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

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.

Systematic in-silico evaluation of fibrosis effects on re-entrant wave dynamics in atrial tissue

Despite the key role of fibrosis in atrial fibrillation (AF), the effects of different spatial distributions and textures of fibrosis on wave propagation mechanisms in AF are not fully understood. To clarify these aspects, we performed a systematic computational study to assess fibrosis effects on the characteristics and stability of re-entrant waves in electrically-remodelled atrial tissues. A stochastic algorithm, which generated fibrotic distributions with controlled overall amount, average size, and orientation of fibrosis elements, was implemented on a monolayer spheric atrial model. 245 simulations were run at changing fibrosis parameters. The emerging propagation patterns were quantified in terms of rate, regularity, and coupling by frequency-domain analysis of correspondent synthetic bipolar electrograms. At the increase of fibrosis amount, the rate of reentrant waves significantly decreased and higher levels of regularity and coupling were observed (p < 0.0001). Higher spatial variability and pattern stochasticity over repetitions was observed for larger amount of fibrosis, especially in the presence of patchy and compact fibrosis. Overall, propagation slowing and organization led to higher stability of re-entrant waves. These results strengthen the evidence that the amount and spatial distribution of fibrosis concur in dictating re-entry dynamics in remodeled tissue and represent key factors in AF maintenance.

The SK4 channel allosteric blocker, BA6b9, reduces atrial fibrillation substrate in rats with reduced ejection fraction

Atrial fibrillation (AF), the most common cardiac arrhythmia, is strongly associated with several comorbidities including heart failure (HF). AF in general, and specifically in the context of HF, is progressive in nature and associated with poor clinical outcomes. Current therapies for AF are limited in number and efficacy and do not target the underlying causes of atrial remodeling such as inflammation or fibrosis. We previously identified the calcium-activated SK4 K+ channels, which are preferentially expressed in the atria relative to the ventricles in both rat and human hearts, as attractive druggable target for AF treatment. Here, we examined the ability of BA6b9, a novel allosteric inhibitor of SK4 channels that targets the specific calmodulin-PIP2 binding domain, to alter AF susceptibility and atrial remodeling in a systolic HF rat postmyocardial infarction (post-MI) model. Daily BA6b9 injection (20 mg/kg/day) for 3 weeks starting 1-week post-MI prolonged the atrial effective refractory period, reduced AF induction and duration, and dramatically prevented atrial structural remodeling. In the post-MI left atrium (LA), pronounced upregulation of the SK4 K+ channel was observed, with corresponding increases in collagen deposition, α-SMA levels, and NLRP3 inflammasome expression. Strikingly, BA6b9 treatment reversed these changes while also significantly reducing the lateralization of the atrial connexin Cx43 in the LA of post-MI rats. Our findings indicate that the blockade of SK4 K+ channels using BA6b9 not only favors rhythm control but also remarkably reduces atrial structural remodeling, a property that is highly desirable for novel AF therapies, particularly in patients with comorbid HF.

Identification of Atrial Transmural Conduction Inhomogeneity Using Unipolar Electrogram Morphology

(1) Background: Structural remodeling plays an important role in the pathophysiology of atrial fibrillation (AF). It is likely that structural remodeling occurs transmurally, giving rise to electrical endo-epicardial asynchrony (EEA). Recent studies have suggested that areas of EEA may be suitable targets for ablation therapy of AF. We hypothesized that the degree of EEA is more pronounced in areas of transmural conduction block (T-CB) than single-sided CB (SS-CB). This study examined the degree to which SS-CB and T-CB enhance EEA and which specific unipolar potential morphology parameters are predictive for SS-CB or T-CB. (2) Methods: Simultaneous endo-epicardial mapping in the human right atrium was performed in 86 patients. Potential morphology parameters included unipolar potential voltages, low-voltage areas, potential complexity (long double and fractionated potentials: LDPs and FPs), and the duration of fractionation. (3) Results: EEA was mostly affected by the presence of T-CB areas. Lower potential voltages and more LDPs and FPs were observed in T-CB areas compared to SS-CB areas. (4) Conclusion: Areas of T-CB could be most accurately predicted by combining epicardial unipolar potential morphology parameters, including voltages, fractionation, and fractionation duration (AUC = 0.91). If transmural areas of CB indeed play a pivotal role in the pathophysiology of AF, they could theoretically be used as target sites for ablation.

Efficacy and safety of hybrid epicardial and endocardial ablation versus endocardial ablation in patients with persistent and longstanding persistent atrial fibrillation: a randomised, controlled trial

Background

Endocardial catheter ablation (CA) has limited long-term benefit for persistent and longstanding persistent atrial fibrillation (PersAF/LSPAF). We hypothesized hybrid epicardial-endocardial ablation (HA) would have superior effectiveness compared to CA, including repeat (rCA), in PersAF/LSPAF.

