Fully automated initiation of simulated episodes of atrial arrhythmias

What determines the optimal pharmacological treatment of atrial fibrillation? Insights from in silico trials in 800 virtual atria

The best pharmacological treatment for each atrial fibrillation (AF) patient is unclear. We aim to exploit AF simulations in 800 virtual atria to identify key patient characteristics that guide the optimal selection of anti-arrhythmic drugs. The virtual cohort considered variability in electrophysiology and low voltage areas (LVA) and was developed and validated against experimental and clinical data from ionic currents to ECG. AF sustained in 494 (62%) atria, with large inward rectifier K+ current (IK1 ) and Na+ /K+ pump (INaK ) densities (IK1 0.11 ± 0.03 vs. 0.07 ± 0.03 S mF-1 ; INaK 0.68 ± 0.15 vs. 0.38 ± 26 S mF-1 ; sustained vs. un-sustained AF). In severely remodelled left atrium, with LVA extensions of more than 40% in the posterior wall, higher IK1 (median density 0.12 ± 0.02 S mF-1 ) was required for AF maintenance, and rotors localized in healthy right atrium. For lower LVA extensions, rotors could also anchor to LVA, in atria presenting short refractoriness (median L-type Ca2+ current, ICaL , density 0.08 ± 0.03 S mF-1 ). This atrial refractoriness, modulated by ICaL and fast Na+ current (INa ), determined pharmacological treatment success for both small and large LVA. Vernakalant was effective in atria presenting long refractoriness (median ICaL density 0.13 ± 0.05 S mF-1 ). For short refractoriness, atria with high INa (median density 8.92 ± 2.59 S mF-1 ) responded more favourably to amiodarone than flecainide, and the opposite was found in atria with low INa (median density 5.33 ± 1.41 S mF-1 ). In silico drug trials in 800 human atria identify inward currents as critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics. KEY POINTS: Atrial fibrillation (AF) maintenance is facilitated by small L-type Ca2+ current (ICaL ) and large inward rectifier K+ current (IK1 ) and Na+ /K+ pump. In severely remodelled left atrium, with low voltage areas (LVA) covering more than 40% of the posterior wall, sustained AF requires higher IK1 and rotors localize in healthy right atrium. For lower LVA extensions, rotors can also anchor to LVA, if the atria present short refractoriness (low ICaL ) Vernakalant is effective in atria presenting long refractoriness (high ICaL ). For short refractoriness, atria with fast Na+ current (INa ) up-regulation respond more favourably to amiodarone than flecainide, and the opposite is found in atria with low INa . The inward currents (ICaL and INa ) are critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics.

In-silico drug trials for precision medicine in atrial fibrillation: From ionic mechanisms to electrocardiogram-based predictions in structurally-healthy human atria

Atrial fibrillation (AF) inducibility, sustainability and response to pharmacological treatment of individual patients are expected to be determined by their ionic current properties, especially in structurally-healthy atria. Mechanisms underlying AF and optimal cardioversion are however still unclear. In this study, in-silico drug trials were conducted using a population of human structurally-healthy atria models to 1) identify key ionic current properties determining AF inducibility, maintenance and pharmacological cardioversion, and 2) compare the prognostic value for predicting individual AF cardioversion of ionic current properties and electrocardiogram (ECG) metrics. In the population of structurally-healthy atria, 477 AF episodes were induced in ionic current profiles with both steep action potential duration (APD) restitution (eliciting APD alternans), and high excitability (enabling propagation at fast rates that transformed alternans into discordant). High excitability also favored 211 sustained AF episodes, so its decrease, through prolonged refractoriness, explained pharmacological cardioversion. In-silico trials over 200 AF episodes, 100 ionic profiles and 10 antiarrhythmic compounds were consistent with previous clinical trials, and identified optimal treatments for individual electrophysiological properties of the atria. Algorithms trained on 211 simulated AF episodes exhibited >70% accuracy in predictions of cardioversion for individual treatments using either ionic current profiles or ECG metrics. In structurally-healthy atria, AF inducibility and sustainability are enabled by discordant alternans, under high excitability and steep restitution conditions. Successful pharmacological cardioversion is predicted with 70% accuracy from either ionic or ECG properties, and it is optimal for treatments maximizing refractoriness (thus reducing excitability) for the given ionic current profile of the atria.

