New Approaches in the Management of Sudden Cardiac Death in Patients with Heart Failure-Targeting the Sympathetic Nervous System

Neurohumoral Activation in Heart Failure

In patients with heart failure (HF), the neuroendocrine systems of the sympathetic nervous system (SNS), the renin-angiotensin-aldosterone system (RAAS) and the arginine vasopressin (AVP) system, are activated to various degrees producing often-observed tachycardia and concomitant increased systemic vascular resistance. Furthermore, sustained neurohormonal activation plays a key role in the progression of HF and may be responsible for the pathogenetic mechanisms leading to the perpetuation of the pathophysiology and worsening of the HF signs and symptoms. There are biomarkers of activation of these neurohormonal pathways, such as the natriuretic peptides, catecholamine levels and neprilysin and various newer ones, which may be employed to better understand the mechanisms of HF drugs and also aid in defining the subgroups of patients who might benefit from specific therapies, irrespective of the degree of left ventricular dysfunction. These therapies are directed against these neurohumoral systems (neurohumoral antagonists) and classically comprise beta blockers, angiotensin-converting enzyme (ACE) inhibitors/angiotensin receptor blockers and vaptans. Recently, the RAAS blockade has been refined by the introduction of the angiotensin receptor-neprilysin inhibitor (ARNI) sacubitril/valsartan, which combines the RAAS inhibition and neprilysin blocking, enhancing the actions of natriuretic peptides. All these issues relating to the neurohumoral activation in HF are herein reviewed, and the underlying mechanisms are pictorially illustrated.

Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network

Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrate that the HRV approach, in conjunction with linear features and the DL method, achieved a higher accuracy rate, averaging 99.30%, reaching 97% sensitivity, 99.60% specificity, and 97.87% precision. Future research should focus on further exploring and refining DL methods in the context of HRV analysis to improve SCD prediction.

At the heart of the matter: how mental stress and negative emotions affect atrial fibrillation

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia, affecting 2%-3% of the world's population. Mental and emotional stress, as well as some mental health conditions (e.g., depression) have been shown to significantly impact the heart and have been suggested to act both as independent risk factors and triggers in the onset of AF. In this paper, we review the current literature to examine the role that mental and emotional stress have in the onset of AF and summarise the current knowledge on the interaction between the brain and heart, and the cortical and subcortical pathways involved in the response to stress. Review of the evidence suggests that mental and emotional stress negatively affect the cardiac system, potentially increasing the risk for developing and/or triggering AF. Further studies are required to further understand the cortical and sub-cortical structures involved in the mental stress response and how these interact with the cardiac system, which may help in defining new strategies and interventions to prevent the development of, and improve the management of AF.

Imaging of cardiac sympathetic dysfunction with 18F-FDOPA PET/CT in patients with heart failure: a pilot study

BACKGROUND

Routine use of cardiac sympathetic imaging in HF has been limited by the lower availability/sensitivity of radiotracers. This study was aimed to assess the feasibility of 18F-FDOPA (commonly available PET-radiotracer) in assessment of cardiac autonomic dysfunction.

METHODS

Twenty-four controls (46.5 ± 11.1 years, 16men) and 24 patients (43.5 ± 11.0 years, 18men) with diagnosed HF (Framingham-Criteria) underwent cardiac-PET/CT. Region(s) Of Interest were drawn over entire left ventricular myocardium (LV), individual walls, and mediastinum (M). Coefficient of Variation (CV) was calculated from individual wall counts.

RESULTS

HF patients had significantly lower myocardial 18F-FDOPA uptake (P < .001, independent t test) than controls [32.4% ± 9.5% global reduction; highest in apex (39.9% ± 7.0%)]. A cut-off of LV/M ≤ 1.68 could differentiate patients from controls with sensitivity and specificity of 100% and 95.8%, respectively. LV/M correlated positively with EF (Pearson coefficient = 0.460, P .031). During follow-up, 3 patients were lost to follow-up, 4 died (survival-20.5 ± 4 months), 2 worsened, and 15 remained stable/showed mild improvement. Patients who worsened/died during follow-up had higher CV than those with stable/improving symptoms [0.16 ± 0.05 vs 0.11 ± 0.05, P value .069 (independent t test); Cox regression P = .084].

CONCLUSION

Myocardial 18F-FDOPA uptake in patients with HF is significantly reduced. Higher reduction is seen in those with lower EF. CV, a maker of regional heterogeneity, is a potential prognostic marker.

Editorial for the IJMS Special Issue "Progress in Understanding of Cardiac Arrhythmia Mechanisms and Antiarrhythmic Targets"

Cardiac rhythm disorders, in particular life-threatening ventricular fibrillation and stroke-provoking fibrillation of the atria, are a permanent focus of both clinical and experimental cardiologists [...].

