Why the study?
Identifying catheter ablation targets for ventricular tachycardias remains challenging because most mapping techniques ignore tissue repolarization, and the re-entry vulnerability index mapping procedure requires further development for clinical implementation.
Does the RVI mapping algorithm identify re-entrant circuits and vulnerable regions for VT ablation in computational models?
Does the RVI mapping algorithm identify re-entrant circuits and vulnerable regions for VT ablation in computational models?
In-silico modeling demonstrates that the RVI algorithm can identify vulnerable regions for VT ablation using clinically relevant low-resolution mapping grids, potentially guiding ablation without inducing VT.
RVI mapping merits animal validation for VT substrates; leaves open clinical translation pending human trials.
Identification of targets for catheter ablation of ventricular tachycardias (VTs) remains a significant challenge. VTs are often driven by re-entrant circuits resulting from a complex interaction between the front (activation) and tail (repolarization) of the electrical wavefront. Most mapping techniques do not take into account the tissue repolarization which may hinder the detection of ablation targets. The re-entry vulnerability index (RVI), a recently proposed mapping procedure, incorporates both activation and repolarization times to uncover VT circuits. The method showed potential in a series of experiments, but it still requires further development to enable its incorporation into a clinical protocol. Here, in-silico experiments were conducted to thoroughly assess RVI maps constructed under clinically-relevant mapping conditions. Within idealized as well as anatomically realistic infarct models, we show that parameters of the algorithm such as the search radius can significantly alter the specificity and sensitivity of the RVI maps. When constructed on sparse grids obtained following various placements of clinical recording catheters, RVI maps can identify vulnerable regions as long as two electrodes were placed on both sides of the line of block. Moreover, maps computed during pacing without inducing VT can reveal areas of abnormal repolarization and slow conduction but not directly vulnerability. In conclusion, the RVI algorithm can detect re-entrant circuits during VT from low resolution mapping grids resembling the clinical setting. Furthermore, RVI maps may provide information about the underlying tissue electrophysiology to guide catheter ablation without the need of inducing potentially harmful VT during the clinical procedure. Finally, the ability of the RVI maps to identify vulnerable regions with specificity in high resolution computer models could potentially improve the prediction of optimal ablation targets of simulation-based strategies.
No takes yet. Share an insight, caveat, or question.
Campos et al. (2019) studied this question.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: