Discrepancy Between CARTO and Rhythmia Maps for Defining the Left Atrial Low-Voltage Areas in Atrial Fibrillation Ablation.

Kaseno Kenichi,Hasegawa Kanae,Miyazaki Shinsuke,Mukai Moe,Aoyama Daisetsu,Nodera Minoru, Hirano Koudai, Otake Mika,Nomura Ryouhei, Miyahara Kousuke, Ishikawa Rie,Matsui Akira,Yamaguchi Junya,Shiomi Yuichiro,Tama Naoto,Ikeda Hiroyuki,Fukuoka Yoshitomo,Ishida Kentaro,Uzui Hiroyasu,Tada Hiroshi

Heart and Vessels（2021）SCI 4区

University of Fukui

Cited 9|Views22

Abstract

Reported mapping procedures of left atrial (LA) low-voltage areas (LVAs) vary widely. This study aimed to compare the PentaRay®/CARTO®3 (PentaRay map) and Orion™/Rhythmia™ (Orion map) systems for LA voltage mapping. This study included 15 patients who underwent successful pulmonary vein isolation (PVI) for atrial fibrillation. After PVI, PentaRay and Orion maps created for all patients were compared. LVAs were defined as sites with ≥ 3 adjacent low-voltage points < 0.5 mV. LVAs were indicated in 8 (53%) among 15 patients, and the average values of the measured LVAs was comparable between the systems (PentaRay map = 5.4 ± 8.7 cm2; Orion map = 4.3 ± 6.4 cm2, p = 0.69). However, in 2 of 8 patients with LVAs, the Orion map indicated LVAs at the septum and posterolateral sites of the LA, respectively, whereas the PentaRay map indicated no LVAs. In those patients, sharp electrograms of > 0.5 mV were properly recorded at the septum and posterolateral sites during appropriate beats in the PentaRay map. The PentaRay map had a shorter procedure time than the Orion map (12 ± 3 min vs. 23 ± 8 min, respectively; p < 0.01). Our study results showed a discrepancy in the LVA evaluation between the PentaRay and Orion maps. In 2 of 15 patients, the Orion map indicated LVAs at the sites where > 0.5-mV electrograms were properly recorded in the PentaRay map.

Translated text

Key words

Atrial fibrillation,Fibrosis,Low-voltage area,CARTO,Rhythmia

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper