 48.2 ± 4.9 min. All virtual analyses were successfully achieved during clinical PVI procedures. We developed a highly efficient, realistic, in situ procedural simulation model reflective of individual anatomy, fiber orientation, fibrosis, and electrophysiology that can be applied during AF ablation.

Challenges Associated with Interpreting Mechanisms of AF

Determining optimal treatment strategies for complex arrhythmogenesis in AF is confounded by the lack of consensus regarding the mechanisms causing AF. Studies report different mechanisms for AF, ranging from hierarchical drivers to anarchical multiple activation wavelets. Differences in the assessment of AF mechanisms are likely due to AF being recorded across diverse models using different investigational tools, spatial scales and clinical populations. The authors review different AF mechanisms, including anatomical and functional re-entry, hierarchical drivers and anarchical multiple wavelets. They then describe different cardiac mapping techniques and analysis tools, including activation mapping, phase mapping and fibrosis identification. They explain and review different data challenges, including differences between recording devices in spatial and temporal resolutions, spatial coverage and recording surface, and report clinical outcomes using different data modalities. They suggest future research directions for investigating the mechanisms underlying human AF.

Cardiac Fibroblast Diversity

Cardiac fibrosis is a pathological condition that occurs after injury and during aging. Currently, there are limited means to effectively reduce or reverse fibrosis. Key to identifying methods for curbing excess deposition of extracellular matrix is a better understanding of the cardiac fibroblast, the cell responsible for collagen production. In recent years, the diversity and functions of these enigmatic cells have been gradually revealed. In this review, I outline current approaches for identifying and classifying cardiac fibroblasts. An emphasis is placed on new insights into the heterogeneity of these cells as determined by lineage tracing and single-cell sequencing in development, adult, and disease states. These recent advances in our understanding of the fibroblast provide a platform for future development of novel therapeutics to combat cardiac fibrosis.

Computational models in cardiology

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post-infarction patients

Precision Cardiology is a targeted strategy for cardiovascular disease prevention and treatment that accounts for individual variability. Computational heart modeling is one of the novel approaches that have been developed under the umbrella of Precision Cardiology. Personalized computational modeling of patient hearts has made strides in the development of models that incorporate the individual geometry and structure of the heart as well as other patient-specific information. Of these developments, one of the potentially most impactful is the research aimed at noninvasively predicting the targets of ablation of lethal arrhythmia, ventricular tachycardia (VT), using patient-specific models. The approach has been successfully applied to patients with ischemic cardiomyopathy in proof-of-concept studies. The goal of this paper is to review the strategies for computational VT ablation guidance in ischemic cardiomyopathy patients, from model developments to the intricacies of the actual clinical application. To provide context in describing the road these computational modeling applications have undertaken, we first review the state of the art in VT ablation in the clinic, emphasizing the benefits that personalized computational prediction of ablation targets could bring to the clinical electrophysiology practice. This article is characterized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Translational, Genomic, and Systems Medicine > Translational Medicine.

Pharmacological Inhibition of Serine Proteases to Reduce Cardiac Inflammation and Fibrosis in Atrial Fibrillation

Systemic inflammation correlates with an increased risk of atrial fibrillation (AF) and thrombogenesis. Systemic inflammation alters vessel permeability, allowing inflammatory and immune cell migration toward target organs, including the heart. Among inflammatory cells infiltrating the atria, macrophages and mast cell have recently attracted the interest of basic researchers due to the pathogenic mechanisms triggered by their activation. This chemotactic invasion is likely implicated in short- and long-term changes in cardiac cell-to-cell communication and in triggering fibrous tissue accumulation in the atrial myocardium and electrophysiological re-arrangements of atrial cardiomyocytes, thus favoring the onset and progression of AF. Serine proteases are a large and heterogeneous class of proteases involved in several processes that are important for cardiac function and are involved in cardiac diseases, such as (i) coagulation, (ii) fibrinolysis, (iii) extracellular matrix degradation, (iv) activation of receptors (i.e., protease-activated receptors [PPARs]), and (v) modulation of the activity of endogenous signals. The recognition of serine proteases substrates and their involvement in inflammatory/profibrotic mechanisms allowed the identification of novel cardio-protective mechanisms for commonly used drugs that inhibit serine proteases. The aim of this review is to summarize knowledge on the role of inflammation and fibrosis as determinants of AF. Moreover, we will recapitulate current findings on the role of serine proteases in the pathogenesis of AF and the possible beneficial effects of drugs inhibiting serine proteases in reducing the risk of AF through decrease of cardiac inflammation and fibrosis. These drugs include thrombin and factor Xa inhibitors (used as oral anticoagulants), dipeptidyl-peptidase 4 (DPP4) inhibitors, used for type-2 diabetes, as well as novel experimental inhibitors of mast cell chymases.

Identifying Potential Re-Entrant Circuit Locations From Atrial Fibre Maps

Re-entrant circuits have been identified as potential drivers of atrial fibrillation (AF). In this paper, we develop a novel computational framework for finding the locations of re-entrant circuits from high resolution fibre orientation data. The technique follows a statistical approach whereby we generate continuous fibre tracts across the tissue and couple adjacent fibres stochastically if they are within a given distance of each other. By varying the connection distance, we identify which regions are most susceptible to forming re-entrant circuits if muscle fibres are uncoupled, through the action of fibrosis or otherwise. Our results highlight the sleeves of the pulmonary veins, the posterior left atrium and the left atrial appendage as the regions most susceptible to re-entrant circuit formation. This is consistent with known risk locations in clinical AF. If the model can be personalised for individual patients undergoing ablation, future versions may be able to suggest suitable ablation targets.

