Non-invasive assessment of the ventricular tachycardia isthmus during sinus rhythm

Abstract

Abstract Background The ablation of ventricular tachycardias (VT) presents significant challenges due to the complex and time-consuming approaches involved in identifying arrhythmogenic regions. Combining non-invasive electrocardiographic imaging (ECGI) with endocardial mapping could potentially enhance the efficacy of interventions. Objective To evaluate the capability of ECGI in guiding operators towards areas of VT isthmuses during sinus rhythm. Methods Patients with scar-related VT, confirmed by late gadolinium enhancement cardiac magnetic resonance, were prospectively enrolled. We compared the areas of slowed conduction identified by ECGI with the VT sustaining regions identified endocardially. Simultaneous Electroanatomical Mapping (EAM) and ECGI mapping were performed during VT ablation procedures. In patients in whom a hemodynamically stable VT was maintained, the reentry circuit was mapped endocardially, and the ablation target was identified. The left ventricle was segmented into 9 regions, categorized as either VT-isthmus or non-VT isthmus based on EAM findings. Prior to ablation, local activation times (LAT) and conduction velocity (CV) ECGI sinus maps were computed. Slow conduction was quantified non-invasively by computing different ECGI metrics per region, including the mean and the standard deviation (SD) of LAT and CV, the regional Total Activation Time (rTAT) – time span between the earliest and latest activated nodes–, and the Slow Conduction Velocity (SCV) – the percentage of nodes where velocity falls below 60 cm/s -. Results The study included 20 consecutive patients with ischemic cardiomyopathy who were referred for VT ablation. A total of 21 regions linked to the VT isthmus were identified endocardially in 17 patients (age: 66.1±8.8 years, 94.1% male, left ventricular ejection fraction: 26.4±7.3%). Figure 1 illustrates the correlation between the VT circuit and the ECGI sinus map in two patients with extensive left ventricular fibrosis. ECGI metrics related to VT isthmuses exhibited a higher standard deviation in both LAT (18.1±2.0 vs 9.8±1.2 ms; p=0.001), and CV (50.4±4.7 vs 37.1±2.8 cm/s; p=0.011), and a greater percentage of nodes with slowed conduction (13.7±2.7 vs 7.4±1.6 %; p=0.029), compared to non-VT isthmus regions, Figure 2. Multivariate logistic regression analysis revealed that SD-LAT was the strongest predictor of VT isthmus localization. Conclusion ECGI has demonstrated the capability to pinpoint slowed conduction areas responsible for VT in sinus rhythm, showing promise in planning and guiding VT ablation strategies. This advancement paves the way for further research into using this non-invasive tool to improve risk stratification methods for ventricular arrhythmias.