Stellate Ganglia and Cardiac Sympathetic Overactivation in Heart Failure

Heart failure (HF) is a major public health problem worldwide, especially coronary heart disease (myocardial infarction)-induced HF with reduced ejection fraction (HFrEF), which accounts for over 50% of all HF cases. An estimated 6 million American adults have HF. As a major feature of HF, cardiac sympathetic overactivation triggers arrhythmias and sudden cardiac death, which accounts for nearly 50–60% of mortality in HF patients. Regulation of cardiac sympathetic activation is highly integrated by the regulatory circuitry at multiple levels, including afferent, central, and efferent components of the sympathetic nervous system. Much evidence, from other investigators and us, has confirmed the afferent and central neural mechanisms causing sympathoexcitation in HF. The stellate ganglion is a peripheral sympathetic ganglion formed by the fusion of the 7th cervical and 1st thoracic sympathetic ganglion. As the efferent component of the sympathetic nervous system, cardiac postganglionic sympathetic neurons located in stellate ganglia provide local neural coordination independent of higher brain centers. Structural and functional impairments of cardiac postganglionic sympathetic neurons can be involved in cardiac sympathetic overactivation in HF because normally, many effects of the cardiac sympathetic nervous system on cardiac function are mediated via neurotransmitters (e.g., norepinephrine) released from cardiac postganglionic sympathetic neurons innervating the heart. This review provides an overview of cardiac sympathetic remodeling in stellate ganglia and potential mechanisms and the role of cardiac sympathetic remodeling in cardiac sympathetic overactivation and arrhythmias in HF. Targeting cardiac sympathetic remodeling in stellate ganglia could be a therapeutic strategy against malignant cardiac arrhythmias in HF.

Autoencoded deep features for semi-automatic, weakly supervised physiological signal labelling

BACKGROUND AND AIMS

Machine Learning is transforming data processing in medical research and clinical practice. Missing data labels are a common limitation to training Machine Learning models. To overcome missing labels in a large dataset of microneurography recordings, a novel autoencoder based semi-supervised, iterative group-labelling methodology was developed.

METHODS

Autoencoders were systematically optimised to extract features from a dataset of 478621 signal excerpts from human microneurography recordings. Selected features were clusters with k-means and randomly selected representations of the corresponding original signals labelled as valid or non-valid muscle sympathetic nerve activity (MSNA) bursts in an iterative, purifying procedure by an expert rater. A deep neural network was trained based on the fully labelled dataset.

RESULTS

Three autoencoders, two based on fully connected neural networks and one based on convolutional neural network, were chosen for feature learning. Iterative clustering followed by labelling of complete clusters resulted in all 478621 signal peak excerpts being labelled as valid or non-valid within 13 iterations. Neural networks trained with the labelled dataset achieved, in a cross validation step with a testing dataset not included in training, on average 93.13% accuracy and 91% area under the receiver operating curve (AUC ROC).

DISCUSSION

The described labelling procedure enabled efficient labelling of a large dataset of physiological signal based on expert ratings. The procedure based on autoencoders may be broadly applicable to a wide range of datasets without labels that require expert input and may be utilised for Machine Learning applications if weak-labels were available.

K-means panning - Developing a new standard in automated MSNA signal recognition with a weakly supervised learning approach

BACKGROUND

Accessibility of labelled datasets is often a key limitation for the application of Machine Learning in clinical research. A novel semi-automated weak-labelling approach based on unsupervised clustering was developed to classify a large dataset of microneurography signals and subsequently used to train a Neural Network to reproduce the labelling process.

METHODS

Clusters of microneurography signals were created with k-means and then labelled in terms of the validity of the signals contained in each cluster. Only purely positive or negative clusters were labelled, whereas clusters with mixed content were passed on to the next iteration of the algorithm to undergo another cycle of unsupervised clustering and labelling of the clusters. After several iterations of this process, only pure labelled clusters remained which were used to train a Deep Neural Network.

RESULTS

Overall, 334,548 individual signal peaks form the integrated data were extracted and more than 99.99% of the data was labelled in six iterations of this novel application of weak labelling with the help of a domain expert. A Deep Neural Network trained based on this dataset achieved consistent accuracies above 95%.

DISCUSSION

Data extraction and the novel iterative approach of labelling unsupervised clusters enabled creation of a large, labelled dataset combining unsupervised learning and expert ratings of signal-peaks on cluster basis in a time effective manner. Further research is needed to validate the methodology and employ it on other types of physiologic data for which it may enable efficient generation of large labelled datasets.

