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Reclassification of moderate aortic stenosis based on data-driven phenotyping of hemodynamic progression

Authors :
Iksung Cho
William D. Kim
Subin Kim
Kyu-Yong Ko
Yeonchan Seong
Dae-Young Kim
Jiwon Seo
Chi Young Shim
Jong-Won Ha
Makoto Mori
Aakriti Gupta
Seng Chan You
Geu-Ru Hong
Harlan M. Krumholz
Source :
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract The management and follow-up of moderate aortic stenosis (AS) lacks consensus as the progression patterns are not well understood. This study aimed to identify the hemodynamic progression of AS, and associated risk factors and outcomes. We included patients with moderate AS with at least three transthoracic echocardiography (TTE) studies performed between 2010 and 2021. Latent class trajectory modeling was used to classify AS groups with distinctive hemodynamic trajectories, which were determined by serial systolic mean pressure gradient (MPG) measurements. Outcomes were defined as all-cause mortality and aortic valve replacement (AVR). A total of 686 patients with 3093 TTE studies were included in the analysis. Latent class model identified two distinct AS trajectory groups based on their MPG: a slow progression group (44.6%) and a rapid progression group (55.4%). Initial MPG was significantly higher in the rapid progression group (28.2 ± 5.6 mmHg vs. 22.9 ± 2.8 mmHg, P

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.11758045f9de4044b23a9409157d1ff6
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-023-33683-1