Showing papers on "Cardiac arrhythmia published in 2021"

2020 ESC Guidelines for the management of adult congenital heart disease.

Abstract: a Interní oddělení Nemocnice Přerov, AGEL Středomoravská nemocniční, a.s., a I. interní klinika – kardiologická, Fakultní nemocnice Olomouc b Kardiocentrum Lipsko, Německo c Kardiologické oddělení, Nemocnice Na Homolce, Praha d Centrum pro vrozené srdeční vady v dospělosti, Oddělení kardiochirurgie, Nemocnice Na Homolce, Praha e Centrum pro vrozené srdeční vady, Klinika kardiovaskulární chirurgie, 2. lékařská fakulta Univerzity Karlovy a Fakultní nemocnice v Motole, Praha f Interní kardiologická klinika, Fakultní nemocnice Brno a Centrum komplexní péče o vrozené srdeční vady v dospělosti, Brno

Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge.

Abstract: BackgroundAtrial fibrillation is the most frequent cardiac arrhythmia. It has been estimated that 6–12 million people worldwide will suffer this condition in the US by 2050 and 17.9 million people ...

Short-coupled ventricular fibrillation represents a distinct phenotype among latent causes of unexplained cardiac arrest: a report from the CASPER registry.

Abstract: AIMS The term idiopathic ventricular fibrillation (IVF) describes survivors of unexplained cardiac arrest (UCA) without a specific diagnosis after clinical and genetic testing. Previous reports have described a subset of IVF individuals with ventricular arrhythmia initiated by short-coupled trigger premature ventricular contractions (PVCs) for which the term short-coupled ventricular fibrillation (SCVF) has been proposed. The aim of this article is to establish the phenotype and frequency of SCVF in a large cohort of UCA survivors. METHODS AND RESULTS We performed a multicentre study including consecutive UCA survivors from the CASPER registry. Short-coupled ventricular fibrillation was defined as otherwise unexplained ventricular fibrillation initiated by a trigger PVC with a coupling interval of <350 ms. Among 364 UCA survivors, 24/364 (6.6%) met diagnostic criteria for SCVF. The diagnosis of SCVF was obtained in 19/24 (79%) individuals by documented ventricular fibrillation during follow-up. Ventricular arrhythmia was initiated by a mean PVC coupling interval of 274 ± 32 ms. Electrical storm occurred in 21% of SCVF probands but not in any UCA proband (P < 0.001). The median time to recurrent ventricular arrhythmia in SCVF was 31 months. Recurrent ventricular fibrillation resulted in quinidine administration in 12/24 SCVF (50%) with excellent arrhythmia control. CONCLUSION Short-coupled ventricular fibrillation is a distinct primary arrhythmia syndrome accounting for at least 6.6% of UCA. As documentation of ventricular fibrillation onset is necessary for the diagnosis, most cases are diagnosed at the time of recurrent arrhythmia, thus the true prevalence of SCVF remains still unknown. Quinidine is effective in SCVF and should be considered as first-line treatment for patients with recurrent episodes.

Nonalcoholic Fatty Liver Disease: An Emerging Driver of Cardiac Arrhythmia.

Abstract: Cardiac arrhythmias and the resulting sudden cardiac death are significant cardiovascular complications that continue to impose a heavy burden on patients and society. An emerging body of evidence indicates that nonalcoholic fatty liver disease (NAFLD) is closely associated with the risk of cardiac arrhythmias, independent of other conventional cardiometabolic comorbidities. Although most studies focus on the relationship between NAFLD and atrial fibrillation, associations with ventricular arrhythmias and cardiac conduction defects have also been reported. Mechanistic investigations suggest that a number of NAFLD-related pathophysiological alterations may potentially elicit structural, electrical, and autonomic remodeling in the heart, contributing to arrhythmogenic substrates in the heart. NAFLD is now the most common liver and metabolic disease in the world. However, the upsurge in the prevalence of NAFLD as an emerging risk factor for cardiac arrhythmias has received little attention. In this review, we summarize the clinical evidence and putative pathophysiological mechanisms for the emerging roles of NAFLD in cardiac arrhythmias, with the purpose of highlighting the notion that NAFLD may serve as an independent risk factor and a potential driving force in the development and progression of cardiac arrhythmias.

Sleep - the yet underappreciated player in cardiovascular diseases: A clinical review from the German Cardiac Society Working Group on Sleep Disordered Breathing.

