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 Published and unpublished studies indexed from inception until 2023 were included.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.

Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.
Humans ; Electrocardiography/methods* ; Mental Health ; Algorithms ; Signal Processing, Computer-Assisted ; Machine Learning ; Arrhythmias, Cardiac/diagnosis* ; Cardiovascular Diseases ; Neural Networks, Computer ; Mental Disorders

Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F ₁-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Humans ; Arrhythmias, Cardiac/diagnosis* ; Algorithms ; Electrocardiography/methods* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Deep Learning ; Classification Algorithms

OBJECTIVES: The Charlson comorbidity index reflects overall comorbidity burden and has been applied in cardiovascular medicine. However, its role in predicting in-hospital mortality in patients with acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VA) remains unclear. This study aims to evaluate the predictive value of the Charlson comorbidity index in this setting and to construct a nomogram model for early risk identification and individualized management to improve outcomes. METHODS: Using the open-access critical care database MIMIC-IV (Medical Information Mart for Intensive Care IV), we identified intensive care unit (ICU) patients diagnosed with AMI complicated by VA. Patients were grouped according to in-hospital survival. The predictive performance of the Charlson comorbidity index and other clinical variables for in-hospital mortality was analyzed. Key predictors were selected using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable Logistic regression. A nomogram model was constructed based on the regression results. Model performance was assessed using receiver operating characteristic (ROC) curves and calibration plots. RESULTS: A total of 1 492 patients with AMI and VA were included, of whom 340 died and 1 152 survived during hospitalization. Significant differences were observed between survivors and non-survivors in sex distribution, vital signs, comorbidity burden, organ function, and laboratory parameters (all P<0.05). The area under the curve (AUC) of the Charlson comorbidity index for predicting in-hospital mortality was 0.712 (95% CI 0.681 to 0.742), significantly higher than albumin, international normalized ratio (INR), hemoglobin, body temperature, and platelet count (all P<0.001), but comparable to Sequential Organ Failure Assessment (SOFA) score (P>0.05). LASSO regression identified seven key predictors: the Charlson comorbidity index (quartile groups: T1, <6; T2, ≥6-<7; T3, ≥7-<9; T4, ≥9), ventricular fibrillation, age, systolic blood pressure, respiratory rate, body temperature, and SOFA score. Multivariate Logistic regression showed that compared with T1, mortality risk increased significantly in T2 (OR=1.996, 95% CI 1.135 to 3.486, P=0.016), T3 (OR=3.386, 95% CI 2.192 to 5.302, P<0.001), and T4 (OR=5.679, 95% CI 3.711 to 8.842, P<0.001). Age (OR=1.056, P<0.001), respiratory rate (OR=1.069, P<0.001), SOFA score (OR=1.223, P<0.001), and ventricular fibrillation (OR=2.174, P<0.001) were independent risk factors, while systolic blood pressure (OR=0.984, P<0.001) and body temperature (OR=0.648, P<0.001) were protective factors. The nomogram incorporating these predictors achieved an AUC of 0.849 (95% CI 0.826 to 0.871) with high discrimination and good calibration (mean absolute error=0.014). CONCLUSIONS The Charlson comorbidity index is an independent predictor of in-hospital mortality in AMI patients complicated by VA, with performance comparable to the SOFA score. The nomogram model based on the Charlson comorbidity index and additional clinical variables effectively estimates mortality risk and provides a valuable reference for clinical decision-making.
Humans ; Nomograms ; Hospital Mortality ; Myocardial Infarction/complications* ; Male ; Female ; Comorbidity ; Middle Aged ; Aged ; Arrhythmias, Cardiac/complications* ; ROC Curve ; Intensive Care Units

Humans ; Diabetes Mellitus, Type 2/drug therapy* ; Sodium-Glucose Transporter 2 Inhibitors/therapeutic use* ; Arrhythmias, Cardiac/etiology* ; Glucose ; Sodium

