• Brugada syndrome
• electroanatomical mapping
• mappaggio elettroanatomico
• morte cardiaca improvvisa
• risk stratification
• sindrome di Brugada
• sopraslivellamento del punto j unipolare
• stratificazione del rischio
• sudden cardiac death
• unipolar j point elevation

BACKGROUND
The Brugada syndrome (BrS) is an inherited disorder associated with risk of ventricular fibrillation and sudden cardiac death in a structurally normal heart. Differential action potential duration shortening across the right ventricular (RV) myocardial wall is primarily responsible for the Brugada Syndrome (BrS) phenotype. To date, data on electrical substrate characterization in humans with BrS phenotype is limited. Risk evaluation especially in asymptomatic BrS patients with spontaneous type-1 pattern is still controversial.
PURPOSE
We analyzed J-point elevation in endocardial unipolar electrograms (Uni-JEl). We hypothesized that Uni-JEl mapping could be assumed as a marker of transmural repolarization dispersion resulting from the differences between epicardial and endocardial cellular action potential shortening. Our aim was to evaluate Uni-JEl in BrS and to compare Uni-JEl with others BrS electrophysiological markers.
METHODS
64 patients were included in our analysis. 9 normal patients provided control data. 55 sponteous type-1 BrS. Underlying cardiac diseases were excluded. All subjects underwent 3D RV electroanatomical mapping (EAM) with CARTO3® navigation System. 26 subjects presented a diagnostic type-1 electrocardiographic pattern during electrophysiological study (Ptype1, persistent type1), the others 29 subjects (NPtype1, non-persistent type 1) underwent to an ajmaline test to unveil type 1 ECG. After EPS among BrS patients we identified 2 subgroups: non-inducibility of VT/VF during EPS (EPS-) and inducibility of VT/VF during EPS (EPS+). Only 3 patients suffered an aborted sudden death or appropriate ICD therapies during follow-up (aSCD/ICD+). Electrophysiological data and signals were exported and OpenEP, a toolbox of Matlab, was used to convert Carto proprietary data formats in Matlab format. UniJEL was calculated for each point map as the unipolar value at J point on surface electrocardiogram (ECG). UniJEL values were then interpolated in Paraview to create UniJEL maps, interpolating data points on the mesh cell. Finally, a region of interest (ROI) corresponding to RV free wall was selected. Mean Uni-JEI (MUniJEL) and ROI’s area with a UniJEL over the threshold of 2,5 mV (AOT in cm2; AOT% in percentage of the ROI area) were calculated. For all BrS subjects UniJEL analysis was then statistically compared with others validated electrophysiological markers recorded during EAM: inducibility at EPS, presence of unipolar voltage anomalies areas (uniAVA) and presence of slow conduction areas7. Using CARTO3® software, uniAVAs were defined, for each patient, as RV areas (in cm2) with endocardial unipolar voltage amplitude< 5.3 mV8. A threshold of 7 cm2 was used to distinguish low-risk BrS patients to high-risk patients (uniAVA>7 cm2). The presence of slow conduction areas was evaluated using CARTO3® software to calculate the total RV activation, time defined as the time interval between earliest RV’s LAT (local activation time) and latest RV’s LAT, and the number of isochronal steps (with 5 ms steps isochronal maps) in the RVOT. Continuous variables were presented as mean values ± standard deviation (SD). A non-parametric Wilcoxon’s test was used to statistically compare continuous variables. A value of p<0.05 was considered statistically significant. Correlation between continuous variables was assessed using Pearson's correlation coefficient R.
RESULTS
BrS patients showed AOT and AOT% higher than controls (14.53±9.68 cm2 vs 6.34±5.41 cm2 ;35.31±20.98 vs 20.02±16.05; p<0.05). PType1 patients showed AOT and AOT% higher than NPtype1 patients (17.92±10.53 cm2 vs 11.49±7.84 cm2; p<0.05). Only Ptype1 showed an mUniJEL higher than controls (17.92±10.53 cm2 vs 11.49±7.84 cm2; p<0.05). No difference was shown between EPS- and EPS+ patients. BrS patients showed longer RVAT and more ISOstep than controls (65.44±25.18 ms vs 102.30±35.3 ms; 8.33±3.28 step vs 14.17±2.67 step; p<0.05). All BrS patients showed uniAVA (8,96 cm2±26,15 cm2), 16/55 BrS patients (29%) showed uniAVA>7 cm2 (high risk patients). None of control group showed significant uniAVA (uniAVA>1 cm2). A correlation was found between AOT and RVAT in BrS population (R=0.42). In “high risk patients” (uniAVA>7 cm2) a correlation was found between AOT and uniAVA (R=0.47). In all BrS patients RVAT was found to be correlated with uniAVA (R=0.47). In BrS patients with ISOstep>15 a correlation was found between ISOsteps and uniAVA (R=0.42).
CONCLUSIONS
In this work we introduced a novel workflow for the electrical substrate characterization of subjects with BrS phenotype. The results from our analysis indicate that a higher transmural repolarization dispersion can be found in BrS patients respect to normal subjects and in
Ptype1 BrS patients respect to NPtype1 patients. RV activation and RVOT activation are significantly longer in BrS patient respect to control patients. All BrS patients showed endocardial unipolar voltage anomalies. A correlation was found in BrS patients between transmural repolarization dispersion, conduction anomalies and voltage anomalies. These results suggest that both repolarization and depolarization anomalies characterize the RVOT of BrS patients. In conclusion, the diagnosis and risk stratification of asymptomatic BrS patients remains a significant challenge. Integrated analysis of transmural repolarization dispersion, voltage anomalies and conduction anomalies during EAM could be a useful tool for a better characterization of the electric substrate in those patients.