Abstract: Patients with a wide variety of cardiovascular diseases, including arterial and pulmonary hypertension, arrhythmia, coronary artery disease and heart failure, are more likely to report impaired sle...

Electroimmunology and cardiac arrhythmia

Abstract: Conduction disorders and arrhythmias remain difficult to treat and are increasingly prevalent owing to the increasing age and body mass of the general population, because both are risk factors for arrhythmia. Many of the underlying conditions that give rise to arrhythmia - including atrial fibrillation and ventricular arrhythmia, which frequently occur in patients with acute myocardial ischaemia or heart failure - can have an inflammatory component. In the past, inflammation was viewed mostly as an epiphenomenon associated with arrhythmia; however, the recently discovered inflammatory and non-canonical functions of cardiac immune cells indicate that leukocytes can be arrhythmogenic either by altering tissue composition or by interacting with cardiomyocytes; for example, by changing their phenotype or perhaps even by directly interfering with conduction. In this Review, we discuss the electrophysiological properties of leukocytes and how these cells relate to conduction in the heart. Given the thematic parallels, we also summarize the interactions between immune cells and neural systems that influence information transfer, extrapolating findings from the field of neuroscience to the heart and defining common themes. We aim to bridge the knowledge gap between electrophysiology and immunology, to promote conceptual connections between these two fields and to explore promising opportunities for future research.

Coffee Consumption and Incident Tachyarrhythmias: Reported Behavior, Mendelian Randomization, and Their Interactions.

Abstract: Importance The notion that caffeine increases the risk of cardiac arrhythmias is common. However, evidence that the consumption of caffeinated products increases the risk of arrhythmias remains poorly substantiated. Objective To assess the association between consumption of common caffeinated products and the risk of arrhythmias. Design, Setting, and Participants This prospective cohort study analyzed longitudinal data from the UK Biobank between January 1, 2006, and December 31, 2018. After exclusion criteria were applied, 386 258 individuals were available for analyses. Exposures Daily coffee intake and genetic polymorphisms that affect caffeine metabolism. Main Outcomes and Measures Any cardiac arrhythmia, including atrial fibrillation or flutter, supraventricular tachycardia, ventricular tachycardia, premature atrial complexes, and premature ventricular complexes. Results A total of 386 258 individuals (mean [SD] age, 56 [8] years; 52.3% female) were assessed. During a mean (SD) follow-up of 4.5 (3.1) years, 16 979 participants developed an incident arrhythmia. After adjustment for demographic characteristics, comorbid conditions, and lifestyle habits, each additional cup of habitual coffee consumed was associated with a 3% lower risk of incident arrhythmia (hazard ratio [HR], 0.97; 95% CI, 0.96-0.98;P Conclusions and Relevance In this prospective cohort study, greater amounts of habitual coffee consumption were inversely associated with a lower risk of arrhythmia, with no evidence that genetically mediated caffeine metabolism affected that association. Mendelian randomization failed to provide evidence that caffeine consumption was associated with arrhythmias.

Detection of common risk factors for diagnosis of cardiac arrhythmia using machine learning algorithm

Abstract: This article aims to establish an accurate and innovative objective framework for classification of cardiac arrhythmia patients by trying to measure the importance of specific factors that are potentially relevant to its diagnosis. Cardiac arrhythmia (CA) is a group of condition related to the irregular heartbeats. It is very essential to prevent a CAs, as they are the most common cause of natural death in all over the world. According to the health reports, more than 4.5 lakh cardiac patients fatalities annually in the United States alone. To diagnose cardiac diseases, patient’s reported qualitative symptoms can be useful. However, this strategy may fail sometimes due to less accuracy and false positive cases. Therefore in this work, we strive to find a quantitative basis for more reliable and accurate diagnosis of cardiac arrhythmias. This research used the openly available MIMIC-III database to obtain large quantities of clinical monitoring data from patients over the age of sixteen admitted to intensive care units (ICUs). The database was processed on the Health Sciences and Technology (HEST) Cluster, filtered with in a specified time frame(24hrs, 12hrs and 6hrs) and organized into a multi-class and a single-class and finally split into train, validation, and test sets with respective weights of 0.7, 0.2, and 0.1. We used random forest classifier model for the diagnosis of cardiac arrhythmia and measure the importance of different features like respiratory rate, blood pressure, sodium, potassium, calcium, among the other features. Hyperparameter optimization techniques like grid search and genetic algorithms are compared to find the maximum number and depth of trees in the forest. The model achieved, at its best, an Area Under the Receiver Operator Curve (AUC) score of 0.9787 and, thus, confirmed the importance of several previously suggested factors in the diagnosis of cardiac arrhythmias. We substantiated claims that each of sodium, calcium, potassium, respiratory rates and blood pressure can be used for the early diagnosis of cardiac arrhythmias.

Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges

Abstract: Cardiovascular diseases (CVDs) in India and globally are the major cause of mortality, as revealed by the World Health Organization (WHO). The irregularities in the pace of heartbeats, called cardiac arrhythmias or heart arrhythmias, are one of the commonly diagnosed CVDs caused by ischemic heart disease, hypertension, alcohol intake, and stressful lifestyle. Other than the listed CVDs, the abnormality in the cardiac rhythm caused by the long term mental stress (stimulated by Autonomic Nervous System (ANS)) is a challenging issue for researchers. Early detection of cardiac arrhythmias through automatic electronic techniques is an important research field since the invention of electrocardiogram (ECG or EKG) and advanced machine learning algorithms. ECG (EKG) provides the record of variations in electrical activity associated with the cardiac cycle, used by cardiologists and researchers as a gold standard to study the heart function. The present work is aimed to provide an extensive survey of work done by researchers in the area of automated ECG analysis and classification of regular & irregular classes of heartbeats by conventional and modern artificial intelligence (AI) methods. The artificial intelligence (AI) based methods have emerged popularly during the last decade for the automatic and early diagnosis of clinical symptoms of arrhythmias. In this work, the literature is explored for the last two decades to review the performance of AI and other computer-based techniques to analyze the ECG signals for the prediction of cardiac (heart rhythm) disorders. The existing ECG feature extraction techniques and machine learning (ML) methods used for ECG signal analysis and classification are compared using the performance metrics like specificity, sensitivity, accuracy, positive predictivity value, etc. Some popular AI methods, which include, artificial neural networks (ANN), Fuzzy logic systems, and other machine learning algorithms (support vector machines (SVM), k-nearest neighbor (KNN), etc.) are considered in this review work for the applications of cardiac arrhythmia classification. The popular ECG databases available publicly to evaluate the classification accuracy of the classifier are also mentioned. The aim is to provide the reader, the prerequisites, the methods used in the last two decades, and the systematic approach, all at one place to further purse a research work in the area of cardiovascular abnormalities detection using the ECG signal. As a contribution to the current work, future challenges for real-time remote ECG acquisition and analysis using the emerging technologies like wireless body sensor network (WBSN) and the internet of things (IoT) are identified.

Causes of hospitalization in the USA between 2005 and 2018

Abstract: In this report, we identify the 10 most common causes of hospitalizations in the USA in 2005–2018 using the discharge data from the National Inpatient Sample database. We show that sepsis has been the leading cause of hospitalizations in the USA followed by heart failure, which has consistently been within the three most common causes of hospitalizations since 2005. In addition, we show a high burden of cardiovascular diseases as a cause of hospitalization over the study period with a consistent presence of cardiac arrhythmias as one of the top 10 causes of hospitalizations in the USA and emergence of acute myocardial infarction as one of the top 10 causes after 2014.

Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks

Abstract: Background Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG recordings is not practical for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to develop computer-aided-diagnosis (CAD) systems to automatically identify arrhythmias. Methods This paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The proposed approach identifies arrhythmia classes using Convolutional Neural Network (CNN) trained by two-dimensional (2D) ECG beat images. Firstly, ECG signals, which consist of 5 different arrhythmias, are segmented into heartbeats which are transformed into 2D grayscale images. Afterward, the images are used as input for training a new CNN architecture to classify heartbeats. Results The experimental results show that the classification performance of the proposed approach reaches an overall accuracy of 99.7%, sensitivity of 99.7%, and specificity of 99.22% in the classification of five different ECG arrhythmias. Further, the proposed CNN architecture is compared to other popular CNN architectures such as LeNet and ResNet-50 to evaluate the performance of the study. Conclusions Test results demonstrate that the deep network trained by ECG images provides outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Moreover, the proposed method has lower computational costs compared to existing methods and is more suitable for mobile device-based diagnosis systems as it does not involve any complex preprocessing process. Hence, the proposed approach provides a simple and robust automatic cardiac arrhythmia detection scheme for the classification of ECG arrhythmias.

