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Aims Electroanatomical maps using automated conduction velocity (CV) algorithms are now being calculated using two-dimensional (2D) mapping tools. We studied the accuracy of mapping surface 2D CV, compared to the three-dimensional (3D) vectors, and the influence of mapping resolution in non-scarred animal and human heart models. Methods and results Two models were used: a healthy porcine Langendorff model with transmural needle electrodes and a computer stimulation model of the ventricles built from an MRI-segmented, excised human heart. Local activation times (LATs) within the 3D volume of the mesh were used to calculate true 3D CVs (direction and velocity) for different pixel resolutions ranging between 500 μm and 4 mm (3D CVs). CV was also calculated for endocardial surface-only LATs (2D CV). In the experimental model, surface (2D) CV was faster on the epicardium (0.509 m/s) compared to the endocardium (0.262 m/s). In stimulation models, 2D CV significantly exceeded 3D CVs across all mapping resolutions and increased as resolution decreased. Three-dimensional and 2D left ventricle CV at 500 μm resolution increased from 429.2 ± 189.3 to 527.7 ± 253.8 mm/s (P < 0.01), respectively, with modest correlation (R = 0.64). Decreasing the resolution to 4 mm significantly increased 2D CV and weakened the correlation (R = 0.46). The majority of CV vectors were not parallel ( Conclusion Ventricular CV is overestimated when using 2D LAT-based CV calculation of the mapping surface and significantly compounded by mapping resolution. Three-dimensional electric field-based approaches are needed in mapping true CV on mapping surfaces.

Background: Conventional mapping of focal ventricular arrhythmias relies on unipolar electrogram characteristics and early local activation times. Deep intramural foci are common and associated with high recurrence rates following catheter-based radiofrequency ablation. We assessed the accuracy of unipolar morphological patterns and mapping surface indices to predict the site and depth of ventricular arrhythmogenic focal sources. Methods: An experimental beating-heart model used Langendorff-perfused, healthy swine hearts. A custom 56-pole electrode array catheter was positioned on the left ventricle. A plunge needle was placed perpendicular in the center of the grid to simulate arrhythmic foci at variable depths. Unipolar electrograms and local activation times were generated. Simulation models from 2 human hearts were also included with grids positioned simultaneously on the endocardium-epicardium from multiple left ventricular, septal, and outflow tract sites. Results: A unipolar Q or QS complex lacks specificity for superficial arrhythmic foci, as this morphology pattern occupies a large surface area and is the predominant pattern as intramural depth increases without developing a R component. There is progressive displacement from the arrhythmic focus to the surface exit as intramural focus depth increases. A shorter total activation time over the overlying electrode array, larger surface area within initial 20 ms activation, and a dual surface breakout pattern all indicate a deep focus. Conclusions: Displacement from the focal intramural origin to the exit site on the mapping surface could lead to erroneous lesion delivery strategies. Traditional unipolar electrogram features lack specificity to predict the intramural arrhythmic source; however, novel endocardial-epicardial mapping surface indices can be used to determine the depth of arrhythmic foci.

Supplementary figures S1, S2, and S3 of the following article Rodriguez Padilla J, Petras A, Magat J, Bayer J, Bihan-Poudec Y, El-Hamrani D, Ramlugun G, Neic A, Augustin C, Vaillant F, Constantin M, Benoist D, Pourtau L, Dubes V, Rogier J, Labrousse L, Bernus O, Quesson B, Haissaguerre M, Gsell M, Plank G, Ozenne V, Vigmond E. Impact of Intraventricular Septal Fiber Orientation on Cardiac Electromechanical Function. Am J Physiol Heart Circ Physiol. 2022 Mar 18 Epub ahead of print. PMID: 35302879. Legends of the supplementary figures. Figure S1. Helix angle as function of position and height for the three different sheep hearts. Solid lines indicate the measurement and the shaded areas the standard deviation. Figure S2. Sheet azimutal angle as function of position and height for the three different sheep hearts. Solid lines indicate the measurement and the shaded areas the standard deviation. Figure S3. Anatomical images, the cFA maps, the helix angle maps and the myocardial disarray index (MDI) maps in the IVS in long axis orientation for the large mammalian samples (Pig, Sheep #1#2#3 and Human). Black and pink arrows indicate the delineation of the dual-layer. Purple arrows indicate the presence trabeculation in the right ventricle (purple arrow). Supplementary data The supplementary data are available at this link: https://doi.org/10.5281/zenodo.5789035

