Abstract
The study of stress hyperglycemia ratio (SHR) aims to further investigate the relationship between chronic glucose factors and adverse clinical events, particularly cardiovascular outcomes, in critically ill patients. However, prior research has not explored the correlation between SHR and the prognosis of patients with mitral valve disease (MVD). This study evaluates the relationship between SHR and 28-day all-cause mortality in critically ill MVD patients admitted to the intensive care unit. Patient data were extracted from the MIMIC-IV (medical information mart for intensive care IV) database. Participants were categorized into four groups based on SHR quartiles. The primary outcome was 28-day all-cause mortality, and Kaplan-Meier curves were used to compare outcomes across different glucose metabolism states. Cox proportional hazards regression and restricted cubic splines (RCS) were employed to assess the relationship between the SHR index and the outcomes. Five machine learning algorithms were utilized for mortality risk prediction, with SHapley additive explanations (SHAP) applied to identify key predictors. Among 3506 participants, 1986 (56.65%) were male and 1520 (43.35%) were female. Kaplan-Meier curves showed that higher SHR levels were associated with increased 28-day all-cause mortality (log-rank P = 0.01). This correlation persisted across glucose metabolism groups. Cox regression analysis revealed that the highest SHR quartile was linked to higher mortality risk, particularly in non-diabetic patients. RCS analysis confirmed a U-shaped relationship between SHR and mortality. Subgroup analysis showed that SHR was associated with mortality risk across various subgroups, with stronger associations in females and prediabetic patients. Among eight machine learning models developed based on eight RFE-selected variables, the XGBoost model achieved the best predictive performance for 28-day mortality (AUC = 0.819). SHR can serve as a key prognostic tool for mortality in mitral valve disease (MVD) patients, especially among women and patients with prediabetes. This integrated approach may inform personalized glucose management strategies, potentially improving clinical outcomes.