Methods

CEASE-AF (NCT02695277) is a prospective, multi-center, randomized controlled trial. Nine hospitals in Poland, Czech Republic, Germany, United Kingdom, and the Netherlands enrolled eligible participants with symptomatic, drug refractory PersAF and left atrial diameter (LAD) > 4.0 cm or LSPAF. Randomization was 2:1 to HA or CA by an independent statistician and stratified by site. Treatment assignments were masked to the core rhythm monitoring laboratory. For HA, pulmonary veins (PV) and left posterior atrial wall were isolated with thoracoscopic epicardial ablation including left atrial appendage exclusion. Endocardial touch-up ablation was performed 91-180 days post-index procedure. For CA, endocardial PV isolation and optional substrate ablation were performed. rCA was permitted between days 91-180. Primary effectiveness was freedom from AF/atrial flutter/atrial tachycardia >30-s through 12-months absent class I/III anti-arrhythmic drugs except those not exceeding previously failed doses. It was assessed in the modified intention-to-treat (mITT) population who had the index procedure and follow-up data. Major complications were assessed in the ITT population who had the index procedure. Thirty-six month follow-up continues.

Findings

Enrollment began November 20, 2015 and ended May 22, 2020. In 154 ITT patients (102 HA; 52 CA), 75% were male, mean age was 60.7 ± 7.9 years, mean LAD was 4.7 ± 0.4 cm, and 81% had PersAF. Primary effectiveness was 71.6% (68/95) in HA versus 39.2% (20/51) in CA (absolute benefit increase: 32.4% [95% CI 14.3%-48.0%], p < 0.001). Major complications through 30-days after index procedures plus 30-days after second stage/rCA were similar (HA: 7.8% [8/102] versus CA: 5.8% [3/52], p = 0.75).

Interpretation

HA had superior effectiveness compared to CA/rCA in PersAF/LSPAF without significant procedural risk increase.

Funding

AtriCure, Inc.

Machine Learning and the Conundrum of Stroke Risk Prediction

Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.

Women Have More Recurrences of Atrial Fibrillation than Men after Thoracoscopic Ablation and Suffer More from Established Risk Factors

Introduction. Atrial fibrillation (AF) is more prevalent in men than in women. However, women with AF are more symptomatic, have a worse quality of life, a higher stroke risk and may therefore benefit most from ablation. In this study we aim to identify the risk of recurrent AF after thoracoscopic ablation, and assess the differential impact of the risk factors for recurrence between women and men. Method. This is a single center cohort study, including patients undergoing thoracoscopic ablation for advanced AF between 2008 and 2019. All patients were clinically followed up for two years with quarterly 24 h Holter monitoring and ECGs for the detection of recurrent AF. Left atrial appendage (LAA) tissue was collected for collagen analysis. Results. We included 571 patients, of whom 143 (25%) were women. Women were older than men (63 ± 8.3 y vs. 59 ± 8.5, p < 0.001), but had fewer cardiovascular risk factors, myocardial infarctions (1.4% vs. 6.5%, p = 0.03) and, in particular, vascular disease (7.0% vs. 16.1%, p = 0.01). Women suffered more from AF recurrence, driven by more atrial tachycardias, and sex was an independent risk factor for recurrence (HR1.41 [1.04–1.91], p = 0.028]). The presence of vascular disease was associated with an increased risk for AF recurrence in women, but not in men. In LAA histology, women had more collagen than men, as had patients with persistent compared to paroxysmal AF. Conclusion. Women had 15% more recurrences, driven by more atrial tachycardias, which may be explained by a more fibrotic atrial substrate. What’s new? Women undergoing thoracoscopic AF ablation have a higher risk of recurrent AF, driven by more atrial tachycardias. Among patients with left atrial enlargement or persistent AF, women have worse outcomes than men. Vascular disease was a risk factor for recurrence in women, but not in men. In a histopathologic analysis of the left atrial appendage, women had more collagen than men, as had patients with persistent compared to paroxysmal AF.

Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass

BACKGROUND

This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors.

METHODS

Four ML algorithms were employed to analyze 394 patients undergoing CABG, and their performances were compared: Multivariate Adaptive Regression Spline, Neural Network, Random Forest, and Support Vector Machine. Each algorithm was applied to the training data set to choose the most important features and to build a predictive model. The better performance for each model was obtained by a hyperparameters search, and the Receiver Operating Characteristic Area Under the Curve metric was selected to choose the best model. The best instances of each model were fed with the test data set, and some metrics were generated to assess the performance of the models on the unseen data set. A traditional logistic regression was also performed to be compared with the machine learning models.

RESULTS

Random Forest model showed the best performance, and the top five predictive features included age, preoperative creatinine values, time of aortic cross-clamping, body surface area, and Logistic Euro-Score.

CONCLUSIONS

The use of ML for clinical predictions requires an accurate evaluation of the models and their hyperparameters. Random Forest outperformed all other models in the clinical prediction of POAF following CABG.

Quantitative assessment of transmural fibrosis profile in the human atrium: evidence for a three-dimensional arrhythmic substrate by slice-to-slice histology

AIMS

Intramural fibrosis represents a crucial factor in the formation of a three-dimensional (3D) substrate for atrial fibrillation (AF). However, the transmural distribution of fibrosis and its relationship with atrial overload remain largely unknown. The aim of this study is to quantify the transmural profile of atrial fibrosis in patients with different degrees of atrial dilatation and arrhythmic profiles by a high-resolution 3D histology method.

