Search Results (106)

Atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF) coexist in 40–60% of cases and mutually reinforce each other through adverse electrical, cellular, and functional remodelling. There is considerable overlap in signs and symptoms, and diagnosis may be challenging due to nonspecific clinical presentations and chronic course. AF is clearly linked with worsening morbidity and mortality in HFpEF with higher rates of HF hospitalizations, HF progression, stroke, systemic embolism, and all-cause death. Optimal management of HFpEF-AF patients requires aggressive treatment of comorbidities and risk factor modification. Sodium-glucose cotransporter 2 (SGLT2) inhibitors have demonstrated consistent benefit with respect to HF hospitalizations, symptoms and exercise haemodynamics, and potential to reduce AF burden. Gastric inhibitory polypeptide (GIP)/glucagon-like peptide-1 (GLP-1) agonists, mineralocorticoid receptor antagonists (MRAs), angiotensin receptor-neprilysin inhibitors (ARNIs), and statins may provide benefit in selected phenotypes, though evidence remains heterogeneous. A rhythm control strategy in the early clinical course of HFpEF might be a reasonable strategy to improve symptoms and delay both AF and HFpEF disease progression. Catheter ablation appears to improve exercise haemodynamics and quality of life, and observational data suggest it may reduce mortality and HF hospitalization, though current evidence is inconsistent and not yet definitive. Emerging device-based and molecular therapies could represent promising avenues for future research. Overall, early detection of AF, comprehensive risk-factor modification, and tailored rhythm-control strategies are central to improving outcomes in the HFpEF-AF overlap syndrome. Full article

Background: Device-detected subclinical atrial fibrillation (SCAF) and atrial high-rate episodes (AHRE) are increasingly recognized in patients with cardiac implantable electronic devices and through long-term rhythm monitoring. Although often asymptomatic, these episodes are associated with a higher risk of clinical atrial fibrillation (AF), stroke, and heart failure. Aims: This narrative review summarizes clinical, electrocardiographic, echocardiographic, and circulating biomarkers associated with the development and progression of device-detected SCAF/AHRE. Methods: We performed a comprehensive search of PubMed, Embase, and Scopus using combinations of the terms “subclinical atrial fibrillation”, “atrial high-rate episodes”, “device-detected AF”, “predictive factors”, “P-wave morphology”, “echocardiographic parameters”, “left atrial strain”, and “biological markers”. We included English-language-only studies of patients with cardiac implantable electronic devices or long-term monitoring and reporting incident SCAF/AHRE or AF as outcomes, published in the last 10 years. Results: Older age, high body mass index, heart failure, obstructive sleep apnea, and C₂HEST score are consistently associated with SCAF. On-surface electrocardiogram (ECG) and device electrograms, prolonged and dispersed P-wave indices, low atrial sensing amplitude, and specific pacing configurations, particularly right ventricular apical pacing with wide QRS, predict incident and longer-lasting AHRE. Echocardiographic markers of atrial cardiomyopathy, including increased left atrial volume and impaired atrial strain, together with indices of left ventricular diastolic dysfunction, further refine risk. Among circulating biomarkers, galectin-3 and high-sensitivity C-reactive protein show the most reproducible associations with incident AHRE. Conclusions: A multiparametric approach combining clinical profile, ECG features, advanced echocardiography, and selected biomarkers may improve identification of patients at risk for device-detected SCAF. Further prospective studies are needed to define risk thresholds that justify intensified rhythm surveillance and early initiation of anticoagulation or rhythm control strategies, especially in AHRE shorter than 24 h. Full article

