Search Results (119)

Ventricular arrhythmias (VAs) are critical cardiovascular diseases that require rapid and accurate detection. Conventional approaches relying on multi-lead ECG or deep learning models have limitations in computational cost, interpretability, and real-time applicability on wearable devices. To address these issues, a lightweight and interpretable framework based on multiple complex networks was proposed for the detection of life-threatening VAs using short-term single-lead ECG signals. The input signals were decomposed using the fixed-frequency-range empirical wavelet transform, and sub-bands were subsequently analyzed through multiscale visibility graphs, recurrence networks, cross-recurrence networks, and joint recurrence networks. Eight topological features were extracted and input into an XGBoost classifier for VA identification. Ten-fold cross-validation results on the MIT-BIH VFDB and CUDB databases demonstrated that the proposed method achieved a sensitivity of 99.02 ± 0.53%, a specificity of 98.44 ± 0.43%, and an accuracy of 98.73 ± 0.02% for 10 s ECG segments. The model also maintained robust performance on shorter segments, with 97.23 ± 0.76% sensitivity, 98.85 ± 0.95% specificity, and 96.62 ± 0.02% accuracy on 2 s segments. The results outperformed existing feature-based and deep learning approaches while preserving model interpretability. Furthermore, the proposed method supports mobile deployment, facilitating real-time use in wearable healthcare applications. Full article

Background/Objectives: Nonvalvular atrial fibrillation (NVAF) is a prevalent arrhythmia associated with elevated risks of stroke, systemic embolism, and mortality. Emerging evidence underscores the pivotal role of inflammation in NVAF pathogenesis. The CHA₂DS₂-VA score is currently the most powerful tool used in the management of patients with atrial fibrillation, and integrating novel inflammatory biomarkers—neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI)—into this score may enhance prognostic accuracy and guide personalized therapy. Methods: In this observational case–control study, a cohort of 330 NVAF patients and 201 controls, inflammatory and biochemical parameters were measured and compared, we employed multivariate logistic regression and ROC analyses to validate the discriminative power of novel inflammatory indexes and novel CHA₂DS₂-VA score, setting a new benchmark for biomarker integration in NVAF management. Results: Inflammatory indexes (NLR, PLR, SII, SIRI) were significantly higher in NVAF patients compared to controls (p < 0.001). Multivariate analysis identified NLR (OR = 4.02), PLR (OR = 1.04), SII (OR = 1.01), and SIRI (OR = 1.87) as independent NVAF risk markers. The CHA₂DS₂-VA score showed the strongest association with NVAF (OR = 5.55), and an optimal cutoff of ≥2 yielded 88.18% sensitivity and 74.63% specificity. Conclusions: Inflammatory markers NLR, PLR, SII, and SIRI, when assessed alongside the CHA₂DS₂-VA score, offer significant and complementary prognostic insight for patients with NVAF. These findings support the integration of inflammatory indexes into routine clinical risk assessment models to enhance early identification of high-risk individuals and inform personalized therapeutic strategies. Moreover, our findings provide a rationale for developing composite risk scores in future studies that integrate inflammatory biomarkers with the CHA₂DS₂-VA score (e.g., a CHA₂DS₂-VA-Inflammation Score). Further large-scale, longitudinal studies are warranted to validate these results and explore the benefits of inflammation-targeted interventions. Full article