Acetylcholine Delays Atrial Activation to Facilitate Atrial Fibrillation

Background

Acetylcholine (ACh) shortens action potential duration (APD) in human atria. APD shortening facilitates atrial fibrillation (AF) by reducing the wavelength for reentry. However, the influence of ACh on electrical conduction in human atria and its contribution to AF are unclear, particularly when combined with impaired conduction from interstitial fibrosis.

Objective

To investigate the effect of ACh on human atrial conduction and its role in AF with computational, experimental, and clinical approaches.

Methods

S1S2 pacing (S1 = 600 ms and S2 = variable cycle lengths) was applied to the following human AF computer models: a left atrial appendage (LAA) myocyte to quantify the effects of ACh on APD, maximum upstroke velocity (V _max ), and resting membrane potential (RMP); a monolayer of LAA myocytes to quantify the effects of ACh on conduction; and 3) an intact left atrium (LA) to determine the effects of ACh on arrhythmogenicity. Heterogeneous ACh and interstitial fibrosis were applied to the monolayer and LA models. To corroborate the simulations, APD and RMP from isolated human atrial myocytes were recorded before and after 0.1 μM ACh. At the tissue level, LAAs from AF patients were optically mapped ex vivo using Di-4-ANEPPS. The difference in total activation time (AT) was determined between AT initially recorded with S1 pacing, and AT recorded during subsequent S1 pacing without (n = 6) or with (n = 7) 100 μM ACh.

Results

In LAA myocyte simulations, S1 pacing with 0.1 μM ACh shortened APD by 41 ms, hyperpolarized RMP by 7 mV, and increased V _max by 27 mV/ms. In human atrial myocytes, 0.1 μM ACh shortened APD by 48 ms, hyperpolarized RMP by 3 mV, and increased V _max by 6 mV/ms. In LAA monolayer simulations, S1 pacing with ACh hyperpolarized RMP to delay total AT by 32 ms without and 35 ms with fibrosis. This led to unidirectional conduction block and sustained reentry in fibrotic LA with heterogeneous ACh during S2 pacing. In AF patient LAAs, S1 pacing with ACh increased total AT from 39.3 ± 26 ms to 71.4 ± 31.2 ms (p = 0.036) compared to no change without ACh (56.7 ± 29.3 ms to 50.0 ± 21.9 ms, p = 0.140).

Conclusion

In fibrotic atria with heterogeneous parasympathetic activation, ACh facilitates AF by shortening APD and slowing conduction to promote unidirectional conduction block and reentry.

Patient-specific simulations predict efficacy of ablation of interatrial connections for treatment of persistent atrial fibrillation

Aims

Treatments for persistent atrial fibrillation (AF) offer limited efficacy. One potential strategy aims to return the right atrium (RA) to sinus rhythm (SR) by ablating interatrial connections (IAC) to isolate the atria, but there is limited clinical data to evaluate this ablation approach. We aimed to use simulation to evaluate and predict patient-specific suitability for ablation of IAC to treat AF.

Methods and results

Persistent AF was simulated in 12 patient-specific geometries, incorporating electrophysiological heterogeneity and fibres, with IAC at Bachmann’s bundle, the coronary sinus, and fossa ovalis. Simulations were performed to test the effect of left atrial (LA)-to-RA frequency gradient and fibrotic remodelling on IAC ablation efficacy. During AF, we simulated ablation of one, two, or all three IAC, with or without pulmonary vein isolation and determined if this altered or terminated the arrhythmia. For models without structural remodelling, ablating all IAC terminated RA arrhythmia in 83% of cases. Models with the LA-to-RA frequency gradient removed had an increased success rate (100% success). Ablation of IACs is less effective in cases with fibrotic remodelling (interstitial fibrosis 50% success rate; combination remodelling 67%). Mean number of phase singularities in the RA was higher pre-ablation for IAC failure (success 0.6 ± 0.8 vs. failure 3.2 ± 2.5, P < 0.001).

Conclusion

This simulation study predicts that IAC ablation is effective in returning the RA to SR for many cases. Patient-specific modelling approaches have the potential to stratify patients prior to ablation by predicting if drivers are located in the LA or RA. We present a platform for predicting efficacy and informing patient selection for speculative treatments.

Arrhythmia dynamics in computational models of the atria following virtual ablation of re-entrant drivers

Aims

Efforts to improve ablation success rates in persistent atrial fibrillation (AF) patients by targeting re-entrant driver (RD) sites have been hindered by weak mechanistic understanding regarding emergent RDs localization following initial fibrotic substrate modification. This study aimed to systematically assess arrhythmia dynamics after virtual ablation of RD sites in computational models.

Methods and results

Simulations were conducted in 12 patient-specific atrial models reconstructed from pre-procedure late gadolinium-enhanced magnetic resonance imaging scans. In a previous study involving these same models, we comprehensively characterized pre-ablation RDs in simulations conducted with either 'average human AF'-based electrophysiology (i.e. EPavg) or ±10% action potential duration or conduction velocity (i.e. EPvar). Re-entrant drivers seen under the EPavg condition were virtually ablated and the AF initiation protocol was re-applied. Twenty-one emergent RDs were observed in 9/12 atrial models (1.75 ± 1.35 emergent RDs per model); these dynamically localized to boundary regions between fibrotic and non-fibrotic tissue. Most emergent RD locations (15/21, 71.4%) were within 0.1 cm of sites where RDs were seen pre-ablation in simulations under EPvar conditions. Importantly, this suggests that the level of uncertainty in our models' ability to predict patient-specific ablation targets can be substantially mitigated by running additional simulations that include virtual ablation of RDs. In 7/12 atrial models, at least one episode of macro-reentry around ablation lesion(s) was observed.

