An electrocardiographic diagnostic model for differentiating left from right ventricular outflow tract tachycardia origin

作者：He, Zhuoqiao; Liu, Ming; Yu, Min; Lu, Nan; Li, Jia; Xu, Tan; Zhu, Jinxiu; O'Gara, Mary Clare; O'Meara, Michael; Ye, Hong; Tan, Xuerui^*

来源：Journal of Cardiovascular Electrophysiology, 2018, 29(6): 908-915.

DOI：10.1111/jce.13493

• catheter ablation
• electrocardiogram
• premature ventricular complex
• ventricular outflow tract
• ventricular tachycardia

Introduction: Although several electrocardiographic (ECG) algorithms have been proposed for differentiating the origins of outflow tract ventricular arrhythmias, the most optimal one has not been agreed on. The purpose of this study was to establish an ECG diagnostic model based on the previous ECG algorithms.
Methods and results: The following ECG diagnostic model Y = - 1.15 x (TZ) - 0.494 x (V2S/V3R), was developed by standard 12 lead ECG algorithms in 488 patients with idiopathic premature ventricular contractions or ventricular tachycardia with a left bundle branch block pattern and inferior axis QRS morphology. Binary logistic regression analysis was performed to establish the ECG diagnostic model. The ECG diagnostic model consisted of two ECG algorithms- the transition zone (TZ) index and V2S/V3R index. The area under the curve by receiver operating characteristic curve analysis for the ECG diagnostic model was 0.88, with a cut-off value of >= -0.76 predicting a left ventricular outflow tract (LVOT) origin with a sensitivity of 82% and a specificity of 86%, which was higher than other ECG algorithms in this study. The predictive accuracy of the ECG diagnostic model was also the best among all ECG algorithms in patients with a lead V3 precordial transition. This model was tested prospectively in 207 patients with a sensitivity of 90%, a specificity of 87%, and Youden index of 0.77.
Conclusions: A highly accurate ECG diagnostic model for correctly differentiating LVOT origin from right ventricular outflow tract origin was developed.

• 出版日期2018-6
• 单位汕头大学