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

Diagnosis of outflow tract ventricular arrhythmia (OTVA) localization by an electrocardiographic complex is key to successful catheter ablation for OTVA. However, diagnosing the origin of OTVA with a precordial transition in lead V3 (V3TZ) is challenging. This study aimed to create the best practical electrocardiogram algorithm to differentiate the left ventricular outflow tract (LVOT) from the right ventricular outflow tract (RVOT) of OTVA origin with V3TZ using machine learning.

Of 498 consecutive patients undergoing catheter ablation for OTVA, we included 104 patients who underwent ablation for OTVA with V3TZ and identified the origin of LVOT (n = 62) and RVOT (n = 42) from the results. We analyzed the standard 12-lead electrocardiogram preoperatively and measured 128 elements in each case. The study population was randomly divided into training group (70%) and testing group (30%), and decision tree analysis was performed using the measured elements as features. The performance of the algorithm created in the training group was verified in the testing group.

Four measurements were identified as important features: the aVF/II R-wave ratio, the V2S/V3R index, the QRS amplitude in lead V3, and the R-wave deflection slope in lead V3. Among them, the aVF/II R-wave ratio and the V2S/V3R index had a particularly strong influence on the algorithm. The performance of this algorithm was extremely high, with an accuracy of 94.4%, precision of 91.5%, recall of 100%, and an F1-score of 0.96.

The novel algorithm created using machine learning is useful in diagnosing the origin of OTVA with V3TZ.