Lead I R‐wave amplitude to distinguish ventricular arrhythmias with lead V₃ transition originating from the left versus right ventricular outflow tract

Abstract

BackgroundThe electrophysiology algorithm for localizing left or right origins of outflow tract ventricular arrhythmias (OT‐VAs) with lead V3 transition still needs further investigation in clinical practice.HypothesisLead I R‐wave amplitude is effective in distinguishing the left or right origin of OT‐VAs with lead V3 transition.MethodsWe measured lead I R‐wave amplitude in 82 OT‐VA patients with lead V3 transition and a positive complex in lead I who underwent successful catheter ablation from the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT). The optimal R‐wave threshold was identified, compared with the V2S/V3R index, transitional zone (TZ) index, and V2 transition ratio, and validated in a prospective cohort study.ResultsLead I R‐wave amplitude for LVOT origins was significantly higher than that for RVOT origins (0.55 ± 0.13 vs. 0.32 ± 0.15 mV; p < .001). The area under the curve (AUC) for lead I R‐wave amplitude as assessed by receiver operating characteristic (ROC) analysis was 0.926, with a cutoff value of ≥0.45 predicting LVOT origin with 92.9% sensitivity and 88.2% specificity, superior to the V2S/V3R index, TZ index, and V2 transition ratio. VAs in the LVOT group mainly originated from the right coronary cusp (RCC) and left and right coronary cusp junction (L‐RCC). In the prospective study, lead I R‐wave amplitude identified the LVOT origin with 92.3% accuracy.ConclusionLead I R‐wave amplitude provides a useful and simple criterion to identify RCC or L‐RCC origin in OT‐VAs with lead V3 transition.

A novel practical algorithm using machine learning to differentiate outflow tract ventricular arrhythmia origins

“…In particular, the outflow tract ventricular arrhythmia (OTVA) with a precordial transition in lead V3 (V3TZ) has been recognized as one of the most difficult QRS morphologies to differentiate the origin. 1 Although several algorithms for the diagnosis of the origin of OTVA with V3TZ have been developed so far, [2][3][4][5][6][7][8][9][10][11][12][13][14] the accuracy is not definitive with limited performance in each criterion. 15,16 To address this challenge, we aimed to propose a new algorithm to differentiate the left ventricular outflow tract (LVOT) from the right ventricular outflow tract (RVOT) of OTVA origin with V3TZ using artificial intelligence (AI).…”