Background/Objectives: Wearable electrocardiogram (ECG) monitoring has become increasingly important in detecting atrial fibrillation (AF) and subclinical arrhythmias by addressing diagnostic gaps inherent to intermittent or asymptomatic presentations. In contemporary clinical practice, two major types of wearable ECG monitors—patch-type and lead-type—are widely used, each with distinct advantages and limitations. This study aims to compare these modalities and evaluate their respective strengths and constraints in real-world settings. Methods: We retrospectively analyzed 639 consecutive outpatients (mean age 61.7 ± 14.5 years; 56.7% male) who underwent wearable ECG monitoring between March 2022 and October 2023. Patients were stratified into patch-type (n = 466; 72.9%) and lead-type (n = 173; 27.1%) groups. Baseline characteristics were comparable. Indications, monitoring duration, arrhythmia detection, and noise rates were assessed. Results: Baseline characteristics did not differ significantly between the two groups. Lead-type monitoring was often prescribed for symptomatic patients (87.9% vs. 75.8%; p = 0.001), Symptomatic patients were older than asymptomatic patients (p = 0.040), whereas the proportion of males was higher in the asymptomatic group (p < 0.001). AF detection rates were comparable between the two groups (24.0% vs. 24.9%; p = 0.911). Patch-type monitoring achieved significantly longer recording duration (p < 0.001) and higher pause event detection (p = 0.004), but at the cost of increased noise burden (p < 0.001). Conclusions: Both patch-type and lead-type wearable ECGs are clinical applicable for arrhythmia surveillance in real-world practice. While AF detection rates were similar, the patch-type monitoring provided more extended observation periods and enhanced pause detection, though accompanied by a higher noise burden. These findings suggest that device selection should be individualized based on patient symptoms, monitoring goals, and tolerability. This study provides practical insights for optimizing wearable ECG use in routine practice. Full article

Background: Artificial intelligence (AI) is rapidly transforming clinical medicine by enabling earlier disease detection, personalized risk stratification, precision diagnostics, and optimized therapeutic decision-making across multiple specialties. Methods: This narrative review synthesizes the most recent evidence from prospective randomized controlled trials, large cohort studies, and real-world implementations of AI in cardiology, pulmonology, neurology, hepatology, pancreatic diseases, and other key areas of internal medicine. Studies were selected based on clinical impact, external validation, and regulatory approval status where applicable. Results: AI systems now outperform traditional clinical tools in numerous high-stakes applications: >88% freedom from atrial fibrillation at 1 year with AI-guided ablation, noninferior stent optimization versus OCT guidance, >95% sensitivity for atrial fibrillation and low ejection fraction detection on single-lead ECG, substantial increases in adenoma detection rate and melanoma triage accuracy, automated pancreatic cancer detection on routine CT with 89–90% sensitivity, and significant improvements in palliative care consultation rates and post-PCI outcomes using AI-supported telemedicine. Over 850 FDA-cleared AI devices exist as of November 2025, with cardiology and radiology dominating clinical adoption. Conclusions: AI has transitioned from experimental to clinically indispensable in multiple specialties, delivering measurable reductions in mortality, morbidity, hospitalizations, and healthcare resource utilization. Remaining challenges include external validation gaps, bias mitigation, and the need for large-scale prospective trials before universal implementation. Full article

Atrial fibrillation (AF) is the most common sustained arrhythmia and a growing global health burden, yet conventional monitoring with Holter devices, event recorders and implantable loop recorders often fails to adequately capture recurrence. Rapid advances in digital health, wearable biosensors and artificial intelligence (AI) have transformed consumer smartwatches and wearables into potential clinical tools capable of continuous, real-world rhythm surveillance. This narrative review synthesizes contemporary evidence on smartwatch-based AF monitoring, spanning core technologies—photoplethysmography, single-lead electrocardiography and AI fusion algorithms—and validation studies across post-ablation follow-up. Compared with traditional modalities, smartwatch-based AF monitoring demonstrates improved detection of AF recurrence, enhanced characterization of AF burden, symptom–rhythm correlation, and greater patient engagement. At the same time, key limitations are critically examined, including motion artifacts, false-positive alerts, short recording windows, adherence dependence, digital literacy and access gaps, as well as unresolved issues around regulation, interoperability and data privacy. By integrating engineering advances with guideline-directed care pathways, smartwatch-based AF monitoring holds promise to complement, rather than immediately replace, established diagnostic tools and to enable more proactive, individualized AF management. Future work must focus on robust clinical validation, equitable implementation and clear regulatory frameworks to safely scale these technologies. Full article

