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 Published and unpublished studies indexed from inception until 2023 were included.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

BACKGROUND: Dysfunction of the gap junction channel protein connexin 43 (Cx43) contributes to myocardial ischemia/reperfusion (I/R)-induced ventricular arrhythmias. Cx43 can be regulated by small ubiquitin-like modifier (SUMO) modification. Protein inhibitor of activated STAT Y (PIASy) is an E3 SUMO ligase for its target proteins. However, whether Cx43 is a target protein of PIASy and whether Cx43 SUMOylation plays a role in I/R-induced arrhythmias are largely unknown. METHODS: Male Sprague-Dawley rats were infected with PIASy short hairpin ribonucleic acid (shRNA) using recombinant adeno-associated virus subtype 9 (rAAV9). Two weeks later, the rats were subjected to 45 min of left coronary artery occlusion followed by 2 h reperfusion. Electrocardiogram was recorded to assess arrhythmias. Rat ventricular tissues were collected for molecular biological measurements. RESULTS: Following 45 min of ischemia, QRS duration and QTc intervals statistically significantly increased, but these values decreased after transfecting PIASy shRNA. PIASy downregulation ameliorated ventricular arrhythmias induced by myocardial I/R, as evidenced by the decreased incidence of ventricular tachycardia and ventricular fibrillation, and reduced arrythmia score. In addition, myocardial I/R statistically significantly induced PIASy expression and Cx43 SUMOylation, accompanied by reduced Cx43 phosphorylation and plakophilin 2 (PKP2) expression. Moreover, PIASy downregulation remarkably reduced Cx43 SUMOylation, accompanied by increased Cx43 phosphorylation and PKP2 expression after I/R. CONCLUSION PIASy downregulation inhibited Cx43 SUMOylation and increased PKP2 expression, thereby improving ventricular arrhythmias in ischemic/reperfused rats heart.
Rats ; Male ; Animals ; Myocardial Reperfusion Injury/metabolism* ; Connexin 43/genetics* ; Sumoylation ; Down-Regulation ; Rats, Sprague-Dawley ; Arrhythmias, Cardiac/drug therapy* ; Myocardial Ischemia/metabolism* ; RNA, Small Interfering/metabolism*

Zhenwu Decoction is recorded in Treatise on Febrile Diseases by an outstanding physician ZHANG Zhong-jing in the Han dynasty. With effect of warming yang, transforming Qi, and promoting urination, Zhenwu Decoction is mainly used to treat edema due to yang deficiency. The studies of the severe and critical cases and the pathophysiological mechanisms have demonstrated that the record of Zhenwu Decoction in Treatise on Febrile Diseases describes the clinical symptoms and therapeutic regimen of acute heart failure. The syndrome treated by this formula may be related to the misdiagnosis and wrong treatment. Due to the difficult distinguishing between cardiogenic dyspnea and pulmonary dyspnea, high doses of Ephedrae Herba may be misused for inducing sweating, which may finally lead to the acute aggravation of heart failure, electrolyte disorder, and pulmonary infection. The syndrome treated by Zhenwu Decoction can illustrate the lack of experience of ancient physicians in treating acute heart failure. The description of "trembling and shivering" may be the clinical manifestation of heart failure, which is an upgraded version of "trembling and shaking" treated by Linggui Zhugan Decoction.(1)In terms of diseases, Zhenwu Decoction is suitable for the treatment of acute or chronic heart failure, cardiorenal syndrome, and diuretic resistance. The decoction is especially suitable for treating whole heart failure, acute heart failure, heart failure with reduced ejection fraction, and heart failure with the syndrome of sold and dampness. In addition, it can be used to treat both type Ⅱ and type Ⅳ cardiorenal syndrome.(2)In terms of symptoms, Zhenwu Decoction can be used for treating chest tightness, palpitations, lower limb edema, difficult urination or increased urine output, fear of cold, pale fat tongue with teeth marks, white and slippery tongue fur, and deep or slow pulse.(3)In terms of the pharmacological mechanism, Zhenwu Decoction treats heart failure following the principle of promoting urination, expanding blood vessels, and invigorating heart in modern medicine. Aconiti Lateralis Radix Praparata is the sovereign herb in the formula, with the recommended dosage of 30-60 g. However, arrhythmia may be caused by high doses of Aconiti Lateralis Radix Praparata, which should be used with concern. In addition to Zhenwu Decoction, Shenqi Pills, Renshen Decoction, Wuling Powder, and Fangji Huangqi Decoction with the effect of invigorating spleen, replenishing Qi, warming Yang, and promoting urination can be used in the recovery stage. The therapy of reinforcing Yang was the last choice for critical cases due to the lack of medical conditions, unclear clinical diagnosis in history, which should be treated objectively now.
Humans ; Cardio-Renal Syndrome/drug therapy* ; Drugs, Chinese Herbal/pharmacology* ; Medicine, Chinese Traditional ; Heart Failure/drug therapy* ; Arrhythmias, Cardiac/drug therapy* ; Critical Care

