BACKGROUND AND OBJECTIVE

COVID-19 has been associated with cardiac injury, often detectable through electrocardiographic (ECG) changes. This study seeks to characterize the cardiovascular and electrocardiographic profiles of adult patients diagnosed with COVID-19.

This study included adult patients with confirmed COVID-19 from June 2021 to June 2022. Clinical profiles and 12-lead ECG tracings were obtained from electronic medical records and reviewed independently by three cardiologists. Descriptive analysis was performed to summarize the cardiovascular and electrocardiographic findings in this population.

The study included 998 COVID-19 patients (mean age: 50 years; 53.7% male). The most common comorbidities were hypertension, diabetes, and dyslipidemia. A majority (31.36%) presented with severe COVID-19 infection. The most frequent significant ECG abnormalities observed at admission were sinus tachycardia (22.8%), and atrial fibrillation (11.02%). Additional ischemic findings included ST segment depression (2.91%), T-wave inversion (1.70%), and ST segment elevation (2.71%).

The baseline ECG findings among COVID-19 patients were predominantly normal; however, significant abnormalities were also identified. The most frequent abnormalities included sinus tachycardia, atrial fibrillation, and ischemic changes, all of which may have clinical implications.

INTRODUCTION: Arrhythmias, especially atrial fibrillation (AF) and ventricular arrhythmias, are independent risk factors of mortality in patients with ischaemic heart disease (IHD). While there is a growing body of evidence that suggests an association between obstructive sleep apnoea (OSA) and cardiac arrhythmias, evidence on this relationship in patients with IHD has been scant and inconsistent. We hypothesised that in patients with IHD, severe OSA is associated with an increased risk of nocturnal arrhythmias. METHODS: We studied 103 consecutive patients with IHD who underwent an overnight polysomnography. Exposed subjects were defined as patients who had an apnoea-hypopnoea index (AHI) ≥30/h (severe OSA), and nonexposed subjects were defined as patients who had an AHI <30/h (nonsevere OSA). All electrocardiograms (ECGs) were interpreted by the Somte ECG analysis software and confirmed by a physician blinded to the presence or absence of exposure. Arrhythmias were categorised as supraventricular and ventricular. Arrhythmia subtypes (ventricular, atrial and conduction delay) were analysed as dichotomous outcomes using multiple logistic regression models. RESULTS: Atrial fibrillation and AF/flutter (odds ratio 13.5, 95% confidence interval 1.66-109.83; P = 0.003) were found to be more common in the severe OSA group than in the nonsevere OSA group. This association remained significant after adjustment for potential confounders. There was no significant difference in the prevalence of ventricular and conduction delay arrhythmias between the two groups. CONCLUSION In patients with IHD, there was a significant association between severe OSA and nocturnal AF/flutter. This underscores the need to evaluate for OSA in patients with IHD, as it may have important implications on clinical outcomes.
Humans ; Sleep Apnea, Obstructive/diagnosis* ; Atrial Fibrillation/diagnosis* ; Male ; Female ; Middle Aged ; Polysomnography ; Electrocardiography ; Myocardial Ischemia/complications* ; Aged ; Risk Factors ; Logistic Models

The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.
Humans ; Atrial Fibrillation/diagnosis* ; Machine Learning ; Deep Learning ; Risk Assessment/methods*

BACKGROUND AND OBJECTIVE: Hypertension (HT) and atrial fibrillation (AF) are highly prevalent cardiovascular conditions that frequently coexist. Coronary artery disease (CAD) is a major global cause of mortality. The co-occurrence of HT, AF, and CAD presents significant management challenges. This study aims to explore the clinical characteristics and risk factors associated with CAD in patients with HT and persistent AF (HT-AF). METHODS: In this retrospective cross-sectional study, data were collected from 384 hospitalized HT-AF patients at the People's Liberation Army General Hospital between January 2010 and December 2019. CAD diagnosis was confirmed by coronary angiography or computed tomography angiography. Clinical characteristics and comorbidities were compared between patients with and without CAD. Multivariate logistic regression analyses were performed to identify independent risk factors associated with CAD development. RESULTS: The prevalence of CAD among HT-AF patients was 66.41% (255/384). Cardiovascular complications, particularly heart failure (44.7% vs 25.6%, P < 0.05), were significantly more prevalent in the CAD group than in the non-CAD group. Only age was identified as an independent risk factor for CAD (adjusted OR: 1.047; 95% CI: 1.022-1.073; P = 0.000). Of all HT-AF patients, 54.7% had a CHA₂DS₂-VASc score of ≥4, indicating high stroke risk. There was a slightly higher anticoagulant usage rate in the CAD group than those without CAD (8.6% vs 4.7%, P = 0.157), and the overall anticoagulant usage remained low. CONCLUSION: There is a high prevalence of CAD among hospitalized HT-AF patients, among whom age is the sole independent risk factor for CAD. Despite a high stroke risk, the utilization of oral anticoagulants is alarmingly low.
Humans ; Atrial Fibrillation/epidemiology* ; Coronary Artery Disease/etiology* ; Hypertension/epidemiology* ; Male ; Female ; Risk Factors ; Middle Aged ; Retrospective Studies ; Cross-Sectional Studies ; Aged ; Prevalence