Conclusion

Arrhythmia episodes after virtual RD ablation are perpetuated by both emergent RDs and by macro-reentrant circuits formed around lesions. Custom-tailoring of ablation procedures based on models should take steps to mitigate these sources of AF recurrence.

Computationally guided personalized targeted ablation of persistent atrial fibrillation

Atrial fibrillation (AF) — the most common arrhythmia — significantly increases the risk of stroke and heart failure. Although catheter ablation can restore normal heart rhythms, patients with persistent AF who develop atrial fibrosis often undergo multiple failed ablations and thus increased procedural risks. Here, we present personalized computational modelling for the reliable predetermination of ablation targets, which are then used to guide the ablation procedure in patients with persistent AF and atrial fibrosis. We first show that a computational model of the atria of patients identifies fibrotic tissue that if ablated will not sustain AF. We then integrated the target-ablation sites in a clinical-mapping system, and tested its feasibility in 10 patients with persistent AF. The computational prediction of ablation targets avoids lengthy electrical mapping and could improve the accuracy and efficacy of targeted AF ablation in patients whilst eliminating the need for repeat procedures.

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.

Heterogeneous Effects of Fibroblast-Myocyte Coupling in Different Regions of the Human Atria Under Conditions of Atrial Fibrillation

Background: Atrial fibrillation (AF), the most common cardiac arrhythmia, is characterized by alteration of the action potential (AP) propagation. Under persistent AF, myocytes undergo electrophysiological and structural remodeling, which involves fibroblast proliferation and differentiation, modifying the substrate for AP propagation. The aim of this study was to analyze the effects on the AP of fibroblast-myocyte coupling during AF and its propagation in different regions of the atria.

Methods: Isolated myocytes were coupled to different numbers of fibroblasts using the established AP models and tissue simulations were performed by randomly distributing fibroblasts. Fibroblast formulations were updated to match recent experimental data. Major ion current conductances of the myocyte model were modified to simulate AP heterogeneity in four different atrial regions (right atrium posterior wall, crista terminalis, left atrium posterior wall, and pulmonary vein) according to experimental and computational studies.

Results: The results of the coupled myocyte-fibroblast simulations suggest that a more depolarized membrane potential and higher fibroblast membrane capacitance have a greater impact on AP duration and myocyte maximum depolarization velocity. The number of coupled fibroblasts and the stimulation frequency are determining factors in altering myocyte AP. Strand simulations show that conduction velocity tends to homogenize in all regions, while the left atrium is more likely to be affected by fibroblast and AP propagation block is more likely to occur. The pulmonary vein is the most affected region, even at low fibroblast densities. In 2D sheets with randomly placed fibroblasts, wavebreaks are observed in the low density (10%) central fibrotic zone and when fibroblast density increases (40%) propagation in the fibrotic region is practically blocked. At densities of 10 and 20% the width of the vulnerable window increases with respect to control but is decreased at 40%.

Conclusion: Myocyte-fibroblast coupling characteristics heterogeneously affect AP propagation and features in the different atrial zones, and myocytes from the left atria are more sensitive to fibroblast coupling.

Mapping and Imaging in Non-paroxysmal AF

Despite intense research efforts, maintenance of sinus rhythm in patients with non-paroxysmal AF remains challenging with suboptimal outcomes. A major limitation to the success of current ablation-based treatments is that our understanding of AF pathophysiology is incomplete. Advances in imaging and mapping tools have been reported to improve ablation outcomes. However, the role of these new approaches on the clinical care of patients with AF remains to be validated and better understood before wide adoption can occur. This article reviews the current techniques of imaging and mapping that can be applied in the management of patients with non-paroxysmal AF with a focus on their relevance to catheter ablation. Future applications and opportunities for new knowledge are also discussed.

Understanding AF Mechanisms Through Computational Modelling and Simulations

AF is a progressive disease of the atria, involving complex mechanisms related to its initiation, maintenance and progression. Computational modelling provides a framework for integration of experimental and clinical findings, and has emerged as an essential part of mechanistic research in AF. The authors summarise recent advancements in development of multi-scale AF models and focus on the mechanistic links between alternations in atrial structure and electrophysiology with AF. Key AF mechanisms that have been explored using atrial modelling are pulmonary vein ectopy; atrial fibrosis and fibrosis distribution; atrial wall thickness heterogeneity; atrial adipose tissue infiltration; development of repolarisation alternans; cardiac ion channel mutations; and atrial stretch with mechano-electrical feedback. They review modelling approaches that capture variability at the cohort level and provide cohort-specific mechanistic insights. The authors conclude with a summary of future perspectives, as envisioned for the contributions of atrial modelling in the mechanistic understanding of AF.

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.

A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram

OBJECTIVE

Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and abnormal propagation of the slow wave. This study aims to disambiguate the abnormally functioning wave from one of normalcy using multi-electrode abdominal waveforms of the electrogastrogram (EGG).

METHODS

Human stomach and abdominal models are extracted from computed tomography scans. Normal and abnormal slow waves are simulated along stomach surfaces. Current dipoles at the stomachs surface are propagated to virtual electrodes on the abdomen with a forward model. We establish a deep convolutional neural network (CNN) framework to classify normal and abnormal slow waves from the multi-electrode waveforms. We investigate the effects of non-idealized measurements on performance, including shifted electrode array positioning, smaller array sizes, high body mass index (BMI), and low signal-to-noise ratio (SNR). We compare the performance of our deep CNN to a linear discriminant classifier using wave propagation spatial features.

RESULTS

A deep CNN framework demonstrated robust classification, with accuracy above 90% for all SNR above 0 dB, horizontal shifts within 3 cm, vertical shifts within 6 cm, and abdominal tissue depth within 6 cm. The linear discriminant classifier was much more vulnerable to SNR, electrode placement, and BMI.

