New expectations for diastolic function assessment in transthoracic echocardiography based on a semi-automated computing of strain-volume loops

Early diagnosis of heart failure with preserved ejection fraction (HFpEF) by determination of diastolic dysfunction is challenging. Strain-volume loop (SVL) is a new tool to analyse left ventricular function. We propose a new semi-automated method to calculate SVL area and explore the added value of...

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Bibliographic Details
Published inEuropean heart journal cardiovascular imaging Vol. 21; no. 12; p. 1366
Main Authors Hubert, Arnaud, Le Rolle, Virginie, Galli, Elena, Bidaud, Auriane, Hernandez, Alfredo, Donal, Erwan
Format Journal Article
LanguageEnglish
Published England 01.12.2020
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Summary:Early diagnosis of heart failure with preserved ejection fraction (HFpEF) by determination of diastolic dysfunction is challenging. Strain-volume loop (SVL) is a new tool to analyse left ventricular function. We propose a new semi-automated method to calculate SVL area and explore the added value of this index for diastolic function assessment. Fifty patients (25 amyloidosis, 25 HFpEF) were included in the study and compared with 25 healthy control subjects. Left ventricular ejection fraction was preserved and similar between groups. Classical indices of diastolic function were pathological in HFpEF and amyloidosis groups with greater left atrial volume index, greater mitral average E/e' ratio, faster tricuspid regurgitation (P < 0.0001 compared with controls). SVL analysis demonstrated a significant difference of the global area between groups, with the smaller area in amyloidosis group, the greater in controls and a mid-range value in HFpEF group (37 vs. 120 vs. 72 mL.%, respectively, P < 0.0001). Applying a linear discriminant analysis (LDA) classifier, results show a mean area under the curve of 0.91 for the comparison between HFpEF and amyloidosis groups. SVLs area is efficient to identify patients with a diastolic dysfunction. This new semi-automated tool is very promising for future development of automated diagnosis with machine-learning algorithms.
ISSN:2047-2412
DOI:10.1093/ehjci/jeaa123