OBJECTIVES: To analyze the efficacy and safety of concomitant left atrial appendage clipping during heart valve surgery for valvular heart disease patients with atrial fibrillation. METHODS: Fifty-eight patients who underwent concomitant left atrial appendage clipping during cardiac valve surgery in the Second Affiliated Hospital of Nanchang University from January 2017 to June 2023 were included in the analysis, including 1 case who underwent aortic valve replacement, 49 cases who underwent mitral valve replace-ment (or valvuloplasty)+tricuspid valvuloplasty, and 8 cases who underwent double valve replacement+tricuspid valvuloplasty (3 cases combined with coronary artery bypass grafting). The patients were followed up for 3-36 months [(16.69±6.61) months] after operation, and the changes of cardiac function and the occurrence of serious adverse complications were evaluated. RESULTS: The cardiopulmonary bypass time ranged from 75 to 145 min [(102.50±21.03) min], and the aortic cross-clamp time ranged from 35 to 80 min [(58.02±14.63) min]. The length of postoperative intensive care unit stay was 1 to 5 days [(2.47±0.82) d], and the length of postoperative hospital stay was 7 to 22 days [(10.84±2.69) d]. Cardiac ultrasound indicated complete closure of the left atrial appendage in all cases. During the follow-up, New York Heart Association (NYHA) functional classifications were improved in 54 patients. No left atrial appendage-related bleeding events or other perioperative complications were observed; and no cerebral infarction, limb embolism events, or mortality cases occurred during the follow-up. CONCLUSIONS For valvular heart disease patients with atrial fibrillation, concomitant left atrial appendage clipping during cardiac valve surgery demonstrates efficacy and safety, with no severe adverse events during a medium-term follow-up.
Humans ; Atrial Appendage/surgery* ; Atrial Fibrillation/complications* ; Male ; Female ; Heart Valve Diseases/complications* ; Aged ; Middle Aged ; Heart Valve Prosthesis Implantation/methods* ; Treatment Outcome ; Cardiac Surgical Procedures/methods* ; Mitral Valve/surgery*

This study aims to evaluate the application of flower petal-structured electrodes in pulsed field ablation (PFA) technology, with a particular focus on their performance in myocardial tissue ablation. Through a combination of simulation techniques and in vitro experiments, the study investigates the effects of different voltage levels, electrode-to-tissue contact distances, and their impact on ablation depth, continuity, and transmurality. The research methods include the construction of a myocardial tissue simulation model, electric field distribution simulation using COMSOL Multiphysics, and in vitro ablation experiments on potato tissue. The results indicate that as voltage increases, the ablation depth significantly increases. At a voltage of 2500 V, a transmural ablation depth of 4 mm can be achieved, and the ablation area remains relatively continuous. The in vitro experiments confirm the consistency of the simulation results, and pulsed field ablation does not induce significant temperature rise, confirming its non-thermal characteristic. The conclusion suggests that PFA technology requires less electrode contact and offers higher ablation efficiency, providing a new technological pathway for the clinical treatment of atrial fibrillation and effectively reducing the risk of complications associated with traditional ablation techniques.
Electrodes ; Catheter Ablation/instrumentation* ; Computer Simulation ; Flowers ; Atrial Fibrillation/surgery* ; Myocardium

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. It has a high prevalence and poor prognosis. The application of antiarrhythmic drugs and even surgery cannot completely treat the disease, and there are many sequelae. AF can be classified into the category of "palpitation" in Chinese medicine according to its symptoms. Acupuncture has a significant effect on AF. The authors find that an important mechanism of acupuncture in AF treatment is to regulate the cardiac vagus nerve. Therefore, this article intends to review the distribution and function of vagus nerve in the heart, the application and the regulatroy effect for the treatment of AF.
Atrial Fibrillation/physiopathology* ; Humans ; Acupuncture Therapy ; Vagus Nerve/physiology* ; Animals

