Ho Gordon, Villongco Christopher T, Yousefian Omid, Bradshaw Aaron, Nguyen Andrew, Faiwiszewski Yonatan, Hayase Justin, Rappel Wouter-Jan, McCulloch Andrew D, Krummen David E
Department of Medicine, University of California, San Diego, CA, USA.
Veterans Affairs San Diego Healthcare System, San Diego, CA, USA.
J Cardiovasc Electrophysiol. 2017 Oct;28(10):1158-1166. doi: 10.1111/jce.13283. Epub 2017 Aug 4.
Ventricular fibrillation is a common life-threatening arrhythmia. The ECG of VF appears chaotic but may allow identification of sustaining mechanisms to guide therapy.
We hypothesized that rotors and focal sources manifest distinct features on the ECG, and computational modeling may identify mechanisms of such features.
VF induction was attempted in 31 patients referred for ventricular arrhythmia ablation. Simultaneous surface ECG and intracardiac electrograms were recorded using biventricular basket catheters. Endocardial phase maps were used to mechanistically classify each VF cycle as rotor or focally driven. ECGs were analyzed from patients demonstrating both mechanisms in the primary analysis and from all patients with induced VF in the secondary analysis. The ECG voltage variation during each mechanism was compared. Biventricular computer simulations of VF driven by focal sources or rotors were created and resulting ECGs of each VF mechanism were compared.
Rotor-based VF exhibited greater voltage variation than focal source-based VF in both the primary analysis (n = 8, 110 ± 24% vs. 55 ± 32%, P = 0.02) and the secondary analysis (n = 18, 103 ± 30% vs. 67 ± 34%, P = 0.009). Computational VF simulations also revealed greater voltage variation in rotors compared to focal sources (110 ± 19% vs. 33 ± 16%, P = 0.001), and demonstrated that this variation was due to wavebreak, secondary rotor initiation, and rotor meander.
Clinical and computational studies reveal that quantitative criteria of ECG voltage variation differ significantly between VF-sustaining rotors and focal sources, and provide insight into the mechanisms of such variation. Future studies should prospectively evaluate if these criteria can separate clinical VF mechanisms and guide therapy.
心室颤动是一种常见的危及生命的心律失常。室颤的心电图看起来杂乱无章，但可能有助于识别维持机制以指导治疗。
我们假设转子和局灶性起源在心电图上表现出不同的特征，并且计算模型可以识别这些特征的机制。
对31例因室性心律失常接受消融治疗的患者尝试诱发室颤。使用双心室篮状导管同步记录体表心电图和心内电图。心内膜相位图用于将每个室颤周期机械分类为转子驱动或局灶驱动。在主要分析中，对显示两种机制的患者的心电图进行分析，在次要分析中，对所有诱发室颤的患者的心电图进行分析。比较每种机制下心电图电压的变化。创建了由局灶性起源或转子驱动的室颤的双心室计算机模拟，并比较了每种室颤机制产生的心电图。
在主要分析（n = 8，110 ± 24% 对 55 ± 32%，P = 0.02）和次要分析（n = 18，103 ± 30% 对 67 ± 34%，P = 0.009）中，基于转子的室颤比基于局灶性起源的室颤表现出更大的电压变化。室颤的计算模拟还显示，与局灶性起源相比，转子的电压变化更大（110 ± 19% 对 33 ± 16%，P = 0.001），并表明这种变化是由于波破碎、次级转子起始和转子蜿蜒。
临床和计算研究表明，维持室颤的转子和局灶性起源之间，心电图电压变化的定量标准存在显著差异，并为这种变化的机制提供了见解。未来的研究应前瞻性地评估这些标准是否可以区分临床室颤机制并指导治疗。
