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

作者：Yingjian Pei, Yajun Ma, Ying Xiang, Guitao Zhang, Feng Yao, Wenbo Li, Yinghua Zhou, Shujuan Li · 发表于：Cardiovascular Diabetology · 年份：2025 · DOI：10.1186/s12933-025-02644-5 · 被引用次数：27 · 研究领域：Hyperglycemia and glycemic control in critically ill and hospitalized patients、Sepsis Diagnosis and Treatment、Cardiac and Coronary Surgery Techniques

BACKGROUND: 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. METHODS: 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. RESULTS: 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 wi...