Structural and Physiological Imaging to Predict the Risk of Lethal Ventricular Arrhythmias and Sudden Death

Role of Nuclear Imaging in Cardiac Stereotactic Body Radiotherapy for Ablation of Ventricular Tachycardia

Ventricular tachycardia (VT) is a life-threatening arrhythmia common in patients with structural heart disease or nonischemic cardiomyopathy. Many VTs originate from regions of fibrotic scar tissue, where delayed electrical signals exit scar and re-enter viable myocardium. Cardiac stereotactic body radiotherapy (SBRT) has emerged as a completely noninvasive alternative to catheter ablation for the treatment of recurrent or refractory ventricular tachycardia. While there is no common consensus on the ideal imaging workflow, therapy planning for cardiac SBRT often combines information from a plurality of imaging modalities including MRI, CT, electroanatomic mapping and nuclear imaging. MRI and CT provide detailed anatomic information, and late enhancement contrast imaging can indicate regions of fibrosis. Electroanatomic maps indicate regions of heterogenous conduction voltage or early activation which are indicative of arrhythmogenic tissue. Some early clinical adopters performing cardiac SBRT report the use of myocardial perfusion and viability nuclear imaging to identify regions of scar. Nuclear imaging of hibernating myocardium, inflammation and sympathetic innervation have been studied for ventricular arrhythmia prognosis and in research relating to catheter ablation of VT but have yet to be studied in their potential applications for cardiac SBRT. The integration of information from these many imaging modalities to identify a target for ablation can be challenging. Multimodality image registration and dedicated therapy planning tools may enable higher target accuracy, accelerate therapy planning workflows and improve patient outcomes. Understanding the pathophysiology of ventricular arrhythmias, and localizing the arrhythmogenic tissues, is vital for successful ablation with cardiac SBRT. Nuclear imaging provides an arsenal of imaging strategies to identify regional scar, hibernation, inflammation, and sympathetic denervation with some advantages over alternative imaging strategies.

Neurometabolism and Ventricular Dyssynchrony in Patients With Heart Failure and Reduced Ejection Fraction

BACKGROUND

The brain coordinates the heart through the autonomic nervous system (ANS). Numerous mediator signals along the brain-heart axis interact with the neuronal-metabolic system in heart failure (HF). Disturbances in cardio-neural interactions influence the disease progression in patients with HF.

OBJECTIVES

The purpose of this study was to investigate the interactome between ANS-associated neurometabolism and ventricular dyssynchrony in patients with heart failure with reduced ejection fraction (HFrEF). Further, we studied the association of neurometabolism with major arrhythmic events (MAEs).

METHODS

A total of 197 patients with HFrEF who underwent gated single-photon emission computed tomography myocardial perfusion imaging and the brain ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography were prospectively enrolled. Relationships between the brain metabolism and MAEs were assessed using Cox models and mediation analyses. Finally, metabolic central autonomic networks were constructed and statistically compared between patients with and without MAEs.

RESULTS

In total, 35 (17.8%) patients experienced MAEs during a median follow-up of 3.1 years. In patients with HFrEF (age 58 years [IQR: 50-64 years], left ventricular ejection fraction: 20.0% [IQR: 15.0%-25.0%]), glucose hypometabolism in the insula, hippocampus, amygdala, cingulate gyrus, and caudate nucleus were independent predictors for MAEs (all P < 0.05). Cerebral hypometabolism was related to ventricular dyssynchrony, which was the predominant risk factor of MAEs. Additionally, patients who experienced MAEs presented hypoconnectivity in the metabolic central autonomic network compared with those without MAEs (P < 0.05).

CONCLUSIONS

We found an interaction of the neuronal metabolic-ventricular dyssynchronization axis in HF, which might be related to MAEs. This new brain-heart axis could expand our understanding of the distinct pathomechanisms of HFrEF.

Detection of myocardial fibrosis: Where we stand

Myocardial fibrosis, resulting from the disturbance of extracellular matrix homeostasis in response to different insults, is a common and important pathological remodeling process that is associated with adverse clinical outcomes, including arrhythmia, heart failure, or even sudden cardiac death. Over the past decades, multiple non-invasive detection methods have been developed. Laboratory biomarkers can aid in both detection and risk stratification by reflecting cellular and even molecular changes in fibrotic processes, yet more evidence that validates their detection accuracy is still warranted. Different non-invasive imaging techniques have been demonstrated to not only detect myocardial fibrosis but also provide information on prognosis and management. Cardiovascular magnetic resonance (CMR) is considered as the gold standard imaging technique to non-invasively identify and quantify myocardial fibrosis with its natural ability for tissue characterization. This review summarizes the current understanding of the non-invasive detection methods of myocardial fibrosis, with the focus on different techniques and clinical applications of CMR.