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models

BACKGROUND

Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability.

METHODS

Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics.

RESULTS

We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14).

CONCLUSION

A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.

A Reproducible Protocol to Assess Arrhythmia Vulnerability in silico: Pacing at the End of the Effective Refractory Period

In both clinical and computational studies, different pacing protocols are used to induce arrhythmia and non-inducibility is often considered as the endpoint of treatment. The need for a standardized methodology is urgent since the choice of the protocol used to induce arrhythmia could lead to contrasting results, e.g., in assessing atrial fibrillation (AF) vulnerabilty. Therefore, we propose a novel method-pacing at the end of the effective refractory period (PEERP)-and compare it to state-of-the-art protocols, such as phase singularity distribution (PSD) and rapid pacing (RP) in a computational study. All methods were tested by pacing from evenly distributed endocardial points at 1 cm inter-point distance in two bi-atrial geometries. Seven different atrial models were implemented: five cases without specific AF-induced remodeling but with decreasing global conduction velocity and two persistent AF cases with an increasing amount of fibrosis resembling different substrate remodeling stages. Compared with PSD and RP, PEERP induced a larger variety of arrhythmia complexity requiring, on average, only 2.7 extra-stimuli and 3 s of simulation time to initiate reentry. Moreover, PEERP and PSD were the protocols which unveiled a larger number of areas vulnerable to sustain stable long living reentries compared to RP. Finally, PEERP can foster standardization and reproducibility, since, in contrast to the other protocols, it is a parameter-free method. Furthermore, we discuss its clinical applicability. We conclude that the choice of the inducing protocol has an influence on both initiation and maintenance of AF and we propose and provide PEERP as a reproducible method to assess arrhythmia vulnerability.

An audit of uncertainty in multi-scale cardiac electrophysiology models

Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Constructing a Human Atrial Fibre Atlas

Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies. We then expressed each fibre field in this atrial coordinate system and calculated an average fibre field. To assess the effects of fibre field on patient-specific modelling predictions, we calculated paced activation time maps and electrical driver locations during AF. In total, 756 activation time maps were calculated (7 anatomies with 9 fibre maps and 2 pacing locations, for the endocardial, epicardial and bilayer surface models of the LA and RA). Patient-specific fibre fields had a relatively small effect on average paced activation maps (range of mean local activation time difference for LA fields: 2.67-3.60 ms, and for RA fields: 2.29-3.44 ms), but had a larger effect on maximum LAT differences (range for LA 12.7-16.6%; range for RA 11.9-15.0%). A total of 126 phase singularity density maps were calculated (7 anatomies with 9 fibre maps for the LA and RA bilayer models). The fibre field corresponding to anatomy 1 had the highest median PS density map correlation coefficient for LA bilayer simulations (0.44 compared to the other correlations, ranging from 0.14 to 0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from 0.37 to 0.56). For sinus rhythm simulations, average activation time is robust to fibre field direction; however, maximum differences can still be significant. Patient specific fibres are more important for arrhythmia simulations, particularly in the left atrium. We propose using the fibre field corresponding to DTMRI dataset 1 for LA simulations, and the average fibre field for RA simulations as these optimally predicted arrhythmia properties.

Pharmacologic TWIK-Related Acid-Sensitive K+ Channel (TASK-1) Potassium Channel Inhibitor A293 Facilitates Acute Cardioversion of Paroxysmal Atrial Fibrillation in a Porcine Large Animal Model

Background The tandem of P domains in a weak inward rectifying K+ channel (TWIK)-related acid-sensitive K⁺ channel (TASK-1; hK_2P3.1) two-pore-domain potassium channel was recently shown to regulate the atrial action potential duration. In the human heart, TASK-1 channels are specifically expressed in the atria. Furthermore, upregulation of atrial TASK-1 currents was described in patients suffering from atrial fibrillation (AF). We therefore hypothesized that TASK-1 channels represent an ideal target for antiarrhythmic therapy of AF. In the present study, we tested the antiarrhythmic effects of the high-affinity TASK-1 inhibitor A293 on cardioversion in a porcine model of paroxysmal AF. Methods and Results Heterologously expressed human and porcine TASK-1 channels are blocked by A293 to a similar extent. Patch clamp measurements from isolated human and porcine atrial cardiomyocytes showed comparable TASK-1 currents. Computational modeling was used to investigate the conditions under which A293 would be antiarrhythmic. German landrace pigs underwent electrophysiological studies under general anesthesia. Paroxysmal AF was induced by right atrial burst stimulation. After induction of AF episodes, intravenous administration of A293 restored sinus rhythm within cardioversion times of 177±63 seconds. Intravenous administration of A293 resulted in significant prolongation of the atrial effective refractory period, measured at cycle lengths of 300, 400 and 500 ms, whereas the surface ECG parameters and the ventricular effective refractory period lengths remained unchanged. Conclusions Pharmacological inhibition of atrial TASK-1 currents exerts antiarrhythmic effects in vivo as well as in silico, resulting in acute cardioversion of paroxysmal AF. Taken together, these experiments indicate the therapeutic potential of A293 for AF treatment.

