Search Results (293)

Subclinical atrial fibrillation (SCAF) episodes are frequently detected in patients with cardiac implantable electronic devices (CIEDs). These asymptomatic arrhythmias are increasingly recognized as potential harbingers of clinical atrial fibrillation and thromboembolic events. However, the management of SCAF—particularly regarding the use of oral anticoagulation (OAC)—remains controversial. This literature review (Medline, Scopus, Goggle scholar, Embase) focuses on using current literature and clinical studies to guide decision-making regarding anticoagulation therapy and other treatment options that can limit complications for patients with SCAF. The decision to initiate anticoagulation in patients with atrial high-rate episodes (AHREs) should be individualized, balancing stroke risk against bleeding potential. Ongoing research and post hoc analyses will further clarify which subgroups may benefit most from therapy, informing future guideline recommendations. Full article

This review investigates recent developments in cardiac mechano-electrical-fluid interaction (MEFI) modeling, with a focus on multiphysics simulation platforms and digital twin frameworks developed between 2015 and 2025. The purpose of the study is to assess how computational modeling methods—particularly finite element and immersed boundary techniques, monolithic and partitioned coupling schemes, and artificial intelligence (AI)-enhanced surrogate modeling—capture the integrated dynamics of cardiac electrophysiology, tissue mechanics, and hemodynamics. The goal is to evaluate the translational potential of MEFI models in clinical applications such as cardiac resynchronization therapy (CRT), arrhythmia classification, atrial fibrillation ablation, and surgical planning. Quantitative results from the literature demonstrate <5% error in pressure–volume loop predictions, >0.90 F1 scores in machine-learning-based arrhythmia detection, and <10% deviation in myocardial strain relative to MRI-based ground truth. These findings highlight both the promise and limitations of current MEFI approaches. While recent advances improve physiological fidelity and predictive accuracy, key challenges remain in achieving multiscale integration, model validation across diverse populations, and real-time clinical applicability. The review concludes by identifying future milestones for clinical translation, including regulatory model certification, standardization of validation protocols, and integration of patient-specific digital twins into electronic health record (EHR) systems. Full article

Background and Objectives: The perioperative use of beta-blockers remains controversial due to conflicting evidence of their risks and benefits. The aim of this study was to evaluate the association between chronic beta-blocker (bb) therapy and perioperative cardiac events in non-cardiac surgeries using 24 h continuous Holter monitoring. Materials and Methods: A prospective observational study was conducted on patients undergoing elective or emergency non-cardiac surgery at a Romanian tertiary care hospital. The patients were divided into two groups: G1 (not receiving Bb) and G2 (on chronic Bb). The incidences of perioperative cardiac events, such as severe bradycardia (<40 b/min), new-onset atrial fibrillation (AF), extrasystolic arrhythmia (Ex), and sustained ventricular tachycardia (sVT) and arterial hypotension, were compared between the two groups using clinical, electrocardiography (ECG), and Holter ECG data. Beta-blocker indications, complications, and outcomes were analyzed using chi-squared tests and logistic regression. Results: A total of 100 consecutive patients (63% men, mean age of 53.7 years) were enrolled in the study. G2 included 30% (n = 30) of patients on chronic beta-blocker therapy. The indications included atrial fibrillation (46.7%, n = 14), arterial hypertension (36.7%, n = 11), extrasystolic arrhythmias (10%, n = 3), and chronic coronary syndrome (6.6%, n = 2). Beta-blocker use was significantly associated with severe bradycardia (n = 6; p < 0.001) in G2, whereas one patient in G1 had bradycardia, and 15 and 1 patients had hypotension (p < 0.001) in G1 and G2, respectively. The bradycardia and arterial hypotension cases were promptly treated and did not influence the patients’ prognoses. The 14 patients with AF in G2 had a 15-fold higher odds of requiring beta-blockers (p < 0.001, odds ratio (OR) = 15.145). No significant associations were found between beta-blocker use and the surgery duration (p = 0.155) or sustained ventricular tachycardia (p = 0.857). Ten patients developed paroxysmal postoperative atrial fibrillation (AF), which was related to longer surgery durations (165 (150–180) vs. 120 (90–150) minutes; p = 0.002) and postoperative anemia [hemoglobin (Hg): 10.4 (9.37–12.6) vs. 12.1 (11–13.2) g/dL; p = 0.041]. Conclusions: Patients under chronic beta-blocker therapy undergoing non-cardiac surgery have a higher risk of perioperative bradycardia and hypotension. Continuous Holter monitoring proved effective in detecting transient arrhythmic events, emphasizing the need for careful perioperative surveillance of these patients, especially the elderly, in order to prevent cardiovascular complications These findings emphasize the necessity of tailored perioperative beta-blocker strategies and support further large-scale investigations to optimize risk stratification and management protocols. Full article