METHODS AND RESULTS

Serial microtome-cut tissue slices, sampling the entire atrial wall thickness at 5 µm spatial resolution, were obtained from right atrial appendage specimens in 23 cardiac surgery patients. Atrial slices were picrosirius red stained, imaged by polarized light microscopy, and analysed by a custom-made segmentation algorithm. In all patients, the intramural fibrosis content displayed a progressive decrease alongside tissue depth, passing from 68.6 ± 11.6% in the subepicardium to 10-13% in the subendocardium. Distinct transmural fibrotic profiles were observed in patients with atrial dilatation with respect to control patients, where the first showed a slower decrease of fibrosis along tissue depth (exponential decay constant: 171.2 ± 54.5 vs. 80.9 ± 24.4 µm, P < 0.005). Similar slow fibrotic profiles were observed in patients with AF (142.8 ± 41.7 µm). Subepicardial and midwall levels of fibrosis correlated with the degree of atrial dilatation (ρ = 0.72, P < 0.001), while no correlation was found in subendocardial layers.

CONCLUSIONS

Quantification of fibrosis transmural profile at high resolution is feasible by slice-to-slice histology. Deeper penetration of fibrosis in subepicardial and midwall layers in dilated atria may concur to the formation of a 3D arrhythmic substrate.

Detection of endo-epicardial atrial low-voltage areas using unipolar and omnipolar voltage mapping

Background: Low-voltage areas (LVA) can be located exclusively at either the endocardium or epicardium. This has only been demonstrated for bipolar voltages, but the value of unipolar and omnipolar voltages recorded from either the endocardium and epicardium in predicting LVAs at the opposite layer remains unknown. The goal of this study was therefore to compare simultaneously recorded endo-epicardial unipolar and omnipolar potentials and to determine whether their voltage characteristics are predictive for opposite LVAs.

Methods: Intra-operative simultaneous endo-epicardial mapping (256 electrodes, interelectrode distances 2 mm) was performed during sinus rhythm at the right atrium in 93 patients (67 ± 9 years, 73 male). Cliques of four electrodes (2 × 2 mm) were used to define maximal omnipolar (Vomni,max) and unipolar (Vuni,max) voltages. LVAs were defined as Vomni,max ≤0.5 mV or Vuni,max ≤1.0 mV.

Results: The majority of both unipolar and omnipolar LVAs were located at only the endocardium (74.2% and 82.0% respectively) or epicardium (52.7% and 47.6% respectively). Of the endocardial unipolar LVAs, 25.8% were also located at the opposite layer and 47.3% vice-versa. In omnipolar LVAs, 18.0% of the endocardial LVAs were also located at the epicardium and 52.4% vice-versa. The combination of epicardial Vuni,max and Vomni,max was most accurate in identifying dual-layer LVAs (50.4%).

Conclusion: Unipolar and omnipolar LVAs are frequently located exclusively at either the endocardium or epicardium. Endo-epicardial LVAs are most accurately identified using combined epicardial unipolar and omnipolar voltages. Therefore, a combined endo-epicardial unipolar and omnipolar mapping approach is favoured as it may be more indicative of possible arrhythmogenic substrates.

The Hybrid Convergent Procedure for Persistent and Long-Standing Persistent Atrial Fibrillation From an Electrophysiologist's Perspective

In atrial fibrillation (AF), the pulmonary veins (PV) are central to arrhythmogenicity and are targeted by PV isolation (PVI). As AF progresses, triggers become more prevalent in non-PV areas including the left atrial posterior wall (LAPW). Reported benefits of LAPW isolation in Cox-maze IV led to exploration of ablation strategies using endocardial catheters. However, no single approach to endocardial LAPW isolation exists. Relative success in comparison to PVI alone has been mixed. The hybrid convergent procedure was developed to combine minimally invasive surgical and electrophysiology techniques to accomplish effective PVI and LAPW isolation. Epicardial LAPW isolation is performed by a cardiothoracic surgeon followed by endocardial ablation by an electrophysiologist who ensures PVI completion and targets any remaining gaps. Safety and effectiveness of hybrid convergent was evaluated in the prospective, multi-center, randomized controlled trial, Convergence of Epicardial and Endocardial Ablation for the Treatment of Symptomatic Persistent AF (CONVERGE). CONVERGE compared the effectiveness of the hybrid convergent procedure to endocardial catheter ablation for treatment of drug-refractory persistent and longstanding persistent AF and demonstrated primary effectiveness of higher freedom from atrial arrhythmias absent new/increased dose previously failed/intolerant anti-arrhythmic drugs through 12 months compared to endocardial catheter ablation. Greater freedom from AF and proportion of patients experiencing ≥90% burden reduction with hybrid convergent ablation were seen through 18 months follow-up. Improved electrophysiology lab efficiency was demonstrated by the reduction in endocardial ablation time with addition of epicardial ablation. This multi-disciplinary heart team procedure may improve outcomes in difficult-to-treat patients with advanced AF. This article is protected by copyright. All rights reserved.

Endomysial fibrosis, rather than overall connective tissue content, is the main determinant of conduction disturbances in human atrial fibrillation

Aims

Although in persistent atrial fibrillation (AF) a complex AF substrate characterized by a high incidence of conduction block has been reported, relatively little is known about AF complexity in paroxysmal AF (pAF). Also, the relative contribution of various aspects of structural alterations to conduction disturbances is not clear. In particular, the contribution of endomysial fibrosis to conduction disturbances during progression of AF has not been studied yet.

Methods and results

During cardiac surgery, epicardial high-density mapping was performed in patients with acutely induced (aAF, n = 11), pAF (n = 12), and longstanding persistent AF (persAF, n = 9) on the right atrial (RA) wall, the posterior left atrial wall (pLA) and the LA appendage (LAA). In RA appendages, overall and endomysial (myocyte-to-myocyte distances) fibrosis and connexin 43 (Cx43) distribution were quantified. Unipolar AF electrogram analysis showed a more complex pattern with a larger number of narrower waves, more breakthroughs and a higher fractionation index (FI) in persAF compared with aAF and pAF, with no differences between aAF and pAF. The FI was consistently higher at the pLA compared with the RA. Structurally, Cx43 lateralization increased with AF progression (aAF = 7.5 ± 8.9%, pAF = 24.7 ± 11.1%, persAF = 35.1 ± 11.4%, P < 0.001). Endomysial but not overall fibrosis correlated with AF complexity (r = 0.57, P = 0.001; r = 0.23, P = 0.20; respectively).

Conclusions

Atrial fibrillation complexity is highly variable in patients with pAF, but not significantly higher than in patients with acutely induced AF, while in patients with persistent AF complexity is higher. Among the structural alterations studied, endomysial fibrosis, but not overall fibrosis, is the strongest determinant of AF complexity.

Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification

Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace the model from different, potentially random, locations to determine whether arrhythmias can be induced in the atria. In this work, we propose to use multi-fidelity Gaussian process classification on Riemannian manifolds to efficiently determine the regions in the atria where arrhythmias are inducible. We build a probabilistic classifier that operates directly on the atrial surface. We take advantage of lower resolution models to explore the atrial surface and combine seamlessly with high-resolution models to identify regions of inducibility. We test our methodology in 9 different cases, with different levels of fibrosis and ablation treatments, totalling 1,800 high resolution and 900 low resolution simulations of atrial fibrillation. When trained with 40 samples, our multi-fidelity classifier that combines low and high resolution models, shows a balanced accuracy that is, on average, 5.7% higher than a nearest neighbor classifier. We hope that this new technique will allow faster and more precise clinical applications of computational models for atrial fibrillation. All data and code accompanying this manuscript will be made publicly available at: https://github.com/fsahli/AtrialMFclass.

Electrophysiological Consequences of Cardiac Fibrosis

For both the atria and ventricles, fibrosis is generally recognized as one of the key determinants of conduction disturbances. By definition, fibrosis refers to an increased amount of fibrous tissue. However, fibrosis is not a singular entity. Various forms can be distinguished, that differ in distribution: replacement fibrosis, endomysial and perimysial fibrosis, and perivascular, endocardial, and epicardial fibrosis. These different forms typically result from diverging pathophysiological mechanisms and can have different consequences for conduction. The impact of fibrosis on propagation depends on exactly how the patterns of electrical connections between myocytes are altered. We will therefore first consider the normal patterns of electrical connections and their regional diversity as determinants of propagation. Subsequently, we will summarize current knowledge on how different forms of fibrosis lead to a loss of electrical connectivity in order to explain their effects on propagation and mechanisms of arrhythmogenesis, including ectopy, reentry, and alternans. Finally, we will discuss a histological quantification of fibrosis. Because of the different forms of fibrosis and their diverging effects on electrical propagation, the total amount of fibrosis is a poor indicator for the effect on conduction. Ideally, an assessment of cardiac fibrosis should exclude fibrous tissue that does not affect conduction and differentiate between the various types that do; in this article, we highlight practical solutions for histological analysis that meet these requirements.

Posterior left atrial epicardial adipose tissue: scope of the problem and impact of new technology

PURPOSE OF REVIEW

Patients with persistent forms of atrial fibrillation are seeking treatments based on the promise of better restoration of sinus rhythm with newer therapies. Successful catheter ablation and maintenance of atrial fibrillation in this subgroup is negatively impacted by the presence of epicardial adipose tissue (EAT) associated with the posterior left atrium.

RECENT FINDINGS

EAT is now understood to be hormonally active and promotes adverse atrial remodelling, including fibrosis and myopathy. Despite being dominantly adipose tissue, it is known to be electrically active, comprising ganglia, neural tissue and ectopic atrial myocardium that may contribute to endo-epicardial dissociation and persistent electrical activity and atrial fibrillation despite good endocardial electrical silencing. Hybrid procedures that include direct epicardial ablation of the posterior wall, including the EAT, are associated with superior outcomes in nonparoxysmal atrial fibrillation.

SUMMARY

Therapies for persistent atrial fibrillation that also ablate the EAT as part of a well tolerated transmural posterior wall ablation may improve outcomes in this challenging subset of patients.

Synergistic antiarrhythmic effect of inward rectifier current inhibition and pulmonary vein isolation in a 3D computer model for atrial fibrillation

AIMS

Recent clinical studies showed that antiarrhythmic drug (AAD) treatment and pulmonary vein isolation (PVI) synergistically reduce atrial fibrillation (AF) recurrences after initially successful ablation. Among newly developed atrial-selective AADs, inhibitors of the G-protein-gated acetylcholine-activated inward rectifier current (IKACh) were shown to effectively suppress AF in an experimental model but have not yet been evaluated clinically. We tested in silico whether inhibition of inward rectifier current or its combination with PVI reduces AF inducibility.

