INTRODUCTION

Ventricular tachycardia (VT) storm is a condition characterized by recurrent ventricular arrhythmias within a 24-hour period, requiring a device or pharmacologic intervention. Despite its clinical significance, data on VT storm prevalence and treatment outcomes in the Filipino population remain limited

We present a 69-year-old male with heart failure from non-ischemic cardiomyopathy and an implantable cardioverter-defibrillator (ICD), who experienced multiple VT episodes unresponsive to amiodarone, lidocaine and mechanical cardioversion. He was initially admitted for catheter ablation but later developed a left ventricular thrombus precluding the procedure. Mexiletine was introduced and successfully suppressed arrhythmia recurrence

This case emphasized the complexity of managing ES, especially in patients with contraindications to ablation. Mexiletine, a class IB antiarrhythmic agent structurally similar to lidocaine, has shown efficacy in refractory VT, especially when standard therapies are ineffective or are contraindicated. Limited data exists on its safety for such cases, particularly in patients with intracardiac thrombus.

Mexiletine may offer a viable treatment option for VT storm in patients ineligible for ablation due to left ventricular thrombus. While it was effective in this case, further studies are needed to validate its safety and long-term outcomes in similar high-risk populations.

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

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.

The data of a patient with carbamate pesticide poisoning were analyzed. Cardiac arrest, oliguria, acute renal injury and pulmonary infection occurred during treatment. After cardiopulmonary resuscitation, tracheal intubation, CRRT, anti-infection and other symptomatic support treatment, the patient recovered and discharged. The myocardial damage caused by carbamate pesticide poisoning is easy to be ignored, and it often causes cardiac manifestations such as arrhythmia and cardiac insufficiency, and the related markers of cardiac injury, electrocardiogram and echocardiogram are also changed. Therefore, the awareness of cardiac damage caused by carbamate pesticide poisoning should be improved.
Humans ; Pesticides ; Carbamates ; Heart Arrest ; Arrhythmias, Cardiac ; Poisoning/therapy* ; Organophosphate Poisoning

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

Electrocardiogram (ECG) is a low-cost, simple, fast, and non-invasive test. It can reflect the heart's electrical activity and provide valuable diagnostic clues about the health of the entire body. Therefore, ECG has been widely used in various biomedical applications such as arrhythmia detection, disease-specific detection, mortality prediction, and biometric recognition. In recent years, ECG-related studies have been carried out using a variety of publicly available datasets, with many differences in the datasets used, data preprocessing methods, targeted challenges, and modeling and analysis techniques. Here we systematically summarize and analyze the ECG-based automatic analysis methods and applications. Specifically, we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes. Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications. Finally, we elucidated some of the challenges in ECG analysis and provided suggestions for further research.
Humans ; Arrhythmias, Cardiac/diagnosis* ; Electrocardiography/methods* ; Algorithms

OBJECTIVE: To retrieve the evidence for threshold setting of multi-parameter electrocardiograph (ECG) monitors in intensive care unit (ICU), and summarize the best evidence. METHODS: After literature retrieval, clinical guidelines, expert consensus, evidence summary and systematic review that met the requirements were screened. Guidelines were evaluated by the appraisal of guidelines for research and evaluation II (AGREE II), expert consensus and systematic review were evaluated by the Australian JBI evidence-based health care center authenticity evaluation tool, and evidence summary was evaluated by the CASE checklist. High-quality literature was selected to extract evidence related to the use and setup of multi-parameter ECG monitors in the ICU. RESULTS: A total of 19 literatures were included, including 7 guidelines, 2 expert consensus, 8 systematic reviews, 1 evidence summary, and 1 national industry standard. After evidence extraction, translation, proofreading and summary, a total of 32 pieces of evidence were integrated. The included evidence involved the environmental preparation for the application of the ECG monitor, the electrical requirements of the ECG monitor, ECG monitor use process, ECG monitor alarm setting principles, ECG monitor alarm heart rate or heart rhythm monitoring setting, ECG monitor alarm blood pressure monitoring setting, ECG monitor alarm respiratory and blood oxygen saturation threshold setting, alarm delay warning time setting, adjusting alarm setting method, evaluating alarm setting time, improving the comfort of monitoring patients, reducing nuisance alarm report the occurrence, alarm priority processing, alarm intelligent processing and so on. CONCLUSIONS This summary of evidence involves many aspects of the setting and application of ECG monitor. According to the latest guidelines and expert consensus, it is updated and revised to guide healthcare workers to monitor patients more scientifically and safely, and aims to ensure patient safety.
Humans ; Clinical Alarms ; Australia ; Intensive Care Units ; Arrhythmias, Cardiac ; Electrocardiography ; Monitoring, Physiologic

Temporary cardiac pacing is an essential technique in the diagnosis and treatment of arrhythmias. Due to its urgency, complexity, and uncertainty, it is necessary to develop an evidence-based emergency operation norms. Currently, there is no specific consensus guidelines at home or abroad. The Emergency Branch of Chinese Medical Association organized relevant experts to draft the Chinese emergency expert consensus on bedside temporary cardiac pacing (2023) to guide the operation and application of bedside cardiac pacing. The formulation of the consensus adopts the consensus meeting method and the evidentiary basis and recommendation grading of the Oxford Center for Evidence-based Medicine in the United States. A total of 13 recommendations were extracted from the discussion on the methods of bedside temporary cardiac pacing, the puncture site of transvenous temporary cardiac pacing, the selection of leads, the placement and placement of leads, pacemaker parameter settings, indications, complications and postoperative management. The recommended consensus includes the choice between transcutaneous and transvenous pacing, preferred venous access for temporary transvenous pacing, the target and best guidance method for implantation of bedside pacing electrodes, recommended default pacemaker settings, recommended indications for sinoatrial node dysfunction, atrioventricular block, acute myocardial infarction, cardiac arrest, ventricular and supraventricular arrhythmias. They also recommended ultrasound guidance and a shortened temporary pacing support time to reduce complications of temporary transvenous cardiac pacing, recommended bedrest, and anticoagulation after temporary transvenous pacing. Bedside temporary cardiac pacing is generally safe and effective. Accurate assessment, correct selection of the pacing mode, and timely performance of bedside temporary cardiac pacing can further improve the survival rate and prognosis of related emergency patients.
Humans ; Cardiac Pacing, Artificial/methods* ; Pacemaker, Artificial ; Arrhythmias, Cardiac/therapy* ; Myocardial Infarction/therapy* ; Electrodes