Repolarization abnormalities unmasked with exercise in sudden cardiac death survivors with structurally normal hearts
Intracardiac mapping studies have demonstrated conduction and repolarization abnormalities in patients with “channelopathies” such as Brugada syndrome (BrS) and also in idiopathic VF, although it is unclear if these are greater in those at higher risk of sudden cardiac death (SCD) events.2 The surface ECG has also been used to quantify such abnormalities using surrogate measures such as QT dispersion, Tpeak‐end interval, or QRS dispersion, but have shown little or no prognostic utility in the specific channel mutations.8 This seeming lack of prognostic utility may relate to not taking autonomic tone into account and/or using tools with limited spatial resolution.
Autonomic tone is well described in modulating this arrhythmogenic potential, yet it has not been widely incorporated into risk‐stratification models.8 To date, most risk stratification is performed under basal, resting conditions, despite most SCD events being observed to occur at times of autonomic stress (both sympathetic and vagal). Therefore, characterizing the changes in depolarization and repolarization patterns during physiological stress could be crucial in unmasking or augmenting the arrhythmogenic state.
Electrocardiographic imaging (ECGi) is a novel tool that utilizes body surface potentials from a 252 electrode vest and inverse solution mathematics to reconstruct >1,200 unipolar electrograms on a digitized heart surface. Its advantage over the surface ECG is in providing more detailed information on depolarization and repolarization patterns on the epicardial surface of the heart and may be similarly utilized during a physiological stress test (e.g., exercise) owing to its noninvasive functionality.
In this study, we tested the hypothesis that patients with structurally normal hearts who have had VF arrests would demonstrate evidence of increased dispersion of repolarization and/or conduction heterogeneities under exercise‐stress conditions using ECGi.