Background: Wearable electrocardiography (ECG) devices such as smartwatches offer a novel means for detecting cardiac arrhythmias, particularly atrial fibrillation (AF), and ST-segment abnormalities. Their role in complementing or replacing traditional ECG methods is being increasingly investigated. Objective: To evaluate the diagnostic performance (sensitivity, specificity) of wearable ECG devices in detecting AF and ST-segment changes, compared to 12-lead ECG as the gold standard. Methods: A systematic search was performed in PubMed, Scopus, and additionally, the SpringerLink platform was consulted up to June 2025, targeting open-access, English-language clinical studies from the last five years. Inclusion criteria: adult population, use of a wearable ECG device, 12-lead ECG comparator, and diagnostic accuracy reporting. Out of 145 records, 5 studies met the inclusion criteria. The systematic review protocol was not prospectively registered in PROSPERO due to the limited number of available studies and the exploratory nature of the topic, which focused on the most recent clinical evaluations of wearable ECG devices. However, the review strictly adhered to the PRISMA 2020 guidelines for systematic reviews to ensure methodological transparency and reproducibility. Results: Five studies encompassing a total of 1133 participants were incorporated into the analysis. Devices evaluated included Apple Watch (Series 4–6), KardiaMobile 6L, FibriCheck, Preventicus, and HUAMI dynamic ECG. Sensitivity ranged from 83% to 100%, and specificity from 79% to 100%. Algorithm improvements and repeated measurements significantly reduced inconclusive recordings. Multichannel ECG methods using smartwatches showed high agreement with 12-lead ECG in ST-elevation myocardial infarction detection. Conclusions: Wearable ECG devices demonstrate high diagnostic performance for AF and ST-segment abnormalities, especially in supervised environments. However, inconclusive recordings and algorithm limitations remain barriers to widespread clinical use. Real-world validation and algorithm refinement are needed for broader adoption. Full article

Background: Atrial high-rate episodes (AHREs) detected by pacemakers are linked to increased stroke risk. The predictive value of high-sensitivity cardiac troponin I (hs-cTnI) for AHREs in pacemaker patients remains uncertain. This study evaluated baseline hs-cTnI as a predictor for new-onset AHREs in this population. Methods: This prospective cohort study enrolled 272 patients undergoing permanent pacemaker implantation. We excluded 40 patients with pre-existing atrial fibrillation (AF), leaving a total of 232 patients (mean age 63.7 years; 53.4% male) in the at-risk cohort. Baseline hs-cTnI and NT-proBNP were measured. The primary endpoint was new-onset AHREs (>175 bpm), detected by device interrogation over a median follow-up of 12 months. Results: New-onset AHREs occurred in 65 (28.0%) patients. Contrary to our hypothesis, baseline hs-cTnI levels did not differ significantly between patients who developed AHREs and those who did not (median 16.5 vs. 15.7 pg/mL, p = 0.148). Multivariable Cox regression confirmed that neither hs-cTnI nor NT-proBNP were independent predictors. Instead, Sick Sinus Syndrome (HR 2.10, p < 0.001), heart failure (HR 1.78, p = 0.010), and Left Atrial Diameter (HR 1.15, p = 0.006) were significant independent predictors. Conclusions: In this high-risk pacemaker cohort, baseline hs-cTnI and NT-proBNP did not predict short-term new-onset AHREs. Established electrical and structural substrates appear to be the overwhelming drivers of arrhythmia in this specific population. Full article

Atrial fibrillation (AF) is the most common type of heart rhythm disorder worldwide. Early recognition of brief episodes of atrial fibrillation can provide important diagnostic information and lead to prompt treatment. AF is mainly characterized by an irregular heartbeat. Today, many personal devices such as smartphones, smartwatches, smart rings, or small wearable medical devices can detect heart rhythm. Sensors can acquire different types of heart-related signals and extract the sequence of inter-beat intervals, i.e., the instantaneous heart rate. Various algorithms, some of which are very complex and require significant computational resources, are used to recognize AF based on inter-beat intervals (RR). This study aims to verify the possibility of using neural networks algorithms directly on a microcontroller connected to sensors for AF detection. Sequences of 25, 50, and 100 RR were extracted from a public database of electrocardiographic signals with annotated episodes of atrial fibrillation. A custom 1D convolutional neural network (1D-CNN) was designed and then validated via a 5-fold subject-wise split cross-validation scheme. In each fold, the model was tested on a set of 3 randomly selected subjects, which had not previously been used for training, to ensure a subject-independent evaluation of model performance. Across all folds, all models achieved high and stable performance, with test accuracies of 0.963 ± 0.031, 0.976 ± 0.022, and 0.980 ± 0.023, respectively, for models using 25 RR, 50 RR, and 100 RR sequences. Precision, recall, F1-score, and AUC-ROC exhibited similarly high performance, confirming robust generalization across unseen subjects. Performance systematically improved with longer RR windows, indicating that richer temporal context enhances discrimination of AF rhythm irregularities. A complete Edge AI prototype integrating a low-power ECG analog front-end, an ARM Cortex M7 microcontroller and an IoT transmitting module was utilized for realistic tests. Inferencing time, peak RAM usage, flash usage and current absorption were measured. The results obtained show the possibility of using neural network algorithms directly on microcontrollers for real-time AF recognition with very low power consumption. The prototype is also capable of sending the suspicious ECG trace to the cloud for final validation by a physician. The proposed methodology can be used for personal screening not only with ECG signals but with any other signal that reproduces the sequence of heartbeats (e.g., photoplethysmographic, pulse oximetric, pressure, accelerometric, etc.). Full article

