Suppr超能文献

心脏再同步治疗中双心室、仅左心室和多点起搏的融合起搏:最新证据与应用策略

Fusion Pacing with Biventricular, Left Ventricular-only and Multipoint Pacing in Cardiac Resynchronisation Therapy: Latest Evidence and Strategies for Use.

作者信息

Waddingham Peter H, Lambiase Pier, Muthumala Amal, Rowland Edward, Chow Anthony Wc

机构信息

St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.

William Harvey Research Institute, Queen Mary University of London, London, UK.

出版信息

Arrhythm Electrophysiol Rev. 2021 Jul;10(2):91-100. doi: 10.15420/aer.2020.49.

Abstract

Despite advances in the field of cardiac resynchronisation therapy (CRT), response rates and durability of therapy remain relatively static. Optimising device timing intervals may be the most common modifiable factor influencing CRT efficacy after implantation. This review addresses the concept of fusion pacing as a method for improving patient outcomes with CRT. Fusion pacing describes the delivery of CRT pacing with a programming strategy to preserve intrinsic atrioventricular (AV) conduction and ventricular activation via the right bundle branch. Several methods have been assessed to achieve fusion pacing. QRS complex duration (QRSd) shortening with CRT is associated with improved clinical response. Dynamic algorithm-based optimisation targeting narrowest QRSd in patients with intact AV conduction has shown promise in people with heart failure with left bundle branch block. Individualised dynamic programming achieving fusion may achieve the greatest magnitude of electrical synchrony, measured by QRSd narrowing.

摘要

尽管心脏再同步治疗(CRT)领域取得了进展,但治疗的有效率和持久性仍相对停滞不前。优化设备的时间间隔可能是影响植入后CRT疗效的最常见可调节因素。本综述探讨了融合起搏这一概念,它是一种可改善CRT患者预后的方法。融合起搏描述的是通过一种编程策略进行CRT起搏,以保留经右束支的固有房室(AV)传导和心室激动。为实现融合起搏,已对多种方法进行了评估。CRT使QRS波群时限(QRSd)缩短与临床反应改善相关。基于动态算法的优化以完整AV传导患者中最窄QRSd为目标,已在左束支传导阻滞的心力衰竭患者中显示出前景。通过实现融合的个体化动态编程可能实现最大程度的电同步,这通过QRSd变窄来衡量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5dd/8335856/bbbdfb0b65f3/aer-10-91-g001.jpg

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