Background: Acute kidney injury (AKI) in critically ill patients is often complicated by arrhythmias, potentially affecting outcomes. This study aimed to develop predictive models for arrhythmias in AKI patients and assess the impact of antiarrhythmic drugs on in-hospital mortality. Methods: We conducted a multi-database retrospective cohort study using MIMIC-IV and eICU databases. XGBoost and Bayesian Information Criterion (BIC) models were employed to identify key predictors of arrhythmias. Weighted log-rank and Cox analysis evaluated the effect of amiodarone and metoprolol on in-hospital mortality. Results: Among 14,035 critically ill AKI patients, 5614 individuals (40%) developed arrhythmias. Both XGBoost and BIC showed predictive power for arrhythmias. The XGBoost model identified HR_max, HR_min, and heart failure as the most important features, while the BIC model highlighted heart failure had the highest odds ratio (OR 1.18, 95% CI 1.16–1.20) as a significant predictor. Patients experiencing arrhythmia is associated with in-hospital mortality (arrhythmia group: 636 (11.3%) vs. non-arrhythmia group: 587 (7.0%), p < 0.01). Antiarrhythmic medications showed a statistically significant effect on in-hospital mortality (amiodarone: HR 0.28, 95% CI 0.19–0.41, p < 0.01). Conclusions: Our predictive models demonstrated a robust discriminatory ability for identifying arrhythmia occurrence in critically ill AKI patients, with identified risk factors showing strong clinical relevance. The significant association between arrhythmia occurrence and increased in-hospital mortality underscores the clinical importance of early identification and management. Furthermore, amiodarone therapy effectively reduced the risk of in-hospital mortality in these patients, even after accounting for time-dependent biases. The findings highlight the necessity of precise arrhythmia definition, careful consideration of time-dependent covariates, and comprehensive model validation for clinically actionable insights. Full article

Cardiomyopathies are a heterogeneous group of heart muscle diseases that can lead to heart failure, arrhythmias, and sudden cardiac death. Traditional animal models and in vitro systems have limitations in replicating the complex pathology of human cardiomyopathies. Induced pluripotent stem cells (iPSCs) offer a transformative platform by enabling the generation of patient-specific cardiomyocytes, thus opening new avenues for disease modeling, drug discovery, and regenerative therapy. This process involves reprogramming somatic cells into iPSCs and subsequently differentiating them into functional cardiomyocytes, which can be characterized using techniques such as electrophysiology, contractility assays, and gene expression profiling. iPSC-derived cardiomyocyte (iPSC-CM) platforms are also being explored for drug screening and personalized medicine, including high-throughput testing for cardiotoxicity and the identification of patient-tailored therapies. While iPSC-CMs already serve as valuable models for understanding disease mechanisms and screening drugs, ongoing advances in maturation and bioengineering are bringing iPSC-based therapies closer to clinical application. Furthermore, the integration of multi-omics approaches and artificial intelligence (AI) is enhancing the predictive power of iPSC models. iPSC-based technologies are paving the way for a new era of personalized cardiology, with the potential to revolutionize the management of cardiomyopathies through patient-specific insights and regenerative strategies. Full article

Cardiovascular diseases increase among pregnant women and complicate 1–4% of pregnancies worldwide. The incidence of maternal deaths due to cardiovascular causes has increased dramatically, rising from 3% three decades ago to 15% in recent years. The aim of this study is to provide a comprehensive overview of the current status of knowledge in sudden maternal death (SMD) described in the literature and to present two cases of autopsy findings in sudden cardiac death in pregnant women. Among the most common causes of sudden maternal deaths are peripartum cardiomyopathies, aortic dissection, acute myocardial infarction, arrhythmias, ischemic heart disease, and coronary artery dissection, and among the less common causes, we list coronary artery dissection, congenital heart diseases, valvulopathies, hypertension, fibroelastosis, and borderline myocarditis. The Centers for Disease Control and Prevention (CDC) reported that over 80% of pregnancy-related deaths were preventable. To reduce the number of maternal deaths caused by cardiovascular diseases, the implementation of specialized multidisciplinary teams has been proposed. Molecular biology techniques are proving their effectiveness in forensic medicine. PCR or DNA sequencing can be utilized in “molecular autopsy”, which holds particular value in cases of sudden death where the forensic autopsy is negative but there is a suspicion that death was caused by arrhythmia. Susceptibility genes can be analyzed, such as KCNQ1, KCNH2, KCNE1, and KCNE2, which are involved in long QT syndrome, the RYR2 gene implicated in catecholaminergic polymorphic ventricular tachycardia type 1, or the SCN5A gene associated with Brugada syndrome. Early identification of risk factors involved in sudden maternal death prenatally and during pregnancy is essential. At the same time, genetic determinations and molecular biology techniques are absolutely necessary to prevent the occurrence of sudden deaths among close relatives. Full article