CONCLUSION

This is the first study to attempt and, moreover, succeed in using a deep CNN to disambiguate normal and abnormal gastric slow wave patterns from high-resolution EGG data.

SIGNIFICANCE

These findings suggest that multi-electrode cutaneous abdominal recordings have the potential to serve as widely deployable clinical screening tools for gastrointestinal foregut disorders.

Sensitivity of Ablation Targets Prediction to Electrophysiological Parameter Variability in Image-Based Computational Models of Ventricular Tachycardia in Post-infarction Patients

Ventricular tachycardia (VT), which could lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders in post-infarction patients and for guiding patient VT ablation. However, it remains unclear how VT dynamics and predicted ablation targets are influenced by inter-patient variability in action potential duration (APD) and conduction velocity (CV). The goal of this study was to systematically assess the effect of changes in the electrophysiological parameters on the induced VTs and predicted ablation targets in personalized models of post-infarction hearts. Simulations were conducted in 5 patient-specific left ventricular models reconstructed from late gadolinium-enhanced magnetic resonance imaging scans. We comprehensively characterized all possible pre-ablation and post-ablation VTs in simulations conducted with either an “average human VT”-based electrophysiological representation (i.e., EP_avg) or with ±10% APD or CV (i.e., EP_var); additional simulations were also executed in some models for an extended range of these parameters. The results showed that: (1) a subset of reentries (76.2–100%, depending on EP parameter set) conducted with ±10% APD/CV was observed in approximately the same locations as reentries observed in EP_avg cases; (2) emergent VTs could be induced sometimes after ablation in EP_avg models, and these emergent VTs often corresponded to the pre-ablation reentries in simulations with EP_var parameter sets. These findings demonstrate that the VT ablation target uncertainty in patient-specific ventricular models with an average representation of VT-remodeled electrophysiology is relatively low and the ablation targets stable, as the localization of the induced VTs was primarily driven by the remodeled structural substrate. Thus, personalized ventricular modeling with an average representation of infarct-remodeled electrophysiology may uncover most targets for VT ablation.

Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes

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We introduce a coordinate system for the atria based on anatomical landmarks.

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We construct the coordinates from solutions to Laplace’s equation.

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We demonstrate the mapping of both scalar and vector data between different atria.

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The coordinate system was used for registration and 2D visualisation of multimodal data.

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Patient specific meshes with atrial structures and fibre direction were constructed using just five landmark points.

Integrating spatial information about atrial physiology and anatomy in a single patient from multimodal datasets, as well as generalizing these data across patients, requires a common coordinate system. In the atria, this is challenging due to the complexity and variability of the anatomy. We aimed to develop and validate a Universal Atrial Coordinate (UAC) system for the following applications: combination and assessment of multimodal data; comparison of spatial data across patients; 2D visualization; and construction of patient specific geometries to test mechanistic hypotheses. Left and right atrial LGE-MRI data were segmented and meshed. Two coordinates were calculated for each atrium by solving Laplace’s equation, with boundary conditions assigned using five landmark points. The coordinate system was used to map spatial information between atrial meshes, including scalar fields measured using different mapping modalities, and atrial anatomic structures and fibre directions from a reference geometry. Average error in point transfer from a source mesh to a destination mesh and back again was less than 0.1 mm for the left atrium and 0.02 mm for the right atrium. Patient specific meshes were constructed using the coordinate system and phase singularity density maps from arrhythmia simulations were visualised in 2D. In conclusion, we have developed a universal atrial coordinate system allowing automatic registration of imaging and electroanatomic mapping data, 2D visualisation, and patient specific model creation.

Multiple factors influence the morphology of the bipolar electrogram: An in silico modeling study

Although bipolar electrograms (Bi-egms) are commonly used for catheter mapping and ablation of cardiac arrhythmias, the accuracy and reproducibility of Bi-egms have not been evaluated. We aimed to clarify the influence of the catheter orientation (CO), catheter contact angle (CA), local conduction velocity (CV), scar size, and catheter type on the Bi-egm morphology using an in silico 3-dimensional realistic model of atrial fibrillation. We constructed a 3-dimensional, realistic, in silico left atrial model with activation wave propagation including bipolar catheter models. Bi-egms were obtained by computing the extracellular potentials from the distal and proximal electrodes. The amplitude and width were measured on virtual Bi-egms obtained under different conditions created by changing the CO according to the wave direction, catheter-atrial wall CA, local CV, size of the non-conductive area, and catheter type. Bipolar voltages were also compared between virtual and clinically acquired Bi-egms. Bi-egm amplitudes were lower for a perpendicular than parallel CO relative to the wave direction (p<0.001), lower for a 90° than 0° CA (p<0.001), and lower for a CV of 0.13m/s than 0.48m/s (p<0.001). Larger sized non-conductive areas were associated with a decreased bipolar amplitude (p<0.001) and increased bipolar width (p<0.001). Among three commercially available catheters (Orion, Pentaray, and Thermocool), those with more narrowly spaced and smaller electrodes produced higher voltages on the virtual Bi-egms (p<0.001). Multiple factors including the CO, CA, CV, and catheter design significantly influence the Bi-egm morphology. Universal voltage cut-off values may not be appropriate for bipolar voltage-guided substrate mapping.

