Left ventricular outflow tract velocity-time integral improves outcome prediction in patients with secondary mitral regurgitation.

Cardiac output Ejection fraction Heart failure LVOT-VTI Mitral regurgitation Stroke volume Systolic function

Journal

International journal of cardiology
ISSN: 1874-1754
Titre abrégé: Int J Cardiol
Pays: Netherlands
ID NLM: 8200291

Informations de publication

Date de publication:
01 Dec 2023
Historique:
received: 20 07 2023
revised: 12 08 2023
accepted: 17 08 2023
pubmed: 22 8 2023
medline: 22 8 2023
entrez: 21 8 2023
Statut: ppublish

Résumé

Left ventricular outflow velocity-time integral (LVOT-VTI) has been shown to improve outcome prediction in different patients' subsets, with or without heart failure (HF). Nevertheless, the prognostic value of LVOT-VTI in patients with HF and secondary mitral regurgitation (MR) has never been investigated so far. Therefore, in the present study, we aimed to assess the prognostic value different metrics of LV forward output, including LVOT-VTI, in HF patients with secondary MR. Consecutive patients with HF and moderate-to-severe/severe secondary MR and systolic dysfunction (i.e., left ventricular ejection fraction [LVEF] <50%) were retrospectively selected and followed-up for the primary endpoint of cardiac death. Out of the 287 patients analyzed (aged 74 ± 11 years, 70% men, 46% ischemic etiology, mean LVEF 30 ± 9%, mean LVOT-VTI 20 ± 5 cm), 71 met the primary endpoint over a 33-month median follow-up (16-47 months). Patients with an LVOT-VTI ≤17 cm (n = 96, 32%) showed the greatest risk of cardiac death (Log Rank 44.3, p < 0.001) and all-cause mortality (Log rank 8.6, p = 0.003). At multivariable regression analysis, all the measures of LV forward volume (namely LVOT-VTI, stroke volume index, cardiac output, and cardiac index) were predictors of poor outcomes. Among these, LVOT-VTI was the most accurate in risk prediction (univariable C-statistics 0.70 [95%CI 0.64-0.77]). Left ventricular forward output, noninvasively estimated through LVOT-VTI, improves outcome prediction in HF patients with low LVEF and secondary MR.

Identifiants

pubmed: 37604287
pii: S0167-5273(23)01151-8
doi: 10.1016/j.ijcard.2023.131272
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

131272

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest None.

Auteurs

Francesco Gentile (F)

Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy.

Francesco Buoncristiani (F)

Fondazione Toscana Gabriele Monasterio, Pisa, Italy.

Paolo Sciarrone (P)

Fondazione Toscana Gabriele Monasterio, Pisa, Italy.

Lorenzo Bazan (L)

Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy.

Giorgia Panichella (G)

Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy.

Simone Gasparini (S)

Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy.

Vlad Chubuchny (V)

Fondazione Toscana Gabriele Monasterio, Pisa, Italy.

Claudia Taddei (C)

Fondazione Toscana Gabriele Monasterio, Pisa, Italy.

Elisa Poggianti (E)

Fondazione Toscana Gabriele Monasterio, Pisa, Italy.

Iacopo Fabiani (I)

Fondazione Toscana Gabriele Monasterio, Pisa, Italy.

Christina Petersen (C)

Fondazione Toscana Gabriele Monasterio, Pisa, Italy.

Patrizio Lancellotti (P)

University of Liège Hospital, Cardiology Department, GIGA Cardiovascular Sciences, CHU Sart Tilman, Liège, Belgium; Gruppo Villa Maria Care and Research, Maria Cecilia Hospital, Cotignola, and Anthea Hospital, Bari, Italy.

Claudio Passino (C)

Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy; Fondazione Toscana Gabriele Monasterio, Pisa, Italy.

Michele Emdin (M)

Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy; Fondazione Toscana Gabriele Monasterio, Pisa, Italy.

Alberto Giannoni (A)

Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy; Fondazione Toscana Gabriele Monasterio, Pisa, Italy. Electronic address: a.giannoni@santannapisa.it.

Classifications MeSH