Publications by authors named "A Yatomi"
Article Synopsis
- VT non-inducibility after radiofrequency (RF) ablation is generally linked to a lower chance of ventricular tachycardia (VT) recurrence, but its routine use as an endpoint is debated.
- A study analyzed 62 patients who did not achieve VT non-inducibility post-ablation, finding that 35% experienced VT recurrences over two years.
- Key factors influencing lower VT recurrence included a left ventricular ejection fraction (LVEF) of 35% or higher and the successful elimination of clinical VT during the procedure.
Article Synopsis
- - The study investigates why atrial fibrillation (AF) recurrence rates differ between sexes, focusing on the roles of non-pulmonary vein (PV) foci and epicardial adipose tissue (EAT).
- - Data from 304 patients revealed that females had more non-PV foci and less EAT around the atrium compared to males, with specific patterns observed in the left atrial wall.
- - Key predictors for AF recurrence included female sex, presence of non-PV foci, left atrial diameter, and septal EAT, with notable differences in predictors between males and females.
Background: Corticosteroids are widely used in patients with cardiac sarcoidosis (CS). In addition, upgrading to cardiac resynchronization therapy (CRT) is sometimes needed. This study aimed to investigate the impact of corticosteroid use on the clinical outcomes following CRT upgrades.
View Article and Find Full Text PDFFactors related to the skin thickness of cardiovascular implantable electronic device pockets.
J Cardiovasc Electrophysiol
August 2022
Introduction: The skin overlying cardiovascular implantable electronic devices (CIEDs) sometimes becomes very thin after implantations, which could cause a device erosion. The factors related to the skin thickness of device pockets have not been elucidated. This study aimed to evaluate the skin thickness of CIED pockets and search for the factors associated with the thickness.
View Article and Find Full Text PDFBackground: Several algorithms have been proposed for differentiating the right and left outflow tracts (RVOT/LVOT) arrhythmia origins from 12-lead electrocardiograms (ECGs); however, the procedure is complicated. A deep learning (DL) model, a form of artificial intelligence, can directly use ECGs and depict the importance of the leads and waveforms. This study aimed to create a visualized DL model that could classify arrhythmia origins more accurately.
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