Impact of congestive heart failure and role of cardiac biomarkers in COVID-19 patients: A systematic review and meta-analysis

Abstract: Background Coronavirus disease 2019 (COVID-19) has been reported to cause worse outcomes in patients with underlying cardiovascular disease, especially in patients with acute cardiac injury, which is determined by elevated levels of high-sensitivity troponin. There is a paucity of data on the impact of congestive heart failure (CHF) on outcomes in COVID-19 patients. Methods We conducted a literature search of PubMed/Medline, EMBASE, and Google Scholar databases from 11/1/2019 till 06/07/2020, and identified all relevant studies reporting cardiovascular comorbidities, cardiac biomarkers, disease severity, and survival. Pooled data from the selected studies was used for metanalysis to identify the impact of risk factors and cardiac biomarker elevation on disease severity and/or mortality. Results We collected pooled data on 5967 COVID-19 patients from 20 individual studies. We found that both non-survivors and those with severe disease had an increased risk of acute cardiac injury and cardiac arrhythmias, our pooled relative risk (RR) was — 8.52 (95% CI 3.63–19.98) (p Conclusion Cardiac involvement in COVID-19 infection appears to significantly adversely impact patient prognosis and survival. Pre-existence of CHF, and high cardiac biomarkers like NT-pro BNP and CK-MB levels in COVID-19 patients correlates with worse outcomes.

Machine learning-data mining integrated approach for premature ventricular contraction prediction

Abstract: Cardiac arrhythmias impose a significant burden on the healthcare environment due to the increasing ratio of mortality worldwide. Arrhythmia and abnormal ECG heartbeat are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is a common form of cardiac arrhythmia which begins from the lower chamber of the heart, and frequent occurrence of PVC beat might lead to mortality. ECG signals are the noninvasive and primary tool used to identify the actual life threat related to the heart. Nowadays, in society, the computer-assisted technique reduces doctors' burden to evaluate heart disease and heart arrhythmia automatically. Regardless of well-equipped and well-developed health facilities that are available for monitoring the cardiac condition, the success stories are yet unsatisfactorily due to the complexity of the cardiac disorder. The most challenging part in ECG signal analysis is to extract the accurate features relevant to the arrhythmia for classification due to the inter-patient variation. There are many morphological changes present in the ECG signals. Hence, there is a gap in the usage of appropriate methods for the extraction of features and classification models, which reduce the biased diagnosis of PVC arrhythmia. To predict PVC arrhythmia accurately is a quite challenging task owing to (a) QRS negative (b) long compensatory pause (c) p-wave (d) biased diagnosis of PVC detection due to the small feature set. This study presents a new approach for PVC prediction using derived predictor variables from the electrocardiograph (ECG-MLII) signals: R–R wave interval, previous R–R wave interval, QRS duration, and verification of P-wave whether it is present or absent using threshold technique. We propose the machine learning-data mining MACDM integrated approach using five different models of multiple logistic regression and four classifiers, namely, Random Forest (RF), K-Nearest Neighbor (KNN), Support vector machine (SVM), and Naive Bayes (NB). The experiment was conducted on the public benchmark MIT-BIH-AR to evaluate the performance of our proposed MACDM technique. The multiple logistic regression models constructed as a function of all independent variables achieved an accuracy of 99.96%, sensitivity 98.9%, specificity 99.20%, PPV 99.25%, and Youden's index parameter 98.24%. Thus, it is proved that this computer-aided method helps our medical practitioners improve the efficiency of their services.

Radiofrequency ablation: technological trends, challenges, and opportunities.

Abstract: More than three decades have passed since utilization of radiofrequency (RF) ablation in the treatment of cardiac arrhythmias. Although several limitations and challenges still exist, with improvements in catheter designs and delivery of energy the way we do RF ablation now is much safer and more efficient. This review article aims to give an overview on historical advances on RF ablation and challenges in performing safe and efficient ablation.

