1345370 - Cardiac magnetic resonance imaging and computational modelling based assessment of ventricular electro-pathology predicts appropriate internal defibrillator therapy

Background: Identifying patients at risk for arrhythmias after a myocardial infarction is still a challenging clinical task. Currently, reduced left ventricular ejection fraction (LVEF) is the most widely used marker to stratify patients for an implantable cardiac defibrillator (ICD) implantation for prevention of sudden cardiac death (SCD). However, relatively few ICD patients experience a life-threatening arrhythmic event. Furthermore, a significant percentage of SCD occurs in patients who do not meet the current ICD selection criteria.

Aim
Improve assessment of post-infarct ventricular electro-pathology and prediction of internal defibrillator therapy by combining cardiac magnetic resonance imaging and advanced computational modelling.

Methods: ADAS 3D LV was used to generate scar maps from 2D late gadolinium enhancement (LGE) images in a prospective cohort of patients with ischemic cardiomyopathy who had undergone cardiac MRI prior to ICD implantation. The extent of variation in scar-characteristics was evaluated by quantifying the borderzone (BZ), scar core, the number and weight of conduction corridors and the surface area between healthy myocardium and total enhancement (BZ+core), and between BZ and core. Electrical simulations were performed on the ADAS models using the Virtual Induction and Treatment of Arrhythmias (VITA) framework. VITA was used to assess the electrical vulnerability of potential reentrant channels and provided simulation metrics such as the number of inducible VTs and their corresponding round-trip-time (RTT), an equivalent of VT cycle-length. All quantified imaging and simulation metrics were correlated with appropriate ICD therapy during follow-up.

Results: 40 patients were included for analysis with a mean follow-up of 7.34 ± 3.6 years. Both myocardium-total enhancement (103.8±35.1 vs. 77.4±33.7, p=.021) and BZ-core (76±27.5 vs. 55.2±27.6, p=.024) surface areas were significantly higher in patients with appropriate ICD therapy. Unique VTs (4.1±3.4 vs. 6.6±4.2, p=.044), mean RTT (76.9±42.6 vs. 116.2±51, p=.012) and max RTT (109.6±78.6 vs. 194.4±105.1, p=.006) were significantly higher in patients with appropriate therapy.
Cox regression demonstrated significantly increased risk of appropriate ICD therapy for the following factors: BZ (HR: 1.085; 95% CI: 1.005-1.170; p=0.036), interface myocardium-total enhancement (HR: 1.021; 95% CI: 1.007-1.036; p=0.003), interface BZ-core (HR: 1.020; 95% CI: 1.003-1.038; p=0.019), unique VTs (HR: 1.12; 95% CI: 1.005-1.249; p=0.040), meanRTT (HR: 1.009; 95% CI: 1.001-1.018; p=0.033), maxRTT (HR: 1.005; 95% CI: 1.000-1.009; p=0.034). The remaining metrics did not show a significant difference between the two groups.

Conclusion: LGE based scarmaps, combined with advanced computational modeling, enable comprehensive, non-invasive assessment of ventricular electro-pathology. A combined workup accurately predicts ICD therapy and could facilitate early identification of high-risk patients in addition to LVEF.