Predicting 28-day all-cause mortality in patients admitted to intensive care units with pre-existing chronic heart failure using the stress hyperglycemia ratio: a machine learning-driven retrospective cohort analysis

Predicting 28-day all-cause mortality in patients admitted to intensive care units with pre-existing chronic heart failure using the stress hyperglycemia ratio: a machine learning-driven retrospective cohort analysis

Abstract Chronic heart failure (CHF) poses a significant threat to human health. The stress hyperglycemia ratio (SHR) is a novel metric for accurately assessing stress hyperglycemia, which has been correlated with adverse outcomes in various major diseases. However, it remains unclear whether SHR is...

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Bibliographic Details
Main Authors: Xiao-han Li, Xing-long Yang, Bin-bin Dong, Qi Liu
Format: Article
Language:English
Published: BMC 2025-01-01
Series:Cardiovascular Diabetology
Subjects:
Stress hyperglycemia ratio
Chronic heart failure
Mortality
Risk factor
Machine learning
Online Access:https://doi.org/10.1186/s12933-025-02577-z
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https://doi.org/10.1186/s12933-025-02577-z

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