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 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 algorithm. Prediction models were constructed using machine learning algorithms. A total of 913 patients were enrolled. The risk of 28-day mortality increased with higher SHR levels (P < 0.001). SHR was independently associated with 28-day all-cause mortality, with an unadjusted hazard ratio (HR) of 1.45 (P < 0.001) and an adjusted HR of 1.43 (P < 0.001). Subgroup analysis found that none of the potential risk factors, such as demographics, comorbidities, and drugs, affected the relationship (P for interaction > 0.05). The area under the ROC (AUC) curve for SHR was larger than those for admission blood glucose and HbA1c; the cut-off for SHR was 0.57. Patients with SHR higher than the cut-off had a significantly lower 28-day survival probability (P < 0.001). SHR was identified as one of the key factors for 28-day mortality by the Boruta algorithm. The predictive performance was verified through four machine learning algorithms, with the neural network algorithm being the best (AUC 0.801). For patients with both acute critical illness and pre-existing CHF, SHR was an independent predictor of 28-day all-cause mortality. Its prognostic performance surpasses those of HbA1c and blood glucose, and prognostic models based on SHR provide clinicians with an effective tool to make therapeutic decisions.