Objective: To investigate the impact of COVID-19 on treatment of patients with acute ST segment elevation myocardial infarction(STEMI) undergoing primary percutaneous coronary intervention(PPCI). Methods: This was a multicenter retrospective study. STEMI patients undergoing PPCI from January 1, 2019 to December 31, 2021 were selected, based on the data of Xinnaolvsetongdao App. Clinical data and treatment time indicators, including symptom to first medical contact (S-FMC), symptom to door (StoD), first medical contact to ECG (FMC-ECG), first medical contact to guide wire (FMC-W), door to balloon (DtoB) and total ischemic time in 2019, 2020 and 2021 were compared. STEMI patients aged<60 years were sub-grouped as the young and middle-aged group, and STEMI patients aged≥60 years were sub-grouped as the elderly group. Results: A total of 7 435 (3 305 in 2019, 1 796 in 2020 and 2 334 in 2021) STEMI patients aged (59.6±12.6) years undergoing PPCI were included in this analysis. There were 5 990 males. For STEMI patients with PPCI in 2019, 2020 and 2021, FMC-ECG was 3 (1, 5) min, 3(1, 7) min and 4 (1, 7) min. FMC-W was 73 (56, 87) min, 78 (62, 95) min and 77 (62, 87) min. DtoB was 73 (56, 85) min, 78 (62, 95) min and 77 (62, 86) min. Total ischemic time was 189 (130, 273) min, 196 (138, 295) min and 209 (143, 276) min. FMC-ECG, FMC-W, DtoB and total ischemic time were longer in 2020 and 2021 than in 2019 (all P<0.05). The proportions of patients with FMC-ECG≤10 min (88.4% (1 588/1 796) vs. 92.7% (3 064/3 305), P<0.05), FMC-W≤120 min (87.9% (1 579/1796) vs. 91.7% (3 030/3 305), P<0.05) and DtoB≤90 min (72.3% (1 298/1 796) vs. 80.8% (2 672/3 305), P<0.05) were lower in 2020 than in 2019, whereas no differences were observed in the proportions of patients with FMC-ECG≤10 min (91.3% (2 131/2 334) vs. 92.7% (3 064/3 305), P=0.054), FMC-W≤120 min (92.0% (2 148/2 334) vs. 91.7% (3 030/3 305), P=0.635) and DtoB≤90 min (80.0% (1 867/2 334) vs. 80.8% (2 672/3 305), P=0.424) in 2021 compared with 2019. In the subgroup analysis, the proportions of patients with FMC-ECG≤10 min, FMC-W≤120 min and DtoB≤90 min were lower in the elderly group than in young and middle-aged group in 2019 (all P<0.05). The proportions of patients with FMC-W≤120 min and DtoB≤90 min were lower in the elderly group than in young and middle-aged group in 2021(all P<0.05). No differences were observed in the proportions of patients with FMC-ECG≤10 min, FMC-W≤120 min and DtoB≤90 min between the two group in 2020 (all P>0.05). Conclusions: Affected by the COVID-19, there is a reduction in the number of PPCI cases and treatment delays in STEMI patients, especially in the elderly. After adjusting the treatment strategy and widely applying the Xinnaolvsetongdao APP, the above indicators are significantly improved in 2021 as compared with 2020.
Aged ; Male ; Middle Aged ; Humans ; ST Elevation Myocardial Infarction ; Beijing ; COVID-19 ; Retrospective Studies ; Anterior Wall Myocardial Infarction ; Arrhythmias, Cardiac ; Percutaneous Coronary Intervention

Humans ; Arrhythmias, Cardiac/history* ; Biomedical Research/history* ; Cardiology/history* ; Cardiovascular System ; China ; History, 20th Century ; History, 21st Century ; Publishing/history*

