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

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

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.

BACKGROUND

Postoperative atrial fibrillation (POAF) is the most common arrythmia to occur after cardiovascular surgery. Inflammation being pivotal in POAF perpetuation has been utilized as a therapeutic target. Owing to their anti-inflammatory and anti-oxidant effects, beta-blockers (BB) and N-acetylcysteine (NAC) became research interests in the pursuit for an effective POAF prevention strategy.

To determine the efficacy of NAC plus BB versus BB alone in preventing POAF in cardiac surgery patients.

A literature search using the following search engines: PubMed/Medline, Cochrane Review Central, Clinical Trials Registry, ResearchGate, Mendeley and Google Scholar for relevant randomized trials were conducted. Published and unpublished studies indexed from inception until 2023 were included. Three independent reviewers evaluated the randomized clinical trials (RCTs) for eligibility. The pooled estimates for POAF prevention as primary outcome and MACE, mortality, myocardial infarction, stroke, ICU LOS and hospital LOS as secondary outcomes were measured using the RStudio statistical software.

Seven eligible RCTs allocated 1069 cardiac surgery patients to NAC + BB (n=539) and BB alone (N = 530) treatment arms. The effect estimate using random effect model disclosed significantly reduced POAF events (RR 0.62, 95% CI [0.44, 0.86], p = 0.005) in those on NAC + BB. While no statistical difference between the study arms were demonstrated in reducing mortality (RR 0.63, 95% CI [0.23, 1.73], p = 0.37); myocardial infarction (RR 1.02, 95% CI [0.49, 2.13], p = 0.96); stroke (RR 0.95, 95% CI [0.24, 3.68], p = 0.94); ICU LOS (std. mean difference 0.14, 95% CI [-0.43, 0.70], p = 0.41), and hospital LOS (std. mean difference 0.08, 95% CI [-0.06, 0.21], p = 0.19).

Among cardiac surgery patients, the use of NAC in combination with BB compared with BB alone significantly reduced POAF.

INTRODUCTION

Patients with atrial fibrillation (AF) undergoing percutaneous coronary intervention (PCI) poses a therapeutic dilemma for the attending physician. Standard anticoagulation with a vitamin K antagonist (VKA) plus dual antiplatelet therapy with a P2Y12 inhibitor and aspirin reduces the risk of stroke and thrombosis, but increases risk of bleeding. The effectiveness and safety of several novel oral anticoagulants are still unclear in these patients.

PubMed, Cochrane and Embase databases were systematically searched for studies from 2016 until 30 November 2023. The search key terms were ‘DOACs,’ ‘atrial fibrillation,’ ‘percutaneous coronary intervention’ and ‘bleeding.’ Two independent reviewers appraised eligible studies using well-defined criteria. The main outcomes of interest were International Society on Thrombosis and Haemostasis (ISTH) major bleeding, stent thrombosis and major adverse cardiovascular events (MACE). The random-effects model was used to derive pooled estimates.

The search yielded four studies which were all randomized controlled trials (RCTs). There were a total of 10,963 participants. Pooled estimates showed a statistically significant difference between direct oral anticoagulants (DOAC) + P2Y12 and VKA + DAPT for ISTH major bleeding (OR 0.62, 95% CI 0.57 – 0.69, p =DISCUSSION

In patients with AF who had undergone PCI, the risk of bleeding was lower among those who received dual therapy with DOAC + P2Y12 than among those who received triple therapy with warfarin, a P2Y12 inhibitor and aspirin. However, dual therapy was noninferior to triple therapy with respect to the risk of stent thrombosis and MACE.

OBJECTIVE

This study aims to determine the clinical profile and incidence of in-hospital outcomes of patients who underwent coronary artery bypass graft surgery (CABG) under the Philippine Health Insurance Corporation (Philhealth) Z Benefit Package (PZBP).

A retrospective descriptive cohort study. A review of medical records was done from July 2017 to October 2023 to collect data and in-hospital outcomes of patients who underwent CABG surgery under Z benefit package.

One hundred twenty-six patients were included in the study. The mean age of patients was 60 years and majority of them were male and came from NCR. Hypertension was the leading cause of comorbidity. The top two most common in-hospital outcomes were hospital-acquired pneumonia and postoperative atrial fibrillation. Most commonly observed caused of increased length in hospitalization was pneumonia. The mean Society of Thoracic Surgeons (STS) (operative mortality) and Euro scores were 1.04 ± 0.82 and 0.88 ± 0.56, respectively. All patients had successful surgical outcome with no mortality and an average length of stay in the hospital of six days.

This study will serve as a future reference to enhance screening criteria and improved in-hospital outcomes for those who will undergo CABG under Z benefit package. The study also showed insights on clinical profile and in-hospital outcomes of patients who underwent CABG in our own institution and this may give way to a larger scale of study involving multiple centers who also offer the said program.

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