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Echocardiographic view and feature selection for the estimation of the response to CRT

Authors :
Alban Gallard
Elena Galli
Arnaud Hubert
Auriane Bidaut
Virginie Le Rolle
Otto Smiseth
Jens-Uwe Voigt
Erwan Donal
Alfredo I. Hernández
Source :
PLoS ONE, Vol 16, Iss 6 (2021)
Publication Year :
2021
Publisher :
Public Library of Science (PLoS), 2021.

Abstract

Cardiac resynchronization therapy (CRT) is an implant-based therapy applied to patients with a specific heart failure (HF) profile. The identification of patients that may benefit from CRT is a challenging task and the application of current guidelines still induce a non-responder rate of about 30%. Several studies have shown that the assessment of left ventricular (LV) mechanics by speckle tracking echocardiography can provide useful information for CRT patient selection. A comprehensive evaluation of LV mechanics is normally performed using three different echocardioraphic views: 4, 3 or 2-chamber views. The aim of this study is to estimate the relative importance of strain-based features extracted from these three views, for the estimation of CRT response. Several features were extracted from the longitudinal strain curves of 130 patients and different methods of feature selection (out-of-bag random forest, wrapping and filtering) have been applied. Results show that more than 50% of the 20 most important features are calculated from the 4-chamber view. Although features from the 2- and 3-chamber views are less represented in the most important features, some of the former have been identified to provide complementary information. A thorough analysis and interpretation of the most informative features is also provided, as a first step towards the construction of a machine-learning chain for an improved selection of CRT candidates.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
19326203
Volume :
16
Issue :
6
Database :
Directory of Open Access Journals
Journal :
PLoS ONE
Publication Type :
Academic Journal
Accession number :
edsdoj.541593259826404d9ff5aefb6998adac
Document Type :
article