METHODS AND RESULTS

We simulated the effect of inward rectifier current blockade (IK blockade), PVI, and their combination on AF inducibility in a detailed three-dimensional model of the human atria with different degrees of fibrosis. IK blockade was simulated with a 30% reduction of its conductivity. Atrial fibrillation was initiated using incremental pacing applied at 20 different locations, in both atria. IK blockade effectively prevented AF induction in simulations without fibrosis as did PVI in simulations without fibrosis and with moderate fibrosis. Both interventions lost their efficacy in severe fibrosis. The combination of IK blockade and PVI prevented AF in simulations without fibrosis, with moderate fibrosis, and even with severe fibrosis. The combined therapy strongly decreased the number of fibrillation waves, due to a synergistic reduction of wavefront generation rate while the wavefront lifespan remained unchanged.

CONCLUSION

Newly developed blockers of atrial-specific inward rectifier currents, such as IKAch, might prevent AF occurrences and when combined with PVI effectively supress AF recurrences in human.

Simulation of diffuse and stringy fibrosis in a bilayer interconnected cable model of the left atrium

AIMS

The aim of this study is to design a computer model of the left atrium for investigating fibre-orientation-dependent microstructure such as stringy fibrosis.

METHODS AND RESULTS

We developed an approach for automatic construction of bilayer interconnected cable models from left atrial geometry and epi- and endocardial fibre orientation. The model consisted of two layers (epi- and endocardium) of longitudinal and transverse cables intertwined-like fabric threads, with a spatial discretization of 100 µm. Model validation was performed by comparison with cubic volumetric models in normal conditions. Then, diffuse (n = 2904), stringy (n = 3600), and mixed fibrosis patterns (n = 6840) were randomly generated by uncoupling longitudinal and transverse connections in the interconnected cable model. Fibrosis density was varied from 0% to 40% and mean stringy obstacle length from 0.1 to 2 mm. Total activation time, apparent anisotropy ratio, and local activation time jitter were computed during normal rhythm in each pattern. Non-linear regression formulas were identified for expressing measured propagation parameters as a function of fibrosis density and obstacle length (stringy and mixed patterns). Longer obstacles (even below tissue space constant) were independently associated with prolonged activation times, increased anisotropy, and local fluctuations in activation times. This effect was increased by endo-epicardial dissociation and mitigated when fibrosis was limited to the epicardium.

CONCLUSION

Interconnected cable models enable the study of microstructure in organ-size models despite limitations in the description of transmural structures.

Paradigm shifts in electrophysiological mechanisms of atrial fibrillation

Determining the sequence of activation is a major source of information for understanding the electrophysiological mechanism(s) of atrial fibrillation (AF). However, the complex morphology of the electrograms hampers their analysis, and has stimulated generations of electrophysiologists to develop a large variety of technologies for recording, pre-processing, and analysis of fibrillation electrograms. This variability of approaches is mirrored by a large variability in the interpretation of fibrillation electrograms and, thereby, opinions regarding the basic electrophysiological mechanism(s) of AF vary widely. Multiple wavelets, different types of re-entry including rotors, double layers, multiple focal activation patterns all have been advocated, and a comprehensive and commonly accepted paradigm for the fundamental mechanisms of AF is still lacking. Here, we summarize the Maastricht perspective and Cleveland perspective regarding AF mechanism(s). We also describe some of the key observations in mapping of AF reported over the past decades, and how they changed over the years, often as results of new techniques introduced in the experimental field of AF research.

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.

Automatic reconstruction of the left atrium activation from sparse intracardiac contact recordings by inverse estimate of fibre structure and anisotropic conduction in a patient-specific model

AIMS

Electric conduction in the atria is direction-dependent, being faster in fibre direction, and possibly heterogeneous due to structural remodelling. Intracardiac recordings of atrial activation may convey such information, but only with high-quality data. The aim of this study was to apply a patient-specific approach to enable such assessment even when data are scarce, noisy, and incomplete.

METHODS AND RESULTS

Contact intracardiac recordings in the left atrium from nine patients who underwent ablation therapy were collected before pulmonary veins isolation and retrospectively included in the study. The Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps (PIEMAP), previously developed, has been used to reconstruct the conductivity tensor from sparse recordings of the activation. Regional fibre direction and conduction velocity were estimated from the fitted conductivity tensor and extensively cross-validated by clustered and sparse data removal. Electrical conductivity was successfully reconstructed in all patients. Cross-validation with respect to the measurements was excellent in seven patients (Pearson correlation r > 0.93) and modest in two patients (r = 0.62 and r = 0.74). Bland-Altman analysis showed a neglectable bias with respect to the measurements and the limit-of-agreement at -22.2 and 23.0 ms. Conduction velocity in the fibre direction was 82 ± 25 cm/s, whereas cross-fibre velocity was 46 ± 7 cm/s. Anisotropic ratio was 1.91±0.16. No significant inter-patient variability was observed. Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps correctly predicted activation times in late regions in all patients (r = 0.88) and was robust to a sparser dataset (r = 0.95).

CONCLUSION

Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps offers a novel approach to extrapolate the activation in unmapped regions and to assess conduction properties of the atria. It could be seamlessly integrated into existing electro-anatomic mapping systems. Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps also enables personalization of cardiac electrophysiology models.

