Transmural voltage gradient dispersion and heterogeneity in brugada syndrome phenotype - a novel workflow for advanced mapping using endocardial unipolar electrograms J-point elevation

• Published in: Europace (London, England) Vol. 24; no. Supplement_1
• Main Authors: Rossi, A, Hartwig, V, Morelli, M S, Martini, N, Zaurino, N, Notarstefano, P, Nesti, M, Giannoni, A, Mansi, G, Mirizzi, G, Panchetti, L, Garibaldi, S, Startari, U, Piacenti, M, Vanello, N
• Format: Journal Article
• Language: English
• Published: 19.05.2022
• ISSN: 1099-5129; 1532-2092
• DOI: 10.1093/europace/euac053.013

Abstract Funding Acknowledgements Type of funding sources: None. Background Differential action potential duration shortening across the right ventricular (RV) myocardial wall is primarily responsible for the Brugada Syndrome (BrS) phenotype [1]. To date, data on electrical substrate characterization in humans with BrS phenotype is limited and risk evaluation is still controversial. Purpose We hypothesized that Uni-JEl mapping could be used as a marker of transmural voltage gradient dispersion resulting. Our aim was to evaluate Uni-JEl mapping in defining arrhythmogenic substrates in patients with BrS phenotype. Methods 12 patients were included in our analysis. 2 normal patients provided control data and 10 asymptomatic subjects with spontaneous type-1 BrS underwent 3D RV mapping (CARTO3 System, Biosense Webster). Among BrS patients we had 3 patients with arrhythmic events (aborted sudden death or appropriate ICD therapies) during follow-up (median 56, interquartile range: 46-74 months) and 7 patients without arrhythmic events. In the former group we had 1 patient with inducibility of VT/VF during EPS (EPS+) and 2 patients non-inducibles during EPS (EPS-), in the latter group we had 3 patients with EPS+ and 4 patients with EPS-. Electrophysiological data and signals were exported and OpenEP [2] was used to convert Carto proprietary data formats into Matlab format (Fig.1). Uni-JEl was calculated for each point map as the unipolar value at J point on surface electrocardiogram. Uni-JEl values were then interpolated in Paraview to create Uni-JEl maps, interpolating data points on the mesh cell (Fig.1). Finally, a region of interest (ROI) was selected and the calculation of mean Uni-JEI (MUni-JEI, as a measure of voltage gradient dispersion), interquartile range and range (intrqUni-JEI and ∆Uni-JEI, as markers of heterogeneity of dispersion) was performed. Results are shown as mean ± standard deviation for the group of BrS patients and the actual values for the two controls. Results BrS patients showed Muni-JEl, intrqUni-JEl and ∆Uni-JEI higher than controls (2.03 mV ± 0.31 mV vs 0,82 mV and 1,1 mV, 1.90 mV ± 0.82 mV vs 1,04 mV and 1,18 mV 6.26 mV ± 1.98 mV vs 3,54 mV and 4,01 mV, respectively). BrS patients with arrhythmic events during the follow-up showed higher intrqUni-JEl and the ∆Uni-JEI respect to BrS with EPS+ and without arrhythmic events during follow-up (2.31 mV ± 0.44 mV vs 0.78 mV ± 0.11 mV and 6.69 mV ± 2.27 mV vs 3.98 mV ± 0.31 mV). Figure 2 shows some examples of calculated Uni-JEl maps for each group under study. Conclusions In this work we introduced a novel workflow for the electrical substrate characterization of subjects with BrS phenotype. The results from our preliminary analysis indicate that a higher transmural voltage gradient dispersion and heterogeneity can be found in type-1 BrS with respect to normal subjects. Voltage gradient dispersion heterogeneity could be used to better recognize high risk BrS patients regardless of VT/VF inducibility during EPS.