Mechanisms of ventricular arrhythmias elicited by coexistence of multiple electrophysiological remodeling in ischemia: A simulation study

Efficient electrocardiogram generation based on cardiac electric vector simulation model

This study introduces a novel Cardiac Electric Vector Simulation Model (CEVSM) to address the computational inefficiencies and low fidelity of traditional electrophysiological models in generating electrocardiograms (ECGs). Our approach leverages CEVSM to efficiently produce reliable ECG samples, facilitating data augmentation essential for the computer-aided diagnosis of myocardial infarction (MI). Significantly, experimental results show that our model dramatically reduces computation time compared to conventional models, with the self-adapting regression transformation matrix method (SRTM) providing clear advantages. SRTM not only achieves high fidelity in ECG simulations but also ensures exceptional consistency with the gold standard method, greatly enhancing MI localization accuracy by data augmentation. These advancements highlight the potential of our model to generate dependable ECG training samples, making it highly suitable for data augmentation and significantly advancing the development and validation of intelligent MI diagnostic systems. Furthermore, this study demonstrates the feasibility of applying life system simulations in the training of medical big models.

A simulation study on the antiarrhythmic mechanisms of established agents in myocardial ischemia and infarction

Patients with myocardial ischemia and infarction are at increased risk of arrhythmias, which in turn, can exacerbate the overall risk of mortality. Despite the observed reduction in recurrent arrhythmias through antiarrhythmic drug therapy, the precise mechanisms underlying their effectiveness in treating ischemic heart disease remain unclear. Moreover, there is a lack of specialized drugs designed explicitly for the treatment of myocardial ischemic arrhythmia. This study employs an electrophysiological simulation approach to investigate the potential antiarrhythmic effects and underlying mechanisms of various pharmacological agents in the context of ischemia and myocardial infarction (MI). Based on physiological experimental data, computational models are developed to simulate the effects of a series of pharmacological agents (amiodarone, telmisartan, E-4031, chromanol 293B, and glibenclamide) on cellular electrophysiology and utilized to further evaluate their antiarrhythmic effectiveness during ischemia. On 2D and 3D tissues with multiple pathological conditions, the simulation results indicate that the antiarrhythmic effect of glibenclamide is primarily attributed to the suppression of efflux of potassium ion to facilitate the restitution of [K+]o, as opposed to recovery of IKATP during myocardial ischemia. This discovery implies that, during acute cardiac ischemia, pro-arrhythmogenic alterations in cardiac tissue's excitability and conduction properties are more significantly influenced by electrophysiological changes in the depolarization rate, as opposed to variations in the action potential duration (APD). These findings offer specific insights into potentially effective targets for investigating ischemic arrhythmias, providing significant guidance for clinical interventions in acute coronary syndrome.

An ECG generative model of myocardial infarction

Background and Objective Computer-aided diagnosis (CAD) of Myocardial Infarction (MI) using machine learning depends on a large amount of clinical Electrocardiogram (ECG) data. Existing infarct ECG databases face the problem of class imbalance. Data augmentation using generative simulation models is a new approach to effectively address this problem. Methods A multiscale ECG generative model was established for ECG data augmentation. In the cellular layer, an ischemic Action Potential (AP) model was established to generate APs in cardiomyocytes with different transmural regions of infraction or different ischemic durations. In the tissue layer, a probability-driven cellular automata excitation propagation model was established to simulate the propagation speed and direction of excitation. An infarct tissue model and a coronary artery model were established to describe the spatiotemporal diversity of MI. A ventricle model, a human torso model, and a computational model of surface ECG based on field source theory were established in the heart-torso layer. Results The model generated pathological 12-lead ECGs of MI with different topography and different extent. When simulating different ventricular wall infarction, the lesions appear in the same leads as the clinical 12-lead ECG. The ST-segment decreases and the T-wave amplitude decreases, similar to the clinical ECG features when simulating subendocardial ischemia. The average fidelity of the 12-lead ECG the model generated is 95.6%, according to the designed DTW-GRA distance algorithm. Conclusions The generative model considers the electrophysiological properties of the natural heart, the pathology of myocardial infarction, and the diversity of clinical ECGs. The model can provide many reliable samples for machine learning of MI.

In-hospital arrhythmic burden reduction in diabetic patients with acute myocardial infarction treated with SGLT2-inhibitors: Insights from the SGLT2-I AMI PROTECT study

Background

Sodium-glucose co-transporter 2 inhibitors (SGLT2-i) have shown significant cardiovascular benefits in patients with and without type 2 diabetes mellitus (T2DM). They have also gained interest for their potential anti-arrhythmic role and their ability to reduce the occurrence of atrial fibrillation (AF) and ventricular arrhythmias (VAs) in T2DM and heart failure patients.

Objectives

To investigate in-hospital new-onset cardiac arrhythmias in a cohort of T2DM patients presenting with acute myocardial infarction (AMI) treated with SGLT2-i vs. other oral anti-diabetic agents (non-SGLT2-i users).

Methods

Patients from the SGLT2-I AMI PROTECT registry (NCT05261867) were stratified according to the use of SGLT2-i before admission for AMI, divided into SGLT2-i users vs. non-SGLT2-i users. In-hospital outcomes included the occurrence of in-hospital new-onset cardiac arrhythmias (NOCAs), defined as a composite of new-onset AF and sustained new-onset ventricular tachycardia (VT) and/or ventricular fibrillation (VF) during hospitalization.

Results

The study population comprised 646 AMI patients categorized into SGLT2-i users (111 patients) and non-SGLT2-i users (535 patients). SGLT2-i users had a lower rate of NOCAs compared with non-SGLT2-i users (6.3 vs. 15.7%, p = 0.010). Moreover, SGLT2-i was associated with a lower rate of AF and VT/VF considered individually (p = 0.032). In the multivariate logistic regression model, after adjusting for all confounding factors, the use of SGLT2-i was identified as an independent predictor of the lower occurrence of NOCAs (OR = 0.35; 95%CI 0.14-0.86; p = 0.022). At multinomial logistic regression, after adjusting for potential confounders, SGLT2-i therapy remained an independent predictor of VT/VF occurrence (OR = 0.20; 95%CI 0.04-0.97; p = 0.046) but not of AF occurrence.

Conclusions

In T2DM patients, the use of SGLT2-i was associated with a lower risk of new-onset arrhythmic events during hospitalization for AMI. In particular, the primary effect was expressed in the reduction of VAs. These findings emphasize the cardioprotective effects of SGLT2-i in the setting of AMI beyond glycemic control.

Trial registration

Data are part of the observational international registry: SGLT2-I AMI PROTECT. ClinicalTrials.gov, identifier: NCT05261867.