Cardiac fiber direction is an important factor determining the propagation of electrical activity, as well as the development of mechanical force. In this article, we imaged the ventricles of several species with special attention to the intraventricular septum to determine the functional consequences of septal fiber organization. First, we identified a dual-layer organization of the fiber orientation in the intraventricular septum of ex vivo sheep hearts using diffusion tensor imaging at high field MRI. To expand the scope of the results, we investigated the presence of a similar fiber organization in five mammalian species (rat, canine, pig, sheep, and human) and highlighted the continuity of the layer with the moderator band in large mammalian species. We implemented the measured septal fiber fields in three-dimensional electromechanical computer models to assess the impact of the fiber orientation. The downward fibers produced a diamond activation pattern superficially in the right ventricle. Electromechanically, there was very little change in pressure volume loops although the stress distribution was altered. In conclusion, we clarified that the right ventricular septum has a downwardly directed superficial layer in larger mammalian species, which can have modest effects on stress distribution.

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.

Reliable patient-specific ventricular repolarization times (RTs) can identify regions of functional block or afterdepolarizations, indicating arrhythmogenic cardiac tissue and the risk of sudden cardiac death. Unipolar electrograms (UEs) record electric potentials, and the Wyatt method has been shown to be accurate for estimating RT from a UE. High-pass filtering is an important step in processing UEs, however, it is known to distort the T-wave phase of the UE, which may compromise the accuracy of the Wyatt method. The aim of this study was to examine the effects of high-pass filtering, and improve RT estimates derived from filtered UEs. We first generated a comprehensive set of UEs, corresponding to early and late activation and repolarization, that were then high-pass filtered with settings that mimicked the CARTO filter. We trained a deep neural network (DNN) to output a probabilistic estimation of RT and a measure of confidence, using the filtered synthetic UEs and their true RTs. Unfiltered ex-vivo human UEs were also filtered and the trained DNN used to estimate RT. Even a modest 2 Hz high-pass filter imposes a significant error on RT estimation using the Wyatt method. The DNN outperformed the Wyatt method in 62.75% of cases, and produced a significantly lower absolute error (p=8.99E-13), with a median of 16.91 ms, on 102 ex-vivo UEs. We also applied the DNN to patient UEs from CARTO, from which an RT map was computed. In conclusion, DNNs trained on synthetic UEs improve the RT estimation from filtered UEs, which leads to more reliable repolarization maps that help to identify patient-specific repolarization abnormalities.

The atrial-specific ultrarapid delayed rectifier K+ current (IKur) inactivates slowly but completely at depolarized voltages. The consequences for IKur rate-dependence have not been analyzed in detail and currently available mathematical action-potential (AP) models do not take into account experimentally observed IKur inactivation dynamics. Here, we developed an updated formulation of IKur inactivation that accurately reproduces time-, voltage-, and frequency-dependent inactivation. We then modified the human atrial cardiomyocyte Courtemanche AP model to incorporate realistic IKur inactivation properties. Despite markedly different inactivation dynamics, there was no difference in AP parameters across a wide range of stimulation frequencies between the original and updated models. Using the updated model, we showed that, under physiological stimulation conditions, IKur does not inactivate significantly even at high atrial rates because the transmembrane potential spends little time at voltages associated with inactivation. Thus, channel dynamics are determined principally by activation kinetics. IKur magnitude decreases at higher rates because of AP changes that reduce IKur activation. Nevertheless, the relative contribution of IKur to AP repolarization increases at higher frequencies because of reduced activation of the rapid delayed-rectifier current IKr. Consequently, IKur block produces dose-dependent termination of simulated atrial fibrillation (AF) in the absence of AF-induced electrical remodeling. The inclusion of AF-related ionic remodeling stabilizes simulated AF and greatly reduces the predicted antiarrhythmic efficacy of IKur block. Our results explain a range of experimental observations, including recently reported positive rate-dependent IKur-blocking effects on human atrial APs, and provide insights relevant to the potential value of IKur as an antiarrhythmic target for the treatment of AF.

To show how atrial fibrillation rotor activity on the heart surface manifests as phase on the torso, fibrillation was induced on a geometrically accurate computer model of the human atria. The Hilbert transform, time embedding, and filament detection were compared. Electrical activity on the epicardium was used to compute potentials on different surfaces from the atria to the torso. The Hilbert transform produces erroneous phase when pacing for longer than the action potential duration. The number of phase singularities, frequency content, and the dominant frequency decreased with distance from the heart, except for the convex hull.

