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

He, Zhuoqiao; Liu, Ming; Yu, Min; Lu, Nan; Li, Jia; Xu, Tan; Zhu, Jun; O'Gara, Mary Clare; O’Meara, Michael; Ye, Hong; Tan, Xuerui

doi:10.1111/jce.13493

Cited by 27 publications

(29 citation statements)

References 20 publications

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“…We used DeLong's test (DeLong et al, 1988) to demonstrate that the automated ECG feature extraction method had a significantly higher AUC compared with that attained by the conventional QRS morphological feature extraction approach with a P-value = 0.035. The comparison of our approach against methods from 12 prior studies (Kamakura et al, 1998;Zhang et al, 2009;Betensky et al, 2011;Yoshida et al, 2011Yoshida et al, , 2014Cheng et al, 2013Cheng et al, , 2018Nakano et al, 2014;Efimova et al, 2015;He et al, 2018;Xie et al, 2018;Di et al, 2019) shows that our algorithm achieved the highest performance scores (shown in Table 3). Additionally, we evaluated the general classification capability of each criterion proposed by previous studies using the database in this study.…”

Section: Discussionmentioning

confidence: 79%

“…The classification confusion matrix for these three methods shows correct and incorrect frequency counts in Supplementary Section A and Table 3. Furthermore, we compared our approach against related methods from 12 prior studies (Kamakura et al, 1998;Zhang et al, 2009;Betensky et al, 2011;Yoshida et al, 2011Yoshida et al, , 2014Cheng et al, 2013Cheng et al, , 2018Nakano et al, 2014;Efimova et al, 2015;He et al, 2018;Xie et al, 2018;Di et al, 2019). ACC, F 1 -score, SE, SP, positive predictive value, negative predictive value, and AUC were used to compare performances and are shown in Table 3.…”

Section: Resultsmentioning

confidence: 99%

“…An accurate prediction of RVOT and LVOT origins of OTVT can optimize the CA strategy, reduce ablation duration, and avoid operative complications. Previous studies (Kamakura et al, 1998;Hachiya et al, 2000;Ito et al, 2003;Joshi and Wilber, 2005;Tanner et al, 2005;Haqqani et al, 2009;Zhang et al, 2009;Betensky et al, 2011;Yoshida et al, 2011Yoshida et al, , 2014Cheng et al, 2013Cheng et al, , 2018Nakano et al, 2014;Efimova et al, 2015;He et al, 2018;Xie et al, 2018;Di et al, 2019;Enriquez et al, 2019;Yamada, 2019) propose several criteria or models to estimate RVOT and LVOT origins. However, these results have been limited by sample size, scope of studies, ECG measurement efficiency, and generalizability of the models.…”

Section: Introductionmentioning

confidence: 99%

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A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

Zheng

Abudayyeh

et al. 2021

Front. Physiol.

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IntroductionMultiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.MethodsWe randomly sampled training, validation, and testing data sets from 420 patients who underwent successful catheter ablation (CA) to treat VT or PVC, containing 340 (81%), 38 (9%), and 42 (10%) patients, respectively. We iteratively trained a machine learning algorithm supplied with 1,600,800 features extracted via our proprietary algorithm from 12-lead ECGs of the patients in the training cohort. The area under the curve (AUC) of the receiver operating characteristic curve was calculated from the internal validation data set to choose an optimal discretization cutoff threshold.ResultsThe proposed approach attained the following performance: accuracy (ACC) of 97.62 (87.44–99.99), weighted F1-score of 98.46 (90–100), AUC of 98.99 (96.89–100), sensitivity (SE) of 96.97 (82.54–99.89), and specificity (SP) of 100 (62.97–100).ConclusionsThe proposed multistage diagnostic scheme attained clinical-grade precision of prediction for LVOT and RVOT locations of VT origin with fewer applicability restrictions than prior studies.

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Section: Discussionmentioning

confidence: 79%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

Zheng

Abudayyeh

et al. 2021

Front. Physiol.

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“…The stepwise algorithm is beneficial for improving the accuracy of predicting the LVOT origin, which makes contributions to guiding to choose the right access, reducing the scope of intracardiac mapping, saving the mapping time, and improving the success rate of ablation. To distinguish LVOT from RVOT origin accurately, He et al [26] used similar ECG algorithms to develop an ECG diagnostic equation: Y = -1.15 × (TZ) -0.494 × (V2S/V3R). However, its usage and promotion would be restricted because of the complex calculation.…”

Section: Discussionmentioning

confidence: 99%

A Simplified Two-Stepwise Electrocardiographic Algorithm to Distinguish Left from Right Ventricular Outflow Tract Tachycardia Origin

Yu¹,

Li²,

Zhang³

et al. 2020

Cardiology

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Background: There are several electrocardiographic algorithms to predict the origin of idiopathic outflow tract ventricular arrhythmias (OT-VAs). This study aimed to develop a more accurate and efficient stepwise electrocardiographic algorithm to discriminate left ventricular outflow tract (LVOT) from right ventricular outflow tract (RVOT) origin. Methods and Results: We analyzed 12-lead electrocardiographic characteristics of 173 consecutive OT-VAs patients who underwent successful radiofrequency catheter ablation in the RVOT (n = 124) or LVOT (n = 49). Based on the areas under the receiver operating characteristic curves, the combination of transitional zone (TZ) index <0 and V2S/V3R index ≤1.5 exhibited 93.5% sensitivity, 85.9% specificity, and 87.3% accuracy. A further analysis was performed in the 71 OT-VAs with a V3-lead precordial transition. The sensitivity, specificity, and accuracy of the integration of V2S/V3R index ≤1.5 and R-wave deflection interval in lead V3 >80 ms were 91.7, 83.1, and 85.9%, respectively. In the prospective evaluation, the combination of TZ index and V2S/V3R index could identify the correct origin sites with 91.2% accuracy in the overall analysis, and the integration of V2S/V3R index ≤1.5 and R-wave deflection interval in lead V3 >80 ms exhibited 94% accuracy in V3-lead precordial transition. Conclusions: The combination of TZ index <0 and V2S/V3R index ≤1.5 is a simple and efficient stepwise electrocardiographic algorithm for predicting LVOT origin. For the OT-VAs with a V3-lead precordial transition, the integration of V2S/V3R index ≤1.5 and R-wave deflection interval in lead V3 >80 ms would be a better choice.

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“…An accurate prediction of RVOT and LVOT origins of OTVT can optimize the CA strategy, reduce ablation duration, and avoid operative complications. Previous studies 3,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] have proposed several criteria or models to estimate RVOT and LVOT origins. However, these results have been limited by sample size, the scope of studies, ECG measurement efficiency, and the generalizability of the models.…”

Section: Introductionmentioning

confidence: 99%

A High Precision Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

Zheng

Abudayyeh

et al. 2020

Preprint

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

Abstract: A highly accurate ECG diagnostic model for correctly differentiating LVOT origin from right ventricular outflow tract origin was developed.

Cited by 27 publications

References 20 publications

A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

A Simplified Two-Stepwise Electrocardiographic Algorithm to Distinguish Left from Right Ventricular Outflow Tract Tachycardia Origin

A High Precision Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

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