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

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.

Computational Modeling and Simulation of the Fibrotic Human Atria

Patient-specific modeling of atrial electrical activity enables the execution of simulations that can provide mechanistic insights and provide novel solutions to vexing clinical problems. The geometry and fibrotic remodeling of the heart can be reconstructed from clinical-grade medical scans and used to inform personalized models with detail incorporated at the cell- and tissue-scale to represent changes in image-identified diseased regions. Here, we provide a rubric for the reconstruction of realistic atrial models from pre-segmented 3D renderings of the left atrium with fibrotic tissue regions delineated, which are the output from clinical-grade systems for quantifying fibrosis. We then provide a roadmap for using those models to carry out patient-specific characterization of the fibrotic substrate in terms of its potential to harbor reentrant drivers via cardiac electrophysiology simulations.

Patchy fibrosis promotes trigger-substrate interactions that both generate and maintain atrial fibrillation

Fibrosis has been mechanistically linked to arrhythmogenesis in multiple cardiovascular conditions, including atrial fibrillation (AF). Previous studies have demonstrated that fibrosis can create functional barriers to conduction which may promote excitation wavebreak and the generation of re-entry, while also acting to pin re-entrant excitation in stable rotors during AF. However, few studies have investigated the role of fibrosis in the generation of AF triggers in detail. We apply our in-house computational framework to study the impact of fibrosis on the generation of AF triggers and trigger-substrate interactions in two- and three-dimensional atrial tissue models. Our models include a reduced and efficient description of stochastic, spontaneous cellular triggers as well as a simple model of heterogeneous inter-cellular coupling. Our results demonstrate that fibrosis promotes the emergence of focal excitations, primarily through reducing the electrotonic load on individual fibre strands. This enables excitation to robustly initiate within these single strands before spreading to neighbouring strands and inducing a full tissue focal excitation. Enhanced conduction block can allow trigger-substrate interactions that result in the emergence of complex, re-entrant excitation patterns. This study provides new insight into the mechanisms by which fibrosis promotes the triggers and substrate necessary to induce and sustain arrhythmia.

A new procedure for elimination of atrial fibrillation associated with mitral valve disease: a proof-of-concept study

BACKGROUND

Left atrial enlargement and fibrosis have been linked to the pathogenesis of atrial fibrillation (AF). The authors aimed to introduce a novel concept and develop a new procedure for AF treatment based on these characteristics.

METHODS

The study included three stages. The first stage was a descriptive study to clarify the characteristics of the left atrial enlargement and fibrosis' distribution in patients with mitral valve disease and long-standing persistent AF. Based on these characteristics, the authors introduced a novel concept for AF treatment, and then translated it into a new procedure. The second stage was a proof-of-concept study with this new procedure. The third stage was a comparative effectiveness research to compare the clinical outcomes between patients with this new procedure and those who received Cox-Maze IV treatment.

RESULTS

Based on the nonuniform fashion of left atrial enlargement and fibrosis' distribution, the authors introduced a novel concept: reconstructing a left atrium with appropriate geometry and uniform fibrosis' distribution for proper cardiac conduction, and translated it into a new procedure: left atrial geometric volume reduction combined with left appendage base closure. As compared to the Cox-Maze IV procedure, the new procedure spent significantly shorter total surgery time, cardiopulmonary bypass time, and aortic cross-clamp time ( P <0.001). Besides, the new procedure was related to a shorter ICU stay period (odd ratio (OR)=0.45, 95% CI=0.26-0.78), lower costs (OR=0.15, 95% CI=0.08-0.29), and a higher rate of A wave of transmitral and transtricuspid flow reappearance (OR=1.76, 95% CI=1.02-3.04).

CONCLUSIONS

The new procedure is safe and effective for eliminating AF associated with mitral valve disease.

Comparing Inducibility of Re-Entrant Arrhythmia in Patient-Specific Computational Models to Clinical Atrial Fibrillation Phenotypes

BACKGROUND

Computational models of fibrosis-mediated, re-entrant left atrial (LA) arrhythmia can identify possible substrate for persistent atrial fibrillation (AF) ablation. Contemporary models use a 1-size-fits-all approach to represent electrophysiological properties, limiting agreement between simulations and patient outcomes.

