Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature.

Computational models of atrial fibrillation (AF) can help improve success rates of interventions, such as ablation. However, evaluating the efficacy of different treatments requires performing multiple costly simulations by pacing at different points and checking whether AF has been induced or not, hindering the clinical application of these models. In this work, we propose a classification method that can predict AF inducibility in patient-specific cardiac models without running additional simulations.

High-density and high-coverage composite atrial activation maps: an in-silico validation study.

Objective: Repetitive atrial activation patterns (RAAPs) during complex atrial tachycardia could be associated with localized mechanisms that can be targeted. Clinically available electroanatomical mapping systems are limited by either the spatial coverage or electrode density of the mapping catheters, preventing the adequate visualization of transiently occurring RAAPs. This work proposes a technique to overcome this shortcoming by stitching spatially overlapping conduction patterns together to a larger image- called a composite map.

Digital Twinning of Cardiac Electrophysiology Models From the Surface ECG: A Geodesic Backpropagation Approach.

The eikonal equation has become an indispensable tool for modeling cardiac electrical activation accurately and efficiently. In principle, by matching clinically recorded and eikonal-based electrocardiograms (ECGs), it is possible to build patient-specific models of cardiac electrophysiology in a purely non-invasive manner. Nonetheless, the fitting procedure remains a challenging task.

Synchronization of repolarization after cardiac resynchronization therapy: A combined clinical and modeling study.

Introduction: The changes in ventricular repolarization after cardiac resynchronization therapy (CRT) are poorly understood. This knowledge gap is addressed using a multimodality approach including electrocardiographic and echocardiographic measurements in patients and using patient-specific computational modeling.

Methods: In 33 patients electrocardiographic and echocardiographic measurements were performed before and at various intervals after CRT, both during CRT-ON and temporary CRT-OFF.

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.

Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks.

Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data.

Space-time shape uncertainties in the forward and inverse problem of electrocardiography.

In electrocardiography, the "classic" inverse problem is the reconstruction of electric potentials at a surface enclosing the heart from remote recordings at the body surface and an accurate description of the anatomy. The latter being affected by noise and obtained with limited resolution due to clinical constraints, a possibly large uncertainty may be perpetuated in the inverse reconstruction. The purpose of this work is to study the effect of shape uncertainty on the forward and the inverse problem of electrocardiography.

GEASI: Geodesic-based earliest activation sites identification in cardiac models.

The identification of the initial ventricular activation sequence is a critical step for the correct personalization of patient-specific cardiac models. In healthy conditions, the Purkinje network is the main source of the electrical activation, but under pathological conditions the so-called earliest activation sites (EASs) are possibly sparser and more localized. Yet, their number, location and timing may not be easily inferred from remote recordings, such as the epicardial activation or the 12-lead electrocardiogram (ECG), due to the underlying complexity of the model.

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.

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.

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.

PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps.

Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task.

Reconstruction of three-dimensional biventricular activation based on the 12-lead electrocardiogram via patient-specific modelling.

Aims: Non-invasive imaging of electrical activation requires high-density body surface potential mapping. The nine electrodes of the 12-lead electrocardiogram (ECG) are insufficient for a reliable reconstruction with standard inverse methods. Patient-specific modelling may offer an alternative route to physiologically constraint the reconstruction.

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

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

The influence of scar on the spatio-temporal relationship between electrical and mechanical activation in heart failure patients.

Aims: The aim of this study was to determine the relationship between electrical and mechanical activation in heart failure (HF) patients and whether electromechanical coupling is affected by scar.

Methods And Results: Seventy HF patients referred for cardiac resynchronization therapy or biological therapy underwent endocardial anatomo-electromechanical mapping (AEMM) and delayed-enhancement magnetic resonance (CMR) scans. Area strain and activation times were derived from AEMM data, allowing to correlate mechanical and electrical activation in time and space with unprecedented accuracy.

Beat-to-beat P-wave morphological variability in patients with paroxysmal atrial fibrillation: an in silico study.

Aims: P-wave beat-to-beat morphological variability can identify patients prone to paroxysmal atrial fibrillation (AF). To date, no computational study has been carried out to mechanistically explain such finding. The aim of this study was to provide a pathophysiological explanation, by using a computer model of the human atria, of the correlation between P-wave beat-to-beat variability and the risk of AF.

Evaluation of a Rapid Anisotropic Model for ECG Simulation.

State-of-the-art cardiac electrophysiology models that are able to deliver physiologically motivated activation maps and electrocardiograms (ECGs) can only be solved on high-performance computing architectures. This makes it nearly impossible to adopt such models in clinical practice. ECG imaging tools typically rely on simplified models, but these neglect the anisotropic electric conductivity of the tissue in the forward problem.

Impact of mechanical deformation on pseudo-ECG: a simulation study.

Aims: Electrophysiological simulations may help to investigate causes and possible treatments of ventricular conduction disturbances. Most electrophysiological models do not take into account that the heart moves during the cardiac cycle. We used an electro-mechanical model to study the effect of mechanical deformation on the results of electrophysiological simulations.

Verification of cardiac mechanics software: benchmark problems and solutions for testing active and passive material behaviour.

Models of cardiac mechanics are increasingly used to investigate cardiac physiology. These models are characterized by a high level of complexity, including the particular anisotropic material properties of biological tissue and the actively contracting material. A large number of independent simulation codes have been developed, but a consistent way of verifying the accuracy and replicability of simulations is lacking.

High-order finite element methods for cardiac monodomain simulations.

Computational modeling of tissue-scale cardiac electrophysiology requires numerically converged solutions to avoid spurious artifacts. The steep gradients inherent to cardiac action potential propagation necessitate fine spatial scales and therefore a substantial computational burden. The use of high-order interpolation methods has previously been proposed for these simulations due to their theoretical convergence advantage.