Noninvasive assessment of the ventricular arrhythmogenic substrate using electrocardiographic imaging during sinus...

Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) identifies structural properties associated with ventricular tachycardia (VT) but lacks functional information. Noninvasive identification of slow conduction regions using electrocardiographic imaging (ECGI) could complement LGE-CMR to aid VT risk stratification.

This study aimed to evaluate the relationship between ventricular arrhythmogenic substrate and regional ECGI markers during sinus rhythm in patients with ischemic cardiomyopathy (ICM).

Seventy-two patients were included: 29 with ICM evidenced by LGE-CMR referred for VT ablation, 17 with ICM with implantable cardioverter-defibrillator for primary prevention without documented VT, and 26 controls. ECGI-derived regional activation dispersion (rAD) and pseudo-regional conduction velocity (pseudo-rCV) were analyzed and compared between scarred and healthy regions based on LGE, and between patients with ICM with and without previous VT. In a subgroup with electroanatomic mapping (n = 16), deceleration zones were correlated with ECGI.

Scarred regions showed higher rAD (46.3 ± 2.2 vs 30.1 ± 1.7 ms, P < .001) and reduced pseudo-rCV (149.9 ± 3.0 vs 165.7 ± 2.4 cm/s, P < .001) than healthy regions. In the subgroup with sinus rhythm electroanatomic mapping, regions containing deceleration zones showed increased rAD (64.9 ± 5.4 vs 43.1 ± 3.1 ms, P < .001), and ECGI identified 70.4% of those zones. At the patient level, the mean of the 3 regions with the highest activation dispersion differentiated patients with ICM with and without previous VT (rAD ≥60.0 ms, sensitivity 75.9%, area under the curve [AUC] 0.75, P = .005) and identified patients with ICM from controls (rAD ≥39.5 ms, AUC 0.93).

This study introduces a novel regional ECGI methodology demonstrating that rAD identifies abnormalities linked to arrhythmogenic substrate and could help identify patients at risk of VT. These findings highlight ECGI's potential as a complementary tool to LGE-CMR.