Background: Chronic liver disease (CLD) and cirrhosis contribute to approximately 2 million deaths annually, with primary causes including alcohol-related liver disease (ALD), metabolic dysfunction-associated steatotic liver disease (MASLD), and chronic hepatitis B and C infections. Among these, MASLD has emerged as a significant global health concern, closely linked to metabolic disorders and a leading cause of liver failure and transplantation. Objective: This review aims to highlight the interplay between cirrhosis and cardiac dysfunction, emphasizing the pathophysiology, diagnostic criteria, and management of cirrhotic cardiomyopathy (CCM). Methods: A comprehensive literature review was conducted to evaluate the hemodynamic and structural cardiac alterations in cirrhosis. Results: Cirrhosis leads to portal hypertension and systemic inflammation, contributing to CCM, which manifests as subclinical cardiac dysfunction, impaired contractility, and electrophysiological abnormalities. Structural changes, such as increased left ventricular mass, myocardial fibrosis, and ion channel dysfunction, further impair cardiac function. Vasodilation in the splanchnic circulation reduces peripheral resistance, triggering compensatory tachycardia, while the activation of the renin–angiotensin–aldosterone system (RAAS) promotes fluid retention and increases cardiac preload. Chronic inflammation and endotoxemia exacerbate myocardial dysfunction. The 2005 World Congress of Gastroenterology (WCG) and the 2019 Cirrhotic Cardiomyopathy Consortium (CCC) criteria provide updated diagnostic frameworks that incorporate global longitudinal strain (GLS) and tissue Doppler imaging (TDI). Prolonged QT intervals and arrhythmias are frequently observed. Managing heart failure in cirrhotic patients remains complex due to intolerance to afterload-reducing agents, and beta-blockers require careful use due to potential systemic hypotension. The interaction between CCM and major interventions, such as transjugular intrahepatic portosystemic shunt (TIPS) and orthotopic liver transplantation (OLT), highlights the critical need for thorough preoperative cardiac evaluation and vigilant postoperative monitoring. Conclusions: CCM is a frequently underdiagnosed yet significant complication of cirrhosis, impacting prognosis, particularly post-liver transplantation. Early identification using echocardiography and thorough evaluations of arrhythmia risk in cirrhotic patients are critical for optimizing management strategies. Future research should focus on targeted therapeutic approaches to mitigate the cardiac burden in cirrhotic patients and improve clinical outcomes. Full article

Atrial fibrillation (AF) is the most frequent cardiac arrhythmia, with an estimated five million cases globally. This condition increases the likelihood of developing cardiovascular complications such as thromboembolic events, with a fivefold increase in risk of both heart failure and stroke. Contemporary challenges include a better understanding AF pathophysiology and optimizing therapeutical options due to the current lack of efficacy and adverse effects of antiarrhythmic drug therapy. Hence, the identification of novel biomarkers in biological samples would greatly impact the diagnostic and therapeutic opportunities offered to AF patients. Long noncoding RNAs, micro RNAs, circular RNAs, and genes involved in heart cell differentiation are particularly relevant to understanding gene regulatory effects on AF pathophysiology. Proteomic remodeling may also play an important role in the structural, electrical, ion channel, and interactome dysfunctions associated with AF pathogenesis. Different devices for processing RNA and proteomic samples vary from RNA sequencing and microarray to a wide range of mass spectrometry techniques such as Orbitrap, Quadrupole, LC-MS, and hybrid systems. Since AF atrial tissue samples require a more invasive approach to be retrieved and analyzed, blood plasma biomarkers were also considered. A range of different sample preprocessing techniques and bioinformatic methods across studies were examined. The objective of this descriptive review is to examine the most recent developments of transcriptomics, proteomics, and bioinformatics in atrial fibrillation. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is correlated with an increased cardiovascular risk, independent of other traditional risk factors. The mechanisms underlying this pathogenic link are complex yet remain incompletely elucidated. Among these, the most significant are visceral adiposity, low-grade inflammation and oxidative stress, endothelial dysfunction, prothrombotic status, insulin resistance, dyslipidemia and postprandial hyperlipemia, gut dysbiosis, and genetic mutations. Cardiovascular diseases are the leading cause of death in patients with MASLD. These patients have an increased incidence of coronary artery disease, carotid artery disease, structural and functional cardiac abnormalities, and valvulopathies, as well as arrhythmias and cardiac conduction disorders. In this review, we present the latest data on the association between MASLD and cardiovascular risk, focusing on the pathogenic mechanisms that explain the correlation between these two pathologies. Given the high rates of cardiovascular morbidity and mortality among patients with MASLD, we consider it imperative to raise awareness of the risks associated with this condition within the general population. Further research is essential to clarify the mechanisms underlying the increased cardiovascular risk linked to MASLD. This understanding may facilitate the identification of new diagnostic and prognostic biomarkers for these patients, as well as novel therapeutic targets. Full article

