Targeting the ectopy-triggering ganglionated plexuses without pulmonary vein isolation prevents atrial fibrillation

Background Ganglionated plexuses (GPs) are implicated in atrial fibrillation (AF). Endocardial high-frequency stimulation (HFS) delivered within the local atrial refractory period can trigger ectopy and AF from specific GP sites (ET-GP). The aim of this study was to understand the role of ET-GP ablation in the treatment of AF. Methods Patients with paroxysmal AF indicated for ablation were recruited. HFS mapping was performed globally around the left atrium to identify ET-GP. ET-GP was defined as atrial ectopy or atrial arrhythmia triggered by HFS. All ET-GP were ablated, and PVs were left electrically connected. Outcomes were compared with a control group receiving pulmonary vein isolation (PVI). Patients were followed-up for 12 months with multiple 48-h Holter ECGs. Primary endpoint was ≥30 s AF/atrial tachycardia in ECGs. Results In total, 67 patients were recruited and randomized to ET-GP ablation (n = 39) or PVI (n = 28). In the ET-GP ablation group, 103 ± 28 HFS sites were tested per patient, identifying 21 ± 10 (20%) GPs. ET-GP ablation used 23.3 ± 4.1 kWs total radiofrequency (RF) energy per patient, compared with 55.7 ± 22.7 kWs in PVI (p = <.0001). Duration of procedure was 3.7 ± 1.0 and 3.3 ± 0.7 h in ET-GP ablation group and PVI, respectively (p = .07). Follow-up at 12 months showed that 61% and 49% were free from ≥30 s of AF/AT with PVI and ET-GP ablation respectively (log-rank p = .27). Conclusions It is feasible to perform detailed global functional mapping with HFS and ablate ET-GP to prevent AF. This provides direct evidence that ET-GPs are part of the AF mechanism. The lower RF requirement implies that ET-GP targets the AF pathway more specifically

Feasibility of mapping and ablating ectopy-triggering ganglionated plexus reproducibly in persistent atrial fibrillation

Background Ablation of autonomic ectopy-triggering ganglionated plexuses (ET-GP) has been used to treat paroxysmal atrial fibrillation (AF). It is not known if ET-GP localisation is reproducible between different stimulators or whether ET-GP can be mapped and ablated in persistent AF. We tested the reproducibility of the left atrial ET-GP location using different high-frequency high-output stimulators in AF. In addition, we tested the feasibility of identifying ET-GP locations in persistent atrial fibrillation. Methods Nine patients undergoing clinically-indicated paroxysmal AF ablation received pacing-synchronised high-frequency stimulation (HFS), delivered in SR during the left atrial refractory period, to compare ET-GP localisation between a custom-built current-controlled stimulator (Tau20) and a voltage-controlled stimulator (Grass S88, SIU5). Two patients with persistent AF underwent cardioversion, left atrial ET-GP mapping with the Tau20 and ablation (Precision™, Tacticath™ [n = 1] or Carto™, SmartTouch™ [n = 1]). Pulmonary vein isolation (PVI) was not performed. Efficacy of ablation at ET-GP sites alone without PVI was assessed at 1 year. Results The mean output to identify ET-GP was 34 mA (n = 5). Reproducibility of response to synchronised HFS was 100% (Tau20 vs Grass S88; [n = 16] [kappa = 1, SE = 0.00, 95% CI 1 to 1)][Tau20 v Tau20; [n = 13] [kappa = 1, SE = 0, 95% CI 1 to 1]). Two patients with persistent AF had 10 and 7 ET-GP sites identified requiring 6 and 3 min of radiofrequency ablation respectively to abolish ET-GP response. Both patients were free from AF for > 365 days without anti-arrhythmics. Conclusions ET-GP sites are identified at the same location by different stimulators. ET-GP ablation alone was able to prevent AF recurrence in persistent AF, and further studies would be warranted

Multi-task learning for left atrial segmentation on GE-MRI

Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative atrial fibrillation (AF) ablation planning and post-operative follow-up studies. In this paper, we present a fully automated framework for left atrial segmentation in gadolinium-enhanced magnetic resonance images (GE-MRI) based on deep learning. We propose a fully convolutional neural network and explore the benefits of multi-task learning for performing both atrial segmentation and pre/post ablation classification. Our results show that, by sharing features between related tasks, the network can gain additional anatomical information and achieve more accurate atrial segmentation, leading to a mean Dice score of 0.901 on a test set of 20 3D MRI images. Code of our proposed algorithm is available at https://github.com/cherise215/atria_segmentation_2018/

Postinfarct ventricular tachycardia substrate: Characterization and ablation of conduction channels using ripple mapping

