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 Published and unpublished studies indexed from inception until 2023 were included.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 ; COVID-19/complications* ; Arrhythmias, Cardiac/diagnosis* ; Anti-Arrhythmia Agents/therapeutic use*

Malaria, one of the major global public health events, is a leading cause of mortality and morbidity among children and adults in tropical and subtropical regions(mainly in sub-Saharan Africa), threatening human health. It is well known that malaria can cause various complications including anemia, blackwater fever, cerebral malaria, and kidney damage. Conventionally, cardiac involvement has not been listed as a common reason affecting morbidity and mortality of malaria, which may be related to ignored cases or insufficient diagnosis. However, the serious clinical consequences such as acute coronary syndrome, heart failure, and malignant arrhythmia caused by malaria have aroused great concern. At present, antimalarials are commonly used for treating malaria in clinical practice. However, inappropriate medication can increase the risk of cardiovascular diseases and cause severe consequences. This review summarized the research advances in the cardiovascular complications including acute myocardial infarction, arrhythmia, hypertension, heart failure, and myocarditis in malaria. The possible mechanisms of cardiovascular diseases caused by malaria were systematically expounded from the hypotheses of cell adhesion, inflammation and cytokines, myocardial apoptosis induced by plasmodium toxin, cardiac injury secondary to acute renal failure, and thrombosis. Furthermore, the effects of quinolines, nucleoprotein synthesis inhibitors, and artemisinin and its derivatives on cardiac structure and function were summarized. Compared with the cardiac toxicity of quinolines in antimalarial therapy, the adverse effects of artemisinin-derived drugs on heart have not been reported in clinical studies. More importantly, the artemisinin-derived drugs demonstrate favorable application prospects in the prevention and treatment of cardiovascular diseases, and are expected to play a role in the treatment of malaria patients with cardiovascular diseases. This review provides reference for the prevention and treatment of malaria-related cardiovascular complications as well as the safe application of antimalarials.
Child ; Adult ; Humans ; Antimalarials/pharmacology* ; Cardiovascular Diseases/drug therapy* ; Artemisinins/pharmacology* ; Quinolines ; Malaria, Cerebral/drug therapy* ; Heart Failure/drug therapy* ; Arrhythmias, Cardiac/drug therapy*

Accurate assessment of the risks associated with traditional Chinese medicine(TCM), such as the potential to induce serious cardiovascular adverse reactions including cardiac arrhythmias, is crucial. This article introduced the pharmacological evaluation strategies for cardiac safety and the progress in cardiac organ research, with a focus on discussing the application prospects of human induced pluripotent stem cells(hiPSCs) and organoids in assessing the risks of TCM-induced cardiac arrhythmias. Compared with traditional animal models, hiPSCs and organoid models provide better reference and predictive capabilities, allowing for more accurate simulation of human cardiac responses. Researchers have successfully generated various cardiac tissue models that mimic the structure and function of the heart to evaluate the effects of TCM on the heart. The hiPSCs model, by reprogramming adult cells into pluripotent stem cells and differentiating them into cardiac cells, enables the generation of personalized cardiac tissue, which better reflects individual differences and drug responses. This provides guidance for the assessment of TCM cardiac toxicity risks. By combining organoid model with cardiac safety pharmacology strategies such as electrocardiogram monitoring and ion channel function assessment, the impact of TCM on the heart can be comprehensively evaluated. In addition, the application of the Comprehensive in Vitro Proarrhythmia Assay(CiPA) approach improves the accuracy of evaluation. Applying the CiPA approach to TCM research reveals potential risks and provides a scientific basis for the clinical application and industrial development of TCM. In conclusion, organoid model and cardiac safety pharmacology evaluation strategies provide important tools for assessing the cardiac toxicity risks of TCM. The combination of hiPSCs model, comprehensive assessment methods, and the CiPA strategy enables an accurate assessment of the risks of TCM-induced cardiac arrhythmias, thus providing a scientific basis for the safe use and international recognition of TCM in clinical practice. This contributes to ensuring the safety and efficacy of TCM and promoting its clinical application and global acceptance.
Animals ; Humans ; Medicine, Chinese Traditional/adverse effects* ; Cardiotoxicity ; Induced Pluripotent Stem Cells ; Arrhythmias, Cardiac/chemically induced* ; Myocytes, Cardiac ; Organoids ; Drugs, Chinese Herbal/adverse effects*