Arrhythmogenic right ventricular cardiomyopathy and sports activity: from molecular pathways in diseased hearts to new insights into the athletic heart mimicry

Abstract: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited disease associated with a high risk of sudden cardiac death. Among other factors, physical exercise has been clearly identified as a strong determinant of phenotypic expression of the disease, arrhythmia risk, and disease progression. Because of this, current guidelines advise that individuals with ARVC should not participate in competitive or frequent high-intensity endurance exercise. Exercise-induced electrical and morphological para-physiological remodelling (the so-called 'athlete's heart') may mimic several of the classic features of ARVC. Therefore, the current International Task Force Criteria for disease diagnosis may not perform as well in athletes. Clear adjudication between the two conditions is often a real challenge, with false positives, that may lead to unnecessary treatments, and false negatives, which may leave patients unprotected, both of which are equally inacceptable. This review aims to summarize the molecular interactions caused by physical activity in inducing cardiac structural alterations, and the impact of sports on arrhythmia occurrence and other clinical consequences in patients with ARVC, and help the physicians in setting the two conditions apart.

Human RyR2 (Ryanodine Receptor 2) Loss-of-Function Mutations: Clinical Phenotypes and In Vitro Characterization.

Abstract: Background: The overall objective of the present study is to extend our understanding of the clinical phenotype and underlying mechanism of a newly discovered cardiac arrhythmia syndrome through a multicenter study. Gain-of-function mutations in the cardiac Ca2+release channel (RyR2 [ryanodine receptor 2]) cause catecholaminergic polymorphic ventricular tachycardia, whereas loss-of-function RyR2 mutations are linked to a new cardiac arrhythmia disorder termed Ca2+-release deficiency syndrome (CRDS). Catecholaminergic polymorphic ventricular tachycardia is an inherited arrhythmia disorder characterized by stress-induced bidirectional and polymorphic ventricular tachyarrhythmias and is routinely diagnosed by using exercise stress testing. Conversely, RyR2-CRDS is characterized by ventricular arrhythmias and sudden cardiac death but a negative exercise stress testing for catecholaminergic polymorphic ventricular tachycardia. There are currently no clinical diagnostic tests for CRDS and affected patients may manifest with sudden cardiac death as their first symptom. In the absence of effective clinical diagnostic tools, in vitro functional characterization of associated RyR2 mutations provides an alternative means to identify potential cases of CRDS. Methods: We searched for patients presenting with phenotypes compatible with CRDS that have RyR2 mutations and performed in vitro functional characterization. Results: We found that 3 novel (G570D, R4147K, and A4203V) and 2 previously reported (M4109R and A4204V) RyR2 mutations associated with CRDS phenotypes markedly reduced caffeine-induced Ca2+release and store overload-induced Ca2+release. We also characterized 2 additional loss-of-function RyR2 mutations previously reported (Q3925E and L4769S) that are located in the central and channel pore-forming domains critical for Ca2+activation and channel gating. Q3925E was identified through postmortem genetic testing in an individual who died suddenly, while L4769S is a variant of uncertain significance reported in ClinVar, suggesting that RyR2 CRDS may be under detected. Conclusions: These findings provide further support for the existence of an emerging RyR2 loss-of-function associated arrhythmia syndrome (CRDS) and shed new insights into the disease mechanism.

Relationship between non-alcoholic fatty liver disease and cardiac arrhythmia: a systematic review and meta-analysis

Abstract: Objective We performed a meta-analysis to create a quantitative estimate of the association between non-alcoholic fatty liver disease (NAFLD) and the risk of cardiac arrhythmia (including atrial fibrillation (AF), prolonged QT interval, premature atrial/ventricular contraction [PAC/PVC] and heart block). Methods A literature review was conducted using PubMed, Embase, Web of Science and the Cochrane Library database to identify observational studies of the link between NAFLD and cardiac arrhythmia. Effect sizes were expressed as odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CIs). The method of analysis of AF was also analysed separately, according to the effect estimate (OR or HR). Results Nineteen studies of 7,012,960 individuals were included. NAFLD was independently associated with higher risks of AF (OR 1.71, 95% CI: 1.14-2.57; HR 1.12, 95% CI: 1.11-1.13), prolonged QT interval (OR 2.86, 95% CI: 1.64-4.99), PAC/PVC (OR 2.53, 95% CI: 1.70-3.78) and heart block (OR 2.65, 95% CI: 1.88-3.72). The heterogeneity of the data with respect to AF and prolonged QT was moderate on sensitivity analysis. Conclusions We found a significantly higher risk of cardiac arrhythmia in patients with NAFLD, but the observational design of the studies does not permit conclusions regarding causality.

Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis.