Computational modeling: What does it tell us about atrial fibrillation therapy?

Atrial fibrillation (AF) is a complex cardiac arrhythmia with diverse etiology that negatively affects morbidity and mortality of millions of patients. Technological and experimental advances have provided a wealth of information on the pathogenesis of AF, highlighting a multitude of mechanisms involved in arrhythmia initiation and maintenance, and disease progression. However, it remains challenging to identify the predominant mechanisms for specific subgroups of AF patients, which, together with an incomplete understanding of the pleiotropic effects of antiarrhythmic therapies, likely contributes to the suboptimal efficacy of current antiarrhythmic approaches. Computer modeling of cardiac electrophysiology has advanced in parallel to experimental research and provides an integrative framework to attempt to overcome some of these challenges. Multi-scale cardiac modeling and simulation integrate structural and functional data from experimental and clinical work with knowledge of atrial electrophysiological mechanisms and dynamics, thereby improving our understanding of AF mechanisms and therapy. In this review, we describe recent advances in our quantitative understanding of AF through mathematical models. We discuss computational modeling of AF mechanisms and therapy using detailed, mechanistic cell/tissue-level models, including approaches to incorporate variability in patient populations. We also highlight efforts using whole-atria models to improve catheter ablation therapies. Finally, we describe recent efforts and suggest future extensions to model clinical concepts of AF using patient-level models.

Patient-Specific Identification of Atrial Flutter Vulnerability-A Computational Approach to Reveal Latent Reentry Pathways

Atypical atrial flutter (AFlut) is a reentrant arrhythmia which patients frequently develop after ablation for atrial fibrillation (AF). Indeed, substrate modifications during AF ablation can increase the likelihood to develop AFlut and it is clinically not feasible to reliably and sensitively test if a patient is vulnerable to AFlut. Here, we present a novel method based on personalized computational models to identify pathways along which AFlut can be sustained in an individual patient. We build a personalized model of atrial excitation propagation considering the anatomy as well as the spatial distribution of anisotropic conduction velocity and repolarization characteristics based on a combination of a priori knowledge on the population level and information derived from measurements performed in the individual patient. The fast marching scheme is employed to compute activation times for stimuli from all parts of the atria. Potential flutter pathways are then identified by tracing loops from wave front collision sites and constricting them using a geometric snake approach under consideration of the heterogeneous wavelength condition. In this way, all pathways along which AFlut can be sustained are identified. Flutter pathways can be instantiated by using an eikonal-diffusion phase extrapolation approach and a dynamic multifront fast marching simulation. In these dynamic simulations, the initial pattern eventually turns into the one driven by the dominant pathway, which is the only pathway that can be observed clinically. We assessed the sensitivity of the flutter pathway maps with respect to conduction velocity and its anisotropy. Moreover, we demonstrate the application of tailored models considering disease-specific repolarization properties (healthy, AF-remodeled, potassium channel mutations) as well as applicabiltiy on a clinical dataset. Finally, we tested how AFlut vulnerability of these substrates is modulated by exemplary antiarrhythmic drugs (amiodarone, dronedarone). Our novel method allows to assess the vulnerability of an individual patient to develop AFlut based on the personal anatomical, electrophysiological, and pharmacological characteristics. In contrast to clinical electrophysiological studies, our computational approach provides the means to identify all possible AFlut pathways and not just the currently dominant one. This allows to consider all relevant AFlut pathways when tailoring clinical ablation therapy in order to reduce the development and recurrence of AFlut.

