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A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia

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Journal of Interventional Cardiac Electrophysiology Aims and scope Submit manuscript

Abstract

Background

Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Published algorithms to distinguish ventricular tachycardia (VT) and supraventricular tachycardia (SVT) have limited diagnostic capabilities.

Methods

A total of 278 patients with WQCT from January 2010 to March 2022 were enrolled. The electrophysiological study confirmed SVT in 154 patients and VT in 65 ones. Two hundred nineteen WQCT 12-lead ECGs were randomly divided into development cohort (n = 165) and testing cohort (n = 54) data sets. The development cohort was split into a training group (n = 115) and an internal validation group (n = 50). Forty ECG features extracted from the 219 WQCT ECGs are fed into 9 iteratively trained ML algorithms. This novel ML algorithm was also compared with four published algorithms.

Results

In the development cohort, the Gradient Boosting Machine (GBM) model displayed the maximum area under curve (AUC) (0.91, 95% confidence interval (CI) 0.81–1.00). In the testing cohort, the GBM model had a higher AUC of 0.97 compared to 4 validated ECG algorithms, namely, Brugada (0.68), avR (0.62), RWPTII (0.72), and LLA algorithms (0.70). Accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the GBM model were 0.94, 0.97, 0.90, 0.94, and 0.95, respectively.

Conclusions

A GBM ML model contributes to distinguishing SVT from VT based on surface ECG features. In addition, we were able to identify important indicators for distinguishing WQCT.

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Data availability

If there is a reasonable request, all the data can be obtained through the corresponding author.

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Funding

This study was funded by 333 project of Jiangsu Province (BRA2017544) and the Zhongnanshan Medical Foundation of Guangdong Province (ZNSA‐2020017).

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Author notes

  1. Zhen-Zhen Li and Wei Zhao have contributed equally to this work.

Authors and Affiliations

  1. Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China

    Zhen-Zhen Li, Wei Zhao, YangMing Mao, Dan Bo, QiuShi Chen & FengXiang Zhang

  2. Department of Cardiology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, 210021, Jiangsu, China

    Zhen-Zhen Li

  3. Heart Center, National University, Singapore, Singapore

    Pipin Kojodjojo

Authors
  1. Zhen-Zhen Li
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  2. Wei Zhao
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  4. Dan Bo
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  7. FengXiang Zhang
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Correspondence to FengXiang Zhang.

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Ethical approval

This study was approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (Ethics No:2022-SR-638).

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Li, ZZ., Zhao, W., Mao, Y. et al. A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia. J Interv Card Electrophysiol (2024). https://doi.org/10.1007/s10840-024-01743-9

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