Abstract
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.
Highlights
Multiple 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
One population-based study (Dukes et al, 2015) of 1,139 older adults without any heart-failure signs or systolic dysfunction shows that premature ventricular complexes (PVC) and ventricular tachycardia (VT) burden are significantly associated with an increased risk of adjusted decreased left ventricular ejection fraction and increased adjusted risk of incident heart failure and death
This study consists of four phases: (Dukes et al, 2015) a feature extraction phase in which two feature extraction methods are studied and compared—our proprietary automated ECG feature extraction method and a method based on conventional QRS morphological ECG measurements (Cronin et al, 2019) a training phase in which the extreme gradient boosting tree classification model is supplied by the features generated in the feature extraction phase (Joshi and Wilber, 2005) a validation phase aimed at finding important features as optimal model input and deciding the optimal discretization cutoff threshold that was applied in the testing phase; and (Latchamsetty et al, 2015) a testing phase aimed at evaluating, interpreting, and reporting the model performance
Summary
Multiple 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. 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, 2011, 2014; Cheng et al, 2013, 2018; Nakano 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. We develop an optimal multistage scheme that automatically extracts features from standard 12-lead ECGs and incorporates these features into a machine learning model to predict RVOT and LVOT origins of VT or PVC with clinical-grade precision and provides multiprospective analysis for the most important ECG features
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