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.

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 Published and unpublished studies indexed from inception until 2023 were included.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

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

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

Severe hypokalemia is defined as the concentration of serum potassium lower than 2.5 mmol/L, which may lead to serious arrhythmias and cause mortality. We report an unusual case of potentially fatal ventricular arrhythmias induced by severe hypokalemia in a patient undergoing laparoscopic partial nephrectomy in Peking University Third Hospital due to irregular use of indapamide before operation. Indapamide is a sulfonamide diuretic with vasodilative and calcium antagonistic effects, which enhances sodium delivery to the renal distal tubules resulting in a dose-related increase in urinary potassium excretion and decreases serum potassium concentrations. The electrolyte disorder caused by the diuretic is more likely to occur in the elderly patients, especially those with malnutrition or long-term fasting. Hence, the serum potassium concentration of the patients under indapamide therapy, especially elderly patients, should be monitored carefully. Meanwhile, the potassium concentration measured by arterial blood gas analysis is different from that measured by venous blood or laboratory test. According to the previous research, the concentration of potassium in venous blood was slightly higher than that in arterial blood, and the difference value was 0.1-0.5 mmol/L. This error should be taken into account when rapid intravenous potassium supplementation or reduction of blood potassium level was carried out clinically. In the correction of severe hypokalemia, the standard approach often did not work well for treating severe hypokalemia. The tailored rapid potassium supplementation strategy shortened the time of hypokalemia and was a safe and better treatment option to remedy life-threatening arrhythmias caused by severe hypokalemia with a high success rate. Through the anesthesia management of this case, we conclude that for the elderly patients who take indapamide or other potassium excretion diuretics, the electrolyte concentration and the general volume state of the patients should be comprehensively measured and fully evaluated before operation. It may be necessary for us to reexamine the serum electrolyte concentration before anesthesia induction on the morning of surgery in patients with the history of hypokalemia. For severe hypokalemia detected after anesthesia, central venous cannulation access for individualized rapid potassium supplementation is an effective approach to reverse the life-threatening arrhythmias caused by severe hypokalemia and ensure the safety of the patients.
Humans ; Aged ; Hypokalemia/complications* ; Indapamide/adverse effects* ; Arrhythmias, Cardiac/therapy* ; Diuretics/adverse effects* ; Potassium ; Electrolytes/adverse effects* ; Anesthesia, General/adverse effects*

OBJECTIVE: To investigate whether Suxiao Jiuxin Pills (SJP), a Chinese herbal remedy, is an anti-ventricular fibrillation (VF) agent. METHODS: VF was induced by isoproterenolol (ISO) intraperitoneal injection followed by electrical pacing in mice and rabbits. The effects of SJP on the L-type calcium channel current (CaV1.2), voltage-dependent sodium channel current (I_Na), rapid and slow delayed rectifier potassium channel current (I_Kr and I_Ks, respectively) were studied by whole-cell patch-clamp method. Computer simulation was implemented to incorporate the experimental data of SJP effects on the CaV1.2 current into the action potential (AP) and pseudo-electrocardiography (pseudo-ECG) models. RESULTS: SJP prevented VF induction and reduced VF durations significantly in mice and rabbits. Patch-clamp experiments revealed that SJP decreased the peak amplitude of the CaV1.2 current with a half maximal concentration (IC₅₀) value of 16.9 mg/L (SJP-30 mg/L, -32.8 ± 6.1 pA; Verapamil, -16.2 ±1.8 pA; vs. control, -234.5 ±16.7 pA, P<0.01, respectively). The steady-state activation curve, inactivation curve, and the recovery from inactivation of the CaV1.2 current were not shifted significantly. Specifically, SJP did not altered I_Na, I_Kr, and I_Ks currents significantly (SJP vs. control, P>0.05). Computer simulation showed that SJP-reduced CaV1.2 current shortened the AP duration, transiting VF into sinus rhythm in pseudo-ECG. CONCLUSION SJP reduced VF via inhibiting the CaV1.2 current with in vivo, in vitro, and in silico studies, which provide experimental basis for SJP anti-VF clinical application.
Animals ; Rabbits ; Mice ; Calcium ; Computer Simulation ; Arrhythmias, Cardiac ; Electrocardiography

Humans ; Arrhythmias, Cardiac ; Tachycardia, Ventricular/surgery*

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

In view of the high incidence of malignant diseases such as malignant arrhythmias in the elderly population, accidental injuries such as falls, and the problem of no witnesses when danger occurs, the study developed a human vital signs and body posture monitoring and positioning alarm system. Through the collection and analysis of electrocardiogram (ECG), respiration (RESP) and acceleration (ACC) signals, the system monitors human vital signs and body posture in real time, automatically judges critical states such as malignant arrhythmias and accidental falls on the local device side, and then issues alarm information, opens the positioning function, and uploads physiological information and patient location information through 4G communication. Experiments have shown that the system can accurately determine the occurrence of ventricular fibrillation and falls, and issue position and alarm information.
Humans ; Aged ; Arrhythmias, Cardiac/diagnosis* ; Ventricular Fibrillation ; Electrocardiography ; Accidental Falls ; Vital Signs ; Posture ; Monitoring, Physiologic