Persistent Atrial Fibrillation: The Role of Left Atrial Posterior Wall Isolation and Ablation Strategies

Atrial fibrillation (AF) is a global disease with rapidly rising incidence and prevalence. It is associated with a higher risk of stroke, dementia, cognitive decline, sudden and cardiovascular death, heart failure and impairment in quality of life. The disease is a major burden on the healthcare system. Paroxysmal AF is typically managed with medications or endocardial catheter ablation to good effect. However, a large proportion of patients with AF have persistent or long-standing persistent AF, which are more complex forms of the condition and thus more difficult to treat. This is in part due to the progressive electro-anatomical changes that occur with AF persistence and the spread of arrhythmogenic triggers and substrates outside of the pulmonary veins. The posterior wall of the left atrium is a common site for these changes and has become a target of ablation strategies to treat these more resistant forms of AF. In this review, we discuss the role of the posterior left atrial wall in persistent and long-standing persistent AF, the limitations of current endocardial-focused treatment strategies, and future perspectives on hybrid epicardial–endocardial approaches to posterior wall isolation or ablation.

A Computational Study of the Electrophysiological Substrate in Patients Suffering From Atrial Fibrillation

In the context of cardiac electrophysiology, we propose a novel computational approach to highlight and explain the long-debated mechanisms behind atrial fibrillation (AF) and to reliably numerically predict its induction and sustainment. A key role is played, in this respect, by a new way of setting a parametrization of electrophysiological mathematical models based on conduction velocities; these latter are estimated from high-density mapping data, which provide a detailed characterization of patients' electrophysiological substrate during sinus rhythm. We integrate numerically approximated conduction velocities into a mathematical model consisting of a coupled system of partial and ordinary differential equations, formed by the monodomain equation and the Courtemanche-Ramirez-Nattel model. Our new model parametrization is then adopted to predict the formation and self-sustainment of localized reentries characterizing atrial fibrillation, by numerically simulating the onset of ectopic beats from the pulmonary veins. We investigate the paroxysmal and the persistent form of AF starting from electro-anatomical maps of two patients. The model's response to stimulation shows how substrate characteristics play a key role in inducing and sustaining these arrhythmias. Localized reentries are less frequent and less stable in case of paroxysmal AF, while they tend to anchor themselves in areas affected by severe slow conduction in case of persistent AF.

Directed graph mapping exceeds phase mapping in discriminating true and false rotors detected with a basket catheter in a complex in-silico excitation pattern

Atrial fibrillation (AF) is the most frequently encountered arrhythmia in clinical practise. One of the major problems in the management of AF is the difficulty in identifying the arrhythmia sources from clinical recordings. That difficulty occurs because it is currently impossible to verify algorithms which determine these sources in clinical data, as high resolution true excitation patterns cannot be recorded in patients. Therefore, alternative approaches, like computer modelling are of great interest. In a recent published study such an approach was applied for the verification of one of the most commonly used algorithms, phase mapping (PM). A meandering rotor was simulated in the right atrium and a basket catheter was placed at 3 different locations: at the Superior Vena Cava (SVC), the Crista Terminalis (CT) and at the Coronary Sinus (CS). It was shown that although PM can identify the true source, it also finds several false sources due to the far-field effects and interpolation errors in all three positions. In addition, the detection efficiency strongly depended on the basket location. Recently, a novel tool was developed to analyse any arrhythmia called Directed Graph Mapping (DGM). DGM is based on network theory and creates a directed graph of the excitation pattern, from which the location and the source of the arrhythmia can be detected. Therefore, the objective of the current study was to compare the efficiency of DGM with PM on the basket dataset of this meandering rotor. The DGM-tool was applied for a wide variety of conduction velocities (minimal and maximal), which are input parameters of DGM. Overall we found that DGM was able to distinguish between the true rotor and false rotors for both the SVC and CT basket positions. For example, for the SVC position with a CVmin=0.01cmms, DGM detected the true core with a prevalence of 82% versus 94% for PM. Three false rotors where detected for 39.16% (DGM) versus 100% (PM); 22.64% (DGM) versus 100% (PM); and 0% (DGM) versus 57% (PM). Increasing CVmin to 0.02cmms had a stronger effect on the false rotors than on the true rotor. This led to a detection rate of 56.6% for the true rotor, while all the other false rotors disappeared. A similar trend was observed for the CT position. For the CS position, DGM already had a low performance for the true rotor for CVmin=0.01cmms (14.7%). For CVmin=0.02cmms the false and the true rotors could therefore not be distinguished. We can conclude that DGM can overcome some of the limitations of PM by varying one of its input parameters (CVmin). The true rotor is less dependent on this parameter than the false rotors, which disappear at a CVmin=0.02cmms. In order to increase to detection rate of the true rotor, one can decrease CVmin and discard the new rotors which also appear at lower values of CVmin.

Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation

AIMS

Atrial fibrillation (AF) is sustained by re-entrant activation patterns. Ablation strategies have been proposed that target regions of tissue that may support re-entrant activation patterns. We aimed to characterize the tissue properties associated with regions that tether re-entrant activation patterns in a validated virtual patient cohort.

