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

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

Abstract Background For critically ill patients with acute kidney injury (AKI), there remains controversy regarding the predictive factors affecting the discontinuation of continuous renal replacement therapy (CRRT). This study aims to explore factors associated with successful CRRT discontinuation...

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Bibliographic Details
Main Authors: Shuyue Sheng, Andong Li, Xiaobin Liu, Tuo Shen, Wei Zhou, Xingping Lv, Yezhou Shen, Chun Wang, Qimin Ma, Lihong Qu, Shaolin Ma, Feng Zhu
Format: Article
Language:English
Published: BMC 2024-11-01
Series:BMC Nephrology
Subjects:
Acute kidney injury
Continuous renal replacement therapy
Machine learning
Prediction algorithm
Online Access:https://doi.org/10.1186/s12882-024-03844-z
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https://doi.org/10.1186/s12882-024-03844-z

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