Islets of heterogeneous myocardium within the scar in cardiac magnetic resonance predict ventricular tachycardia after myocardial infarction

Ablation of Ventricular and Atrial Arrhythmias in the Era of Cardiac Magnetic Resonance

In the past decade, cardiac magnetic resonance (CMR) has undergone remarkable progress, emerging as a pivotal tool in various cardiological scenarios. Its capacity for tissue characterization, both with and without contrast agents, makes CMR the perfect tool to study the substrate of arrhythmia. This review highlights the potential role of CMR in electrophysiology (EP) and its role in the ablation of atrial and ventricular arrhythmias. First, we will discuss the key aspects of ventricular arrhythmia ablation, while in the second part, we will review how CMR is changing the ablation of atrial arrhythmias. The potentiality of CMR in the pre-procedural, intra-procedural, and post-ablation assessment will be reviewed. In particular, CMR is capable of visualizing fibrosis and building 3D reconstruction. Furthermore, it is possible to merge a 3D-rendered shell of the heart into the EP room to guide radiation-free ablation through active or passive tracking. Finally, the accuracy of CMR in depicting ablation lesions and its ability to predict arrhythmia relapses will be discussed.

Effect of myocardial scar size on the risk of ventricular arrhythmias in patients with chronic total coronary occlusion

BACKGROUND

The presence of an untreated chronic total coronary occlusion (CTO) is associated with a higher risk of ventricular arrhythmias (VAs). This increased risk may be modulated by the presence of an existing scar.

OBJECTIVES

To evaluate whether scar size is associated with VA in patients with an implantable cardioverter-defibrillator (ICD) and a CTO.

METHODS

In this retrospective study we included patients with a CTO that received an ICD between 2005 and 2015. Scar size was estimated using the Selvester QRS score on a baseline 12‑lead ECG. The primary endpoint was any appropriate ICD therapy.

RESULTS

Our study population comprised 148 CTO patients with a median scar size at baseline of 18% (IQR, 9-27%). Patients with a scar size ≥18% more often had a CTO located in the left anterior descending artery and a higher proportion of poor left ventricular function (<35%) and infarct-related CTO compared to patients with a smaller scar size (<18%). During a median follow-up of 35 months (interquartile range [IQR], 8-60 months), 42 patients (28%) received appropriate ICD therapy. The cumulative 5-year event rate was higher in the patients with a large scar in comparison to those with a smaller or no scar (36% versus 19%, P = 0.04). Multivariable Cox regression analysis demonstrated that large scar and diabetes mellitus were independent factors associated with appropriate ICD therapy.

CONCLUSION

In ICD recipients with an untreated CTO, a larger scar is an independent factor associated with an increased risk of VA.

Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events

Background

Machine learning analysis of complex myocardial scar patterns affords the potential to enhance risk prediction of life-threatening arrhythmia in stable coronary artery disease (CAD).

Objective

To assess the utility of computational image analysis, alongside a machine learning (ML) approach, to identify scar microstructure features on late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) that predict major arrhythmic events in patients with CAD.

Methods

Patients with stable CAD were prospectively recruited into a CMR registry. Shape-based scar microstructure features characterizing heterogeneous ('peri-infarct') and homogeneous ('core') fibrosis were extracted. An ensemble of machine learning approaches were used for risk stratification, in addition to conventional analysis using Cox modeling.

Results

Of 397 patients (mean LVEF 45.4 ± 16.0) followed for a median of 6 years, 55 patients (14%) experienced a major arrhythmic event. When applied within an ML model for binary classification, peri-infarct zone (PIZ) entropy, peri-infarct components and core interface area outperformed a model representative of the current standard of care (LVEF<35% and NYHA>Class I): AUROC (95%CI) 0.81 (0.81-0.82) vs. 0.64 (0.63-0.65), p = 0.002. In multivariate cox regression analysis, these features again remained significant after adjusting for LVEF<35% and NYHA>Class I: PIZ entropy hazard ratio (HR) 1.88, 95% confidence interval (CI) 1.38-2.56, p < 0.001; number of PIZ components HR 1.34, 95% CI 1.08-1.67, p = 0.009; core interface area HR 1.6, 95% CI 1.29-1.99, p = <0.001.

Conclusion

Machine learning models using LGE-CMR scar microstructure improved arrhythmic risk stratification as compared to guideline-based clinical parameters; highlighting a potential novel approach to identifying candidates for implantable cardioverter defibrillators in stable CAD.

Circle Method for Robust Estimation of Local Conduction Velocity High-Density Maps From Optical Mapping Data: Characterization of Radiofrequency Ablation Sites

Conduction velocity (CV) slowing is associated with atrial fibrillation (AF) and reentrant ventricular tachycardia (VT). Clinical electroanatomical mapping systems used to localize AF or VT sources as ablation targets remain limited by the number of measuring electrodes and signal processing methods to generate high-density local activation time (LAT) and CV maps of heterogeneous atrial or trabeculated ventricular endocardium. The morphology and amplitude of bipolar electrograms depend on the direction of propagating electrical wavefront, making identification of low-amplitude signal sources commonly associated with fibrotic area difficulty. In comparison, unipolar electrograms are not sensitive to wavefront direction, but measurements are susceptible to distal activity. This study proposes a method for local CV calculation from optical mapping measurements, termed the circle method (CM). The local CV is obtained as a weighted sum of CV values calculated along different chords spanning a circle of predefined radius centered at a CV measurement location. As a distinct maximum in LAT differences is along the chord normal to the propagating wavefront, the method is adaptive to the propagating wavefront direction changes, suitable for electrical conductivity characterization of heterogeneous myocardium. In numerical simulations, CM was validated characterizing modeled ablated areas as zones of distinct CV slowing. Experimentally, CM was used to characterize lesions created by radiofrequency ablation (RFA) on isolated hearts of rats, guinea pig, and explanted human hearts. To infer the depth of RFA-created lesions, excitation light bands of different penetration depths were used, and a beat-to-beat CV difference analysis was performed to identify CV alternans. Despite being limited to laboratory research, studies based on CM with optical mapping may lead to new translational insights into better-guided ablation therapies.