Literature DB >> 25242299

The utility of fully automated real-time three-dimensional echocardiography in the evaluation of left ventricular diastolic function.

Koki Nakanishi1, Shota Fukuda2, Hiroyuki Watanabe3, Yoshihiro Seo4, Keitaro Mahara5, Eiichi Hyodo6, Kenichiro Otsuka1, Tomoko Ishizu4, Kenei Shimada1, Tetsuya Sumiyoshi5, Kazutaka Aonuma4, Hitonobu Tomoike5, Junichi Yoshikawa6.   

Abstract

BACKGROUND: A novel real-time three-dimensional echocardiography (RT3DE) system allows fully automated quantification of the left ventricular (LV) volume throughout a cardiac cycle. This study aimed to investigate whether an LV time-volume curve, obtained using fully automated RT3DE, is useful in the evaluation of LV diastolic function.
METHODS: First, 15 patients underwent simultaneous standard two-dimensional echocardiography (2DE), RT3DE, and cardiac catheterization to measure the time constant of the isovolumic-pressure decline (τ). From the LV time-volume curve obtained using RT3DE, peak early filling rate (PFR) during diastole was generated and indexed for LV end-systolic volume. Next 570 patients, who were scheduled for both 2DE and RT3DE examinations, were enrolled to investigate the association between PFR index and 2DE-evidenced diastolic dysfunction and clinical characteristics.
RESULTS: Of the 585 patients, RT3DE analysis was adequate in 542 patients (feasibility 93%). In the 15 patients, PFR index showed significant correlation with τ (r=-0.65, p=0.009). In the remaining 527 patients, PFR index was related to age (r=-0.24, p<0.001) and e' (r=0.41, p<0.001). PFR index decreased in proportion to the grade of 2DE-evidenced diastolic dysfunction. All patients with normal diastolic function had a PFR index greater than 2.0.
CONCLUSIONS: This study demonstrated that a novel, fully automated RT3DE-derived PFR index was the diagnostic tool of choice for the assessment of LV diastolic function.
Copyright © 2014. Published by Elsevier Ltd.

Entities:  

Keywords:  Diagnosis; Diastolic dysfunction; Echocardiography

Mesh:

Year:  2014        PMID: 25242299     DOI: 10.1016/j.jjcc.2014.08.007

Source DB:  PubMed          Journal:  J Cardiol        ISSN: 0914-5087            Impact factor:   3.159


  1 in total

1.  Machine learning based automated dynamic quantification of left heart chamber volumes.

Authors:  Akhil Narang; Victor Mor-Avi; Aldo Prado; Valentina Volpato; David Prater; Gloria Tamborini; Laura Fusini; Mauro Pepi; Neha Goyal; Karima Addetia; Alexandra Gonçalves; Amit R Patel; Roberto M Lang
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2019-05-01       Impact factor: 6.875

  1 in total

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