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Pathophysiological insights into machine learning-based subphenotypes of acute heart failure with preserved ejection fraction.

Authors :
Yohei Sotomi
Shunsuke Tamaki
Shungo Hikoso
Daisaku Nakatani
Katsuki Okada
Tomoharu Dohi
Akihiro Sunaga
Hirota Kida
Taiki Sato
Yuki Matsuoka
Daisuke Sakamoto
Tetsuhisa Kitamura
Sho Komukai
Masahiro Seo
Masamichi Yano
Takaharu Hayashi
Akito Nakagawa
Yusuke Nakagawa
Tomohito Ohtani
Yoshio Yasumura
Source :
Heart; Mar2024, Vol. 110 Issue 6, p441-447, 11p
Publication Year :
2024

Abstract

This document provides supplemental material for a study on heart failure with preserved ejection fraction (HFpEF). It includes tables that present clinical characteristics and biomarker analysis of the patients included and excluded from the study, as well as the different phenotypes and their corresponding biomarker levels. The tables provide data on factors such as age, sex, blood pressure, comorbidities, and laboratory values. This information can be valuable for researchers studying heart failure and its subphenotypes. [Extracted from the article]

Details

Language :
English
ISSN :
13556037
Volume :
110
Issue :
6
Database :
Complementary Index
Journal :
Heart
Publication Type :
Academic Journal
Accession number :
176065329
Full Text :
https://doi.org/10.1136/heartjnl-2023-323059