Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.
Journal:
Cardiovascular diabetology
Published Date:
May 9, 2024
Abstract
BACKGROUND: Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the disease. Previous studies have indicated that the stress hyperglycemia ratio (SHR) can serve as a reliable predictor of adverse outcomes in various cardiovascular and cerebrovascular diseases. However, there is limited research on the relationship between the SHR and adverse outcomes in patients with infectious diseases, particularly in critically ill patients with sepsis. Therefore, this study aimed to explore the association between the SHR and adverse outcomes in critically ill patients with sepsis.
Authors
Keywords
Aged
Biomarkers
Blood Glucose
Cause of Death
China
Critical Illness
Databases, Factual
Decision Support Techniques
Female
Hospital Mortality
Humans
Hyperglycemia
Machine Learning
Male
Middle Aged
Predictive Value of Tests
Prognosis
Retrospective Studies
Risk Assessment
Risk Factors
Sepsis
Time Factors