OBJECTIVES: To develop an early atrial fibrillation (AF) risk prediction model based on large-scale electrocardiogram (ECG) data from the Chinese population. METHODS: The data of multiple ECG records of 30 383 patients admitted in the Chinese PLA General Hospital between 2009 and 2023 were randomly divided into the training set and the internal testing set in a 7:3 ratio. The predictive factors were selected based on the training set using univariate analysis, LASSO regression, and the Boruta algorithm. Cox proportional hazards regression was used to establish the ECG model and the composite model incorporating age, gender, and ECG model score. The discrimination power, calibration, and clinical net benefits of the models were evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curves. RESULTS: The cohort included 51.1% male patients with a median age of the patients of 51 (36, 62) years and an AF incidence of 4.5% (1370/30 383). In the ECG model, the parameters related to the P wave and QRS complex were identified as significant predictors. In the testing set, the AUROC of the ECG model for predicting 5-year AF risk was 0.77 (95% CI: 0.74-0.80), which was increased to 0.81 (95% CI: 0.78-0.83) after incorporating age and gender, with a net reclassification improvement of 0.123 and an integrated discrimination improvement of 0.04 (P<0.05). The calibration curve of the model was close to the diagonal line. Decision curve analysis showed that the clinical net benefit of the composite model was higher than that of the ECG model across the majority of threshold probability. CONCLUSIONS The composite model incorporating quantitative ECG features during sinus rhythm, along with age and gender, can effectively predict AF risk in the Chinese population, thus providing a low-cost screening tool for early AF risk assessment and management.
Humans ; Atrial Fibrillation/epidemiology* ; Electrocardiography ; Middle Aged ; Male ; Female ; China/epidemiology* ; Proportional Hazards Models ; Adult ; Risk Factors ; Risk Assessment ; East Asian People

OBJECTIVES: To design a deep learning model that balances model complexity and performance to enable its integration into wearable ECG monitoring devices for automated diagnosis of atrial fibrillation. METHODS: This study was performed based on data from 84 patients with atrial fibrillation, 25 patients with atrial fibrillation, and 18 subjects without obvious arrhythmia collected from the publicly available datasets LTAFDB, AFDB, and NSRDB, respectively. A lightweight attention network based on depthwise separable convolution and fusion of channel-spatial information, namely DSC-AttNet, was proposed. Depthwise separable convolution was introduced to replace standard convolution and reduce model parameters and computational complexity to realize high efficiency and light weight of the model. The multilayer hybrid attention mechanism was embedded to compute the attentional weights of the channels and spatial information at different scales to improve the feature expression ability of the model. Ten-fold cross-validation was performed on LTAFDB, and external independent testing was conducted on AFDB and NSRDB datasets. RESULTS: DSC-AttNet achieved a ten-fold average accuracy of 97.33% and a precision of 97.30% on the test set, both of which outperformed the other 4 comparison models as well as the 3 classical models. The accuracy of the model on the external test set reached 92.78%, better than those of the 3 classical models. The number of parameters of DSC-AttNet was 1.01M, and the computational volume was 27.19G, both smaller than the 3 classical models. CONCLUSIONS This proposed method has a smaller complexity, achieves better classification performance, and has a better generalization ability for atrial fibrillation classification.
Atrial Fibrillation/diagnosis* ; Humans ; Electrocardiography ; Deep Learning ; Wearable Electronic Devices ; Neural Networks, Computer

OBJECTIVES: To analyze the changes in disease burden and risk factors of atrial fibrillation/atrial flutter (AF/AFL) in China and globally from 1990 to 2021 to provide epidemiological evidence for developing effective preventive measures for AF/AFL in China. METHODS: Based on data from the Global Burden of Disease Study 2021, we analyzed sex- and age-specific AF/AFL burden metrics across 204 countries or territories. Age-standardized incidence rate (ASIR), prevalence, mortality rate (ASMR), and disability-adjusted life-years (DALYs) rate (ASDR) were calculated. Joinpoint regression was employed to compute average annual percentage changes (AAPC) in trends. The risk factors were systematically evaluated and further validated using Mendelian randomization analysis. RESULTS: From 1990 to 2021, China experienced an increase in ASIR of AF/AFL (from 42.63 to 44.93 per 100 000), contrasting with the global decline. While China's ASMR decreased (from 4.93 to 4.33 per 100 000), global ASMR rose slightly (from 4.24 to 4.36 per 100 000). China's ASDR remained stable at 93.29 per 100 000, whereas global ASDR increased marginally (100.81 to 101.40 per 100 000). Significant sex disparities were observed: males showed higher ASIR and ASDR, while females exhibited greater ASMR and ASDR. China had higher incidence and prevalence but lower mortality and DALY rates compared to global averages. In 2021, the key risk factors for AF/AFL included elevated systolic blood pressure, high BMI, smoking, alcohol use, high-sodium diet, and low temperature. Gender-specific patterns emerged: smoking was predominant in males, whereas high BMI disproportionately affected females. CONCLUSIONS AF/AFL incidence and prevalence keep increasing in China, and its large population base and demographic aging pose significant public health challenges. Maintaining healthy lifestyle habits, including smoking cessation, alcohol moderation, and a low-sodium diet can help to lower AF/AFL incidence, and regular screenings is crucial for its early detection and treatment.
Humans ; China/epidemiology* ; Risk Factors ; Atrial Flutter/epidemiology* ; Atrial Fibrillation/epidemiology* ; Mendelian Randomization Analysis ; Male ; Female ; Global Burden of Disease ; Incidence ; Prevalence ; Middle Aged ; Disability-Adjusted Life Years ; Aged