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 – Cardiovascular Diabetology
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 associated with 28-day mortality in patients with pre-existing CHF who were admitted to intensive care units (ICUs). This study retrospectively recruited patients who were admitted to ICUs with both acute critical illness and pre-existing CHF from the Medical Information Mart for Intensive Care (MIMIC) database. Characteristics were compared between the survival and non-survival groups. The relationship between SHR and 28-day all-cause mortality was analyzed using restricted cubic splines, receiver operating characteristic (ROC) curves, Kaplan–Meier survival analysis, and Cox proportional hazards regression analysis. The importance of the potential risk factors was assessed using the Boruta algorith