Atrial fibrillation (AF) is a common cardiac arrhythmia linked to an increased risk of stroke, heart failure, and mortality, yet its diagnosis remains challenging due to its intermittent and often asymptomatic nature. Traditional methods, such as standard electrocardiography (ECG) and prolonged cardiac monitoring, have limitations in terms of cost, accessibility, and diagnostic yield. Artificial intelligence (AI), particularly machine learning (ML) and deep learning, has emerged as a promising tool for AF detection and prediction by analyzing ECG data with high accuracy. AI models can identify subtle patterns in ECG signals that may indicate AF, even when the arrhythmia is not actively present, improving early diagnosis and risk stratification. Additionally, AI-powered ECG analysis has been integrated into wearable and mobile health devices, expanding screening capabilities beyond clinical settings. While studies have demonstrated AI’s effectiveness, challenges such as data bias, model reliability across diverse populations, and regulatory considerations must be addressed before widespread clinical adoption. If these obstacles are overcome, AI has the potential to revolutionize AF management by enabling earlier detection, reducing the need for resource-intensive monitoring, and improving patient outcomes. Full article

Objectives: With the growing importance of mobile technology and artificial intelligence (AI) in healthcare, the development of automated cardiac diagnostic systems has gained strategic significance. This review aims to summarize the current state of knowledge on the use of AI in the analysis of electrocardiographic (ECG) signals obtained from wearable devices, particularly smartwatches, and to outline perspectives for future clinical applications. Methods: A narrative literature review was conducted using PubMed, Web of Science, and Scopus databases. The search focused on combinations of keywords related to AI, ECG, and wearable technologies. After screening and applying inclusion criteria, 152 publications were selected for final analysis. Conclusions: Modern AI algorithms—especially deep neural networks—show promise in detecting arrhythmias, heart failure, prolonged QT syndrome, and other cardiovascular conditions. Smartwatches without ECG sensors, using photoplethysmography (PPG) and machine learning, show potential as supportive tools for preliminary atrial fibrillation (AF) screening at the population level, although further validation in diverse real-world settings is needed. This article explores innovation trends such as genetic data integration, digital twins, federated learning, and local signal processing. Regulatory, technical, and ethical challenges are also discussed, along with the issue of limited clinical evidence. Artificial intelligence enables a significant enhancement of personalized, mobile, and preventive cardiology. Its integration into smartwatch ECG analysis opens a path toward early detection of cardiac disorders and the implementation of population-scale screening approaches. Full article

Post-traumatic cardiac dysfunction is a clinically under-recognized complication of polytrauma, often occurring in the absence of overt structural injury. Traditional diagnostic tools frequently fail to detect early or subclinical myocardial impairment, underscoring the need for more sensitive assessment methods. This review explores the utility of global longitudinal strain (GLS), derived from speckle-tracking echocardiography (STE), as a sensitive biomarker for identifying and managing cardiac dysfunction following traumatic injury. It outlines the complex pathophysiology of trauma-induced myocardial impairment, including mechanical injury, systemic inflammation, oxidative stress, and neuro-hormonal activation. The limitations of conventional diagnostic approaches, such as electrocardiography, left ventricular ejection fraction (LVEF), and cardiac biomarkers, are critically assessed and contrasted with the enhanced diagnostic performance of GLS. GLS has demonstrated superior sensitivity in detecting subclinical myocardial dysfunction even when LVEF remains preserved and is associated with increased risk of long-term cardiovascular complications, including arrhythmias and heart failure. The manuscript highlights the clinical utility of GLS in early diagnosis, risk stratification, treatment monitoring, and long-term follow-up. Integration of GLS with inflammatory and oxidative biomarkers (e.g., IL-6, TNF-α, and MPO) and artificial intelligence-based diagnostic models offers potential for improved precision in trauma cardiology. Full article