OBJECTIVES

The goal of this study was to test the hypothesis that conduction velocity (ϴ) modulation in persistent AF models can improve simulation agreement with clinical arrhythmias.

METHODS

Patients with persistent AF (n = 37) underwent ablation and were followed up for ≥2 years to determine post-ablation outcomes: AF, atrial flutter (AFL), or no recurrence. Patient-specific LA models (n = 74) were constructed using pre-ablation and ≥90 days' post-ablation magnetic resonance imaging data. Simulated pacing gauged in silico arrhythmia inducibility due to AF-like rotors or AFL-like macro re-entrant tachycardias. A physiologically plausible range of ϴ values (±10 or 20% vs. baseline) was tested, and model/clinical agreement was assessed.

RESULTS

Fifteen (41%) patients had a recurrence with AF and 6 (16%) with AFL. Arrhythmia was induced in 1,078 of 5,550 simulations. Using baseline ϴ, model/clinical agreement was 46% (34 of 74 models), improving to 65% (48 of 74) when any possible ϴ value was used (McNemar's test, P = 0.014). ϴ modulation improved model/clinical agreement in both pre-ablation and post-ablation models. Pre-ablation model/clinical agreement was significantly greater for patients with extensive LA fibrosis (>17.2%) and an elevated body mass index (>32.0 kg/m2).

CONCLUSIONS

Simulations in persistent AF models show a 41% relative improvement in model/clinical agreement when ϴ is modulated. Patient-specific calibration of ϴ values could improve model/clinical agreement and model usefulness, especially in patients with higher body mass index or LA fibrosis burden. This could ultimately facilitate better personalized modeling, with immediate clinical implications.

Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models

Background Postablation arrhythmia recurrence occurs in ~40% of patients with persistent atrial fibrillation. Fibrotic remodeling exacerbates arrhythmic activity in persistent atrial fibrillation and can play a key role in reentrant arrhythmia, but emergent interaction between nonconductive ablation-induced scar and native fibrosis (ie, residual fibrosis) is poorly understood. Methods and Results We conducted computational simulations in pre- and postablation left atrial models reconstructed from late gadolinium enhanced magnetic resonance imaging scans to test the hypothesis that ablation in patients with persistent atrial fibrillation creates new substrate conducive to recurrent arrhythmia mediated by anchored reentry. We trained a random forest machine learning classifier to accurately pinpoint specific nonconductive tissue regions (ie, areas of ablation-delivered scar or vein/valve boundaries) with the capacity to serve as substrate for anchored reentry-driven recurrent arrhythmia (area under the curve: 0.91±0.03). Our analysis suggests there is a distinctive nonconductive tissue pattern prone to serving as arrhythmogenic substrate in postablation models, defined by a specific size and proximity to residual fibrosis. Conclusions Overall, this suggests persistent atrial fibrillation ablation transforms substrate that favors functional reentry (ie, rotors meandering in excitable tissue) into an arrhythmogenic milieu more conducive to anchored reentry. Our work also indicates that explainable machine learning and computational simulations can be combined to effectively probe mechanisms of recurrent arrhythmia.

A Review on Atrial Fibrillation (Computer Simulation and Clinical Perspectives)

Atrial fibrillation (AF), a heart condition, has been a well-researched topic for the past few decades. This multidisciplinary field of study deals with signal processing, finite element analysis, mathematical modeling, optimization, and clinical procedure. This article is focused on a comprehensive review of journal articles published in the field of AF. Topics from the age-old fundamental concepts to specialized modern techniques involved in today’s AF research are discussed. It was found that a lot of research articles have already been published in modeling and simulation of AF. In comparison to that, the diagnosis and post-operative procedures for AF patients have not yet been totally understood or explored by the researchers. The simulation and modeling of AF have been investigated by many researchers in this field. Cellular model, tissue model, and geometric model among others have been used to simulate AF. Due to a very complex nature, the causes of AF have not been fully perceived to date, but the simulated results are validated with real-life patient data. Many algorithms have been proposed to detect the source of AF in human atria. There are many ablation strategies for AF patients, but the search for more efficient ablation strategies is still going on. AF management for patients with different stages of AF has been discussed in the literature as well but is somehow limited mostly to the patients with persistent AF. The authors hope that this study helps to find existing research gaps in the analysis and the diagnosis of AF.