The coronavirus disease 2019 (COVID-19) pandemic has revealed a close and multifaceted relationship between viral infection, systemic inflammation, and cardiovascular health. Among the cardiac complications of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), atrial fibrillation (AF)—especially new-onset atrial fibrillation (NOAF)—has emerged as a major determinant of disease severity and prognosis. Clinical studies and meta-analyses show that 5–10% of hospitalized COVID-19 patients develop AF, with markedly higher rates in critically ill individuals. Both pre-existing and NOAF are independently associated with increased risks of intensive care admission, mechanical ventilation, thromboembolic events, and mortality. The underlying mechanisms involve a combination of cytokine-mediated inflammation, endothelial dysfunction, microvascular injury, and dysregulation of the renin–angiotensin–aldosterone system (RAAS). Viral downregulation of angiotensin-converting enzyme 2 (ACE2) receptors contributes to myocardial fibrosis, while hypoxia, oxidative stress, and autonomic imbalance further promote electrical remodeling and arrhythmogenesis. Post-infectious studies indicate that atrial structural changes and autonomic dysfunction may persist for months, predisposing survivors to recurrent arrhythmias. Technological advances in telecardiology and digital medicine have provided new tools for early detection and long-term monitoring. Wearable electroencephalography (ECG) devices, implantable loop recorders (ILRs), and artificial intelligence (AI)-based diagnostic algorithms enable continuous rhythm surveillance and individualized management, improving outcomes in post-COVID patients. This review summarizes current evidence on the epidemiology, pathophysiology, clinical implications, and monitoring strategies of AF in COVID-19. It underscores the importance of integrating telemedicine and AI-assisted diagnostics into cardiovascular care to mitigate the long-term arrhythmic and systemic consequences of SARS-CoV-2 infection. Full article

Atrial Fibrillation (AF) is a common yet often undiagnosed cardiac arrhythmia with serious clinical consequences, including increased risk of stroke, heart failure, and mortality. In this work, we present a novel Embedded Edge system performing real-time AF detection on a low-power Microcontroller Unit (MCU). Rather than relying on full Electrocardiogram (ECG) waveforms or cloud-based analytics, our method extracts Heart Rate Variability (HRV) features from RR-Interval (RRI) and performs classification using a compact Long Short-Term Memory (LSTM) model optimized for embedded deployment. We achieved an overall classification accuracy of 98.46% while maintaining a minimal resource footprint: inference on the target MCU completes in 143 ± 0 ms and consumes 3532 ± 6 μJ per inference. This low power consumption for local inference makes it feasible to strategically keep wireless communication OFF, activating it only to transmit an alert upon AF detection, thereby reinforcing privacy and enabling long-term battery life. Our results demonstrate the feasibility of performing clinically meaningful AF monitoring directly on constrained edge devices, enabling energy-efficient, privacy-preserving, and scalable screening outside traditional clinical settings. This work contributes to the growing field of personalised and decentralised cardiac care, showing that Artificial Intelligence (AI)-driven diagnostics can be both technically practical and clinically relevant when implemented at the edge. Full article