Introduction: Ventricular arrhythmias may lead to sudden cardiac death and, when occurring during the recovery phase after exercise testing, are associated with increased cardiovascular risk. Aim: To investigate the association between psychosocial stress and the risk of premature ventricular contractions (PVCs) during the recovery phase after treadmill testing in asymptomatic individuals. Methods: A total of 282 asymptomatic adults underwent treadmill testing. Participants were categorized into a stress-present group (+S, n = 176) or a stress-absent group (−S, n = 106) based on their self-reported psychosocial stress levels. Inclusion criteria included exercising for at least 6 min and reaching at least 85% of the age-predicted maximum heart rate. Exclusion criteria comprised pre-exercise VAs, unreadable ECGs, chronic medication use, systolic blood pressure ≥180 mmHg, and diastolic blood pressure ≥110 mmHg. This study was registered on ClinicalTrials.gov (NCT05987891). Results: Compared to the −S group, the +S group had a higher body mass index (BMI) (p = 0.0025); 26.5 (23.9; 29.0) and larger waist circumference (p = 0.0001); 95 (86; 103), and reported lower physical activity levels (p = 0.0004). Notably, only psychosocial stress and BMI were statistically associated with PVCs during the recovery phase, immediately following the stress test. For each 1 kg/m² increase in BMI, the risk of PVCs decreased by 9%. Participants reporting psychosocial stress had a 9.03-fold higher risk of PVCs compared to those who did not report stress. Conclusions: Self-reported psychosocial stress significantly increases the risk of PVC occurrence during the recovery phase of treadmill exercise testing in asymptomatic individuals. These findings may support the development of improved PVC detection strategies and enhance cardiovascular risk assessment in clinical settings. Full article

Since the COVID-19 pandemic, the utilization of transthoracic ultrasonography (TTU) in the evaluation of pulmonary field artefacts has become standard practice among clinicians. However, there is a considerable lack of knowledge regarding the assessment of diaphragm mobility in the context of various lung diseases. Although numerous conditions are known to affect diaphragm mobility, including neurological, cardiovascular, and infectious diseases, it appears that pulmonary diseases may also limit the mobility of this major respiratory muscle. Despite the evidence of diaphragm mobility disorders in patients diagnosed with lung cancer, there is a discrepancy in the literature regarding the function of the diaphragm in individuals with chronic obstructive pulmonary disease (COPD). A shared aetiological factor frequently results in the co-occurrence of the aforementioned diseases. It is, however, possible to detect patients whose obstructive airway disease is caused only by the compression of infiltrative and nodal lesions rather than COPD. Bilateral TTU of diaphragmatic mobility in correlation with other available pulmonary function tests and radiological imaging may prove to be a valuable approach to isolating lung cancer patients with COPD overdiagnosis. Conversely, the overdiagnosis of COPD has been implicated in the potentially unnecessary and harmful use of inhaled medications with their adverse effects (e.g., cardiac arrhythmias, limb tremor, cough, and pneumonia), the failure to decrease obstruction in cases of other lung disorders, and the potential to contribute to the delayed diagnosis of the underlying condition responsible for the respiratory symptoms. This paper aims to provide a comprehensive overview of the utilization of ultrasound in the evaluation of diaphragm movement impairments for the detection of obstructions while also delineating the underlying limitations of this technique. Moreover, we propose a diagnostic algorithm for the purpose of excluding unilateral obstruction resulting from infiltrative neoplastic masses based on the ultrasound assessment of diaphragmatic mobility. Full article

ECG monitoring is central to the early detection of cardiac abnormalities. We compared 28 manually selected signal-specific features with 159 automatically extracted signal-independent descriptors from the MIT BIH Arrhythmia Database. ANOVA reduced features to the 10 most informative attributes, which were evaluated alone and in combination with the signal-specific features using Random Forest, SVM, and deep neural networks (CNN, RNN, ANN, LSTM) under an interpatient 80/20 split. Merging the two feature groups delivered the best results: a 128-layer CNN achieved 100% accuracy. Power profiling revealed that deeper models improve accuracy at the cost of runtime, memory, and CPU load, underscoring the trade-off faced in edge deployments. The proposed hybrid feature strategy provides beat-by-beat classification with a reduction in the number of features, enabling real-time ECG screening on wearable and IoT devices. Full article

