Search

Introduction: Pulmonary vein isolation (PVI) is well established as a primary treatment for atrial fibrillation (AF). The POLAR ICE study was designed to collect prospective real world data on the safety and effectiveness of the POLARxTM cryoballoon for PVI to treat paroxysmal AF. Methods: POLAR ICE, a prospective, non-randomized, multicenter (international) registry (NCT04250714), enrolled 399 patients across 19 European centers. Procedural characteristics, such as time to isolation, cryoablations per pulmonary vein (PV), balloon nadir temperature, and occlusion grade were recorded. PVI was confirmed with entrance block testing. Results: Data on 372 de novo PVI procedures (n = 2190 ablations) were collected. Complete PVI was achieved in 96.8% of PVs. Procedure and fluoroscopy times were 68.2 ± 24.6 and 15.6 ± 9.6 min, respectively. Left atrial dwell time was 46.6 ± 18.3 min. Grade 3 or 4 occlusion was achieved in 98.2% of PVs reported and 71.2% of PVs isolation required only a single cryoablation. Of 2190 cryoapplications, 83% had a duration of at least 120 s; nadir temperature of these ablations averaged −56.3 ± 6.5°C. There were 6 phrenic nerve palsy events, 2 of which resolved within 3 months of the procedure. Conclusion: This real-world usage data on a novel cryoballoon suggests this device is effective, safe, and relatively fast in centers with cryoballoon experience. These data are comparable to prior POLARx reports and in keeping with reported data on other cryoballoons. Future studies should examine the long-term outcomes and the relationship between biophysical parameters and outcomes for this novel cryoballoon.

Fibrotic atrial cardiomyopathy is characterized by a replacement of healthy atrial tissue with diffuse patches exhibiting slow electrical conduction properties and altered myocardial tissue structure, which provides a substrate for the maintenance of reentrant activity during atrial fibrillation (AF). Therefore, an early detection of atrial fibrosis could be a valuable risk marker for new-onset AF episodes to select asymptomatic subjects for screening, allowing for timely intervention and optimizing therapy planning. We examined the potential of estimating the fibrotic tissue volume fraction in the atria based on P waves of the 12-lead ECG recorded in sinus rhythm in a quantitative and noninvasive way. Our dataset comprised 68,282 P waves from healthy subjects and 42,227 P waves from AF patients with low voltage areas in the atria, as well as 642,400 simulated P waves of a virtual cohort derived from statistical shape models with different extents of the left atrial myocardium replaced by fibrosis. The root mean squared error for estimating the left atrial fibrotic volume fraction on a clinical test set with a neural network trained on features extracted from simulated and clinical P waves was 16.57 %. Our study shows that the 12-lead ECG contains valuable information on atrial tissue structure. As such it could potentially be employed as an inexpensive and widely available tool to support AF risk stratification in clinical practice

The treatment of atrial fibrillation and other cardiac arrhythmias as a major cause of cardiovascular hospitalization has remained a challenge predominantly for patients with severely remodeled substrate. Individualized ablation strategies are extremely important both for pulmonary vein isolation and subsequent ablations. Current approaches to identifying arrhythmogenic regions rely on electrogram-based features such as activation time and voltage. Novel technologies now enable clinical assessment of the local impedance as tissue property. Previous studies demonstrated its use for ablation monitoring and indicated its potential to differentiate healthy substrate, scar, and pathological tissue. This study investigates the potential of local electrical impedance-based substrate mapping of the atria for human in-vivo data. The presented pipeline for impedance mapping particularly contains options for dealing with undesirable effects originating from cardiac motion, catheter motion, or proximity to other intracardiac devices. Bloodpool impedance was automatically determined as a patient-specific reference. Full-chamber, left atrial impedance maps were drawn up from interpolating the measured impedances to the atrial endocardium. Finally, the origin and magnitude of oscillations of the raw impedance recording were probed into. The most dominant reason for exclusion of impedance samples was the loss of endocardial contact. With median elevations above the bloodpool impedance between 29 and 46 Ω, the impedance within the pulmonary veins significantly exceeded the remaining atrial walls presenting median elevations above the bloodpool impedance between 16 and 20 Ω. Previous ablation lesions were distinguished from their surroundings by a significant drop in local impedance while the corresponding regions did not differ for the control group. The raw impedance was found to oscillate with median amplitudes between 6 and 17 Ω depending on the patient. Oscillations were traced back to an interplay of atrial, ventricular, and respiratory motion. In summary, local impedance measurements demonstrated their capability to distinguish pathological atrial tissue from physiological substrate. Methods to limit the influence of confounding factors that still hinder impedance mapping were presented. Measurements at different frequencies or the combination of multiple electrodes could lead to further improvement. The presented examples indicate that electrogram- and impedance-based substrate mapping have the potential to complement each other toward better patient outcomes in future.

Background: Rate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity. Methods and Results: The S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude. Conclusion: The proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.

[EN] In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas., We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG) through DO637/22-3, LO2093/1-1 and LU 2294/1-1, by the European Union's Horizon 2020 programme (grant agreement No.766082, MY-ATRIA project), by the KIT-Publication Fund of the Karlsruhe Institute of Technology and by the Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2017-2020 from the Ministerio de Ciencia e Innovacion y Universidades (PID2019-104356RB-C41/AEI/10.13039/501100011033)

Intracardiac electrogram recordings during atrial fibrillation (AFib) are characterized by irregular rhythms and complex morphologies. Hence, analysis in the time domain is a difficult task. The so called dominant frequency DF is a spectrum based approach that aims at finding the most relevant frequency in a signal providing information about the rate and dynamics of AFib. However, in recent years various studies reported controversial results regarding the clinical relevance of the DF. In this work, a definition of the DF at a fundamental scale is proposed as the rate at which action potentials are triggered in atrial cells. The most common method to estimate the DF in literature, labeled as DFSpec , is examined in comparison to the proposed definition. A signal processing study using synthetic signals verified that the DFSpec is stable for all changes in morphology of atrial activations. However, it is also demonstrated that the DFSpec becomes unstable for variations above 20% in the cycle length of a signal. Spectrum based DF estimation should be interpreted in a critical manner and is not advisable for study endpoints or clinical markers.

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.