Cardiovascular diseases are the leading cause of death globally, highlighting the need for accurate diagnostic tools. To address this issue, we introduce a novel approach for arrhythmia detection based on electrocardiogram (ECG) that incorporates explainable artificial intelligence through three key methods. First, we developed an enhanced R peak detection method that integrates domain-specific knowledge into the ECG, improving peak identification accuracy by accounting for the characteristic features of R peaks. Second, we proposed an arrhythmia classification method utilizing a modified convolutional neural network (CNN) architecture with additional convolutional and batch normalization layers. This model processes a triad of cardio cycles—the preceding, current, and following cycles—to capture temporal dependencies and hidden features related to arrhythmias. Third, we implemented an interpretation method that explains CNN’s decisions using clinically relevant features, making the results understandable to clinicians. Using the MIT-BIH database, our approach achieved an accuracy of 99.43%, with F1-scores approaching 100% for major arrhythmia classes. The integration of these methods enhances both the performance and transparency of arrhythmia detection systems. Full article

Anderson–Fabry disease (AFD) is a rare X-linked lysosomal storage disorder characterized by the accumulation of globotriaosylceramide, leading to multi-organ involvement and significant morbidity. Cardiovascular manifestations, particularly arrhythmias, are common and pose a considerable risk to affected individuals. This overview examines current approaches to arrhythmic risk stratification in AFD, focusing on the identification, assessment, and management of cardiac arrhythmias associated with the disease. We explore advancements in diagnostic techniques, including echocardiography, cardiac MRI, and ambulatory ECG monitoring, to enhance the detection of arrhythmogenic substrate. Furthermore, we discuss the role of genetic and biochemical markers in predicting arrhythmic risk and the implications for personalized treatment strategies. Current therapeutic interventions, including enzyme replacement therapy and antiarrhythmic medications, are reviewed in the context of their efficacy and limitations. Finally, we highlight ongoing research and future directions with the aim of improving arrhythmic risk assessment and management in AFD. This overview underscores the need for a multidisciplinary approach to optimize care and outcomes for patients with AFD. Full article

Supraventricular arrhythmias (SVAs), including the often-asymptomatic supraventricular extrasystole (SVE), pose significant challenges in early detection and precise diagnosis. These challenges are of paramount importance, as recurrent SVEs may elevate the risk of developing severe SVAs, potentially resulting in cardiac weakening and subsequent heart failure. In the study conducted, an innovative approach was introduced that combined a convolutional neural network (CNN) architecture to enable the early identification and characterization of SVEs within electrocardiogram (ECG) signals. The analysis leveraged a dataset comprising 78 half-hour recordings from the highly regarded MIT-BIH Arrhythmia Database, which included annotation headers serving as labels for each recording. Signals were down-sampled by a factor of 2 and split into windows of 512 samples, with 12,288 observations for training. Following the methodology, classic signal preprocessing techniques (filtering and data normalization) were used. The proposed model was based on the UNET 1D model. A binary cross-entropy loss function, Adam optimizer, and a batch size of 128 were obtained after a hyperparameter tuning. As a training-validation methodology, a 50-fold cross-validation technique was used. The approach demonstrated a Dice coefficient of 79.01%, a precision of 80.96%, and a recall rate of 86.60% in detecting SVE events. These findings were corroborated through meticulous comparison with the annotations provided by the MIT-BIH database. The results underscore the immense potential of CNN and deep learning techniques in the early detection of supraventricular arrhythmias. This approach not only offers a valuable tool for healthcare professionals engaged in telemonitoring and early intervention strategies but also represents a significant contribution to the field of cardiac health monitoring. By facilitating efficient and precise identification of SVEs, our research sets the stage for improved patient outcomes and the prevention of severe SVAs, marking substantial advancements in this critical domain. Full article