Abstract: Background Significant morbidity, mortality, and financial burden are associated with cardiac rhythm abnormalities. Conventional investigative tools are often unsuccessful in detecting cardiac arrhythmias because of their episodic nature. Smartwatches have gained popularity in recent years as a health tool for the detection of cardiac rhythms. Objective This study aims to systematically review and meta-analyze the diagnostic accuracy of smartwatches in the detection of cardiac arrhythmias. Methods A systematic literature search of the Embase, MEDLINE, and Cochrane Library databases was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies reporting the use of a smartwatch for the detection of cardiac arrhythmia. Summary estimates of sensitivity, specificity, and area under the curve were attempted using a bivariate model for the diagnostic meta-analysis. Studies were examined for quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Results A total of 18 studies examining atrial fibrillation detection, bradyarrhythmias and tachyarrhythmias, and premature contractions were analyzed, measuring diagnostic accuracy in 424,371 subjects in total. The signals analyzed by smartwatches were based on photoplethysmography. The overall sensitivity, specificity, and accuracy of smartwatches for detecting cardiac arrhythmias were 100% (95% CI 0.99-1.00), 95% (95% CI 0.93-0.97), and 97% (95% CI 0.96-0.99), respectively. The pooled positive predictive value and negative predictive value for detecting cardiac arrhythmias were 85% (95% CI 0.79-0.90) and 100% (95% CI 1.0-1.0), respectively. Conclusions This review demonstrates the evolving field of digital disease detection. The current diagnostic accuracy of smartwatch technology for the detection of cardiac arrhythmias is high. Although the innovative drive of digital devices in health care will continue to gain momentum toward screening, the process of accurate evidence accrual and regulatory standards ready to accept their introduction is strongly needed. Trial registration PROSPERO International Prospective Register of Systematic Reviews CRD42020213237; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=213237.

Sugar Fructose Triggers Gut Dysbiosis and Metabolic Inflammation with Cardiac Arrhythmogenesis.

Abstract: Fructose is a main dietary sugar involved in the excess sugar intake-mediated progression of cardiovascular diseases and cardiac arrhythmias. Chronic intake of fructose has been the focus on the possible contributor to the metabolic diseases and cardiac inflammation. Recently, the small intestine was identified to be a major organ in fructose metabolism. The overconsumption of fructose induces dysbiosis of the gut microbiota, which, in turn, increases intestinal permeability and activates host inflammation. Endotoxins and metabolites of the gut microbiota, such as lipopolysaccharide, trimethylamine N-oxide, and short-chain fatty acids, also influence the host inflammation and cardiac biofunctions. Thus, high-fructose diets cause heart–gut axis disorders that promote cardiac arrhythmia. Understanding how gut microbiota dysbiosis-mediated inflammation influences the pathogenesis of cardiac arrhythmia may provide mechanisms for cardiac arrhythmogenesis. This narrative review updates our current understanding of the roles of excessive intake of fructose on the heart-gut axis and proposes potential strategies for inflammation-associated cardiac vascular diseases.

Neuroscientific therapies for atrial fibrillation

Abstract: The cardiac autonomic nervous system (ANS) plays an integral role in normal cardiac physiology as well as in disease states that cause cardiac arrhythmias. The cardiac ANS, comprised of a complex neural hierarchy in a nested series of interacting feedback loops, regulates atrial electrophysiology and is itself susceptible to remodelling by atrial rhythm. In light of the challenges of treating atrial fibrillation (AF) with conventional pharmacologic and myoablative techniques, increasingly interest has begun to focus on targeting the cardiac neuraxis for AF. Strong evidence from animal models and clinical patients demonstrates that parasympathetic and sympathetic activity within this neuraxis may trigger AF, and the ANS may either induce atrial remodelling or undergo remodelling itself to serve as a substrate for AF. Multiple nexus points within the cardiac neuraxis are therapeutic targets, and neuroablative and neuromodulatory therapies for AF include ganglionated plexus ablation, epicardial botulinum toxin injection, vagal nerve (tragus) stimulation, renal denervation, stellate ganglion block/resection, baroreceptor activation therapy, and spinal cord stimulation. Pre-clinical and clinical studies on these modalities have had promising results and are reviewed here.

The photoplethysmography dictionary: practical guidance on signal interpretation and clinical scenarios from TeleCheck-AF

Abstract: Within the TeleCheck-AF project, numerous centres in Europe used on-demand photoplethysmography (PPG) technology to remotely assess heart rate and rhythm in conjunction with teleconsultations. Based on the TeleCheck-AF investigator experiences, we aimed to develop an educational structured stepwise practical guide on how to interpret PPG signals and to introduce typical clinical scenarios how on-demand PPG was used. During an online conference, the structured stepwise practical guide on how to interpret PPG signals was discussed and further refined during an internal review process. We provide the number of respective PPG recordings (FibriCheck®) and number of patients managed within a clinical scenario during the TeleCheck-AF project. To interpret PPG recordings, we introduce a structured stepwise practical guide and provide representative PPG recordings. In the TeleCheck-AF project, 2522 subjects collected 90 616 recordings in total. The majority of these recordings were classified by the PPG algorithm as sinus rhythm (57.6%), followed by AF (23.6%). In 9.7% of recordings, the quality was too low to interpret. The most frequent clinical scenarios where PPG technology was used in the TeleCheck-AF project was a follow-up after AF ablation (1110 patients) followed by heart rate and rhythm assessment around (tele)consultation (966 patients). We introduce a newly developed structured stepwise practical guide on PPG signal interpretation developed based on presented experiences from TeleCheck-AF. The present clinical scenarios for the use of on-demand PPG technology derived from the TeleCheck-AF project will help to implement PPG technology in the management of AF patients.

Brugada syndrome and arrhythmogenic cardiomyopathy: overlapping disorders of the connexome?

Abstract: Arrhythmogenic cardiomyopathy (ACM) and Brugada syndrome (BrS) are inherited diseases characterized by an increased risk for arrhythmias and sudden cardiac death. Possible overlap between the two was suggested soon after the description of BrS. Since then, various studies focusing on different aspects have been published pointing to similar findings in the two diseases. More recent findings on the structure of the cardiac cell-cell junctions may unite the pathophysiology of both diseases and give further evidence to the theory that they may in part be variants of the same disease spectrum. In this review, we aim to summarize the studies indicating the pathophysiological, genetic, structural, and electrophysiological overlap between ACM and BrS.

Chronically elevated branched chain amino acid levels are pro-arrhythmic.

Abstract: Aim. Cardiac arrhythmias comprise a major health and economic burden and are associated with significant morbidity and mortality, including cardiac failure, stroke and sudden cardiac death (SCD). Development of efficient preventive and therapeutic strategies is hampered by incomplete knowledge of disease mechanisms and pathways. Our aim is to identify novel mechanisms underlying cardiac arrhythmia and SCD using an unbiased approach. Methods and Results. We employed a phenotype-driven N-ethyl-N-nitrosourea (ENU) mutagenesis screen and identified a mouse line with a high incidence of sudden death at young age (6-9 weeks) in the absence of prior symptoms. Affected mice were found to be homozygous for the nonsense mutation Bcat2p.Q300*/p.Q300* in the Bcat2 gene encoding branched chain amino acid transaminase 2. At the age of 4-5 weeks, Bcat2p.Q300*/p.Q300* mice displayed drastic increase of plasma levels of branch chain amino acids (BCAAs – leucine, isoleucine, valine) due to the incomplete catabolism of BCAAs, in addition to inducible arrhythmias ex vivo as well as cardiac conduction and repolarization disturbances. In line with these findings, plasma BCAA levels were positively correlated to ECG indices of conduction and repolarization in the German community-based KORA F4 Study. Isolated cardiomyocytes from Bcat2p.Q300*/p.Q300* mice revealed action potential (AP) prolongation, pro-arrhythmic events (early and late afterdepolarizations, triggered APs) and dysregulated calcium homeostasis. Incubation of human pluripotent stem cell-derived cardiomyocytes with elevated concentration of BCAAs induced similar calcium dysregulation and pro-arrhythmic events which were prevented by rapamycin, demonstrating the crucial involvement of mTOR pathway activation. Conclusions. Our findings identify for the first time a causative link between elevated BCAAs and arrhythmia, which has implications for arrhythmogenesis in conditions associated with BCAA metabolism dysregulation such as diabetes, metabolic syndrome and heart failure. Translational perspectives. Development of efficient anti-arrhythmic strategies is hampered by incomplete knowledge of disease mechanisms. Using an unbiased approach, we here identified for the first time a pro-arrhythmic effect of increased levels of branched chain amino acids (BCAAs). This is of particular relevance for conditions associated with BCAA dysregulation and increased arrhythmia risk, including heart failure, obesity and diabetes, as well as for athletes supplementing their diet with BCAAs. Such metabolic dysregulation is potentially modifiable through dietary interventions, paving the way for novel preventive strategies. Our findings furthermore identify mTOR inhibition as a potential anti-arrhythmic strategy in patients with metabolic syndrome.

Electrophysiological Mechanisms Underlying T-Wave Alternans and Their Role in Arrhythmogenesis.

Abstract: T-wave alternans (TWA) reflects every-other-beat alterations in the morphology of the electrocardiogram ST segment or T wave in the setting of a constant heart rate, hence, in the absence of heart rate variability. It is believed to be associated with the dispersion of repolarization and has been used as a non-invasive marker for predicting the risk of malignant cardiac arrhythmias and sudden cardiac death as numerous studies have shown. This review aims to provide up-to-date review on both experimental and simulation studies in elucidating possible mechanisms underlying the genesis of TWA at the cellular level, as well as the genesis of spatially concordant/discordant alternans at the tissue level, and their transition to cardiac arrhythmia. Recent progress and future perspectives in antiarrhythmic therapies associated with TWA are also discussed.

ESC working group on cardiac cellular electrophysiology position paper: relevance, opportunities, and limitations of experimental models for cardiac electrophysiology research

Abstract: Cardiac arrhythmias are a major cause of death and disability. A large number of experimental cell and animal models have been developed to study arrhythmogenic diseases. These models have provided important insights into the underlying arrhythmia mechanisms and translational options for their therapeutic management. This position paper from the ESC Working Group on Cardiac Cellular Electrophysiology provides an overview of (i) currently available in vitro, ex vivo, and in vivo electrophysiological research methodologies, (ii) the most commonly used experimental (cellular and animal) models for cardiac arrhythmias including relevant species differences, (iii) the use of human cardiac tissue, induced pluripotent stem cell (hiPSC)-derived and in silico models to study cardiac arrhythmias, and (iv) the availability, relevance, limitations, and opportunities of these cellular and animal models to recapitulate specific acquired and inherited arrhythmogenic diseases, including atrial fibrillation, heart failure, cardiomyopathy, myocarditis, sinus node, and conduction disorders and channelopathies. By promoting a better understanding of these models and their limitations, this position paper aims to improve the quality of basic research in cardiac electrophysiology, with the ultimate goal to facilitate the clinical translation and application of basic electrophysiological research findings on arrhythmia mechanisms and therapies.

Protein kinases mediate anti-inflammatory effects of cannabidiol and estradiol against high glucose in cardiac sodium channels

Abstract: Background: Cardiovascular anomalies are predisposing factors for diabetes-induced morbidity and mortality. Recently, we showed that high glucose induces changes in the biophysical properties of the cardiac voltage-gated sodium channel (Nav1.5) that could be strongly correlated to diabetes-induced arrhythmia. However, the mechanisms underlying hyperglycemia-induced inflammation, and how inflammation provokes cardiac arrhythmia, are not well understood. We hypothesized that inflammation could mediate the high glucose-induced biophyscial changes on Nav1.5 through protein phosphorylation by protein kinases A and C. We also hypothesized that this signaling pathway is, at least partly, involved in the cardiprotective effects of cannabidiol (CBD) and 17β-estradiol (E2). Methods and Results: To test these ideas, we used Chinese hamster ovarian (CHO) cells transiently co-transfected with cDNA encoding human Nav1.5 α-subunit under control, a cocktail of inflammatory mediators or 100 mM glucose conditions (for 24 h). We used electrophysiological experiments and action potential modeling. Inflammatory mediators, similar to 100 mM glucose, right shifted the voltage dependence of conductance and steady-state fast inactivation and increased persistent current leading to computational prolongation of action potential (hyperexcitability) which could result in long QT3 arrhythmia. We also used human iCell cardiomyocytes derived from inducible pluripotent stem cells (iPSC-CMs) as a physiologically relevant system, and they replicated the effects produced by inflammatory mediators observed in CHO cells. In addition, activators of PK-A or PK-C replicated the inflammation-induced gating changes of Nav1.5. Inhibitors of PK-A or PK-C, CBD or E2 mitigated all the potentially deleterious effects provoked by high glucose/inflammation. Conclusion: These findings suggest that PK-A and PK-C may mediate the anti-inflammatory effects of CBD and E2 against high glucose-induced arrhythmia. CBD, via Nav1.5, may be a cardioprotective therapeutic approach in diabetic postmenopausal population.