Suppr超能文献

Potential role of body surface ECG mapping for localization of atrial fibrillation trigger sites.

作者信息

Sippensgroenewegen Arne, Natale Andrea, Marrouche Nassir F, Bash Diana, Cheng Jie

机构信息

Heart Center for Excellence, Borgess Medical Center, Michigan State University, Kalamazoo, MI 49048, USA.

出版信息

J Electrocardiol. 2004;37 Suppl:47-52. doi: 10.1016/j.jelectrocard.2004.08.017.

Abstract

Catheter ablation has revolutionized the clinical management of atrial fibrillation (AF) by offering a curative treatment option for this highly prevalent arrhythmia. Ablation therapy is aimed at electrical isolation of the pulmonary veins (PVs) as a means to prevent rapidly firing focal activation within the PVs from penetrating into the left atrium (LA) and initiate reentrant wavelet propagation. However, non-PV AF trigger sites may be present and lead to unsuccessful ablation or post-ablation AF recurrences. Infrequent trigger firing and the difficulty or inability to induce focal trigger activity in the electrophysiology laboratory limits invasive catheter-based mapping of non-PV trigger sites. Identification of AF trigger sites using the surface electrocardiogram (ECG) P wave morphology is feasible but conventional 12-lead scalar recordings do not offer the resolving power to provide discrete regional localization to potentially target catheter ablation. The present paper includes a review of preliminary clinical data on the use of a 65-lead ECG mapping system (Resolution Medical, Inc) for the non-invasive localization of AF trigger sites. This method utilizes a unique previously developed reference database of 34 mean paced P wave integral map patterns which are each specific to activation arising from a discrete segment in the LA and right atrium (RA). Trigger site localization is obtained by matching the P wave integral map morphology of a premature atrial contraction (PAC) with the reference database of 34 mean paced P wave integral map patterns.

摘要

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验