Key result
Machine learning model differentiates RVOT from LVOT PVC origin with ~0.91 ROC-AUC.
Why the study?
The surface 12-lead ECG is commonly used to differentiate right and left ventricular outflow tract premature ventricular contractions prior to catheter ablation, and machine learning methods may further enhance this diagnostic accuracy.
Does a logistic regression machine learning model combining 64 ECG-derived features, age, and gender improve the classification of RVOT versus LVOT origin in patients undergoing catheter ablation for idiopathic premature ventricular contractions?
Observational (n=43)
Does a logistic regression machine learning model combining 64 ECG-derived features, age, and gender improve the classification of RVOT versus LVOT origin in patients undergoing catheter ablation for idiopathic premature ventricular contractions?
A machine learning model combining surface 12-lead ECG parameters with patient age and gender accurately differentiates between RVOT and LVOT origins of idiopathic PVCs, aiding in pre-procedural ablation planning.
INTRODUCTION: Idiopathic premature ventricular contractions (PVCs) commonly originate from the right and left ventricular outflow tracts (RVOT and LVOT). The surface 12-lead electrocardiogram (ECG) is commonly used to differentiate the anatomic site of origin prior to catheter ablation. Machine learning methods can further enhance diagnostic accuracy. MATERIALS AND METHODS: In this study, 64 ECG-derived features, along with age and gender, were analyzed from 43 patients (65% males, median age 55.6, IQR 30 years), who underwent successful catheter ablation of outflow tract-originating idiopathic PVCs. The site of origin was depicted through 3-D electroanatomical mapping. A logistic regression model was trained to classify the site of origin as either RVOT or LVOT using repeated stratified five-fold cross-validation with five repeats. A model restricted to ECG features and four previously published ECG algorithms was evaluated for comparison. RESULTS: Across the 25 held-out partitions, the model achieved an accuracy of 0.817 ± 0.143, precision of 0.888 ± 0.122, recall of 0.824 ± 0.206, F1-score of 0.836 ± 0.139 and ROC-AUC of 0.917 ± 0.094. Using the pooled out-of-fold predictions, accuracy was 0.860 (95% CI 0.744-0.953) and ROC-AUC 0.910 (95% CI 0.800-0.987). Age was the most discriminative feature, with increasing values favoring LVOT origin. The ECG-only model achieved an ROC-AUC of 0.853 (95% CI 0.721-0.958; ΔAUC +0.057, p = 0.104). CONCLUSIONS: These findings suggest that machine learning models, based on ECG parameters and demographic characteristics, can classify outflow tract ventricular arrhythmias, supporting their use in automated differentiation and pre-procedural planning.
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Botis et al. (2026) conducted an observational in Outflow tract-originating idiopathic premature ventricular contractions (PVCs) (n=43). Logistic regression model using 64 ECG-derived features plus age and gender vs. ECG-only model and four previously published ECG algorithms was evaluated on Classification of premature ventricular contraction site of origin as RVOT vs LVOT (ROC-AUC and accuracy) (ROC-AUC 0.910, 95% CI 0.800-0.987, p=0.104). A machine learning model combining 64 ECG features, age, and gender classified RVOT vs LVOT PVC origin with 0.860 accuracy and 0.910 ROC-AUC (95% CI 0.800-0.987).
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