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.

CMR-Based Risk Stratification of Sudden Cardiac Death and Use of Implantable Cardioverter-Defibrillator in Non-Ischemic Cardiomyopathy

Non-ischemic cardiomyopathy (NICM) is one of the most important entities for arrhythmias and sudden cardiac death (SCD). Previous studies suggest a lower benefit of implantable cardioverter–defibrillator (ICD) therapy in patients with NICM as compared to ischemic cardiomyopathy (ICM). Nevertheless, current guidelines do not differentiate between the two subgroups in recommending ICD implantation. Hence, risk stratification is required to determine the subgroup of patients with NICM who will likely benefit from ICD therapy. Various predictors have been proposed, among others genetic mutations, left-ventricular ejection fraction (LVEF), left-ventricular end-diastolic volume (LVEDD), and T-wave alternans (TWA). In addition to these parameters, cardiovascular magnetic resonance imaging (CMR) has the potential to further improve risk stratification. CMR allows the comprehensive analysis of cardiac function and myocardial tissue composition. A range of CMR parameters have been associated with SCD. Applicable examples include late gadolinium enhancement (LGE), T1 relaxation times, and myocardial strain. This review evaluates the epidemiological aspects of SCD in NICM, the role of CMR for risk stratification, and resulting indications for ICD implantation.

Global and regional cardiac dysfunction quantified by 18F-FDG PET scans can predict ventricular arrhythmia in patients with implantable cardioverter defibrillator

BACKGROUND

A low appropriate therapy rate indicates that a minority of patients will benefit from their implantable cardioverter defibrillator (ICD). Quantitative measurements from 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) may predict ventricular arrhythmia (VA) occurrence after ICD placement.

METHODS

We performed a prospective observational study and recruited patients who required ICD placement. Pre-procedure image scans were performed. Patients were followed up for VA occurrence. Associations between image results and VA were analyzed.

RESULTS

In 51 patients (33 males, 53.9 ± 17.2 years) analyzed, 17 (33.3%) developed VA. Compared with patients without VA, patients with VA had significantly larger values in scar area (17.7 ± 12.4% vs. 7.0 ± 7.9%), phase standard deviation (51.4° ± 14.0° vs. 34.0° ± 15.0°), bandwidth (172.9° ± 39.8° vs. 128.7° ± 49.9°), sum thickening score (STS, 29.5 ± 11.1 vs. 17.8 ± 13.2), and sum motion score (42.9 ± 11.5 vs. 33.0 ± 19.0). Cox regression analysis and receiver operating characteristic curve analysis showed that scar size, dyssynchrony, and STS were associated with VA occurrence (HR, 4.956, 95% CI 1.70-14.46).

CONCLUSION

Larger left ventricular scar burden, increased dyssynchrony, and higher STS quantified by 18F-FDG PET may indicate a higher VA incidence after ICD placement.

Non-fatal cardiovascular events preceding sudden cardiac death in patients with an acute myocardial infarction complicated by heart failure: insights from the high-risk myocardial infarction database

AIMS

Among patients with acute myocardial infarction (AMI) complicated by heart failure [HF; clinical HF or left ventricular (LV) systolic dysfunction], we explored the probability of subsequent non-fatal cardiovascular (CV) events and sudden cardiac death (SCD).

METHODS AND RESULTS

The high-risk myocardial infarction (HRMI) database contains 28 771 patients with signs of HF or reduced LV ejection fraction (<40%) after AMI. We evaluated the temporal association between SCD with preceding non-fatal CV event [HF hospitalization, recurrent myocardial infarction (MI), or stroke]. Median follow-up was 1.9 years. Mean age was 65.0 ± 11.5 years and 70% were male. The incidence of CV death was 7.9 per 100 patient-years and for SCD was 3.1 per patient-years (40% of CV deaths). The incidence of SCD preceded by HF hospitalization was greater than SCD without preceding HF hospitalization (P < 0.05). However, overall, SCD was less likely to be preceded by a non-fatal CV event compared to other causes of death: 9.6% of SCD events were preceded by an MI (vs. 46.6% for non-sudden CV death); 17.0% of SCD events were preceded with an HF hospitalization (vs. 25.4% for non-sudden CV death); and 2.7% of SCD events were preceded by stroke (vs.12.9% for non-sudden CV death).

CONCLUSION

Among patients with AMI complicated by HF, SCD, compared with other causes of death, was less likely to be preceded by a non-fatal CV event. As patients are less likely to have preceding non-fatal CV events to alert the healthcare team of a possible impending SCD event, additional strategies for risk stratification for SCD are needed.