Detection of the most dominant drivers of atrial fibrillation based on repetitive activity in sequential electro-anatomical mapping

FUNDING ACKNOWLEDGEMENTS: Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This work is part of Personalize AF. This project received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860974, This work was also supported by the Swiss National Supercomputing Centre (CSCS), project s1074. INTRODUCTION: Detecting and targeting the most dominant drivers of atrial fibrillation (AF) might improve the outcome of AF ablation procedures. We hypothesize that dominant drivers entrain their vicinity in a temporally and spatially stable manner resulting in local as well as adjacent repetitive activation patterns. PURPOSE: We present a novel approach to detect the most dominant drivers of AF based on driver repetitiveness and the degree to which they entrain their vicinity. METHODS: We retrospectively analyzed a dataset of high-density bi-atrial sequential mapping in ablation-naive persistent AF patients (n=13, 30s recording per site). Candidate drivers (focal activity or re-entry) were detected based on local activation time analysis of unipolar electrograms. Focal activity was defined as radial spread of activation, re-entry as a circular activation sequence on at least 4 splines of the mapping catheter that covered >50% of the AF cycle length. As a measure of driver stability, we determined the driver's local repetitiveness (in % of recording). As a measure of driver-associated entrainment, we determined the directionally coherent repetitiveness of adjacent recordings within 30 mm (Figure 1). Following our hypothesis, most dominant drivers can be recognized by their high local and directional coherent adjacent repetitiveness. RESULTS: A total of 459 recordings were analyzed (35±5 per patient). We detected 130 focal activities (10±3 per patient) and 42 re-entries (4±2 per patient) in total. The spatial distribution of all drivers is shown in Figure 2 (blue bars). Focal activities were more repetitive than re-entries (Figure 2, median [IQR] 3.2% [1.9%, 5.6%] vs. 1.8% [1.8%, 2.2%], p<0.001 Mann-Whitney U-test). When applying a 90th percentile threshold (8.2%) to both the local and directional coherent adjacent repetitiveness to detect the most dominant drivers, we were able to detect 9 dominant drivers (all focal activities, 7 patients). Most dominant drivers were located in the upper right atrium or left pulmonary vein region (Figure 2). Interestingly, only 9 of the 130 initially detected drivers (7%) showed an activation pattern in line with a repetitive entrainment of their vicinity. Furthermore, in 6 patients (46%), no dominant drivers were detected. This might indicate that not all patients have temporally and spatially stable drivers. CONCLUSION: In this abstract we present a novel methodology to detect the most dominant drivers of AF. Using this method, we found that some (but not all) patients have temporally and spatially stable drivers that are able to entrain their vicinity. [Figure: see text] [Figure: see text]