Background: The study examined the electrocardiographic and electrophysiologic characteristics in relation to programmed ventricular stimulation (PVS)-induced ventricular fibrillation (VF) in patients with Brugada syndrome.
Methods And Results: Thirty-four patients with a Brugada-type electrocardiogram (ECG) were enrolled. Twelve patients had a type 1 ECG, 12 had a type 2 ECG, and 10 had a type 3 ECG. PVS was performed with up to 2 ventricular premature beats from the right ventricular apex and outflow tract at 2 basic cycle lengths (600 and 400 ms). VF was induced in 17 of 23 (74%) asymptomatic patients and 10 of 11 (91%) symptomatic patients (p<0.05). The 27 patients in whom VF was induced by PVS and 7 patients without inducible VF were followed up for 47.1+/-33.7 months. One sudden death occurred during the follow-up period among asymptomatic patients with inducible VF, and no sudden death occurred among patients without inducible VF.
Conclusions: In conclusion, inducibility of ventricular arrhythmia is high in patients with Brugada syndrome, but it does not correlate with clinical presentation. Few arrhythmic events occur during follow up. However, the present study data suggest that electrophysiologic study-induced VF does not predict arrhythmic events during follow up.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1253/circj.71.1437 | DOI Listing |
J Int Med Res
January 2025
Cardiovascular Surgery, University Hospital of Lausanne, Lausanne, Switzerland.
Objective: The definition of coronary artery bypass graft (CABG)-associated myocardial infarction (MI) is controversial because the postoperative increases in cardiac enzyme activities are multifactorial in origin.
Methods: We performed a retrospective case-control study of patients who experienced perioperative MI (cardiac enzyme release, electrocardiographic changes, dysfunction on echocardiography) and those without ischemia to identify risk factors and enzyme activity thresholds.
Results: The estimated incidence of CABG-associated MI was 2.
Eur J Prev Cardiol
January 2025
Amsterdam UMC location Vrije Universiteit Amsterdam, Department of General Practice Medicine, De Boelelaan 1117, Amsterdam, The Netherlands.
Aims: To investigate if adding ECG abnormalities as a predictor improves the performance of incident CVD-risk prediction models for people with type 2 diabetes (T2D).
Methods: We evaluated the four major prediction models that are recommended by the guidelines of the American College of Cardiology/American Heart Association and European Society of Cardiology, in 11,224 people with T2D without CVD (coronary heart disease, heart failure, stroke, thrombosis) from the Hoorn Diabetes Care System cohort (1998-2018). Baseline measurements included CVD-risk factors and ECG recordings coded according to the Minnesota Classification as no, minor or major abnormalities.
Int J Environ Res Public Health
January 2025
Study Design and Scientific Writing Laboratory, Centro Universitario FMABC, Santo André 09060-870, SP, Brazil.
The trained heart adapts through geometric changes influenced by concentric and eccentric hypertrophy, depending on the predominance of the isometric or dynamic components of the exercise performed. Additionally, alterations in heart rhythm may occur due to increased vagal system activity. Cardiological evaluation with an electrocardiogram (ECG) aims to identify cardiac conditions that could temporarily or permanently disqualify an athlete from competition.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy.
Diabetes is a chronic condition, and traditional monitoring methods are invasive, significantly reducing the quality of life of the patients. This study proposes the design of an innovative system based on a microcontroller that performs real-time ECG acquisition and evaluates the presence of diabetes using an Edge-AI solution. A spectrogram-based preprocessing method is combined with a 1-Dimensional Convolutional Neural Network (1D-CNN) to analyze the ECG signals directly on the device.
View Article and Find Full Text PDFItal J Pediatr
January 2025
Pediatrics Department, Genetics Unit, Mansoura University, Mansoura, Egypt.
Background: Pompe disease is a rare genetic disorder caused by a deficiency of the enzyme acid alpha-glucosidase. This condition leads to muscle weakness, respiratory problems, and heart abnormalities in affected individuals.
Methods: The aim of the study is to share our experience through cross sectional study of patients with infantile-onset Pompe disease (IOPD) with different genetic variations, resulting in diverse clinical presentations.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!