Current progress of computational modeling for guiding clinical atrial fibrillation ablation

Atrial fibrillation (AF) is one of the most common arrhythmias, associated with high morbidity, mortality, and healthcare costs, and it places a significant burden on both individuals and society. Anti-arrhythmic drugs are the most commonly used strategy for treating AF. However, drug therapy faces challenges because of its limited efficacy and potential side effects. Catheter ablation is widely used as an alternative treatment for AF. Nevertheless, because the mechanism of AF is not fully understood, the recurrence rate after ablation remains high. In addition, the outcomes of ablation can vary significantly between medical institutions and patients, especially for persistent AF. Therefore, the issue of which ablation strategy is optimal is still far from settled. Computational modeling has the advantages of repeatable operation, low cost, freedom from risk, and complete control, and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance. This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF, from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation. Finally, we summarize current developments and challenges and provide our perspectives and suggestions for future directions.

POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium

The numerical simulation of multiple scenarios easily becomes computationally prohibitive for cardiac electrophysiology (EP) problems if relying on usual high-fidelity, full order models (FOMs). Likewise, the use of traditional reduced order models (ROMs) for parametrized PDEs to speed up the solution of the aforementioned problems can be problematic. This is primarily due to the strong variability characterizing the solution set and to the nonlinear nature of the input-output maps that we intend to reconstruct numerically. To enhance ROM efficiency, we proposed a new generation of non-intrusive, nonlinear ROMs, based on deep learning (DL) algorithms, such as convolutional, feedforward, and autoencoder neural networks. In the proposed DL-ROM, both the nonlinear solution manifold and the nonlinear reduced dynamics used to model the system evolution on that manifold can be learnt in a non-intrusive way thanks to DL algorithms trained on a set of FOM snapshots. DL-ROMs were shown to be able to accurately capture complex front propagation processes, both in physiological and pathological cardiac EP, very rapidly once neural networks were trained, however, at the expense of huge training costs. In this study, we show that performing a prior dimensionality reduction on FOM snapshots through randomized proper orthogonal decomposition (POD) enables to speed up training times and to decrease networks complexity. Accuracy and efficiency of this strategy, which we refer to as POD-DL-ROM, are assessed in the context of cardiac EP on an idealized left atrium (LA) geometry and considering snapshots arising from a NURBS (non-uniform rational B-splines)-based isogeometric analysis (IGA) discretization. Once the ROMs have been trained, POD-DL-ROMs can efficiently solve both physiological and pathological cardiac EP problems, for any new scenario, in real-time, even in extremely challenging contexts such as those featuring circuit re-entries, that are among the factors triggering cardiac arrhythmias.

Cycle length statistics during human atrial fibrillation reveal refractory properties of the underlying substrate: a combined in silico and clinical test of concept study

AIMS

The treatment of atrial fibrillation beyond pulmonary vein isolation has remained an unsolved challenge. Targeting regions identified by different substrate mapping approaches for ablation resulted in ambiguous outcomes. With the effective refractory period being a fundamental prerequisite for the maintenance of fibrillatory conduction, this study aims at estimating the effective refractory period with clinically available measurements.

METHODS AND RESULTS

A set of 240 simulations in a spherical model of the left atrium with varying model initialization, combination of cellular refractory properties, and size of a region of lowered effective refractory period was implemented to analyse the capabilities and limitations of cycle length mapping. The minimum observed cycle length and the 25% quantile were compared to the underlying effective refractory period. The density of phase singularities was used as a measure for the complexity of the excitation pattern. Finally, we employed the method in a clinical test of concept including five patients. Areas of lowered effective refractory period could be distinguished from their surroundings in simulated scenarios with successfully induced multi-wavelet re-entry. Larger areas and higher gradients in effective refractory period as well as complex activation patterns favour the method. The 25% quantile of cycle lengths in patients with persistent atrial fibrillation was found to range from 85 to 190 ms.

CONCLUSION

Cycle length mapping is capable of highlighting regions of pathologic refractory properties. In combination with complementary substrate mapping approaches, the method fosters confidence to enhance the treatment of atrial fibrillation beyond pulmonary vein isolation particularly in patients with complex activation patterns.

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.

Spatial concentration and distribution of phase singularities in human atrial fibrillation: Insights for the AF mechanism

BACKGROUND

Atrial fibrillation (AF) is characterized by the repetitive regeneration of unstable rotational events, the pivot of which are known as phase singularities (PSs). The spatial concentration and distribution of PSs have not been systematically investigated using quantitative statistical approaches.