International audience; Atrial fibrosis is known to be a factor in the perpetu ation of atrial arrhythmia. Despite the thinness of atrial tissue, the fibrosis distribution may not be homogeneous through the entire thickness of the atria. This study aims to elucidate the respective influences of a transmural and a non-transmural distribution of fibrosis, described as a slow conduction zone, on the perpetuation of an arrhythmic episode, compared to a control situation. We used a bilayer monodomain representation of the atria that included transmural heterogeneities of fibre organisation. This model allowed long simulations for a sustainable computational load. We observed that when the fibrosis was transmural, the centre of the rotor was anchored in the slow conduction zone and was stable during a 10 seconds simulation, whereas the other simulations showed meandering rotors that disappeared after a few seconds. This study provided insight into the influence of transmural fibrosis on atrial arrhythmia perpetuation. In our model framework, only a transmural fibrosis distribution had a stabilizing effect on reentrant circuits. The bilayer model proved to be a good trade-off between accuracy and speed for observing the influence of transmural heterogeneities on atrial arrhythmia over long periods.

Background Atrial action potential duration (APD) has been shown to decrease with increasing distance from the sinoatrial node in several species, including humans. This gradient has been postulated to be cardioprotective by reducing repolarization gradients. Objectives This study tests the effect of the APD gradient on reentry initiation and characteristics. Methods This study used a geometrically accurate atrial computer model to examine arrhythmogenic consequences of an APD gradient on reentry initiation by ectopic beats applied at several locations. As well, dominant frequency maps of any ensuing reentries were analyzed to determine how APD gradients affected rotor behaviour. Results When the APD gradient was increased, anatomic reentry that used the coronary sinus as a critical pathway was prevented, but initiation of functional reentry was unaffected. If a rotor did form, APD gradients led to more disorganized behaviour. For rotors circulating around the pulmonary veins, discrete interatrial coupling accounted for left atrium–right atrium frequency gradients, irrespective of an APD gradient. Conclusions Gradients are protective against anatomic reentry but also increase the complexity of arrhythmias that arise.

International audience; We introduce a framework for the statistical characterization of heart remodeling from both shape and dynamics of the left ventricle. Shape was characterized by thickness and radius maps, unfolded in a two-dimensional dense Bull's eye. Motion was represented as a mixture of affine transformations in an anatomical space of coordinates. Using this representation, a population can be projected (after defining spa-tiotemporal correspondences) to an atlas space built for a given reference population-here, healthy subjects using a classic PCA approach-yielding a joint model of healthy shape and motion statistics. The reconstruction error on shape and motion can then be exploited to quantify remodeling abnormalities. We demonstrate these concepts on 48 healthy subjects and 62 patients with infarct (29 with one year follow-up) imaged with 3D echocardiography, analyzing a total of 139 sequences.

International audience; Activation mapping in animal models of atrial fibrillation (AF) has shown that activation patterns can repeat for several cycles and then be followed by different changing or repetitive patterns. Subsequent clinical studies have suggested a similar type of activity in human AF. Our purpose was to investigate whether a computer model of human AF can reproduce this behavior. We used a three-dimensional model of the human atria consisting of 0.2-mm volumetric elements with a detailed representation of bundle structures and fiber orientations. Propagating activation was simulated over 9 seconds with a monodomain reaction-diffusion model using human atrial membrane dynamics. AF was induced by rapid pacing. Pattern recurrence was quantified using a similarity measure based on transmembrane voltage at 1000 uniformly distributed points in the model. Recurrence plots demonstrated a continuous evolution of patterns, but the speed of evolution varied considerably. Groups of upto 15 similar cycles could be recognized, separated by rapid changes. In a few cases truly periodic patterns developed, which were related to macroscopic anatomical reentry. The drivers of non-periodic patterns could not be clearly identified. For example, a spiral wave could appear or disappear without an obvious impact on the similarity between cycles. We conclude that our model can indeed reproduce strong variations in similarity between subsequent cycles, but true periodicity only in case of anatomical reentry.

International audience; In this work, we present an optimal control formulation for the bidomain model in order to estimate maximal conductances parameters in the physiological ionic model. We consider a general Hodgkin-Huxley formalism to describe the ionic exchanges at the microcopic level. We consider the parameters as control variables to minimize the mismatch between the measured and the computed potentials under the constraint of the bidomain system. The solution of the optimization problem is based on a gradient descent method, where the gradient is obtained by solving an adjoint problem. We show through some numerical examples the capability of this approach to estimate the values of sodium, calcium and potassium ion channels conductances in the Luo Rudy phase I model.