Early detection and the rise of targeted cancer treatment have led to increased overall survival and decreased mortality among cancer patients. As the cancer survivor population ages, there is an increased risk for cardiovascular disease due to pre-existing comorbidities, deconditioning during therapy, or the natural progression of aging. Furthermore, with emerging oncologic therapies, there is an increased recognition of their potential cardiovascular toxicities. Indeed, heart disease is the leading cause of death in cancer survivors, which may reflect upon both the success of novel oncologic therapies and their potential cardiovascular toxicities. This recognition has driven the development of cardio-oncology, a multi-disciplinary field that involves collaboration between hematologists, oncologists, and cardiologists to screen, prevent, and manage cardiovascular disease in cancer patients and cancer survivors. The field focuses on early cardiovascular detection and prevention for these patients before, during, and after their oncologic treatment. As oncologic therapies evolve and our knowledge of short- and long-term adverse cardiovascular effects grows, it is critical for physicians to identify those at risk for increased morbidity and mortality, while also balancing the importance of their oncologic treatment plan. Multimodality cardiac imaging is the crux of the diagnosis and surveillance of these patients within cardio-oncology, and includes echocardiography, nuclear, computed tomography (CT), and cardiac magnetic resonance (CMR). Cardiac imaging is essential to establish the baseline function and assess various cardiotoxicities, including left ventricular dysfunction, heart failure, atherosclerosis, vascular injury, and arrhythmias. This review will discuss common oncologic therapies and their cardiotoxic profiles, the cardiac multimodality imaging modalities used in cardio-oncology, and the various approaches for the diagnosis and surveillance of this population. Full article

Background and Clinical Significance: Mitral valve prolapse (MVP) is commonly benign, but may result in life-threatening arrhythmias and sudden cardiac death (SCD). Mitral annular disjunction (MAD) often coexists with mitral valve prolapse (MVP) and has been implicated in the development of ventricular arrhythmias through myocardial stretch and fibrosis. Case Presentation: Here, we present a case that highlights the diagnostic value of multimodal imaging in evaluating ventricular ectopy in the context of MVP and MAD. A 72-year-old male presented to the cardiology clinic with palpitations and fatigue, compounded by an arrhythmia identified by his Apple Watch. Holter monitoring revealed premature ventricular contractions (PVCs), with cardiac magnetic resonance imaging (CMR) demonstrating MAD and basal inferolateral scarring. Despite minimal symptoms and normal echocardiographic imaging, CMR findings highlight the utility of advanced cardiovascular imaging in patients with newly detected ventricular arrhythmias. Conclusion: This case highlights the importance of integrating consumer wearables and advanced imaging in evaluating ventricular ectopy and its evolving role in risk stratification for patients with MVP, even in the absence of overt symptoms. Full article

Background: Estrogens play a protective role during the early stages of life. However, endogenous 17β-estradiol (E2) can accelerate atherosclerosis progression. Aim: The purpose of this study was to test for the significance of the sex-specific associations of gonadal hormones with the extent of the inflammatory response, myocardial damage, and ventricular arrhythmia risk in acute myocardial infarction (MI). Materials and Methods: Study design: single-center cohort study. Blood samples for the assessment of sex steroids (E2, total testosterone [T]), oxidized low-density lipoproteins, high-sensitivity C-reactive protein (CRP), white blood cell (WBC) counts, and cardiac enzymes were collected 48 h after the onset of symptoms (and within 6 h after PCI) from 111 patients (37% women) with acute MI. Coronary disease severity, left ventricular systolic function (LV), and indices of ventricular repolarization were assessed using coronary angiography, echocardiography, and a conventional electrocardiogram, respectively. Results: In men with acute MI, peak cardiac enzyme levels were predicted by post-percutaneous coronary intervention (PCI) E2 plasma levels, peak WBC count, and peak CRP plasma levels. T levels and the E2/T ratio were associated with post-PCI CRP in these men. For women, peak WBC count was a marker of highest testosterone, and only WBC count was a significant indicator of myocardial injury extent. The incidence of acute ventricular tachycardia detected in AMI was significantly associated with left ventricular ejection fraction and with peak WBC count (as a tendency) regardless of sex. A longer duration of cardiac repolarization prior to PCI was predicted by lower ejection fractions in men and by age, CRP, and testosterone levels in female patients. Conclusions: During acute MI, elevated endogenous estradiol levels in men and increased leukocytes in women indicate acute myocardial damage. Post-PCI plasma inflammatory markers are sex-specific confounding factors for acute endogenous E2 levels, T levels, and the E2/T ratio. LV systolic function in men and, characteristically, the acute inflammatory response and testosterone levels in women are predictors of longer ventricular repolarization and arrhythmia risk. Full article

