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Factors and machine learning models for predicting successful discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury: a retrospective cohort study based on MIMIC-IV database

Crossref DOI link: https://doi.org/10.1186/s12882-024-03844-z

Published Online: 2024-11-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Sheng, Shuyue

Li, Andong

Liu, Xiaobin

Shen, Tuo

Zhou, Wei

Lv, Xingping

Shen, Yezhou

Wang, Chun

Ma, Qimin

Qu, Lihong

Ma, Shaolin

Zhu, Feng
Funding

Funding for this research was provided by:

National Key Research and Development Program of China Stem Cell and Translational Research (2019YFA0110601, 2019YFA0110601, 2019YFA0110601, 2019YFA0110601, 2019YFA0110601, 2019YFA0110601, 2019YFA0110601, 2019YFA0110601, 2019YFA0110601, 2019YFA0110601, 2019YFA0110601, 2019YFA0110601)
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Text and Data Mining valid from 2024-11-12

Version of Record valid from 2024-11-12
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Article History

Received: 25 August 2024

Accepted: 1 November 2024

First Online: 12 November 2024

Declarations

:

: The Institutional Review Board at the Beth Israel Deaconess Medical Center approved the collection of patient information and creation of the research resource and granted a waiver of informed consent.

: Not applicable.

: The authors declare no competing interests.

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