Ito Yoko, Shiga Keisuke, Yoshida Kentaro, Ogata Kuniomi, Kandori Akihiko, Inaba Takeshi, Nakazawa Yoko, Sekiguchi Yukio, Tada Hiroshi, Sekihara Kensuke, Aonuma Kazutaka
Cardiovascular Division, Institute of Clinical Medicine, University of Tsukuba, Tsukuba, Japan.
Department of Systems Design & Engineering, Tokyo Metropolitan University, Hachioji, Japan.
Heart Rhythm. 2014 Sep;11(9):1605-12. doi: 10.1016/j.hrthm.2014.05.032. Epub 2014 Jun 2.
Although several reports address characteristic 12-lead electrocardiographic findings of outflow tract ventricular arrhythmias (OT-VAs), the accuracy of electrocardiogram-based algorithms to predict the OT-VA origin is sometimes limited.
This study aimed to develop a magnetocardiography (MCG)-based algorithm using a novel adaptive spatial filter to differentiate between VAs originating from the aortic sinus cusp (ASC-VAs) and those originating from the right ventricular outflow tract (RVOT-VAs).
This study comprised 51 patients with an OT-VA as the target of catheter ablation. An algorithm was developed by correlating MCG findings with the successful ablation site. The arrhythmias were classified as RVOT-VAs or ASC-VAs. Three parameters were obtained from 3-dimensional MCG imaging: depth of the origin of the OT-VA in the anteroposterior direction; distance between the earliest atrial activation site, that is, sinus node, and the origin of the OT-VA; and orientation of the arrhythmia propagation at the QRS peak. The distance was indexed to the patient's body surface area (in mm/m2).
Origins of ASC-VAs were significantly deeper (81 ± 6 mm/m(2) vs. 68 ± 8 mm/m(2); P < .01) and farther from the sinus node (55 ± 9 mm/m2 vs. 41 ± 9 mm/m(2); P < .01) than those of RVOT-VAs. ASC-VA propagation had a tendency toward rightward axis. Receiver operating characteristic analyses determined that the depth of the origin was the most powerful predictor, with a sensitivity of 90% and a specificity of 73% (area under the curve = 0.90; P < .01). Discriminant analysis combining all 3 parameters revealed the accuracy of the localization to be 94%.
This MCG-based algorithm appeared to precisely discriminate ASC-VAs from RVOT-VAs. Further investigation is required to validate the clinical value of this technique.
尽管有几份报告阐述了流出道室性心律失常(OT-VAs)的特征性12导联心电图表现,但基于心电图的算法预测OT-VA起源的准确性有时有限。
本研究旨在开发一种基于磁心动图(MCG)的算法,使用新型自适应空间滤波器来区分源自主动脉窦嵴(ASC-VAs)的室性心律失常和源自右心室流出道(RVOT-VAs)的室性心律失常。
本研究纳入51例以OT-VA为导管消融靶点的患者。通过将MCG结果与成功消融部位相关联来开发一种算法。心律失常被分类为RVOT-VAs或ASC-VAs。从三维MCG成像中获得三个参数:OT-VA起源在前后方向上的深度;最早心房激动部位即窦房结与OT-VA起源之间的距离;以及QRS波峰处心律失常传播的方向。该距离根据患者体表面积进行索引(单位为mm/m²)。
与RVOT-VAs相比,ASC-VAs的起源显著更深(81±6mm/m²对68±8mm/m²;P<.01)且离窦房结更远(55±9mm/m²对41±9mm/m²;P<.01)。ASC-VA传播有向右轴的趋势。受试者工作特征分析确定起源深度是最有力的预测指标,敏感性为90%,特异性为73%(曲线下面积=0.90;P<.01)。结合所有三个参数的判别分析显示定位准确性为94%。
这种基于MCG的算法似乎能精确区分ASC-VAs和RVOT-VAs。需要进一步研究来验证该技术的临床价值。