Background Conduction channels have been demonstrated within the postinfarct scar and seem to be co-located with the isthmus of ventricular tachycardia (VT). Mapping the local scar potentials (SPs) that define the conduction channels is often hindered by large far-field electrograms generated by healthy myocardium. Objective The purpose of this study was to map conduction channel using ripple mapping to categorize SPs temporally and anatomically. We tested the hypothesis that ablation of early SPs would eliminate the latest SPs without direct ablation. Methods Ripple maps of postinfarct scar were collected using the PentaRay (Biosense Webster) during normal rhythm. Maps were reviewed in reverse, and clusters of SPs were color-coded on the geometry, by timing, into early, intermediate, late, and terminal. Ablation was delivered sequentially from clusters of early SPs, checking for loss of terminal SPs as the endpoint. Results The protocol was performed in 11 patients. Mean mapping time was 65 ± 23 minutes, and a mean 3050 ± 1839 points was collected. SP timing ranged from 98.1 ± 60.5 ms to 214.8 ± 89.8 ms post QRS peak. Earliest SPs were present at the border, occupying 16.4% of scar, whereas latest SPs occupied 4.8% at the opposing border or core. Analysis took 15 ± 10 minutes to locate channels and identify ablation targets. It was possible to eliminate latest SPs in all patients without direct ablation (mean ablation time 16.3 ± 11.1 minutes). No VT recurrence was recorded (mean follow-up 10.1 ± 7.4 months). Conclusion Conduction channels can be located using ripple mapping to analyze SPs. Ablation at channel entrances can eliminate the latest SPs and is associated with good medium-term results

A Semantic-Wise Convolutional Neural Network Approach for 3-D Left Atrium Segmentation from Late Gadolinium Enhanced Magnetic Resonance Imaging

Several studies suggest that the assessment of viable left atrial (LA) tissue is a relevant information to support catheter ablation in atrial fibrillation (AF). Late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a new emerging technique which is employed for the non-invasive quantification of LA fibrotic tissue. The analysis of LGE MRI relies on manual tracing of LA boundaries. This procedure is time-consuming and prone to high inter-observer variability given the dierent degrees of observers' experience, LA wall thickness and data resolution. Therefore, an automatic approach for the LA wall detection would be highly desirable. This work focuses on the design and development of a semantic-wise convolutional neural network based on the successful architecture U-Net (U-SWCNN). Batch normalization, early stopping and parameter initializers consistent with the activation functions chosen were used; a loss function based on the Dice coefficient was employed. The U-SWCNN was fed with the data available from the 2018 Atrial Segmentation Challenge with two different approaches: the model was trained end-to-end using stacks of 2-D axial slices as a preliminary attempt; then, with the appropriate changes in the baseline architecture, with 3-D data. The training was completed using 95 LGE MRI data, and a post-processing step based on the 3-D morphology was then applied. Mean Dice coefficient of unseen data (5) predicted masks were 0.89 and 0.91 for the 2-D and 3-D approach, respectively. These results suggest that, despite the increase of the number of trainable parameters, the 3-D U-SWCNN learns better features leading to a higher value of the Dice coefficient

Visualizing Localized Reentry With Ultra-High Density Mapping in Iatrogenic Atrial Tachycardia Beware Pseudo-Reentry

Background—The activation pattern of localized reentry (LR) in atrial tachycardia remains incompletely understood. We used the ultra–high density Rhythmia mapping system to study activation patterns in LR. Methods and Results—LR was suggested by small rotatory activations (carousels) containing the full spectrum of the color-coded map. Twenty-three left-sided atrial tachycardias were mapped in 15 patients (age: 64±11 years). 16 253±9192 points were displayed per map, collected over 26±14 minutes. A total of 50 carousels were identified (median 2; quartiles 1–3 per map), although this represented LR in only n=7 out of 50 (14%): here, rotation occurred around a small area of scar (<0.03 mV; 12±6 mm diameter). In LR, electrograms along the carousel encompassed the full tachycardia cycle length, and surrounding activation moved away from the carousel in all directions. Ablating fractionated electrograms (117±18 ms; 44±13% of tachycardia cycle length) within the carousel interrupted the tachycardia in every LR case. All remaining carousels were pseudo-reentrant (n=43/50 [86%]) occurring in areas of wavefront collision (n=21; median 0.5; quartiles 0–2 per map) or as artifact because of annotation of noise or interpolation in areas of incomplete mapping (n=22; median 1, quartiles 0–2 per map). Pseudo-reentrant carousels were incorrectly ablated in 5 cases having been misinterpreted as LR. Conclusions—The activation pattern of LR is of small stable rotational activations (carousels), and this drove 30% (7/23) of our postablation atrial tachycardias. However, this appearance is most often pseudo-reentrant and must be differentiated by interpretation of electrograms in the candidate circuit and activation in the wider surrounding region