Catestatin as a Biomarker of Cardiovascular Diseases: A Clinical Perspective

Accounting for almost one-third of the global mortality, cardiovascular diseases (CVDs) represent a major global health issue. Emerging data suggest that most of the well-established mechanistic explanations regarding the cardiovascular pathophysiology are flawed, and cannot fully explain the progression and long-term effects of these diseases. On the other hand, dysregulation of the sympathetic nervous system (SNS) has emerged as an important player in the pathophysiology of CVDs. Even though upregulated SNS activity is an essential compensatory response to various stress conditions, in the long term, it becomes a major contributor to both cardiac dysfunction and vascular damage. Despite the fact that the importance of SNS hyperactivity in the setting of CVDs has been well-appreciated, its exact quantification and clinical application in either diagnostics or therapy of CVDs is still out of reach. Nevertheless, in recent years a number of novel laboratory biomarkers implicated in the pathophysiology of SNS activation have been explored. Specifically, in this review, we aimed to discuss the role of catestatin, a potent physiological inhibitor of catecholamine spillover that offers cardioprotective effects. Limited data indicate that catestatin could also be a reliable indirect marker of SNS activity and it is likely that high CST levels reflect advanced CV disease burden. Consequently, large-scale studies are required to validate these observations in the upcoming future.

Machine learning powered tools for automated analysis of muscle sympathetic nerve activity recordings

Automated analysis and quantification of physiological signals in clinical practice and medical research can reduce manual labor, increase efficiency, and provide more objective, reproducible results. To build a novel platform for the analysis of muscle sympathetic nerve activity (MSNA), we employed state-of-the-art data processing and machine learning applications. Data processing methods for integrated MSNA recordings were developed to evaluate signals regarding the overall quality of the signal, the validity of individual signal peaks regarding the potential to be MSNA bursts and the timing of their occurrence. An overall probability score was derived from this flexible platform to evaluate each individual signal peak automatically. Overall, three deep neural networks were designed and trained to validate individual signal peaks randomly sampled from recordings representing only electrical noise and valid microneurography recordings. A novel data processing method for the whole signal was developed to differentiate between periods of valid MSNA signal recordings and periods in which the signal was not available or lost due to involuntary movement of the recording electrode. A probabilistic model for timing of the signal bursts was implemented as part of the system. Machine Learning algorithms and data processing tools were implemented to replicate the complex decision-making process of manual MSNA analysis. Validation of manual MSNA analysis including intra- and inter-rater validity and a comparison with automated MSNA tools is required. The developed toolbox for automated MSNA analysis can be extended in a flexible way to include algorithms based on other datasets.

Capillary vascular density in the retina of hypertensive patients is associated with a non-dipping pattern independent of mean ambulatory blood pressure

BACKGROUND

A nocturnal non-dipping pattern has been associated with hypertension-mediated organ damage (HMOD), morbidity and mortality. Retinal imaging through application of modern technologies including optical coherence tomography angiography (OCT-A) can provide detailed insights into early vascular damage. In this observational study, we investigated the relationship of microscopic vascular density in the retina measured with OCT-A and nocturnal blood pressure (BP) dipping.

METHODS

Retinal OCT-A and ambulatory BP monitoring (ABPM) data prospectively obtained from 142 patients referred to a tertiary hypertension clinic were analysed with regression models for associations between BP night-time dipping and retinal capillary vascular density in three different zones around the fovea.

RESULTS

More pronounced nocturnal SBP and DBP dipping was significantly associated with increased vascular density in the central foveal area of the retina. These associations were robust to adjustment for other available risk factors including mean daytime BP. Parafoveal and whole image vascular density did not show equivalent significant associations with nocturnal BP dipping. The results were reproducible when assessed in a subgroup of patients who had concomitant type 2 diabetes.

CONCLUSION

Foveal vascular density was associated with the nocturnal BP dipping pattern in hypertensive patients. These associations were robust to adjustment of relevant factors such as daytime BP. Our findings highlight the importance of nocturnal BP features reflected in ambulatory BP monitoring in the assessment of HMOD. Whether routine assessment of retinal damage markers may improve risk management of hypertensive patients remains to be determined.

Cardiac macrophages prevent sudden death during heart stress

Cardiac arrhythmias are a primary contributor to sudden cardiac death, a major unmet medical need. Because right ventricular (RV) dysfunction increases the risk for sudden cardiac death, we examined responses to RV stress in mice. Among immune cells accumulated in the RV after pressure overload-induced by pulmonary artery banding, interfering with macrophages caused sudden death from severe arrhythmias. We show that cardiac macrophages crucially maintain cardiac impulse conduction by facilitating myocardial intercellular communication through gap junctions. Amphiregulin (AREG) produced by cardiac macrophages is a key mediator that controls connexin 43 phosphorylation and translocation in cardiomyocytes. Deletion of Areg from macrophages led to disorganization of gap junctions and, in turn, lethal arrhythmias during acute stresses, including RV pressure overload and β-adrenergic receptor stimulation. These results suggest that AREG from cardiac resident macrophages is a critical regulator of cardiac impulse conduction and may be a useful therapeutic target for the prevention of sudden death.