METHODS AND RESULTS

Atrial fibrillation patient-specific models (seven paroxysmal and three persistent) were generated and validated against local activation time (LAT) measurements during an S1-S2 pacing protocol from the coronary sinus and high right atrium, respectively. Atrial models were stimulated with burst pacing from three locations in the proximity of each pulmonary vein to initiate re-entrant activation patterns. Five atria exhibited sustained activation patterns for at least 80 s. Models with short maximum action potential durations (APDs) were associated with sustained activation. Phase singularities were mapped across the atria sustained activation patterns. Regions with a low maximum conduction velocity (CV) were associated with tethering of phase singularities. A support vector machine (SVM) was trained on maximum local conduction velocity and action potential duration to identify regions that tether phase singularities. The SVM identified regions of tissue that could support tethering with 91% accuracy. This accuracy increased to 95% when the SVM was also trained on surface area.

CONCLUSION

In a virtual patient cohort, local tissue properties, that can be measured (CV) or estimated (APD; using effective refractory period as a surrogate) clinically, identified regions of tissue that tether phase singularities. Combing CV and APD with atrial surface area further improved the accuracy in identifying regions that tether phase singularities.

Short P-Wave Duration is a Marker of Higher Rate of Atrial Fibrillation Recurrences after Pulmonary Vein Isolation: New Insights into the Pathophysiological Mechanisms Through Computer Simulations

Background Short ECG P-wave duration has recently been demonstrated to be associated with higher risk of atrial fibrillation (AF). The aim of this study was to assess the rate of AF recurrence after pulmonary vein isolation in patients with a short P wave, and to mechanistically elucidate the observation by computer modeling. Methods and Results A total of 282 consecutive patients undergoing a first single-pulmonary vein isolation procedure for paroxysmal or persistent AF were included. Computational models studied the effect of adenosine and sodium conductance on action potential duration and P-wave duration (PWD). About 16% of the patients had a PWD of 110 ms or shorter (median PWD 126 ms, interquartile range, 115 ms-138 ms; range, 71 ms-180 ms). At Cox regression, PWD was significantly associated with AF recurrence (P=0.012). Patients with a PWD <110 ms (hazard ratio [HR], 2.20; 95% CI, 1.24-3.88; P=0.007) and patients with a PWD ≥140 (HR, 1.87, 95% CI, 1.06-3.30; P=0.031) had a nearly 2-fold increase in risk with respect to the other group. In the computational model, adenosine yielded a significant reduction of action potential duration 90 (52%) and PWD (7%). An increased sodium conductance (up to 200%) was robustly accompanied by an increase in conduction velocity (26%), a reduction in action potential duration 90 (28%), and PWD (22%). Conclusions One out of 5 patients referred for pulmonary vein isolation has a short PWD which was associated with a higher rate of AF after the index procedure. Computer simulations suggest that shortening of atrial action potential duration leading to a faster atrial conduction may be the cause of this clinical observation.

Translational applications of computational modelling for patients with cardiac arrhythmias

Cardiac arrhythmia is associated with high morbidity, and its underlying mechanisms are poorly understood. Computational modelling and simulation approaches have the potential to improve standard-of-care therapy for these disorders, offering deeper understanding of complex disease processes and sophisticated translational tools for planning clinical procedures. This review provides a clinician-friendly summary of recent advancements in computational cardiology. Organ-scale models automatically generated from clinical-grade imaging data are used to custom tailor our understanding of arrhythmia drivers, estimate future arrhythmogenic risk and personalise treatment plans. Recent mechanistic insights derived from atrial and ventricular arrhythmia simulations are highlighted, and the potential avenues to patient care (eg, by revealing new antiarrhythmic drug targets) are covered. Computational approaches geared towards improving outcomes in resynchronisation therapy have used simulations to elucidate optimal patient selection and lead location. Technology to personalise catheter ablation procedures are also covered, specifically preliminary outcomes form early-stage or pilot clinical studies. To conclude, future developments in computational cardiology are discussed, including improving the representation of patient-specific fibre orientations and fibrotic remodelling characterisation and how these might improve understanding of arrhythmia mechanisms and provide transformative tools for patient-specific therapy.

Fibrosis, atrial fibrillation and stroke: clinical updates and emerging mechanistic models

The current paradigm of stroke risk assessment and mitigation in patients with atrial fibrillation (AF) is centred around clinical risk factors which, in the presence of AF, lead to thrombus formation. The mechanisms by which these clinical risk factors lead to thromboembolism, including any role played by atrial fibrosis, are not understood. In patients who had embolic stroke of undetermined source (ESUS), the problem is compounded by the absence of AF in a majority of patients despite long-term monitoring. Atrial fibrosis has emerged as a unifying mechanism that independently provides a substrate for arrhythmia and thrombus formation. Fibrosis-based computational models of AF initiation and maintenance promise to identify therapeutic targets in catheter ablation. In ESUS, fibrosis is also increasingly recognised as a major risk factor, but the underlying mechanism of this correlation is unclear. Simulations have uncovered potential vulnerability to arrhythmia induction in patients who had ESUS. Likewise, computational models of fluid dynamics representing blood flow in the left atrium and left atrium appendage have improved our understanding of thrombus formation, in particular left atrium appendage shapes and blood flow changes influenced by atrial remodelling. Multiscale modelling of blood flow dynamics based on structural fibrotic and morphological changes with associated cellular and tissue electrical remodelling leading to electromechanical abnormalities holds tremendous promise in providing a mechanistic understanding of the clinical problem of thromboembolisation. We present a review of clinical knowledge alongside computational modelling frameworks and conclude with a vision of a future paradigm integrating simulations in formulating personalised treatment plans for each patient.

