Novel electrogram featurization reveals a spectrum of response to ablation from atrial tachycardia to types of atrial fibrillation

Background

Although atrial tachycardia (AT) may interconvert with fibrillation (AF) in many patients, it is undefined if this represents a pathophysiological spectrum of organization, or whether it indicates that AF will respond better to ablation.

Objective

To test the hypothesis that the spatial area within which electrograms (EGMs) repeat in synchronized fashion over time indicates a spectrum from AT, in which areas span the entire atria, to AF, in which areas are limited. We further hypothesized that repetitive areas would be larger in AF patients with acute termination than in those with poor response to ablation.

Methods

We studied N=234 patients (47% women, 64±10Y), of whom (i) N=10 had AT, (ii) N=120 had AF that terminated with ablation (“Term”), (ii) N=104 had AF that did not terminate (“Non-term”). All patients had global left atrial mapping by 64 pole baskets (Abbott, IL). Spatial areas of repetitive activity (REACT) were calculated by correlating unipolar EGMs in 2x2 grids for 4 sec, repeated for the entire atria (Figure 1A, B). We quantified global organization by averaging the REACT map for each patient.

Results

Figure 1C shows progressively decreasing areas of repetitive EGM from AT to AF Term to AF Non-term (p<0.001, ANOVA). Figure 1D shows a case of AT in a 71 YO male and global REACT >0.90, a case of AF REACT 0.45 in a 65 YO male with termination by ablation, and a case of AF with REACT 0.19 in an 85 YO male that did not terminate. Further, ROC analysis of REACT analysis in AF cases predicted termination with an AUC of 0.71.

Conclusion

Spatial areas of repeating electrogram shapes indicates a spectrum from AT to AF with good and AF with poor acute response to ablation. Future studies should investigate whether REACT areas can be identified non-invasively, such as by body surface ECG, to guide ablation or prognosis.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): US National Institutes of Health

Spatiotemporal signatures of response to atrial fibrillation ablation

Background

Atrial fibrillation (AF) can have organized regions, in the form of consistent dominant frequency sites, focal or reentrant sites, but it is unclear how these overlap with or differ from focal atrial tachycardias (AT) or potential drivers. We set out to develop an intuitive method based on fundamental electrogram shape and timing to separate types of AF.

Objective

To test the hypothesis that spatial regions of electrogram (EGM) in AF that show similar shapes over time based on cross-correlation analysis may separate patients with differing response to ablation.

Methods

We recruited N=133 patients (63.8±12.1 Y, 32% women), (i) N=10 had AT, (ii) N=122 AF that was or was not terminated by ablation, and (iii) N=1 pacing. All patients had left atrial mapping by 64 pole baskets. We applied repetitive activity (REACT) mapping that correlates EGMs in contiguous 2x2 regions (Fig. 1A) over 4sec. To calibrate REACT, we introduced simulated variations in shape (gaussian noise) and timing (gaussian delay) to pacing EGMs and computed nomograph over 100 random trials (Fig. 1C).

Results

Fig. 1B shows that REACT in a 71-year-old man with AT is more organized than in a 65 YO man with AF (100% vs 40% mapped field). Overall, REACT was higher in AT than AF (0.63±0.15 vs 0.36±0.22, p<0.001). There were 24 cases in which global REACT between AF and AT groups had the overlapping range of values, indicating organized “islands” in AF analogous to AT. From nomograph in Fig. 1C we identified that this overlap reflects 15 ms variation in cycle length and 20% variation in EGM shape (labelled “x” in Fig. 1C).

Conclusion

Basic electrogram properties in AF of similar shapes in spatial areas over time can separate response to ablation and may represent “islands” of AT. Future studies should investigate the mechanisms for such islands and whether they may be targeted for therapy.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): US National Institutes of Health

Automatic left atrial segmentation from cardiac CT using computer graphics imaging and deep learning

Introduction

Segmenting left atrial (LA) substructures, including the LA body, appendage (LAA), and pulmonary veins (PVs), from computed tomography (CT) is central to electroanatomic mapping for ablation and functional studies in patients with atrial fibrillation (AF). However, this process requires manual outlining which needs special training, is subjective, and is difficult to scale. Computer graphics imaging (CGI) has been applied in media, film, and computer-aided design to reliably segment complex structures using their basic geometric representations.

Purpose

We hypothesized that LA substructures can be “virtually” dissected using CGI to separate geometric contours of the “convex ellipsoid” LA, “tubular” PVs, and “conical” LAA. We further hypothesized that the results of virtual dissection can be used to train a deep learning (DL) model to segment raw CT scans.

Methods

First, a mathematical method based on CGI techniques – erosion and dilation – was developed to “virtually dissect” the convex LA body from the original concave shell in publicly available digital atria with diverse simulated morphologies (Fig. 1A). The PVs and LAA were then automatically revealed and labeled by a 3D subtraction approach. Second, we refined precise LA/PV/LAA boundaries by tuning hyper-parameters from N=5 patient shells (Fig. 1B). Third, we used virtual dissection to train a DL model to segment CTs in N=20 patient atria (Fig. 1C). Finally, we applied this pipeline to segment raw CTs in a validation cohort of N=105 patients (23.8% women, 63.8±10.3Y; Fig. 1D).

Results

Virtual dissection accurately identified LA/PV/LAA boundaries in the training set (Dice coefficients 89–98%). In the independent test cohort (N=105), this automated pipeline accurately segmented raw CTs with Dice 81–95% (Fig. 1D) compared to a panel of experts (p<0.001).

Conclusion

CGI of basic cardiac geometry combined with deep learning in small datasets can accurately segment raw CT scans in large populations. This computational pipeline may automate and simplify cardiac image processing and ablation procedures, and could be applied to the ventricle or other organ systems for diverse therapeutic strategies or to train machine learning.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institutes of Health