Cardiac arrhythmias are rhythm disorders of the heart leading to abnormal heart function. For the diagnosis and treatment of the arrhythmias, clinicians insert catheters into the heart and examine the electrical signal propagation in the heart. Among different type of catheters, bipolar catheters have two electrodes at the tip of the catheter with the signal being the difference between the two electrodes, which provides sharper signal than unipolar catheter. However, bipolar electrogram is dependent on many factors including catheter design and orientation, and consequently, knowledge of the determinants of the bipolar electrogram is needed for proper interpretation of the signal. In this study, we examined the effects of many factors on bipolar electrogram using computer simulation. Computer simulation is very useful in this type of study because, in clinical settings, it is not feasible to control each factor precisely. We quantitatively demonstrated the effects of catheter design and orientation, and cardiac wave propagation speed on bipolar electrogram.

High-resolution noncontact charge-density mapping of endocardial activation

BACKGROUND

Spatial resolution in cardiac activation maps based on voltage measurement is limited by far-field interference. Precise characterization of electrical sources would resolve this limitation; however, practical charge-based cardiac mapping has not been achieved.

METHODS

A prototype algorithm, developed from first principles of electrostatic field theory, derives charge density (CD) as a spatial representation of the true sources of the cardiac field. The algorithm processes multiple, simultaneous, noncontact voltage measurements within the cardiac chamber to inversely derive the global distribution of CD sources across the endocardial surface.

RESULTS

Comparison of CD to an established computer-simulated model of atrial conduction demonstrated feasibility in terms of spatial, temporal, and morphologic metrics. Inverse reconstruction matched simulation with median spatial errors of 1.73 mm and 2.41 mm for CD and voltage, respectively. Median temporal error was less than 0.96 ms and morphologic correlation was greater than 0.90 for both CD and voltage. Activation patterns observed in human atrial flutter reproduced those established through contact maps, with a 4-fold improvement in resolution noted for CD over voltage. Global activation maps (charge density-based) are reported in atrial fibrillation with confirmed reduction of far-field interference. Arrhythmia cycle-length slowing and termination achieved through ablation of critical points demonstrated in the maps indicates both mechanistic and pathophysiological relevance.

CONCLUSION

Global maps of cardiac activation based on CD enable classification of conduction patterns and localized nonpulmonary vein therapeutic targets in atrial fibrillation. The measurement capabilities of the approach have roles spanning deep phenotyping to therapeutic application.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01875614.

FUNDING

The National Institute for Health Research (NIHR) Translational Research Program at Royal Papworth Hospital and Acutus Medical.

Atrial Fibrosis: Translational Considerations for the Management of AF Patients

Fibrosis plays a fundamental role in the initiation and maintenance of AF, mainly due to enhanced automaticity and anisotropy-related re-entry. The identification and quantification of atrial fibrosis is achieved either preprocedurally by late gadolinium enhancement MRI or intraprocedurally using electroanatomic voltage mapping. The presence and extent of left atrial fibrosis among AF patients may influence relevant decision making regarding the need for anticoagulation, the adoption of rate versus rhythm control and mainly the type of ablation strategy that will be followed during interventional treatment. Several types of individualised substrate modifications targeting atrial fibrotic areas have been proposed, although their impact on patient outcome needs to be further investigated in adequately powered prospective randomised controlled clinical trials.

The expression profile analysis of atrial mRNA in rats with atrial fibrillation: the role of IGF1 in atrial fibrosis

Background

Structural remodeling is critical to the initiation and maintenance of atrial fibrillation (AF). IGF1, insulin like growth factor 1, has been recognized as contributor to fibrosis. However, the roles and mechanisms of IGF1 in structural remodeling during AF is still unclear.

Methods

We investigated the transcriptional expression profiles of left atria in AF and non-AF rat models by using microarray analysis. And quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the accuracy. After bioinformatics analysis, IGF1 was selected to explore its effects and mechanisms on atrial fibrosis. The fibroblasts were extracted from atria of rats, and randomly divided into negative control group, mIGF1 overexpression group and mIGF1 silencing group. Then 30 healthy male Wistar rats were randomly divided into negative control group (n = 10), pacing group (n = 10), pacing + mIGF1 silencing viruses group (n = 10). Then the intracardiac electrophysiological examination, qRT-PCR, Western Blotting, masson staining were conducted after IGF1 interfering experiments.

Results

A total of 956 differentially expressed transcripts were identified, in which 395 transcripts were down-regulated and 561 transcripts were up-regulated. Bioinformatics analysis was conducted to predict the functions and interactions of the aberrantly expressed genes. The inhibition of IGF1 function in AF model could ameliorate the inducibility of AF. The IGF1 plays a fibrotic role by activating the PI3K-Akt pathway to increase the expression of CTGF and AT1R.

Conclusions

IGF1 develops vital function in regulating structural remodeling during AF, which could illustrate the mechanism of AF pathogenesis and supply potential targets for its precise treatment.

Novel role of the clustered miR-23b-3p and miR-27b-3p in enhanced expression of fibrosis-associated genes by targeting TGFBR3 in atrial fibroblasts