Cardiac tumors represent rare neoplasms, but they include a very wide range of neoplasia—first primary benign and malignant cardiac tumors, then cardiac metastases, with these latter ones being far more common in adulthood. These diagnoses may be challenging because of frequently non-specific signs and symptoms; for example, their clinical management may be difficult because of the site and because of possible hemodynamic or arrhythmogenic consequences, independent from their biology. Cardiac tumors may be asymptomatic and incidentally diagnosed, or they may cause heart failure, life-threatening arrhythmias, or even sudden cardiac death. Although they may still represent a post-mortem finding, the evolution and the larger use of cardiac imaging tools, initially echocardiography, has progressively and significantly increased their in vivo detection. Magnetic resonance imaging and computed tomography may give crucial information as to the composition and localization of cardiac masses, useful for investigating them and for planning surgery. Histology is mandatory for the definite and differential diagnosis of the cardiac masses, for assessing predictive factors in malignancies, and for then establishing the appropriate management of patients. Modern techniques applied to histology, including immunohistochemistry and molecular biology, may be required to characterize cardiac tumors, to properly classify them and to assess predictive and/or prognostic markers. Surgical procedures, including minimally invasive surgery, have also dramatically evolved in the last decades, allowing adequate treatment in most cardiac tumors. Finally, biopsy may be useful in selected cases, particularly when radical surgery is not feasible, and histological diagnosis is fundamental for other possible therapeutic approaches. The scope of this review covers advancements in the imaging diagnosis, histology, and treatment of primary and secondary cardiac tumors. Full article

Background: Artificial intelligence (AI) is poised to significantly impact the future of cardiology and electrophysiology, offering new tools to interpret complex datasets, improve diagnosis, optimize clinical workflows, and personalize therapy. ChatGPT-4o, a leading AI-based language model, exemplifies the transformative potential of AI in clinical research, medical education, and patient care. Aim and Methods: In this paper, we present an exploratory dialogue with ChatGPT to assess the role of AI in shaping the future of cardiology, with a particular focus on arrhythmia management and cardiac electrophysiology. Topics discussed include AI applications in ECG interpretation, arrhythmia detection, procedural guidance during ablation, and risk stratification for sudden cardiac death. We also examine the risks associated with AI use, including overreliance, interpretability challenges, data bias, and generalizability. Conclusions: The integration of AI into cardiovascular care offers the potential to enhance diagnostic accuracy, tailor interventions, and support decision-making. However, the adoption of AI must be carefully balanced with clinical expertise and ethical considerations. By fostering collaboration between clinicians and AI developers, it is possible to guide the development of reliable, transparent, and effective tools that will shape the future of personalized cardiology and electrophysiology. Full article

Background and Objectives: Nonischemic cardiomyopathies comprise a wide spectrum of heart muscle disorders characterized by different morphological, functional, and tissue abnormalities. Cardiovascular magnetic resonance (CMR) represents the gold standard imaging modality for assessing cardiac morphology, systolic function, and tissue characterization, thereby aiding in early diagnosis, precise phenotyping, and tailored treatment. The aim of this review is to provide an up-to-date overview of CMR techniques for studying myocardial perfusion and their applications to nonischemic cardiomyopathy, not only to rule out an underlying ischemic aetiology but also to investigate the pathophysiological characteristics of microcirculatory dysfunction in these patients. Materials and Methods: We performed a structured review of the literature focusing on first-pass gadolinium perfusion sequences, stress protocols, and emerging pixel-wise perfusion mapping approaches. Studies were selected to illustrate the methods for image acquisition, post-processing, and quantification of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR), as well as to highlight associations with clinical endpoints. Results: First-pass CMR perfusion imaging reliably detects diffuse and regional microvascular dysfunction across cardiomyopathies. Semi-quantitative parameters (e.g., upslope, MPRI) and quantitative MBF mapping (mL/g/min) have demonstrated that impaired perfusion correlates with disease severity, extent of fibrosis, and adverse outcomes, including heart failure hospitalization, arrhythmias, and mortality. Novel automated pixel-wise mapping enhances reproducibility and diagnostic accuracy, distinguishing coronary microvascular dysfunction from balanced three-vessel disease. Microvascular dysfunction—present in approximately 50–60% of dilated cardiomyopathy (DCM), 40–80% of hypertrophic cardiomyopathy (HCM), and >95% of cardiac amyloidosis (CA) patients—has emerged as a key driver of adverse outcomes. Perfusion defects appear early, often preceding overt hypertrophy or fibrosis, and provide incremental prognostic value beyond conventional CMR metrics. Conclusions: CMR represents a powerful tool for detecting myocardial perfusion abnormalities in nonischemic cardiomyopathies, improving phenotyping, risk stratification, and personalized management. Further standardization of quantitative perfusion techniques will facilitate broader clinical adoption. Full article