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Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors
Joint Authors
Vallejo, Enrique
Santos-Díaz, Alejandro
Hernández, Salvador
Jiménez-Ángeles, Luis
Valdés-Cristerna, Raquel
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-19
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Cardiac resynchronization therapy (CRT) improves functional classification among patients with left ventricle malfunction and ventricular electric conduction disorders.
However, a high percentage of subjects under CRT (20%–30%) do not show any improvement.
Nonetheless the presence of mechanical contraction dyssynchrony in ventricles has been proposed as an indicator of CRT response.
This work proposes an automated classification model of severity in ventricular contraction dyssynchrony.
The model includes clinical data such as left ventricular ejection fraction (LVEF), QRS and P-R intervals, and the 3 most significant factors extracted from the factor analysis of dynamic structures applied to a set of equilibrium radionuclide angiography images representing the mechanical behavior of cardiac contraction.
A control group of 33 normal volunteers (28±5 years, LVEF of 59.7%±5.8%) and a HF group of 42 subjects (53.12±15.05 years, LVEF < 35%) were studied.
The proposed classifiers had hit rates of 90%, 50%, and 80% to distinguish between absent, mild, and moderate-severe interventricular dyssynchrony, respectively.
For intraventricular dyssynchrony, hit rates of 100%, 50%, and 90% were observed distinguishing between absent, mild, and moderate-severe, respectively.
These results seem promising in using this automated method for clinical follow-up of patients undergoing CRT.
American Psychological Association (APA)
Santos-Díaz, Alejandro& Valdés-Cristerna, Raquel& Vallejo, Enrique& Hernández, Salvador& Jiménez-Ángeles, Luis. 2017. Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142056
Modern Language Association (MLA)
Santos-Díaz, Alejandro…[et al.]. Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1142056
American Medical Association (AMA)
Santos-Díaz, Alejandro& Valdés-Cristerna, Raquel& Vallejo, Enrique& Hernández, Salvador& Jiménez-Ángeles, Luis. Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142056
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142056