Anti-arrhythmic strategies for atrial fibrillation: The role of computational modeling in discovery, development, and optimization

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with increased risk of cerebrovascular stroke, and with several other pathologies, including heart failure. Current therapies for AF are targeted at reducing risk of stroke (anticoagulation) and tachycardia-induced cardiomyopathy (rate or rhythm control). Rate control, typically achieved by atrioventricular nodal blocking drugs, is often insufficient to alleviate symptoms. Rhythm control approaches include antiarrhythmic drugs, electrical cardioversion, and ablation strategies. Here, we offer several examples of how computational modeling can provide a quantitative framework for integrating multiscale data to: (a) gain insight into multiscale mechanisms of AF; (b) identify and test pharmacological and electrical therapy and interventions; and (c) support clinical decisions. We review how modeling approaches have evolved and contributed to the research pipeline and preclinical development and discuss future directions and challenges in the field.

Lessons from computer simulations of ablation of atrial fibrillation

This paper reviews the simulations of catheter ablation in computer models of the atria, from the first attempts to the most recent anatomical models. It describes how postulated substrates of atrial fibrillation can be incorporated into mathematical models, how modelling studies can be designed to test ablation strategies, what their current trade-offs and limitations are, and what clinically relevant lessons can be learnt from these simulations. Drawing a parallel between clinical and modelling studies, six ablation targets are considered: pulmonary vein isolation, linear ablation, ectopic foci, complex fractionated atrial electrogram, rotors and ganglionated plexi. The examples presented for each ablation target illustrate a major advantage of computer models, the ability to identify why a therapy is successful or not in a given atrial fibrillation substrate. The integration of pathophysiological data to create detailed models of arrhythmogenic substrates is expected to solidify the understanding of ablation mechanisms and to provide theoretical arguments supporting substrate-specific ablation strategies.

Modeling left and right atrial contributions to the ECG: A dipole-current source approach

This paper presents the mathematical formulation, the numerical validation and several illustrations of a forward-modeling approach based on dipole-current sources to compute the contribution of a part of the heart to the electrocardiogram (ECG). Clinically relevant applications include identifying in the ECG the contributions from the right and the left atrium. In a Courtemanche-based monodomain computer model of the atria and torso, 1000 dipoles distributed throughout the atrial mid-myocardium are found to be sufficient to reproduce body surface potential maps with a relative error <1% during both sinus rhythm and atrial fibrillation. When the boundary element method is applied to solve the forward problem, this approach enables fast offline computation of the ECG contribution of any anatomical part of the atria by applying the principle of superposition to the dipole sources. In the presence of a right-left activation delay (sinus rhythm), pulmonary vein isolation (sinus rhythm) or left-right differences in refractory period (atrial fibrillation), the decomposition of the ECG is shown to help interpret ECG morphology in relation to the atrial substrate. These tools provide a theoretical basis for a deeper understanding of the genesis of the P wave or fibrillatory waves in normal and pathological cases.

Geometrical measurement of cardiac wavelength in reaction-diffusion models

The dynamics of reentrant arrhythmias often consists in multiple wavelets propagating throughout an excitable medium. An arrhythmia can be sustained only if these reentrant waves have a sufficiently short wavelength defined as the distance traveled by the excitation wave during its refractory period. In a uniform medium, wavelength may be estimated as the product of propagation velocity and refractory period (electrophysiological wavelength). In order to accurately measure wavelength in more general substrates relevant to atrial arrhythmias (heterogeneous and anisotropic), we developed a mathematical framework to define geometrical wavelength at each time instant based on the length of streamlines following the propagation velocity field within refractory regions. Two computational methods were implemented: a Lagrangian approach in which a set of streamlines were integrated, and an Eulerian approach in which wavelength was the solution of a partial differential equation. These methods were compared in 1D/2D tissues and in a model of the left atrium. An advantage of geometrical definition of wavelength is that the wavelength of a wavelet can be tracked over time with high temporal resolution and smaller temporal variability in an anisotropic and heterogeneous medium. The results showed that the average electrophysiological wavelength was consistent with geometrical measurements of wavelength. Wavelets were however often shorter than the electrophysiological wavelength due to interactions with boundaries and other wavelets. These tools may help to assess more accurately the relation between substrate properties and wavelet dynamics in computer models.