Arrhythmia, a common and frequently occurring cardiovascular disease, causes a heavy burden on the public health of China. Approximately 20 million patients are suffering from this disease in China and treated by pharmacological and surgical therapies. However, antiarrhythmic drugs can cause arrhythmia and surgical treatment has the risks of failure and recurrence. Therefore, the clinical outcome of arrhythmia remains to be improved. According to the traditional Chinese medicine(TCM) theory, arrhythmia is a disease of palpitation induced by 7 conditions: liver depression and Qi stagnation, accumulation of turbid phlegm, fluid retention attacking the heart, fire-heat disturbing the heart, stasis obstruction of heart vessel, cold congealing in heart vessel, and the deficiency of Qi, blood, Yin, and Yang. Therefore, this study concisely proposed 7 TCM syndromes of arrhythmia, including the palpitation due to depression, phlegm, fluid retention, fire, blood stasis, cold, and deficiency. The corresponding treatment strategies were recommended as follows: Chaihu Longgu Muli Decoction for the palpitation due to depression, Wendan Decoction for the palpitation due to phlegm, Linggui Zhugan Decoction for the palpitation due to fluid retention, Sanhuang Xiexin Decoction for the palpitation due to fire, Xuefu Zhuyu Decoction for the palpitation due to blood stasis, and Mahuang Fuzi Xixin Decoction for the palpitation due to cold, and Guizhi Gancao Decoction, Guizhi Gancao Longgu Muli Decoction, Huanglian Ejiao Decoction, Zhigancao Decoction, and Guipi Decoction for the palpitation due to the deficiency of Qi, blood, Yin, and Yang. Multiple formulas should be combined if the patient presents several TCM syndromes simultaneously. According to the principles of the correspondence between formula and syndrome and the treatment with consideration to both pathogenesis and pathology and both herbal nature and pharmacology, this study proposed an integrated treatment model of "pathogenesis-pathology-nature-pharmacology" to enhance the clinical efficacy of classic herbal formulas in the treatment of arrhythmia.
Humans ; Medicine, Chinese Traditional ; Syndrome ; Drugs, Chinese Herbal/therapeutic use* ; Heart Failure/drug therapy* ; Arrhythmias, Cardiac/drug therapy* ; China

Chaihu Jia Longgu Muli Decoction was firstly recorded in Treatise on Cold Damage(ZHANG Zhong-jing, Eastern Han dynasty). According to this medical classic, it is originally used in the treatment of the Shaoyang and Yangming syndrome. Based on the modern pathophysiological mechanism, this study interpreted the classic provisions of Chaihu Jia Longgu Muli Decoction. Original records of "chest fullness" "annoyance" "shock" "difficult urination" "delirium" "heavy body and failing to turn over" all have profound pathophysiological basis, involving disorders in cardiovascular, respiratory, nervous, and mental systems. This formula is widely used, which can be applied to treat epilepsy, cerebral arteriosclerosis, cerebral infarction, and other cerebrovascular diseases, hypertension, arrhythmia, and other cardiovascular diseases, insomnia, constipation, anxiety, depression, cardiac neurosis and other acute and chronic diseases as well as diseases in psychosomatic medicine. The clinical indications include Bupleuri Radix-targeted syndrome such as fullness and discomfort in chest and hypochondrium, bitter taste mouth, dry throat, and dizziness, the insomnia, anxiety, depression, susceptibility to fright, upset, dreamfulness and other psychiatric symptoms, red tongue, thick and yellow tongue coating, and wiry hard and powerful pulse. This formula was found to be used in combination with other formulas, such as Gualou Xiebai Decoction, Wendan Decoction, Zhizhu Pills, Juzhijiang Decoction, Suanzaoren Decoction, and Banxia Baizhu Tianma Decoction.
Humans ; Sleep Initiation and Maintenance Disorders/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Hypertension/drug therapy* ; Syndrome ; Arrhythmias, Cardiac/drug therapy* ; Medicine, Chinese Traditional

Arrhythmia is an external manifestation of cardiac electrophysiological disorder. It exists in healthy people and patients with various heart diseases, which is often associated with other cardiovascular diseases. The contraction and diastole of myocardium are inseparable from the movement of ions. There are many ion channels in the membrane and organelle membrane of myocardium. The dynamic balance of myocardial ions is vital in maintaining myocardial electrical homeostasis. Potassium ion channels that have a complex variety and a wide distribution are involved in the whole process of resting potential and action potential of cardiomyocytes. Potassium ion channels play a vital role in maintaining normal electrophysiological activity of myocardium and is one of the pathogenesis of arrhythmia. Traditional Chinese medicine(TCM)has unique advantages in treating arrhythmia for its complex active components and diverse targets. A large number of TCM preparations have definite effect on treating arrhythmia-related diseases, whose antiarrhythmic mechanism may be related to the effect on potassium channel. This article mainly reviewed the relevant studies on the active components in TCM acting on different potassium channels to provide references for clinical drug use and development.
Humans ; Potassium Channels ; Medicine, Chinese Traditional ; Anti-Arrhythmia Agents/therapeutic use* ; Arrhythmias, Cardiac/drug therapy* ; Heart Diseases/drug therapy* ; Ions

Arrhythmia is a significant cardiovascular disease that poses a threat to human health, and its primary diagnosis relies on electrocardiogram (ECG). Implementing computer technology to achieve automatic classification of arrhythmia can effectively avoid human error, improve diagnostic efficiency, and reduce costs. However, most automatic arrhythmia classification algorithms focus on one-dimensional temporal signals, which lack robustness. Therefore, this study proposed an arrhythmia image classification method based on Gramian angular summation field (GASF) and an improved Inception-ResNet-v2 network. Firstly, the data was preprocessed using variational mode decomposition, and data augmentation was performed using a deep convolutional generative adversarial network. Then, GASF was used to transform one-dimensional ECG signals into two-dimensional images, and an improved Inception-ResNet-v2 network was utilized to implement the five arrhythmia classifications recommended by the AAMI (N, V, S, F, and Q). The experimental results on the MIT-BIH Arrhythmia Database showed that the proposed method achieved an overall classification accuracy of 99.52% and 95.48% under the intra-patient and inter-patient paradigms, respectively. The arrhythmia classification performance of the improved Inception-ResNet-v2 network in this study outperforms other methods, providing a new approach for deep learning-based automatic arrhythmia classification.
Humans ; Arrhythmias, Cardiac/diagnostic imaging* ; Cardiovascular Diseases ; Algorithms ; Databases, Factual ; Electrocardiography