The Value of Baseline and Arrhythmic ECG in the Interpretation of Arrhythmic Mechanisms

Clinical Characteristics of Moxifloxacin-Related Arrhythmias and Development of a Predictive Nomogram: A Case Control Study

This study aimed to analyze the incidence, clinical characteristics, and risk factors of moxifloxacin-related arrhythmias and electrocardiographic alterations in hospitalized patients using real-world data. Concurrently, a nomogram was established and validated to provide a practical tool for prediction. Retrospective automatic monitoring of inpatients using moxifloxacin was performed in a Chinese hospital from January 1, 2017, to December 31, 2021, to obtain the incidence of drug-induced arrhythmias and electrocardiographic alterations. Propensity score matching was conducted to balance confounders and analyze clinical characteristics. Based on the risk and protective factors identified through logistic regression analysis, a prediction nomogram was developed and internally validated using the Bootstrap method. Arrhythmias and electrocardiographic alterations occurred in 265 of 21,711 cases taking moxifloxacin, with an incidence of 1.2%. Independent risk factors included medication duration (odds ratio [OR] 1.211, 95% confidence interval [CI] 1.156-1.270), concomitant use of meropenem (OR 4.977, 95% CI 2.568-9.644), aspartate aminotransferase >40 U/L (OR 3.728, 95% CI 1.800-7.721), glucose >6.1 mmol/L (OR 2.377, 95% CI 1.531-3.690), and abnormally elevated level of amino-terminal brain natriuretic peptide precursor (OR 2.908, 95% CI 1.640-5.156). Concomitant use of cardioprotective drugs (OR 0.430, 95% CI 0.220-0.841) was a protective factor. The nomogram showed good differentiation and calibration, with enhanced clinical benefit. The incidence of moxifloxacin-related arrhythmias and electrocardiographic alterations is in the range of common. The nomogram proves valuable in predicting the risk in the moxifloxacin-administered population, offering significant clinical applications.

An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction

This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The model performs pre-processing of the heartbeat signal by automatically and efficiently extracting time-domain, time-frequency-domain and multi-scale features at different scales. These features are imported into an adaptive online convolutional network-based classification inference module for arrhythmia diagnosis. Experimental results show that the AOCT-based deep learning neural network diagnostic module has excellent parallel computing and classification inference capabilities, and the overall performance of the model improves with increasing scales. In particular, when multi-scale features are used as inputs, the model is able to learn both time-frequency domain information and other rich information, thus significantly improving the performance of the end-to-end diagnostic model. The final results show that the AOCT-based deep learning neural network model has an average accuracy of 99.72%, a recall of 99.62%, and an F1 score of 99.3% in diagnosing four common heart diseases.

Catheter Ablation of Life-Threatening Ventricular Arrhythmias in Athletes

A recent surveillance analysis indicates that cardiac arrest/death occurs in ≈1:50,000 professional or semi-professional athletes, and the most common cause is attributable to life-threatening ventricular arrhythmias (VAs). It is critically important to diagnose any inherited/acquired cardiac disease, including coronary artery disease, since it frequently represents the arrhythmogenic substrate in a substantial part of the athletes presenting with major VAs. New insights indicate that athletes develop a specific electro-anatomical remodeling, with peculiar anatomic distribution and VAs patterns. However, because of the scarcity of clinical data concerning the natural history of VAs in sports performers, there are no dedicated recommendations for VA ablation. The treatment remains at the mercy of several individual factors, including the type of VA, the athlete's age, and the operator's expertise. With the present review, we aimed to illustrate the prevalence, electrocardiographic (ECG) features, and imaging correlations of the most common VAs in athletes, focusing on etiology, outcomes, and sports eligibility after catheter ablation.

New Parameter of the Second Half of the P-Wave, P-Wave Duration, and Atrial Conduction Times Predict Atrial Fibrillation during Electrophysiological Studies

OBJECTIVE

Several P-wave parameters reflect atrial conduction characteristics and have been used to predict atrial fibrillation (AF). The aim of this study was to determine the relationship between maximum P-wave duration (PMax) and new P-wave parameters, with atrial conduction times (CT), and to assess their predictive value of AF during electrophysiological studies (AF-EPS).

SUBJECTS AND METHODS

This was a cross-sectional study in 153 randomly selected patients aged 18-70 years, undergoing EPS. The patients were divided into 2 groups designated as no AF-EPS and AF-EPS, depending on whether AF occurred during EPS or not. Different P-wave parameters and atrial CT were compared for both study groups. Subsequently, the predictive value of the P-wave parameters and the atrial CT for AF-EPS was evaluated.

RESULTS

The values of CT, PMax, and maximum Ppeak-Pend interval (Pp-eMax) were significantly higher in patients with AF-EPS. Almost all P-wave parameters were correlated with the left CT. PMax, Pp-eMax, and CT were univariate and multivariate predictors of AF-EPS. The largest ROC area was presented by interatrial CT (0.852; p < 0.001; cutoff value: ≥82.5 ms; sensitivity: 91.1%; specificity: 81.1%). Pp-eMax showed greater sensitivity (79.5%) to discriminate AF-EPS than PMax (72.7%), but the latter had better specificity (60.4% vs. 41.5%).

CONCLUSIONS

Left atrial CT were directly and significantly correlated with PMax and almost all the parameters of the second half of the P-wave. CT, PMax, and Pp-eMax (new parameter) were good predictors of AF-EPS, although CT did more robustly.

Electrocardiographic Characteristics of Breast Cancer Patients Treated with Chemotherapy

Introduction

Patients receiving chemotherapy for breast cancer may be at risk of developing cardiac dysfunction and electrophysiological abnormalities. The aim of this study is to evaluate alterations in electrocardiographic (ECG) parameters in breast cancer patients receiving chemotherapy.

Materials and Methods

This was a prospective single-center cohort study conducted in the Fourth Hospital of Hebei Medical University, China. Participants with breast cancer referred for chemotherapy from May 1, 2019, to October 1, 2019, were invited to participate in the study. Standard 12-lead ECG and echocardiography were performed at baseline or before chemotherapy (prechemotherapy) (T0), after 1 cycle (T1), after 3 cycles (T2), and at the end of chemotherapy (T3).

Results

A total of 64 patients with diagnosed breast cancer undergoing chemotherapy were included. Echocardiographic parameters showed no significant variation during the entire procedure (all P > 0.05). The incidence of abnormal ECG increased from 43.75% at baseline to 65.63% at the end of chemotherapy, of which only the prevalence of fragmented QRS (fQRS) was significantly increased after the drug regimen (26.56% to 53.13%). At the end of the treatment, heart rate, P-wave dispersion, corrected QT interval, T-peak to T-end, RR, SV1, RV5, Sokolow–Lyon index (SLI), and index of cardioelectrophysiological balance deteriorated markedly (all P < 0.05). The area under the curve for SLI and QT dispersion (QTd) derived by ECG was 0.710 and 0.606, respectively. The cutoff value with 2.12 of SLI by ECG had a sensitivity of 67.2% and specificity of 71.9% for differentiating patients after therapy from baselines. The cutoff value with 0.55 of QTd had a sensitivity of 60.9% and specificity of 60.9%.

Conclusions

The current study demonstrated that ECGs can be used to detect electrophysiological abnormalities in breast cancer patients receiving chemotherapy. ECG changes can reflect subclinical cardiac dysfunction before the echocardiographic abnormalities.