A Novel Workflow for Electrophysiology Studies in Patients with Brugada Syndrome.

Brugada Syndrome (BrS) is a primary electrical epicardial disease characterized by ST-segment elevation followed by a negative T-wave in the right precordial leads on the surface electrocardiogram (ECG), also known as the 'type 1' ECG pattern. The risk stratification of asymptomatic individuals with spontaneous type 1 ECG pattern remains challenging. Clinical and electrocardiographic prognostic markers are known. As none of these predictors alone is highly reliable in terms of arrhythmic prognosis, several multi-factor risk scores have been proposed for this purpose. This article presents a new workflow for processing endocardial signals acquired with high-density RV electro-anatomical mapping (HDEAM) from BrS patients. The workflow, which relies solely on Matlab software, calculates various electrical parameters and creates multi-parametric maps of the right ventricle. The workflow, but it has already been employed in several research studies involving patients carried out by our group, showing its potential positive impact in clinical studies. Here, we will provide a technical description of its functionalities, along with the results obtained on a BrS patient who underwent an endocardial HDEAM.

Electrophysiological patterns and structural substrates of Brugada syndrome: Critical appraisal and computational analyses.

Brugada syndrome (BrS) is a cardiac electrophysiological disease with unknown etiology, associated with sudden cardiac death. Symptomatic patients are treated with implanted cardiac defibrillator, but no risk stratification strategy is effective in patients that are at low to medium arrhythmic risk. Cardiac computational modeling is an emerging tool that can be used to verify the hypotheses of pathogenesis and inspire new risk stratification strategies. However, to obtain reliable results computational models must be validated with consistent experimental data. We reviewed the main electrophysiological and structural variables from BrS clinical studies to assess which data could be used to validate a computational approach. Activation delay in the epicardial right ventricular outflow tract is a consistent finding, as well as increased fibrosis and subclinical alterations of right ventricular functional and morphological parameters. The comparison between other electrophysiological variables is hindered by methodological differences between studies, which we commented. We conclude by presenting a recent theory unifying electrophysiological and structural substrate in BrS and illustrate how computational modeling could help translation to risk stratification.

Endocardial repolarization dispersion in BrS: A novel automatic algorithm for mapping activation recovery interval.

INTRODUCTION: Repolarization dispersion in the right ventricular outflow tract (RVOT) contributes to the type-1 electrocardiographic (ECG) phenotype of Brugada syndrome (BrS), while data on the significance and feasibility of mapping repolarization dispersion in BrS patients are scarce. Moreover, the role of endocardial repolarization dispersion in BrS is poorly investigated. We aimed to assess endocardial repolarization patterns through an automated calculation of activation recovery interval (ARI) estimated on unipolar electrograms (UEGs) in spontaneous type-1 BrS patients and controls; we also investigated the relation between ARI and right ventricle activation time (RVAT), and T-wave peak-to-end interval (Tpe) in BrS patients. METHODS: Patients underwent endocardial high-density electroanatomical mapping (HDEAM); BrS showing an overt type-1 ECG were defined as OType1, while those without (latent type-1 ECG and LType1) received ajmaline infusion. BrS patients only underwent programmed ventricular stimulation (PVS). Data were elaborated to obtain ARI corrected with the Bazett formula (ARIc), while RVAT was derived from activation maps. RESULTS: 39 BrS subjects (24 OType1 and 15 LTtype1) and 4 controls were enrolled. OType1 and post-ajmaline LType1 showed longer mean ARIc than controls (306 ± 27.3 ms and 333.3 ± 16.3 ms vs. 281.7 ± 10.3 ms, p = .05 and p < .001, respectively). Ajmaline induced a significant prolongation of ARIc compared to pre-ajmaline LTtype1 (333.3 ± 16.3 vs. 303.4 ± 20.7 ms, p < .001) and OType1 (306 ± 27.3 ms, p < .001). In patients with type-1 ECG (OTtype1 and post-ajmaline LType1) ARIc correlated with RVAT (r = .34, p = .04) and Tpec (r = .60, p < .001), especially in OType1 subjects (r = .55, p = .008 and r = .65 p < .001, respectively). CONCLUSION: ARIc mapping demonstrates increased endocardial repolarization dispersion in RVOT in BrS. Endocardial ARIc positively correlates with RVAT and Tpec, especially in OType1.

Multi-chamber speckle tracking imaging and diagnostic value of left atrial strain in cardiac amyloidosis.

AIMS: Cardiac amyloidosis (CA) affects the four heart chambers, which can all be evaluated through speckle-tracking echocardiography (STE). METHODS AND RESULTS: We evaluated 423 consecutive patients screened for CA over 5 years at two referral centres. CA was diagnosed in 261 patients (62%) with either amyloid transthyretin (ATTR; n = 144, 34%) or amyloid light-chain (AL; n = 117, 28%) CA. Strain parameters of all chambers were altered in CA patients, particularly those with ATTR-CA. Nonetheless, only peak left atrial longitudinal strain (LA-PALS) displayed an independent association with the diagnosis of CA or ATTR-CA beyond standard echocardiographic variables and cardiac biomarkers (Model 1), or with the diagnosis of ATTR-CA beyond the validated IWT score in patients with unexplained left ventricular (LV) hypertrophy. Patients with the most severe impairment of LA strain were those most likely to have CA or ATTR-CA. Specifically, LA-PALS and/or LA-peak atrial contraction strain (PACS) in the first quartile (i.e. LA-PALS <6.65% and/or LA-PACS <3.62%) had a 3.60-fold higher risk of CA, and a 3.68-fold higher risk of ATTR-CA beyond Model 1. Among patients with unexplained LV hypertrophy, those with LA-PALS or LA-PACS in the first quartile had an 8.76-fold higher risk for CA beyond Model 1, and a 2.04-fold higher risk of ATTR-CA beyond the IWT score. CONCLUSIONS: Among STE measures of the four chambers, PALS and PACS are the most informative ones to diagnose CA and ATTR-CA. Patients screened for CA and having LA-PALS and/or LA-PACS in the first quartile have a high likelihood of CA and ATTR-CA.