Humans ; Diabetes Mellitus, Type 2/drug therapy* ; Sodium-Glucose Transporter 2 Inhibitors/therapeutic use* ; Arrhythmias, Cardiac/etiology* ; Glucose ; Sodium

Objective: To investigate the impact of COVID-19 on treatment of patients with acute ST segment elevation myocardial infarction(STEMI) undergoing primary percutaneous coronary intervention(PPCI). Methods: This was a multicenter retrospective study. STEMI patients undergoing PPCI from January 1, 2019 to December 31, 2021 were selected, based on the data of Xinnaolvsetongdao App. Clinical data and treatment time indicators, including symptom to first medical contact (S-FMC), symptom to door (StoD), first medical contact to ECG (FMC-ECG), first medical contact to guide wire (FMC-W), door to balloon (DtoB) and total ischemic time in 2019, 2020 and 2021 were compared. STEMI patients aged<60 years were sub-grouped as the young and middle-aged group, and STEMI patients aged≥60 years were sub-grouped as the elderly group. Results: A total of 7 435 (3 305 in 2019, 1 796 in 2020 and 2 334 in 2021) STEMI patients aged (59.6±12.6) years undergoing PPCI were included in this analysis. There were 5 990 males. For STEMI patients with PPCI in 2019, 2020 and 2021, FMC-ECG was 3 (1, 5) min, 3(1, 7) min and 4 (1, 7) min. FMC-W was 73 (56, 87) min, 78 (62, 95) min and 77 (62, 87) min. DtoB was 73 (56, 85) min, 78 (62, 95) min and 77 (62, 86) min. Total ischemic time was 189 (130, 273) min, 196 (138, 295) min and 209 (143, 276) min. FMC-ECG, FMC-W, DtoB and total ischemic time were longer in 2020 and 2021 than in 2019 (all P<0.05). The proportions of patients with FMC-ECG≤10 min (88.4% (1 588/1 796) vs. 92.7% (3 064/3 305), P<0.05), FMC-W≤120 min (87.9% (1 579/1796) vs. 91.7% (3 030/3 305), P<0.05) and DtoB≤90 min (72.3% (1 298/1 796) vs. 80.8% (2 672/3 305), P<0.05) were lower in 2020 than in 2019, whereas no differences were observed in the proportions of patients with FMC-ECG≤10 min (91.3% (2 131/2 334) vs. 92.7% (3 064/3 305), P=0.054), FMC-W≤120 min (92.0% (2 148/2 334) vs. 91.7% (3 030/3 305), P=0.635) and DtoB≤90 min (80.0% (1 867/2 334) vs. 80.8% (2 672/3 305), P=0.424) in 2021 compared with 2019. In the subgroup analysis, the proportions of patients with FMC-ECG≤10 min, FMC-W≤120 min and DtoB≤90 min were lower in the elderly group than in young and middle-aged group in 2019 (all P<0.05). The proportions of patients with FMC-W≤120 min and DtoB≤90 min were lower in the elderly group than in young and middle-aged group in 2021(all P<0.05). No differences were observed in the proportions of patients with FMC-ECG≤10 min, FMC-W≤120 min and DtoB≤90 min between the two group in 2020 (all P>0.05). Conclusions: Affected by the COVID-19, there is a reduction in the number of PPCI cases and treatment delays in STEMI patients, especially in the elderly. After adjusting the treatment strategy and widely applying the Xinnaolvsetongdao APP, the above indicators are significantly improved in 2021 as compared with 2020.
Aged ; Male ; Middle Aged ; Humans ; ST Elevation Myocardial Infarction ; Beijing ; COVID-19 ; Retrospective Studies ; Anterior Wall Myocardial Infarction ; Arrhythmias, Cardiac ; Percutaneous Coronary Intervention

Humans ; Arrhythmias, Cardiac/history* ; Biomedical Research/history* ; Cardiology/history* ; Cardiovascular System ; China ; History, 20th Century ; History, 21st Century ; Publishing/history*