Alternative splicing allows a single gene to produce a variety of protein isoforms. Changes in splicing isoform usage characterize virtually every stage of the differentiation process and define the physiological differences between cardiomyocytes with different function, at different stages of development, and pathological function. Recent identification of cardiac splicing factors provided insights into the mechanisms underlying alternative splicing and revealed how these splicing factors impact functional properties of the heart. Alterations of the splicing of sarcomeric genes, cell signaling proteins, and ion channels have been associated with the development of pathological conditions such as cardiomyopathy and arrhythmia. RBM20, RBM24, PTBP1, RBFOX, and QKI play key roles in cardiac development and pathology. A better understanding of their regulation will yield insights into healthy cardiac development and inform the development of molecular therapeutics. Full article

Inherited cardiac channelopathies are major causes of sudden cardiac death (SCD) in young people. Genetic testing is focused on the identification of single-nucleotide variants (SNVs) by Next-Generation Sequencing (NGS). However, genetically elusive cases can carry copy number variants (CNVs), which need specific detection tools. We underlie the utility of identifying CNVs by investigating the literature data and internally analyzing cohorts with CNVs in KCNQ1, KCNH2, SCN5A, and RYR2. CNVs were reported in 119 patients from the literature and 21 from our cohort. Young patients with CNVs in KCNQ1 show a Long QT (LQT) phenotype > 480 ms and a higher frequency of syncope. None of them had SCD. All patients with CNV in KCNH2 had a positive phenotype for QT > 480 ms. CNVs in SCN5A were represented by the Brugada pattern, with major cardiac events mainly in males. Conversely, adult females show more supraventricular arrhythmias. RYR2-exon3 deletion showed a broader phenotype, including left ventricular non-compaction (LVNC) and catecholaminergic polymorphic ventricular tachycardia (CPVT). Pediatric patients showed atrial arrhythmias and paroxysmal atrial fibrillation. Relatively higher syncope and SCA were observed in young females. The detection of CNVs can be of greater yield in two groups: familial channelopathies and patients with suspected Jervell and Lange-Nielsen syndrome or CPVT. The limited number of reported individuals makes it mandatory for multicentric studies to give future conclusive results. Full article

Since the first description of catecholaminergic polymorphic ventricular tachycardia (CPVT) in the 1970s, new insights have progressively unraveled the understanding of this inherited arrhythmia syndrome. The identification of new distinct clinical entities related to RYR2, the gene encoding the cardiac ryanodine receptor, has allowed significant refinement in the diagnosis of previously labeled “atypical” CPVT cases. Among RYR2-ryanodinopathies, the characterization of calcium release deficiency syndrome (CRDS) is still in its infancy and represents a diagnostic challenge due to the need for functional studies which may confirm the loss-of-function nature of the RYR2 variant. The present review summarizes current evidence on CRDS. First, by providing an overview on RYR2 structure and function, we will elucidate the different pathophysiological underpinnings of CRDS and CPVT. Second, by retrieving in detail reported CRDS variants and their clinical phenotypes, we will provide, if any, genetic and clinical red flags that should raise suspicion for CRDS in daily clinical practice. Finally, we will discuss available therapies to provide clinicians with practical therapeutic options for CRDS management. Full article

Background/Objectives: Inherited arrhythmogenic syndromes comprise a heterogenic group of genetic entities that lead to malignant arrhythmias and sudden cardiac death. Genetic testing has become crucial to understand the disease etiology and allow for the early identification of relatives at risk; however, it requires an accurate interpretation of the data to achieve a clinically actionable outcome. This is particularly challenging for the large number of rare variants obtained by current high-throughput techniques, which are mostly classified as of unknown significance. Methods: In this work, we present a new algorithm for the genetic interpretation of the remaining rare variants in order to shed light on their potential clinical implications and reduce the burden of unknown significance. Results: Our study illustrates the potential utility of our individualized comprehensive stepwise analyses focused on the rare variants associated with IAS, which are currently classified as ambiguous, to further determine their trends towards pathogenicity or benign traits. Conclusions: We advocate for personalized disease-focused population frequency data and family segregation analyses for all rare variants that remain ambiguous to further clarify their role. The current ambiguity should not influence medical decisions, but a potential deleterious role would suggest a closer clinical follow-up and frequent genetic data review for a more personalized clinical approach. Full article