OBJECTIVE: To explore incidence, risk factors and the relationship between preoperative heart failure and prognosis in elderly patients with hip fracture. METHODS: A retrospective analysis was performed on 1 569 elderly patients with hip fracture treated from January 2012 to December 2019, including 522 males and 1 047 females, aged 81.00 (75.00, 90.00) years old;896 intertrochanteric fractures and 673 femoral neck fractures. Patients were divided into heart failure and non-heart failure groups according to whether they developed heart failure before surgery, and heart failure was set as the dependent variable, with independent variables including age, gender, fracture type, comorbidities and hematological indicators, etc. Univariate analysis was performed at first, and independent variables with statistical differences were included in multivariate Logistic regression analysis. Independent risk factors for preoperative heart failure were obtained. The length of hospital stay, perioperative complications, mortality at 30 days and 1 year after surgery were compared between heart failure and non-heart failure groups. RESULTS: There were 91 patients in heart failure group, including 40 males and 51 females, aged 82.00 (79.00, 87.00) years old;55 patients with intertrochanteric fracture and 36 patients with femoral neck fracture. There were 1 478 patients in non-heart failure groups, including 482 males and 996 females, aged 81.00(75.00, 86.00) years old;841 patients with intertrochanteric fracture and 637 patients with femoral neck fracture. There were significant differences in age, sex, coronary heart disease, arrhythmia and dementia between two groups(P<0.05). Multivariate Logistic analysis of statistically significant factors showed that males(OR=1.609, P=0.032), age(OR=1.032, P=0.031), arrhythmia(OR=2.045, P=0.006), dementia (OR=2.106, P=0.014) were independent risk factor for preoperative heart failure. The 30-day and 1-year mortality rates were 9.9% and 26.4% in heart failure group and 3.6% and 13.8% in non-heart failure group, respectively;and had statistical significance between two groups (P<0.05). There were significant differences in pulmonary infection, cerebrovascular complications and cardiovascular complications between two groups (P<0.05). The duration of hospitalization in heart failure group was (16.21±10.64) d compared with that in non-heart failure group (13.26±8.00) d, and the difference was statistically significant (t=2.513, P=0.012). CONCLUSION Male, old age, arrhythmia and dementia are independent risk factors for heart failure after hip fracture in elderly patients. Patients with preoperative heart failure have a higher incidence of postoperative pulmonary infection, cerebrovascular and cardiovascular complications, higher mortality at 30 d and 1 year after surgery, and longer hospital stay.
Aged ; Female ; Humans ; Male ; Aged, 80 and over ; Retrospective Studies ; Hip Fractures/surgery* ; Femoral Neck Fractures ; Heart Failure/etiology* ; Prognosis ; Heart Diseases ; Risk Factors ; Postoperative Complications/etiology* ; Dementia ; Arrhythmias, Cardiac

OBJECTIVE: To investigate risk factors of acute cerebrovascular events and effects on the prognosis within 1 year after hip fracture surgery. METHODS: A retrospective analysis was performed on 320 elderly patients with hip fracture treated from July 2017 to December 2020, including 111 males and 209 females, aged from 60 to 101 years old with an average of (79.05±8.48) years old. According to whether acute cerebrovascular events occurred within 1 year after surgery, patients were divided into cerebrovascular events and non-cerebrovascular events group. Clinical data of patients were collected, including age, sex, comorbidities, fracture type, white blood cell count, hemoglobin, albumin, activities of daily living (ADL) score, walking ability, type of anesthesia, type of surgery, and length of hospital stay, Univariate analysis and multivariate Logistic regression were used to analyze the independent risk factors of acute cerebrovascular events within 1 year after hip fracture in elderly patients. ADL, walking ability and mortality were compared between the two groups 1 year after surgery. RESULTS: Acute cerebrovascular events occurred in 38 patients (11.9%) within 1 year after surgery. In the cerebrovascular events group, there were 20 males and 18 females, aged (82.53±7.91) years. In the non-cerebrovascular event group, there were 91 males and 191 females, aged with an average of (78.59±8.46) years old . Univariate analysis showed that acute cerebrovascular events were associated with age (t=2.712, P=0.007), male (χ²=6.129, P=0.013), hypertension (χ²=8.449, P=0.004), arrhythmia (χ²=6.360, P=0.012), stroke history (χ²=34.887, P=0.000), diabetes mellitus (χ²=4.574, P=0.032) and length of hospital stay (t=2.249, P=0.025) were closely related. Multivariate Logistic regression analysis showed age (OR=1.068, P=0.018), male (OR=2.875, P=0.008), arrhythmia (OR=2.722, P=0.017) and stroke history (OR=7.382, P=0.000) was an independent risk factor for acute cerebrovascular events 1 year after surgery. The patients with cerebrovascular events died at 1 year after surgery (11 cases) compared with those without cerebrovascular events (41 cases), and the difference was statistically significant(χ²=5.108, P=0.024). ADL scores of patients with cerebrovascular events at 1 year after operation were (58.70±14.45) points compared with those without cerebrovascular events (67.83±10.45) points, and the difference was statistically significant(t=4.122, P=0.000). Independent walking, assisted walking and bed rest were 3, 17 and 7 cases in cerebrovascular event group, and 54, 174 and 13 cases in non-cerebrovascular event group, respectively;and the difference was statistically significant(χ²=11.030, P=0.003). CONCLUSION Acute cerebrovascular events were common in elderly patients 1 year after hip fracture. Age, male, arrhythmia and stroke history were independent risk factors for acute stroke. The patients in the cerebrovascular event group had higher mortality and worse self-care ability and walking ability one year after operation.
Female ; Humans ; Male ; Aged ; Middle Aged ; Aged, 80 and over ; Retrospective Studies ; Activities of Daily Living ; Hip Fractures ; Risk Factors ; Prognosis ; Stroke ; Arrhythmias, Cardiac

OBJECTIVE: Reduce the number of false alarms and measurement time caused by movement interference by the sync waveform of the movement. METHODS: Vital signal monitoring system based on motion sensor was developed, which collected and processed the vital signals continuously, optimized the features and results of vital signals and transmitted the vital signal results and alarms to the interface. RESULTS: The system was tested in many departments, such as digestive department, cardiology department, internal medicine department, hepatobiliary surgery department and emergency department, and the total collection time was 1 940 h. The number of false electrocardiograph (ECG) alarms decreased by 82.8%, and the proportion of correct alarms increased by 28%. The average measurement time of non-invasive blood pressure (NIBP) decreased by 16.1 s. The total number of false respiratory rate measurement decreased by 71.9%. CONCLUSIONS False alarms and measurement failures can be avoided by the vital signal monitoring system based on accelerometer to reduce the alarm fatigue in clinic.
Humans ; Monitoring, Physiologic ; Electrocardiography ; Arrhythmias, Cardiac ; Blood Pressure ; Accelerometry ; Clinical Alarms