Machine learning for distinguishing right from left premature ventricular contraction origin using surface electrocardiogram features

BACKGROUND Precise localization of the site of origin of prema-ture ventricular contractions (PVCs) before ablation can facilitate the planning and execution of the electrophysiological procedure.OBJECTIVE The purpose of this study was to develop a predictive model that can be used to differentiate PVCs between the left ven-tricular outflow tract and right ventricular outflow tract (RVOT) us -ing surface electrocardiogram characteristics.METHODS A total of 851 patients undergoing radiofrequency abla-tion of premature ventricular beats from January 2015 to March 2022 were enrolled. Ninety-two patients were excluded. The other 759 patients were enrolled into the development (n = 605), external validation (n = 104), or prospective cohort (n = 50). The development cohort consisted of the training group (n = 423) and the internal validation group (n = 182). Machine learning algorithms were used to construct predictive models for the origin of PVCs using body surface electrocardiogram features.RESULTS In the development cohort, the Random Forest model showed a maximum receiver operating characteristic curve area of 0.96. In the external validation cohort, the Random Forest model surpasses 4 reported algorithms in predicting performance (accu-racy 94.23%; sensitivity 97.10%; specificity 88.57%). In the pro-spective cohort, the Random Forest model showed good performance (accuracy 94.00%; sensitivity 85.71%; specificity 97.22%).CONCLUSION Random Forest algorithm has improved the accuracy of distinguishing the origin of PVCs, which surpasses 4 previous standards, and would be used to identify the origin of PVCs before the interventional procedure.