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Continuous atrial fibrillation monitoring using a wearable smartwatch: Using long-term Holter as reference.
Digit Health
January 2025
Department of Cardiology, Peking University First Hospital, Beijing, China.
Background: Wearables satisfactorily detect atrial fibrillation (AF) longer than 1 hour. Our study aims to evaluate smartwatch performances for long-term AF monitoring, including AF with short durations.
Methods: This prospective study enrolled AF patients from 2020 to 2023.
The added value of ECG abnormalities in predicting incident cardiovascular disease risk for people with type 2 diabetes: The Hoorn Diabetes Care System cohort.
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.
Implications of an Individualized Resuscitation Strategy Using Continuous Rhythm and Physiologic Status Assessment During Ongoing CPR.
Resuscitation
January 2025
Department of Medicine, University of Washington, Seattle, WA; King County Emergency Medical Services, Seattle-King County Department of Public Health, Seattle, WA.
Background: Prior studies have proposed defibrillator biosignal algorithms which characterize cardiac arrest rhythm and physiologic status. We evaluated whether a novel, individualized resuscitation strategy that integrates multiple ECG and impedance-based algorithms could reduce CPR interruptions and better align rescuer actions with patient-specific physiology.
Methods: In a retrospective cohort of ventricular fibrillation out-of-hospital cardiac arrests, observed rescuer actions (rhythm analysis, shock delivery, pulse checks, and drug therapy) were compared to hypothetical actions recommended by the proposed individualized strategy.
Enhancing cardiovascular disease classification in ECG spectrograms by using multi-branch CNN.
Comput Biol Med
January 2025
Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India. Electronic address:
Cardiovascular disease (CVD) is caused by the abnormal functioning of the heart which results in a high mortality rate across the globe. The accurate and early prediction of various CVDs from the electrocardiogram (ECG) is vital for the prevention of deaths caused by CVD. Artificial intelligence (AI) is used to categorize and accurately predict various CVDs.
View Article and Find Full Text PDFApelinergic System Affects Electrocardiographic Abnormalities Induced by Doxorubicin.
Biomedicines
January 2025
Chair and Department of Experimental and Clinical Physiology, Laboratory of Centre for Preclinical Research, Medical University of Warsaw, Banacha 1b, 02-097 Warsaw, Poland.
: Anthracyclines remain a pivotal element of numerous tumor management regimens; however, their utilization is associated with a range of adverse effects, the most significant of which is cardiotoxicity. Research is constantly being conducted to identify substances that could be incorporated into ongoing cancer chemotherapy to mitigate anthracycline-induced cardiotoxicity. Recently, the apelinergic system has received a lot of attention in this field due to its involvement in cardiovascular regulation.
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