A 21-year-old male patient underwent aortic and mitral valve replacement for progressive cardiac failure due to acute bacterial endocarditis. Ischemic myocardial contracture developed during attempts to restore cardiac activity following hypothermic, ischemic, cardioplegic arrest. An abdominal left ventricular assist device (ALVAD) was implanted and supported the circulation for nearly six days prior to cardiac transplantation. The preoperative EKG showed sinus tachycardia with left anterior hemiblock. Postoperatively, there was complete electromechanical dissociation. The postoperative EKG showed a superior and leftward shift of the axis. There was a marked loss of QRS voltage and variable degrees of atrioventricular block. At times, only P waves were present. On the fourth postoperative day, there was an axis shift to the extreme right. Prior to transplantation, sinus rhythm returned, and the axis shifted leftward once again. The common denominator of all the abnormal postoperative electrocardiograms was the conspicuous low voltage that probably signified early and extensive myocardial damage. To our knowledge, this is the first instance wherein a sequential electrocardiographic analysis of stone heart syndrome has been undertaken.
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Methods Mol Biol
December 2024
Manaaki Manawa - The Centre for Heart Research and the Department of Physiology, University of Auckland, Auckland, New Zealand.
The use of large animals in research provides a unique bridge between preclinical findings and clinical relevance, offering a valuable perspective for advancing our understanding of the complexities of heart failure. Multiple models of heart failure have been established with advantages and limitations of each model. Many insights have been gained from these models for understanding both pathophysiological mechanisms and therapeutic interventions for heart failure.
View Article and Find Full Text PDFHeart Rhythm
December 2024
Heart Rhythm Science Center, Minneapolis Heart Institute Foundation, Minneapolis, Minnesota.
Background: The adaptive cardiac resynchronization therapy (CRT) (aCRT) algorithm provides an important clinical benefit. However, a significant number of patients are nonresponders.
Objectives: The goals of this study were to quantify electrical synchrony in patients programmed with aCRT and to assess the echocardiographic effects of optimization in CRT nonresponders and incomplete responders.
J Electrocardiol
November 2024
Qingdao Municipal Hospital (Group), Qingdao, Shandong Province, China. Electronic address:
Objective: The present study was conducted to assess the accuracy and reliability of portable 12‑lead electrocardiography (ECG) devices in patients with heart disease.
Materials And Methods: This single-center, prospective, blinded study enrolled 62 patients between September and October 2023 from the Heart Center of a Class III hospital. In sequential tests on each patient, heart rate (HR) and the PR, QT, QTc and QRS intervals of ECG recordings obtained with a portable 12‑lead device (Weheal, CN) were compared with those obtained via conventional 12‑lead ECG.
Med Image Anal
February 2025
University of Oxford, Oxford, United Kingdom.
Cardiac digital twins are computational tools capturing key functional and anatomical characteristics of patient hearts for investigating disease phenotypes and predicting responses to therapy. When paired with large-scale computational resources and large clinical datasets, digital twin technology can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an open-source automated pipeline for personalising ventricular electrophysiological function based on routinely acquired magnetic resonance imaging (MRI) data and the standard 12-lead electrocardiogram (ECG).
View Article and Find Full Text PDFSensors (Basel)
November 2024
School of Integrated Circuits, Shandong University, Jinan 250101, China.
Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their high weight compression rate. However, multi-class classification of ECG signals based on bCNNs is challenging due to the accuracy loss introduced by the binarization operation. In this paper, an effective multi-classifier system is proposed for electrocardiogram (ECG) signals using a binarized depthwise separable convolutional neural network (bDSCNN) with the merged convolution-pooling (MCP) method.
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