Alcohol consumption combined with dietary low-carbohydrate/high-protein intake increased the left ventricular systolic dysfunction risk and lethal ventricular arrhythmia susceptibility in apolipoprotein E/low-density lipoprotein receptor double-knockout mice

Alcohol abuse is positively associated with cardiovascular disease. Dietary low-carbohydrate/high-protein (LCHP) intake confers a greater mortality risk. Here, the impact of ethanol consumption in combination with dietary LCHP intake on left ventricular (LV) systolic function and lethal ventricular arrhythmia susceptibility were investigated in apolipoprotein E/low-density lipoprotein receptor double-knockout (AL) mice. The underlying mechanisms, cardiac sympathovagal balance, beta-adrenergic receptor (ADRB) levels, and gap junction channel protein connexin 43 (Cx43) expression, were examined. Male AL mice fed an LCHP diet with or without ethanol were bred for 16 weeks. Age-matched male AL and wild-type mice received standard chow diet and served as controls. The following were used to assess LV systolic function, lethal ventricular arrhythmia susceptibility, cardiac sympathovagal balance, Cx43 expression, and ADRB levels: The results demonstrated that ethanol consumption in combination with dietary LCHP intake worsened LCHP-induced LV systolic dysfunction in AL mice and enhanced their susceptibility in the ventricular arrhythmia-evoked test. There were concomitant increases in LV weight, LF/HF ratio shown by HRV, TH, ADRB1, ADRB2, and Cx43 expressions by LV fluorescence immunohistochemistry, and LV Cx43 messenger ribonucleic acid expression by PCR. In AL mice, alcohol consumption combined with dietary LCHP intake may thus promote a shift in cardiac sympathovagal balance toward sympathetic predominance, the increases in beta-adrenergic receptors (ADRB1 and ADRB2), and then affect the gap junction channel protein Cx43, which in turn could contribute to increased risks of LV systolic dysfunction and susceptibility to lethal ventricular arrhythmia.

Benefits of early administration of Sacubitril/Valsartan in patients with ST-elevation myocardial infarction after primary percutaneous coronary intervention

OBJECTIVE

To evaluate the effects of early administration of Sacubitril/Valsartan (Sac/Val) in patients with ST-elevation myocardial infarction after primary percutaneous coronary intervention (pPCI).

METHODS

This prospective, controlled, single-center study randomized 186 ST-segment elevation myocardial infarction patients to one of the following two groups: Sac/Val group: early administration of Sac/Val within 24 hours after pPCI; control group: conventional angiotensin-converting enzyme inhibitors (ACEI) application. The creatine Kinase (CK) peak after the surgery, the incidence of acute heart failure during hospitalization, level of NT-proBNP and left ventricular ejection fraction (LVEF) measured by ultrasound before discharge and soluble suppression of tumorigenicity2 (sST2), LVEF, infarct size determined by single photon emission computed tomography (SPECT), readmission rate within 6 months were recorded and compared between two groups.

RESULTS

Compared to the control group, Sac/Val could decrease the CK peak and the incidence of acute heart failure after pPCI; the level of NT-proBNP was lower and LVEF was higher before discharge in the Sac/Val group. After 6 months, the patients who had taken Sac/Val had a higher LVEF, a smaller infarct size determined by SPECT, lower sST2 and readmission rate.

CONCLUSION

Patients with ST-elevation myocardial infarction after primary percutaneous coronary intervention could benefit from early administration of Sacubitril/Valsartan, the effect was superior to conventional ACEI.

The risk and prevention of sudden death in patients with heart failure with reduced ejection fraction

PURPOSE OF REVIEW

Patients with heart failure are at increased risk of sudden cardiac death. The methods to predict patients at high risk of sudden cardiac death in heart failure are neither sensitive nor specific; both overestimating risk in those with ejection fractions less than 35% and not identifying those at risk with ejection fractions greater than 35%.

RECENT FINDINGS

The absolute risk of sudden cardiac death in patients with heart failure have decreased over the past 20 years. New novel tools are being developed and tested to identify those at higher risk of sudden cardiac death. Reduction in the risk of sudden cardiac death has been achieved with the use of beta-blockers, spironolactone, sacubitril-valsartan, cardiac resynchronization and implantable cardioverter defibrillators.

SUMMARY

The use of contemporary treatments for patients with heart failure can reduce the risk of sudden cardiac death, but research is required to identify those at highest risk.