Literature DB >> 28017302

Cardiodynamicsgram as a New Diagnostic Tool in Coronary Artery Disease Patients With Nondiagnostic Electrocardiograms.

Muqing Deng1, Min Tang2, Cong Wang3, Liang Shan4, Linfeng Zhang4, Jingtao Zhang4, Weiming Wu1, Ling Xia5.   

Abstract

Cardiodynamicsgram (CDG) has emerged recently as a noninvasive spatiotemporal electrocardiographic method for subtle cardiac dynamics information analysis within electrocardiogram (ECG). This study sought to evaluate the clinical utility of CDG for early coronary artery disease (CAD) detection in suspected patients with CAD presenting with nondiagnostic ECGs. A total of 421 suspected patients with CAD presenting with nondiagnostic ECG were enrolled. Standard 12-lead ECG and CDG were performed simultaneously, 1 day before invasive coronary angiography. Diagnostic accuracy of CDG for early CAD detection was assessed with reference to coronary angiography as the gold standard. Coronary angiography showed ≥1 coronary arteries stenosis of >50% in 347 patients. Of these 347 patients with CAD, 294 patients were positive in CDG. Of 74 non-CAD controls, 63 patients were negative in CDG. Diagnostic accuracy of CDG at presentation for CAD was 84.6%, sensitivity 84.7%, and specificity 83.7%. In patients presenting with nondiagnostic ECGs, an abnormal status can be detected early through noninvasive CDG. CDG is highly sensitive for the early diagnosis of CAD. Underlying causes of false-negative CDG findings included number of diseased coronary arteries and collateral circulation. Subtle myocardial damage that was not detectable on coronary angiography might be the major cause of false-positive findings.
Copyright © 2016 Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 28017302     DOI: 10.1016/j.amjcard.2016.11.028

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  3 in total

1.  Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram.

Authors:  Xiaoye Zhao; Jucheng Zhang; Yinglan Gong; Lihua Xu; Haipeng Liu; Shujun Wei; Yuan Wu; Ganhua Cha; Haicheng Wei; Jiandong Mao; Ling Xia
Journal:  Front Physiol       Date:  2022-05-30       Impact factor: 4.755

2.  The predictive value of Cardiodynamicsgram in myocardial perfusion abnormalities.

Authors:  Xunde Dong; Jinhe Zhang; Hongji Lai; Min Tang; Shanxing Ou; Jianhong Dou; Cong Wang
Journal:  PLoS One       Date:  2018-12-17       Impact factor: 3.240

3.  All-Cause Death Prediction Method for CHD Based on Graph Convolutional Networks.

Authors:  Yutao Xue; Kaizhi Chen; Huizhong Lin; Shangping Zhong
Journal:  Comput Intell Neurosci       Date:  2022-07-18
  3 in total

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