Automated Identification of Ventricular Tachycardia Ablation Targets: Multicenter Validation and Workflow Characterization

Decrement evoked potentials (EPs) (DeEPs) constitute an accepted method to identify physiological ventricular tachycardia (VT) ablation targets without inducing VT. The feasibility of automated software (SW) in the detection of arrhythmogenic VT substrate has been documented. However, multicenter validation of automated SW and workflow has yet to be characterized. The objective of this study was to describe the functionality of a novel DeEP SW (Biosense Webster, Diamond Bar, CA, USA) and evaluate the independent performance of the automated algorithm using multicenter data. VT ablation cases were performed in the catheterization laboratory and retrospectively analyzed using the DeEP SW. The algorithm indicated and mapped DeEPs by first identifying capture in surface electrocardiograms (ECGs). Once capture was confirmed, the EPs of S1 paces were detected. The algorithm checked for the stability of S1 EPs by comparing the last 3 of the 8 morphologies and attributing standard deviation values. The extra-stimulus EP was then detected by comparing it to the S1 EP. Once detected, the DeEP value was computed from the extra-stimulus and displayed as a sphere on a voltage map. A total of 5,885 DeEP signals were extracted from 21 substrate mapping cases conducted at 3 different centers (in Spain, Canada, and Australia). A gold standard was established from ECGs manually marked by subject experts. Once the algorithm was deployed, 91.6% of S2 algorithm markings coincided with the gold standard, 1.9% were false-positives, and 0.1% were false-negatives. Also, 6.4% were non-specific DeEP detections. In conclusion, the automated DeEP algorithm identifies and displays DeEP points, revealing VT substrates in a multicenter validation study. The automation of identification and mapping display is expected to improve efficiency.