Atrial fibrillation (AF) is the most common type of arrhythmia in cardiovascular diseases. Atrial fibrosis is an important pathophysiological contributor to AF. This study aimed to investigate the role of the clustered miR‐23b‐3p and miR‐27b‐3p in atrial fibrosis. Human atrial fibroblasts (HAFs) were isolated from atrial appendage tissue of patients with sinus rhythm. A cell model of atrial fibrosis was achieved in Ang‐II‐induced HAFs. Cell proliferation and migration were detected. We found that miR‐23b‐3p and miR‐27b‐3p were markedly increased in atrial appendage tissues of AF patients and in Ang‐II‐treated HAFs. Overexpression of miR‐23b‐3p and miR‐27b‐3p enhanced the expression of collagen, type I, alpha 1 (COL1A1), COL3A1 and ACTA2 in HAFs without significant effects on their proliferation and migration. Luciferase assay showed that miR‐23b‐3p and miR‐27b‐3p targeted two different sites in 3ʹ‐UTR of transforming growth factor (TGF)‐β1 receptor 3 (TGFBR3) respectively. Consistently, TGFBR3 siRNA could increase fibrosis‐related genes expression, along with the Smad1 inactivation and Smad3 activation in HAFs. Additionally, overexpression of TGFBR3 could alleviate the increase of COL1A1, COL3A1 and ACTA2 in HAFs after transfection with miR‐23b‐3p and miR‐27b‐3p respectively. Moreover, Smad3 was activated in HAFs in response to Ang‐II treatment and inactivation of Smad3 attenuated up‐regulation of miR‐23b‐3p and miR‐27b‐3p in Ang‐II‐treated HAFs. Taken together, these results suggest that the clustered miR‐23b‐3p and miR‐27b‐3p consistently promote atrial fibrosis by targeting TGFBR3 to activate Smad3 signalling in HAFs, suggesting that miR‐23b‐3p and miR‐27b‐3p are potential therapeutic targets for atrial fibrosis.

The role of personalized atrial modeling in understanding atrial fibrillation mechanisms and improving treatment

Atrial fibrillation is the most common arrhythmia in humans and is associated with high morbidity, mortality and health-related expenses. Computational approaches have been increasingly utilized in atrial electrophysiology. In this review we summarize the recent advancements in atrial fibrillation modeling at the organ scale. Multi-scale atrial models now incorporate high level detail of atrial anatomy, tissue ultrastructure and fibrosis distribution. We provide the state-of-the art methodologies in developing personalized atrial fibrillation models with realistic geometry and tissue properties. We then focus on the use of multi-scale atrial models to gain mechanistic insights in AF. Simulations using atrial models have provided important insight in the mechanisms underlying AF, showing the importance of the atrial fibrotic substrate and altered atrial electrophysiology in initiation and maintenance of AF. Last, we summarize the translational evidence that supports incorporation of computational modeling in clinical practice for development of personalized treatment strategies for patients with AF. In early-stages clinical studies, AF models successfully identify patients where pulmonary vein isolation alone is not adequate for treatment of AF and suggest novel targets for ablation. We conclude with a summary of the future developments envisioned for the field of atrial computational electrophysiology.

Dynamical anchoring of distant arrhythmia sources by fibrotic regions via restructuring of the activation pattern

Rotors are functional reentry sources identified in clinically relevant cardiac arrhythmias, such as ventricular and atrial fibrillation. Ablation targeting rotor sites has resulted in arrhythmia termination. Recent clinical, experimental and modelling studies demonstrate that rotors are often anchored around fibrotic scars or regions with increased fibrosis. However, the mechanisms leading to abundance of rotors at these locations are not clear. The current study explores the hypothesis whether fibrotic scars just serve as anchoring sites for the rotors or whether there are other active processes which drive the rotors to these fibrotic regions. Rotors were induced at different distances from fibrotic scars of various sizes and degree of fibrosis. Simulations were performed in a 2D model of human ventricular tissue and in a patient-specific model of the left ventricle of a patient with remote myocardial infarction. In both the 2D and the patient-specific model we found that without fibrotic scars, the rotors were stable at the site of their initiation. However, in the presence of a scar, rotors were eventually dynamically anchored from large distances by the fibrotic scar via a process of dynamical reorganization of the excitation pattern. This process coalesces with a change from polymorphic to monomorphic ventricular tachycardia.

Rotors are waves of cardiac excitation like a tornado causing cardiac arrhythmia. Recent research shows that they are found in ventricular and atrial fibrillation. Burning (via ablation) the site of a rotor can result in the termination of the arrhythmia. Recent studies showed that rotors are often anchored to regions surrounding scar tissue, where part of the tissue still survived called fibrotic tissue. However, it is unclear why these rotors anchor to these locations. Therefore, in this work, we investigated why rotors are so abundant in fibrotic tissue with the help of computer simulations. We performed simulations in a 2D model of human ventricular tissue and in a patient-specific model of a patient with an infarction. We found that even when rotors are initially at large distances from the fibrotic region, they are attracted by this region, to finally end up at the fibrotic tissue. We called this process dynamical anchoring and explained how the process works.

Human Atrial Fibrillation Drivers Resolved With Integrated Functional and Structural Imaging to Benefit Clinical Mapping

OBJECTIVES

This study sought to improve atrial fibrillation (AF) driver identification by integrating clinical multielectrode mapping with driver fingerprints defined by high-resolution ex vivo 3-dimensional (3D) functional and structural imaging.

BACKGROUND

Clinical multielectrode mapping of AF drivers suffers from variable contact, signal processing, and structural complexity within the 3D human atrial wall, raising questions on the validity of such drivers.

METHODS

Sustained AF was mapped in coronary-perfused explanted human hearts (n = 11) with transmural near-infrared optical mapping (∼0.3 mm² resolution). Simultaneously, custom FIRMap catheters (∼9 × 9 mm² resolution) mapped endocardial and epicardial surfaces, which were analyzed by Focal Impulse and Rotor Mapping activation and Rotational Activity Profile (Abbott Labs, Chicago, Illinois). Functional maps were integrated with contrast-enhanced cardiac magnetic resonance imaging (∼0.1 mm³ resolution) analysis of 3D fibrosis architecture.