OBJECTIVES

We utilized a geospatial statistical approach to determine the presence of local spatial concentration and global clustering of PSs in biatrial human AF recordings.

METHODS

64-electrode conventional basket (~5 min, n = 18 patients, persistent AF) recordings were studied. Phase maps were produced using a Hilbert-transform based approach. PSs were characterized spatially using the following approaches: (i) local "hotspots" of high phase singularity (PS) concentration using Getis-Ord Gi* (Z ≥ 1.96, P ≤ .05) and (ii) global spatial clustering using Moran's I (inverse distance matrix).

RESULTS

Episodes of AF were analyzed from basket catheter recordings (H: 41 epochs, 120 000 s, n = 18 patients). The Getis-Ord Gi* statistic showed local PS hotspots in 12/41 basket recordings. As a metric of spatial clustering, Moran's I showed an overall mean of 0.033 (95% CI: 0.0003-0.065), consistent with the notion of complete spatial randomness.

CONCLUSION

Using a systematic, quantitative geospatial statistical approach, evidence for the existence of spatial concentrations ("hotspots") of PSs were detectable in human AF, along with evidence of spatial clustering. Geospatial statistical approaches offer a new approach to map and ablate PS clusters using substrate-based approaches.

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodelling processes. Atrial remodelling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modelling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modelling in AF management. We summarize clinical challenges that may benefit from computational modelling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modelling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.

Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate

Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrate's arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7 ± 5.45%) than non-inducible models (11.07 ± 3.61%; p<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (p=0.90), meaning that the intrinsic pro-arrhythmic substrate properties of fibrosis in ESUS and AFib are indistinguishable. This suggests that some ESUS patients have latent pre-clinical fibrotic substrate that could be a future source of arrhythmogenicity. Thus, our work prompts the hypothesis that ESUS patients with fibrotic atria are spared from AFib due to an absence of arrhythmia triggers.

OptoGap is an optogenetics-enabled assay for quantification of cell-cell coupling in multicellular cardiac tissue

Intercellular electrical coupling is an essential means of communication between cells. It is important to obtain quantitative knowledge of such coupling between cardiomyocytes and non-excitable cells when, for example, pathological electrical coupling between myofibroblasts and cardiomyocytes yields increased arrhythmia risk or during the integration of donor (e.g., cardiac progenitor) cells with native cardiomyocytes in cell-therapy approaches. Currently, there is no direct method for assessing heterocellular coupling within multicellular tissue. Here we demonstrate experimentally and computationally a new contactless assay for electrical coupling, OptoGap, based on selective illumination of inexcitable cells that express optogenetic actuators and optical sensing of the response of coupled excitable cells (e.g., cardiomyocytes) that are light-insensitive. Cell-cell coupling is quantified by the energy required to elicit an action potential via junctional current from the light-stimulated cell(s). The proposed technique is experimentally validated against the standard indirect approach, GapFRAP, using light-sensitive cardiac fibroblasts and non-transformed cardiomyocytes in a two-dimensional setting. Its potential applicability to the complex three-dimensional setting of the native heart is corroborated by computational modelling and proper calibration. Lastly, the sensitivity of OptoGap to intrinsic cell-scale excitability is robustly characterized via computational analysis.

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.

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.

Arrhythmogenic propensity of the fibrotic substrate after atrial fibrillation ablation: a longitudinal study using magnetic resonance imaging-based atrial models

AIMS

Inadequate modification of the atrial fibrotic substrate necessary to sustain re-entrant drivers (RDs) may explain atrial fibrillation (AF) recurrence following failed pulmonary vein isolation (PVI). Personalized computational models of the fibrotic atrial substrate derived from late gadolinium enhanced (LGE)-magnetic resonance imaging (MRI) can be used to non-invasively determine the presence of RDs. The objective of this study is to assess the changes of the arrhythmogenic propensity of the fibrotic substrate after PVI.

