Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors.
Comput Math Methods Med
; 2017: 3087407, 2017.
Article
em En
| MEDLINE
| ID: mdl-28348637
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Informática Médica
/
Processamento de Sinais Assistido por Computador
/
Coração
/
Insuficiência Cardíaca
Tipo de estudo:
Prognostic_studies
Limite:
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
Comput Math Methods Med
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
México