RESULTS

During sustained AF, near-infrared optical mapping identified 1 to 2 intramural, spatially stable re-entrant AF drivers per heart. Driver targeted ablation affecting 2.2 ± 1.1% of the atrial surface terminated and prevented AF. Driver regions had significantly higher phase singularity density and dominant frequency than neighboring nondriver regions. Focal Impulse and Rotor Mapping had 80% sensitivity to near-infrared optical mapping-defined driver locations (16 of 20), and matched 14 of 20 driver visualizations: 10 of 14 re-entries seen with Rotational Activity Profile; and 4 of 6 breakthrough/focal patterns. Focal Impulse and Rotor Mapping detected 1.1 ± 0.9 false-positive rotational activity profiles per recording, but these regions had lower intramural contrast-enhanced cardiac magnetic resonance imaging fibrosis than did driver regions (14.9 ± 7.9% vs. 23.2 ± 10.5%; p < 0.005).

CONCLUSIONS

The study revealed that both re-entrant and breakthrough/focal AF driver patterns visualized by surface-only clinical multielectrodes can represent projections of 3D intramural microanatomic re-entries. Integration of multielectrode mapping and 3D fibrosis analysis may enhance AF driver detection, thereby improving the efficacy of driver-targeted ablation.

Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models

Patient specific models created from contrast-enhanced (i.e. late-gadolinium, LGE) MRI images can be used for prediction of reentry location and clinical ablation planning. However, there is still a need for direct and systematic comparison between characteristics of ventricular tachycardia (VT) morphologies predicted in computational models and those acquired in clinical or experimental protocols. In this study, we aimed to: 1) assess the differences in VT morphologies predicted by modeling and recorded in experiments in terms of patterns and location of reentries, earliest and latest activation sites, and cycle lengths; and 2) define the optimal range of infarct tissue threshold values which provide best match between simulation and experimental results. To achieve these goals, we utilized LGE-MRI images from 4 swine hearts with inducible monomorphic VT. The images were segmented to identify non-infarcted myocardium, semi viable gray zone (GZ), and core scar based on pixel intensity. Several models were reconstructed from each LGE-MRI scan, with voxels of intensity between that of non-infarcted myocardium and 20-50% of the maximum intensity (in 10% increments) in the infarct region classified as GZ. VT induction was simulated in each model. Our simulation results showed that using GZ intensity thresholds of 20% or 30% resulted in the best match of simulated propagation patterns and reentry locations with those from the experiment. Overall, we matched 70% (7/10) morphologies for all the hearts. Our simulation shows that MRI-based computational models of hearts with myocardial infarction can accurately reproduce the majority of experimentally recorded post-infarction VTs.

Image-Based Computational Evaluation of the Effects of Atrial Wall Thickness and Fibrosis on Re-entrant Drivers for Atrial Fibrillation

Introduction: Catheter ablation (CA) is a common treatment for atrial fibrillation (AF), but the knowledge of optimal ablation sites, and hence clinical outcomes, are suboptimal. Increasing evidence suggest that ablation strategies based on patient-specific substrates information, such as distributions of fibrosis and atrial wall thickness (AWT), may be used to improve therapy. We hypothesized that competing influences of large AWT gradients and fibrotic patches on conductive properties of atrial tissue can determine locations of re-entrant drivers (RDs) sustaining AF. Methods: Two sets of models were used: (1) a simple model of 3D atrial tissue slab with a step change in AWT and a synthetic fibrosis patch, and (2) 3D models based on patient-specific right atrial (RA) and left atrial (LA) geometries. The latter were obtained from four healthy volunteers and two AF patients, respectively, using magnetic resonance imaging (MRI). A synthetic fibrotic patch was added in the RA and fibrosis distributions in the LA were obtained from gadolinium-enhanced MRI of the same patients. In all models, 3D geometry was combined with the Fenton-Karma atrial cell model to simulate RDs. Results: In the slab, RDs drifted toward, and then along the AWT step. However, with additional fibrosis, the RDs were localized in regions between the step and fibrosis. In the RA, RDs drifted toward and anchored to a large AWT gradient between the crista terminalis (CT) region and the surrounding atrial wall. Without such a gradient, RDs drifted toward the superior vena cava (SVC) or the tricuspid valve (TSV). With additional fibrosis, RDs initiated away from the CT anchored to the fibrotic patch, whereas RDs initiated close to the CT region remained localized between the two structures. In the LA, AWT was more uniform and RDs drifted toward the pulmonary veins (PVs). However, with additional fibrotic patches, RDs either anchored to them or multiplied. Conclusion: In the RA, RD locations are determined by both fibrosis and AWT gradients at the CT region. In the LA, they are determined by fibrosis due to the absence of large AWT gradients. These results elucidate mechanisms behind the stabilization of RDs sustaining AF and can help guide ablation therapy.

Wavelength and Fibrosis Affect Phase Singularity Locations During Atrial Fibrillation

The mechanisms underlying atrial fibrillation (AF), the most common sustained cardiac rhythm disturbance, remain elusive. Atrial fibrosis plays an important role in the development of AF and rotor dynamics. Both electrical wavelength (WL) and the degree of atrial fibrosis change as AF progresses. However, their combined effect on rotor core location remains unknown. The aim of this study was to analyze the effects of WL change on rotor core location in both fibrotic and non-fibrotic atria. Three patient specific fibrosis distributions (total fibrosis content: 16.6, 22.8, and 19.2%) obtained from clinical imaging data of persistent AF patients were incorporated in a bilayer atrial computational model. Fibrotic effects were modeled as myocyte-fibroblast coupling + conductivity remodeling; structural remodeling; ionic current changes + conductivity remodeling; and combinations of these methods. To change WL, action potential duration (APD) was varied from 120 to 240ms, representing the range of clinically observed AF cycle length, by modifying the inward rectifier potassium current (IK1) conductance between 80 and 140% of the original value. Phase singularities (PSs) were computed to identify rotor core locations. Our results show that IK1 conductance variation resulted in a decrease of APD and WL across the atria. For large WL in the absence of fibrosis, PSs anchored to regions with high APD gradient at the center of the left atrium (LA) anterior wall and near the junctions of the inferior pulmonary veins (PVs) with the LA. Decreasing the WL induced more PSs, whose distribution became less clustered. With fibrosis, PS locations depended on the fibrosis distribution and the fibrosis implementation method. The proportion of PSs in fibrotic areas and along the borders varied with both WL and fibrosis modeling method: for patient one, this was 4.2-14.9% as IK1 varied for the structural remodeling representation, but 12.3-88.4% using the combination of structural remodeling with myocyte-fibroblast coupling. The degree and distribution of fibrosis and the choice of implementation technique had a larger effect on PS locations than the WL variation. Thus, distinguishing the fibrotic mechanisms present in a patient is important for interpreting clinical fibrosis maps to create personalized models.