Background/Objectives: Current guidelines recommend 24–48 h Holter for risk stratification and atrial fibrillation (AF) screening in hypertrophic cardiomyopathy (HCM). However, the limited duration of this approach may not provide optimal sensitivity. In addition, extended ECG monitoring has been demonstrated to be more effective in detecting arrhythmias in other clinical entities. We aimed to assess the utility of extended ECG monitoring for 30 days in a non-high-risk cohort of HCM patients. Methods: We conducted a prospective multicentre study with 113 non-high-risk HCM patients who underwent 30-day ECG monitoring with a dedicated device. We compared the detection of relevant arrhythmias (AF, atrial flutter, and non-sustained ventricular tachycardia) during 30-day monitoring with the findings observed during the first 24 h. Results: Extended ECG monitoring detected relevant arrhythmias in 63.7% of patients, compared with 12.4% during the first 24 h (p < 0.001). This difference was mainly driven by non-sustained ventricular tachycardia (NSVT) (61.1% vs. 8.9%, p < 0.001). Atrial fibrillation episodes were detected in 10.6% of patients after completing prolonged monitoring vs. 6.2% during the first 24 h (p = 0.066). Extended monitoring resulted in a reclassification of 21.2% of patients to a higher sudden cardiac death (SCD) risk category using the HCM-SCD calculator. Conclusions: Extended ECG monitoring significantly enhances the detection of arrhythmias in HCM. Using this technique, NSVT were detected in most patients of a non-high-risk HCM cohort. Further investigation is warranted to determine the role of extended monitoring in SCD risk stratification and AF screening. Full article

Background and Clinical Significance: Early lead failure after dual-chamber pacemaker implantation is rare but clinically significant, particularly when associated with thromboembolic complications. Technical pitfalls at the time of implantation, such as suture fixation without protective sleeves, may be predisposed to premature lead damage and abrupt device malfunction. This case highlights the role of device interrogation in diagnosing arrhythmia-related stroke, the challenges of reimplantation in the setting of venous occlusion and anticoagulation, and the value of leadless pacing as a safe rescue strategy. Case Presentation: A 78-year-old man with a history of complete atrioventricular block underwent dual-chamber pacemaker implantation one year earlier. He presented to the emergency department with acute aphasia, right-sided hemiparesis, and facial asymmetry. Stroke was diagnosed, and new-onset atrial fibrillation was documented. Device interrogation revealed an abrupt fall in lead impedance followed by a sharp rise consistent with lead insulation failure and premature battery depletion. Fluoroscopy demonstrated multiple focal narrowings of the leads and complete left subclavian vein occlusion, making conventional transvenous reimplantation unfeasible, while extraction was judged high risk. Right-sided reimplantation was avoided due to hemorrhagic risk under anticoagulation. A leadless pacemaker was implanted successfully in the apico-septal region of the right ventricle via ultrasound-guided femoral access. Hemostasis was secured with a figure-of-8 suture fixed inside a 3-way tap, providing constant compression and preventing hematoma. At two-months follow-up, device function was stable and neurological recovery was favorable (mRS = 2). Conclusions: This case underscores how multiple adverse factors—stroke, arrhythmia detection, early device failure, venous occlusion, and anticoagulation—may converge in a single patient, and demonstrates leadless pacing as a safe and effective rescue strategy in such complex scenarios. Full article

Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of ≥95% and an atrial fibrillation detection sensitivity/specificity of ≥96%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools. Full article

Artificial intelligence (AI) has increasingly become a transformative tool in cardiology, particularly in diagnosing and managing atrial fibrillation (AF), the most prevalent cardiac arrhythmia. This review aims to critically assess and synthesize current AI methodologies and their clinical relevance in AF diagnosis, risk prediction, and therapeutic guidance. It systematically evaluates recent advancements in AI methodologies, including machine learning, deep learning, and natural language processing, for AF detection, risk stratification, and therapeutic decision-making. AI-driven tools have demonstrated superior accuracy and efficiency in interpreting electrocardiograms (ECGs), continuous monitoring via wearable devices, and predicting AF onset and progression compared to traditional clinical approaches. Deep learning algorithms, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have revolutionized ECG analysis, identifying subtle waveform features predictive of AF development. Additionally, AI models significantly enhance clinical decision-making by personalizing anticoagulation therapy, optimizing rhythm versus rate-control strategies, and predicting procedural outcomes for catheter ablation. Despite considerable potential, practical adoption of AI in clinical practice is constrained by challenges including data privacy, explainability, and integration into clinical workflows. Addressing these challenges through robust validation studies, transparent algorithm development, and interdisciplinary collaborations will be crucial. In conclusion, AI represents a paradigm shift in AF management, promising improvements in diagnostic precision, personalized care, and patient outcomes. This review highlights the growing clinical importance of AI in AF care and provides a consolidated perspective on current applications, limitations, and future directions. Full article

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