Dynamics of Atrial Fibrillation Mechanisms and Comorbidities

Atrial fibrillation (AF) contributes to morbidity and mortality of millions of individuals. Its molecular, cellular, neurohumoral, and hemodynamic pathophysiological mechanisms are complex, and there is increasing awareness that a wide range of comorbidities can contribute to AF-promoting atrial remodeling. Moreover, recent research has highlighted that AF risk is not constant and that the temporal variation in concomitant conditions contributes to the complexity of AF dynamics. In this review, we provide an overview of fundamental AF mechanisms related to established and emerging comorbidities or risk factors and their role in the AF-promoting effects. We focus on the accumulating evidence for the relevance of temporally dynamic changes in these risk factors and the consequence for AF initiation and maintenance. Finally, we highlight the important implications for future research and clinical practice resulting from the dynamic interaction between AF risk factors and mechanisms.

A Novel Tool for the Identification and Characterization of Repetitive Patterns in High-Density Contact Mapping of Atrial Fibrillation

Introduction

Electrical contact mapping provides a detailed view of conduction patterns in the atria during atrial fibrillation (AF). Identification of repetitive wave front propagation mechanisms potentially initiating or sustaining AF might provide more insights into temporal and spatial distribution of candidate AF mechanism and identify targets for catheter ablation. We developed a novel tool based on recurrence plots to automatically identify and characterize repetitive conduction patterns in high-density contact mapping of AF.

Materials and Methods

Recurrence plots were constructed by first transforming atrial electrograms recorded by a multi-electrode array to activation-phase signals and then quantifying the degree of similarity between snapshots of the activation-phase in the electrode array. An AF cycle length dependent distance threshold was applied to discriminate between repetitive and non-repetitive snapshots. Intervals containing repetitive conduction patterns were detected in a recurrence plot as regions with a high recurrence rate. Intervals that contained similar repetitive patterns were then grouped into clusters. To demonstrate the ability to detect and quantify the incidence, duration and size of repetitive patterns, the tool was applied to left and right atrial recordings in a goat model of different duration of persistent AF [3 weeks AF (3 wkAF, n = 8) and 22 weeks AF (22 wkAF, n = 8)], using a 249-electrode mapping array (2.4 mm inter-electrode distance).

Results

Recurrence plots identified frequent recurrences of activation patterns in all recordings and indicated a strong correlation between recurrence plot threshold and AF cycle length. Prolonged AF duration was associated with shorter repetitive pattern duration [mean maximum duration 3 wkAF: 74 cycles, 95% confidence interval (54-94) vs. 22 wkAF: 41 cycles (21-62), p = 0.03], and smaller recurrent regions within repetitive patterns [3 wkAF 1.7 cm² (1.0-2.3) vs. 22 wkAF 0.5 cm² (0.0-1.2), p = 0.02]. Both breakthrough patterns and re-entry were identified as repetitive conduction patterns.

Conclusion

Recurrence plots provide a novel way to delineate high-density contact mapping of AF. Dominant repetitive conduction patterns were identified in a goat model of sustained AF. Application of the developed methodology using the new generation of multi-electrode catheters could identify additional targets for catheter ablation of AF.

Long-standing Persistent Atrial Fibrillation Ablation: the Role of the Inter- and Intra-atrial Bundles

Catheter ablation has become the mainstream treatment of atrial fibrillation, but still remains a challenge in those patient with persistent and long standing persistent atrial fibrillation. In addition of isolation of the pulmonary veins, any other areas that can trigger or perpetuate atrial fibrillation need to be isolated. Current technologies may allow to effectively deliver permanently lasting lesions, and therefore improve clinical outcomes after ablation. The specialized conduction system including the Bachmann and septopulmonary bundles, are important substrate targets for the management of atrial fibrillation. The anatomical location of these fibers, and the corresponding approach for ablation are described in this case.

Identifying locations of re-entrant drivers from patient-specific distribution of fibrosis in the left atrium

Clinical evidence suggests a link between fibrosis in the left atrium (LA) and atrial fibrillation (AF), the most common sustained arrhythmia. Image-derived fibrosis is increasingly used for patient stratification and therapy guidance. However, locations of re-entrant drivers (RDs) sustaining AF are unknown and therapy success rates remain suboptimal. This study used image-derived LA models to explore the dynamics of RD stabilization in fibrotic regions and generate maps of RD locations. LA models with patient-specific geometry and fibrosis distribution were derived from late gadolinium enhanced magnetic resonance imaging of 6 AF patients. In each model, RDs were initiated at multiple locations, and their trajectories were tracked and overlaid on the LA fibrosis distributions to identify the most likely regions where the RDs stabilized. The simulations showed that the RD dynamics were strongly influenced by the amount and spatial distribution of fibrosis. In patients with fibrosis burden greater than 25%, RDs anchored to specific locations near large fibrotic patches. In patients with fibrosis burden below 25%, RDs either moved near small fibrotic patches or anchored to anatomical features. The patient-specific maps of RD locations showed that areas that harboured the RDs were much smaller than the entire fibrotic areas, indicating potential targets for ablation therapy. Ablating the predicted locations and connecting them to the existing pulmonary vein ablation lesions was the most effective in-silico ablation strategy.