METHODS AND RESULTS

Pre- and post-ablation individualized left atrial models were constructed from 12 AF patients who underwent pre- and post-PVI LGE-MRI, in six of whom PVI failed. Pre-ablation AF sustained by RDs was induced in 10 models. RDs in the post-ablation models were classified as either preserved or emergent. Pre-ablation models derived from patients for whom the procedure failed exhibited a higher number of RDs and larger areas defined as promoting RD formation when compared with atrial models from patients who had successful ablation, 2.6 ± 0.9 vs. 1.8 ± 0.2 and 18.9 ± 1.6% vs. 13.8 ± 1.5%, respectively. In cases of successful ablation, PVI eliminated completely the RDs sustaining AF. Preserved RDs unaffected by ablation were documented only in post-ablation models of patients who experienced recurrent AF (2/5 models); all of these models had also one or more emergent RDs at locations distinct from those of pre-ablation RDs. Emergent RDs occurred in regions that had the same characteristics of the fibrosis spatial distribution (entropy and density) as regions that harboured RDs in pre-ablation models.

CONCLUSION

Recurrent AF after PVI in the fibrotic atria may be attributable to both preserved RDs that sustain AF pre- and post-ablation, and the emergence of new RDs following ablation. The same levels of fibrosis entropy and density underlie the pro-RD propensity in both pre- and post-ablation substrates.

Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation

BACKGROUND

Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI.

METHODS

This single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI.

RESULTS

In our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47).

CONCLUSIONS

ML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.

Characterization of the human myocardium by optical coherence tomography

Imaging of cardiac tissue structure plays a critical role in the treatment and understanding of cardiovascular disease. Optical coherence tomography (OCT) offers the potential to provide valuable, high-resolution imaging of cardiac tissue. However, there is a lack of comprehensive OCT imaging data of the human heart, which could improve identification of structural substrates underlying cardiac abnormalities. The objective of this study was to provide qualitative and quantitative analysis of OCT image features throughout the human heart. Fifty human hearts were acquired, and tissues from all chambers were imaged with OCT. Histology was obtained to verify tissue composition. Statistical differences between OCT image features corresponding to different tissue types and chambers were estimated using analysis of variance. OCT imaging provided features that were able to distinguish structures such as thickened collagen, as well as adipose tissue and fibrotic myocardium. Statistically significant differences were found between atria and ventricles in attenuation coefficient, and between adipose and all other tissue types. This study provides an overview of OCT image features throughout the human heart, which can be used for guiding future applications such as OCT-integrated catheter-based treatments or ex vivo investigation of structural substrates.

Research Needs and Priorities for Catheter Ablation of Atrial Fibrillation: A Report From a National Heart, Lung, and Blood Institute Virtual Workshop

Catheter ablation has brought major advances in the management of patients with atrial fibrillation (AF). As evidenced by multiple randomized trials, AF catheter ablation can reduce the risk of recurrent AF and improve quality of life. In some studies, AF ablation significantly reduced cardiovascular hospitalizations. Despite the existing data on AF catheter ablation, numerous knowledge gaps remain concerning this intervention. This report is based on a recent virtual workshop convened by the National Heart, Lung, and Blood Institute to identify key research opportunities in AF ablation. We outline knowledge gaps related to emerging technologies, the relationship between cardiac structure and function and the success of AF ablation in patient subgroups in whom clinical benefit from ablation varies, and potential platforms to advance clinical research in this area. This report also considers the potential value and challenges of a sham ablation randomized trial. Prioritized research opportunities are identified and highlighted to empower relevant stakeholders to collaborate in designing and conducting effective, cost-efficient, and transformative research to optimize the use and outcomes of AF ablation.

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.

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.

Relaxin and fibrosis: Emerging targets, challenges, and future directions

The peptide hormone relaxin is well-known for its anti-fibrotic actions in several organs, particularly from numerous studies conducted in animals. Acting through its cognate G protein-coupled receptor, relaxin family peptide receptor 1 (RXFP1), serelaxin (recombinant human relaxin) has been shown to consistently inhibit the excessive extracellular matrix production (fibrosis) that results from the aberrant wound-healing response to tissue injury and/or chronic inflammation, and at multiple levels. Furthermore, it can reduce established scarring by promoting the degradation of aberrant extracellular matrix components. Following on from the review that describes the mechanisms and signaling pathways associated with the extracellular matrix remodeling effects of serelaxin (Ng et al., 2019), this review focuses on newly identified tissue targets of serelaxin therapy in fibrosis, and the limitations associated with (se)relaxin research.

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.