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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.
Premature Coronary Artery Disease Presenting as STEMI in a Teenager.
J Investig Med High Impact Case Rep
January 2025
LSU Health Shreveport, LA, USA.
An 18-year-old teenager with significant atherosclerotic cardiovascular disease (ASCVD) risk factors developed acute chest pain. His electrocardiogram showed inferior ST-segment elevations. Emergent coronary angiogram revealed complete thrombotic occlusion of the right coronary artery.
View Article and Find Full Text PDFEdge-AI Enabled Wearable Device for Non-Invasive Type 1 Diabetes Detection Using ECG Signals.
Bioengineering (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 PDFConstruction and evaluation of a triage assessment model for patients with acute non-traumatic chest pain: mixed retrospective and prospective observational study.
BMC Emerg Med
January 2025
Shengli Clinical Medical College of Fujian Medical University, Department of Emergency, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Key Laboratory of Emergency Medicine, Fuzhou, Fujian, China.
Background: Acute non-traumatic chest pain is one of the common complaints in the emergency department and is closely associated with fatal disease. Triage assessment urgently requires the use of simple, rapid tools to screen patients with chest pain for high-risk condition to improve patient outcomes.
Methods: After data preprocessing and feature selection, univariate and multiple logistic regression analyses were performed to identify potential predictors associated with acute non-traumatic chest pain.
Wearable heart rate variability analysis system based on ionic conductive hydrogels.
Talanta
January 2025
Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, 300072, China; School of Exercise and Health, Tianjin University of Sport, Tianjin, 300211, China. Electronic address:
Developing a wearable device that can continuously and reliably detect and evaluate heart rate variability (HRV) parameters is critical for the diabetic population with cardiac autonomic neuropathy (CAN). In this work, we proposed a zwitterionic conducting hydrogel that enabled a reliable and comfortable wearable device for the evaluation and detection of the autonomic nervous system (ANS). The hydrogel can achieve a strain of 2003 %, an electrical conductivity of 190 mS/m, and is capable of adhering to a variety of materials, including wood, plastic, and glass.
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