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 Published and unpublished studies indexed from inception until 2023 were included.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

OBJECTIVE: To observe the clinical efficacy of wrist-ankle acupuncture in the treatment of functional frequent premature ventricular contractions (PVCs). METHODS: A total of 64 patients with functional frequent PVCs were randomly divided into an observation group and a control group, 32 cases in each group. The observation group was treated with wrist-ankle acupuncture at bilateral upper 1 and upper 2 on the wrist. The control group received sham acupuncture at the same points as the observation group. Both groups were treated once every day from Monday to Friday, with the needles retained for 60 min each time, for a total of 4 weeks. The TCM syndrome score, the 24-hour PVC count, and MOS 36-item short form health survey (SF-36) score were compared between the two groups before and after treatment, and the clinical efficacy was evaluated after treatment. RESULTS: After treatment, the TCM syndrome scores and the 24-hour PVC counts in both groups were reduced compared with those before treatment (P<0.01), and the above indexes in the observation group were lower than those in the control group (P<0.01). After treatment，scores of all SF-36 items in the observation group were increased compared with those before treatment (P<0.01); in the control group, the scores of general health (GH), social function (SF) and role-emotional (RE) were increased compared with those before treatment (P<0.05). After treatment, scores of all SF-36 items in the observation group were higher than those in the control group (P<0.01). The total effective rate in the observation group was 90.6% (29/32), which was higher than 46.9% (15/32) in the control group (P<0.01). CONCLUSION Wrist-ankle acupuncture has a significant clinical efficacy in the treatment of functional frequent PVCs. It can effectively improve symptoms such as chest tightness and palpitations, reduce 24-hour PVC count, and improve patients' quality of life.
Humans ; Acupuncture Therapy ; Male ; Female ; Middle Aged ; Adult ; Ventricular Premature Complexes/physiopathology* ; Ankle/physiopathology* ; Wrist/physiopathology* ; Acupuncture Points ; Treatment Outcome ; Aged ; Young Adult

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

A case of an elderly patient with severe aortic insufficiency who carried high risks for surgical valve replacement. After a detailed preoperative evaluation, the patient successfully received transapical transcatheter aortic valve replacement. Postoperatively, complete left bundle branch block developed, resulting in impaired left ventricular function. Despite guideline-directed medical therapy for heart failure, cardiac function showed no significant recovery. At 4.5 months post-surgery, left bundle branch pacing was performed, leading to a marked improvement in cardiac function.
Aged ; Humans ; Male ; Aortic Valve Insufficiency/surgery* ; Bundle-Branch Block/etiology* ; Cardiac Pacing, Artificial ; Postoperative Complications/therapy* ; Transcatheter Aortic Valve Replacement/adverse effects*

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

The electrocardiographic analysis of heart blocks provides great opportunities for the discussion of mechanisms of electrical cardiac conduction, serving as “teachable moments” in medicine. Recognition of heart blocks can sometimes be a challenge as they can present in many forms, different severities and levels of blocks that present as varied patterns on electrocardiographic tracing. The ultimate key to correct diagnosis rests on adequate understanding of normal electrophysiology of the electrical system of the heart. While it is vital to recognize the pattern, we should always know and understand the physiology behind the pattern. This article presents a detailed analysis of a case of heart block which can easily be misinterpreted on first look. The case is featured not for its rarity but for the interesting concepts in cardiac electrophysiology that are highlighted. Navigation of the different elements of tracing can be an adventure and a great learning experience enjoyed by both students and experts.
Heart Block ; Electrocardiography

Background and Objective: Premature ventricular complex (PVC) burden exhibits one of three circadian types, classified as fast-type, slow-type, and independent-type PVC. It is unknown whether PVC circadian types have different heart rate variability (HRV) parameter values. Therefore, this study aimed to evaluate differences in HRV circadian rhythm among fast-, slow-, and independent-type PVC. Methods: This cross-sectional observational study consecutively recruited 65 idiopathic PVC subjects (23 fast-, 20 slow-, and 22 independent-type) as well as five control subjects. Each subject underwent a 24-hour Holter to examine PVC burden and HRV. HRV analysis included components that primarily reflect global, parasympathetic, and sympathetic activities. Repeated measures analysis of variance was used to compare differences in HRV circadian rhythm by PVC type. Results. The average PVC burden was 15.7%, 8.4%, and 13.6% in fast-, slow-, and independent-type idiopathic PVC subjects, respectively. Global, parasympathetic nervous system, and sympathetic nervous system HRV parameters were significantly lower in independenttype PVC versus fast- and slow-type PVC throughout the day and night. Furthermore, we unexpectedly found that tendency towards sympathetic activity dominance during nighttime was only in independent-type PVC. Conclusion The HRV parameters are reduced in patients with independent-type PVC compared to fast- and slowtype PVC. Future research is warranted to determine possible differences in the prognosis between the three PVC types.
Ventricular Premature Complexes ; Circadian Rhythm ; Autonomic Nervous System

Humans ; Mutation, Missense ; KCNQ1 Potassium Channel/genetics* ; Long QT Syndrome/genetics* ; Phenotype ; Mutation ; Pedigree

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