The Fibrotic Substrate in Persistent Atrial Fibrillation Patients: Comparison Between Predictions From Computational Modeling and Measurements From Focal Impulse and Rotor Mapping

Focal impulse and rotor mapping (FIRM) involves intracardiac detection and catheter ablation of re-entrant drivers (RDs), some of which may contribute to arrhythmia perpetuation in persistent atrial fibrillation (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) has the potential to non-invasively identify all areas of the fibrotic substrate where RDs could potentially be sustained, including locations where RDs may not manifest during mapped AF episodes. The objective of this study was to carry out multi-modal assessment of the arrhythmogenic propensity of the fibrotic substrate in PsAF patients by comparing locations of RD-harboring regions found in simulations and detected by FIRM (RD_sim and RD_FIRM) and analyze implications for ablation strategies predicated on targeting RDs. For 11 PsAF patients who underwent pre-procedure LGE-MRI and FIRM-guided ablation, we retrospectively simulated AF in individualized atrial models, with geometry and fibrosis distribution reconstructed from pre-ablation LGE-MRI scans, and identified RD_sim sites. Regions harboring RD_sim and RD_FIRM were compared. RD_sim were found in 38 atrial regions (median [inter-quartile range (IQR)] = 4 [3; 4] per model). RD_FIRM were identified and subsequently ablated in 24 atrial regions (2 [1; 3] per patient), which was significantly fewer than the number of RD_sim-harboring regions in corresponding models (p < 0.05). Computational modeling predicted RD_sim in 20 of 24 (83%) atrial regions identified as RD_FIRM-harboring during clinical mapping. In a large number of cases, we uncovered RD_sim-harboring regions in which RD_FIRM were never observed (18/22 regions that differed between the two modalities; 82%); we termed such cases “latent” RD_sim sites. During follow-up (230 [180; 326] days), AF recurrence occurred in 7/11 (64%) individuals. Interestingly, latent RD_sim sites were observed in all seven computational models corresponding to patients who experienced recurrent AF (2 [2; 2] per patient); in contrast, latent RD_sim sites were only discovered in two of four patients who were free from AF during follow-up (0.5 [0; 1.5] per patient; p < 0.05 vs. patients with AF recurrence). We conclude that substrate-based ablation based on computational modeling could improve outcomes.

Strategies of data layout and cache writing for input-output optimization in high performance scientific computing: Applications to the forward electrocardiographic problem

Input-output (I/O) optimization at the low-level design of data layout on disk drastically impacts the efficiency of high performance computing (HPC) applications. However, such a low-level optimization is in general challenging, especially when using popular scientific file formats designed with an emphasis on portability and flexibility. To reconcile these two aspects, we present a novel low-level data layout for HPC applications, fully independent of the number of dimensions in the dataset. The new data layout improves reading and writing efficiency in large HPC applications using many processors, and in particular during parallel post-processing. Furthermore, its combination with a cached write mode, in order to aggregate multiple writes into larger ones, substantially decreased the writing times of the proposed strategy. When applied to our simulation framework for the forward calculation of the human electrocardiogram, the combined strategy resulted in drastic improvements in I/O performance, of up to 40% in writing and 93–98% in reading for post-processing tasks. Given the generality of the proposed strategies and scientific file formats used, our results may represent significant improvements in I/O performance of HPC applications across multiple disciplines, reducing execution and post-processing times and leading to a more efficient use of HPC resource envelopes.

Age as a Critical Determinant of Atrial Fibrillation: A Two-sided Relationship

The incidence of atrial fibrillation (AF), the most common sustained arrhythmia and a major public health burden, increases exponentially with age. However, mechanisms underlying this long-recognized association remain incompletely understood. Experimental and human studies have demonstrated the involvement of aging in several arrhythmogenic processes, including atrial electrical and structural remodelling, disturbed calcium homeostasis, and enhanced atrial ectopic activity/increased vulnerability to re-entry induction. Given this wide range of putative mechanisms, the task of delineating the specific effects of aging responsible for AF promotion is not simple, as aging is itself associated with increasing prevalence of a host of AF-predisposing conditions, including heart failure, coronary artery disease, and hypertension. Although we usually think of old age promoting AF, there is also evidence that young age may actually have a protective effect against AF occurrence. For example, the low AF incidence among populations of young patients with significant structural congenital heart disease and substantial atrial enlargement/remodelling suggests that younger age might protect against fibrillation in the diseased atrium; efforts at understating how younger age may prevent AF might be helpful in elucidating missing mechanistic links between AF and age. The goal of this paper is to review the epidemiologic and pathophysiologic evidence regarding mechanisms underlying age-related AF. Although the therapeutic options for AF have recently improved, major gaps still remain and a better understanding of the special relationship between age and AF may be important for the identification of new targets for therapeutic innovation.