Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
Ann Noninvasive Electrocardiol. 2022 Jan;27(1):e12890. doi: 10.1111/anec.12890. Epub 2021 Sep 25.
Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification.
A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one "all-inclusive" model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance.
The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X-lead QRS amplitude change, Y-lead QRS amplitude change, and Z-lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95).
Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.
使用新的计算方法量化宽复合心动过速（WCT）与基础心电图（ECG）之间平均电向量变化的程度，可实现自动区分室性心动过速（VT）和室上性宽复合心动过速（SWCT）。目前尚不清楚在三个正交向量心电图（VCG）导联（X、Y 和 Z 导联）内量化平均电向量变化是否能改善自动 VT 和 SWCT 分类。
使用配对的 WCT 和基础 ECG 推导队列来推导五个逻辑回归模型：（i）一个新的 WCT 区分模型（即 VCG 模型），（ii）三个先前开发的 WCT 区分模型（即 WCT 公式、VT 预测模型和 WCT 公式 II），和（iii）一个“全包”模型（即混合模型）。使用配对的 WCT 和基础 ECG 的独立验证队列来试验和比较每个模型的性能。
由 WCT QRS 持续时间、基础 QRS 持续时间、QRS 持续时间绝对变化、X 导联 QRS 振幅变化、Y 导联 QRS 振幅变化和 Z 导联 QRS 振幅变化组成的 VCG 模型，在推导队列中表现出有效的 WCT 区分（曲线下面积 [AUC] 0.94）。对于验证队列，VCG 模型（AUC 0.94）的诊断性能与 WCT 公式（AUC 0.95）、VT 预测模型（AUC 0.91）、WCT 公式 II（AUC 0.94）和混合模型（AUC 0.95）相当。
从数学合成的 VCG 信号中推导的定制计算可以用来制定自动区分 WCT 的有效方法。
