A prediction model for moderate to severe acute kidney injury in people with heart failure-Reference-Cited by-同舟云学术

A prediction model for moderate to severe acute kidney injury in people with heart failure

Published:2024-08-20 Issue:1 Volume:11 Page:
ISSN:2054-9369
Container-title:Military Medical Research
language:en
Short-container-title:Military Med Res

Author:

Yang Yu-Qi,Da Jing-Jing,Nie Sheng,Yuan Jing,Liu Bi-Cheng,Liu Hua-Feng,Yang Qiong-Qiong,Li Hua,Xu Gang,Weng Jian-Ping,Kong Yao-Zhong,Wan Qi-Jun,Li Gui-Sen,Chen Chun-Bo,Xu Hong,Hu Ying,Shi Yong-Jun,Zhou Yi-Lun,Su Guo-Bin,Tang Ying,Hou Fan-Fan,Zha Yan

Funder

National Key R&D Program of China

Key Technologies R&D Program of Guangdong Province

Guizhou Provincial Science and Technology Department

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1186/s40779-024-00558-z.pdf

Reference5 articles.

1. Damman K, Valente MAE, Voors AA, O’Connor CM, van Veldhuisen DJ, Hillege HL. Renal impairment, worsening renal function, and outcome in patients with heart failure: an updated meta-analysis. Eur Heart J. 2014;35(7):455–69.

2. Ronco C, Bellomo R, Kellum JA. Acute kidney injury. Lancet. 2019;394(10212):1949–64.

3. Yang L, Xing G, Wang L, Wu Y, Li S, Xu G, et al. Acute kidney injury in China: a cross-sectional survey. Lancet. 2015;386(10002):1465–71.

4. Hong C, Sun Z, Hao Y, Dong Z, Gu Z, Huang Z. Identifying patients with heart failure who are susceptible to de novo acute kidney injury: machine learning approach. JMIR Med Inform. 2022;10(10):e37484.

5. Liu WT, Liu XQ, Jiang TT, Wang MY, Huang Y, Huang YL, et al. Using a machine learning model to predict the development of acute kidney injury in patients with heart failure. Front Cardiovasc Med. 2022;9:911987.