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从整合生理学角度看T波交替用于心脏性猝死预测的基础。

Basis for sudden cardiac death prediction by T-wave alternans from an integrative physiology perspective.

作者信息

Verrier Richard L, Kumar Kapil, Nearing Bruce D

机构信息

Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Cardiovascular Institute, Boston, Massachusetts 02115, USA.

出版信息

Heart Rhythm. 2009 Mar;6(3):416-22. doi: 10.1016/j.hrthm.2008.11.019. Epub 2008 Nov 27.

Abstract

Detection of microvolt levels of T-wave alternans (TWA) has been shown to be useful in identifying individuals at heightened risk for sudden cardiac death. The mechanistic bases for TWA are complex, at the cellular level involving multiple mechanisms, particularly instabilities in membrane voltage (i.e., steep action potential duration restitution slope) and disruptions in intracellular calcium cycling dynamics. The integrative factors influencing TWA at the systemic level are also multifold. We focus on three main variables: heart rate, autonomic nervous system activity, and myocardial ischemia. Clinically, there is growing interest in extending TWA testing to include ambulatory ECG monitoring as well as exercise. The former modality permits assessment of the influence of diverse provocative stimuli of daily life, including physical activity, circadian factors, mental stress, and sleep-state related disturbances in respiratory and cardiovascular function. Two major emerging concepts in clinical TWA testing are discussed: quantitative analysis of TWA level to complement the current binary classification scheme, and risk stratification of patients with preserved left ventricular function, the population with the largest absolute number of sudden cardiac deaths.

摘要

已证明检测微伏级的T波交替(TWA)有助于识别心脏性猝死风险增加的个体。TWA的机制基础很复杂,在细胞水平涉及多种机制,特别是膜电压的不稳定性(即陡峭的动作电位时程恢复斜率)和细胞内钙循环动力学的破坏。在系统水平上影响TWA的综合因素也多种多样。我们关注三个主要变量:心率、自主神经系统活动和心肌缺血。临床上,将TWA检测扩展到包括动态心电图监测以及运动的兴趣日益增加。前一种方式允许评估日常生活中各种激发刺激的影响,包括身体活动、昼夜节律因素、精神压力以及与睡眠状态相关的呼吸和心血管功能紊乱。讨论了临床TWA检测中两个主要的新兴概念:TWA水平的定量分析以补充当前的二元分类方案,以及对左心室功能保留患者(心脏性猝死绝对数量最多的人群)进行风险分层。

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