Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery

The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia levels, which was associated with adverse clinical outcomes. This study aims to evaluate the relationship between the postoperative SHR index and all-cause mortality in patients undergoing cardiac surgery. Data for this study were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patients were categorized into four groups based on postoperative SHR index quartiles. The primary outcome was 30-day all-cause mortality, while the secondary outcomes included in-hospital, 90-day and 360-day all-cause mortality. The SHR index was analyzed using quartiles, and Kaplan-Meier curves were generated to compare outcomes across groups. Cox proportional hazards regression and restricted cubic splines (RCS) were employed to assess the relationship between the SHR index and the outcomes. LASSO regression was used for feature selection. Six machine learning algorithms were used to predict in-hospital all-cause mortality and were further extended to predict 360-day all-cause mortality. The SHapley Additive exPlanations method was used for visualizing model characteristics and individual case predictions. A total of 3,848 participants were included in the study, with a mean age of 68 ± 12 years and female participants comprised 30.6% (1,179). Higher postoperative SHR index levels were associated with an increased risk of in-hospital, 90-day and 360-day all-cause mortality as shown by Kaplan-Meier curves (log-rank P < 0.05). Cox regression analysis revealed that the highest postoperative SHR quartile was associated with a significantly higher risk of mortality at these time points (P < 0.05). RCS analysis demonstrated nonlinear relationships between the postoperative SHR index and all-cause mortality (P for nonlinear < 0.05). The Naive Bayes model achieves the highest area under the curve (AUC) for predicting both in-hospital mortality (0.7936) and 360-day all-cause mortality (0.7410). In patients undergoing cardiac surgery, higher postoperative SHR index levels were significantly associated with increased risk of in-hospital, 90-day and 360-day all-cause mortality. The SHR index may serve as a valid tool for assessing the severity after cardiac surgery and guiding treatment decisions.