Cardiac imaging for the prediction of sudden cardiac arrest in patients with heart failure

The identification of heart failure (HF) patients at risk for arrhythmic sudden cardiac arrest (SCA) is a major challenge in the cardiovascular field. In addition to optimal medical treatment for HF, implantable cardioverter defibrillator (ICD) is currently recommended to prevent SCA in patients with reduced left ventricular ejection fraction (LVEF). The indication for an ICD implantation, in addition to HF etiology, New York Health Association (NYHA) class and life expectancy, mainly depends on LVEF value at echocardiography. However, the actual role of LVEF in the prediction of SCA has recently been debated, while newer multimodality imaging techniques with increased prognostic accuracy have been developed. Speckle tracking imaging allows the quantification of mechanical dispersion, a marker of electrophysiological heterogeneity predisposing to malignant arrhythmias, while advanced cardiac magnetic resonance techniques such as myocardial T1-mapping and extracellular volume fraction assessment allow the evaluation of interstitial diffuse fibrosis. Nuclear imaging is helpful for the appraisal of sympathetic nervous system dysfunction, while newer computed tomography techniques assessing myocardial delayed enhancement allow the identification of focal myocardial scar. This review will focus on the most modern advances in the field of cardiovascular imaging along with its applications for the prediction of SCA in patients with HF. Modern artificial in-telligence applications in cardiovascular imaging will also be discussed.

Automated summary

The identification of heart failure (HF) patients at risk for arrhythmic sudden cardiac arrest (SCA) is challenging. Implantable cardioverter defibrillator (ICD) is recommended to prevent SCA in HF patients with reduced left ventricular ejection fraction (LVEF), but the role of LVEF in predicting SCA has been debated. Advanced imaging techniques such as speckle tracking, cardiac magnetic resonance, nuclear imaging, and computed tomography have been developed to assess electrophysiological heterogeneity, fibrosis, sympathetic nervous system dysfunction, and myocardial scar for predicting SCA in HF patients. Modern artificial intelligence applications in cardiovascular imaging are also discussed.