Zhu Wei, Wen Dingke, Duan Lijuan, Fan Chaofeng, Jiang Yan
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
West China School of Nursing, Sichuan University, Chengdu, China.
Front Neurol. 2025 May 29;16:1526169. doi: 10.3389/fneur.2025.1526169. eCollection 2025.
Research on the associations between the stress hyperglycemia ratio (SHR) and adverse outcomes in patients with hemorrhagic stroke is limited. Therefore, we aimed to investigate the relationship between the SHR and all-cause mortality in patients with hemorrhagic stroke.
Clinical data of patients with hemorrhagic stroke were extracted from the Medical Information Mart for Intensive Care (MIMIC-IV) database. The patients were divided into four groups based on the SHR quartiles. Outcomes including 28-, 90-, and 365-day all-cause mortality were analyzed. Kaplan-Meier curves, Cox proportional hazard regression, and restricted cubic splines were used to investigate the relationships between the SHR and all-cause mortality. A machine learning prediction model integrating SHR was developed to assess its prognostic value for all-cause mortality.
The final analysis cohort consisted of 939 patients. Compared to the lowest SHR quartile, the highest quartile had significantly increased mortality risks at 28 days [hazard ratio (HR) = 4.53, 95% CI: 2.75-7.46; < 0.001], 90 days (HR = 3.29, 2.19-4.95; < 0.001), and 365 days (HR = 2.25, 1.60-3.17; < 0.001). A significant upward trend in mortality risk was observed across ascending SHR quartiles (-trend < 0.001 for all time points). Restricted cubic spline analysis demonstrated non-linear associations between SHR and all-cause mortality at 28 and 90 days (-non-linear < 0.05), while the overall trend remained significantly positive. The machine learning models identified SHR as a key predictor, with area under the curves (AUC) of 0.771 (28-day), 0.778 (90-day), and 0.778 (365-day).
This study revealed threshold-dependent associations between the SHR and short- and long-term all-cause mortality in patients with hemorrhagic stroke. The SHR was a reliable predictor for adverse outcomes in patients with hemorrhagic stroke.
关于应激性高血糖比值（SHR）与出血性中风患者不良结局之间关联的研究有限。因此，我们旨在探讨SHR与出血性中风患者全因死亡率之间的关系。
从重症监护医学信息数据库（MIMIC-IV）中提取出血性中风患者的临床数据。根据SHR四分位数将患者分为四组。分析包括28天、90天和365天全因死亡率在内的结局。采用Kaplan-Meier曲线、Cox比例风险回归和受限立方样条来研究SHR与全因死亡率之间的关系。开发了一个整合SHR的机器学习预测模型，以评估其对全因死亡率的预后价值。
最终分析队列包括939例患者。与最低SHR四分位数组相比，最高四分位数组在28天（风险比[HR]=4.53，95%置信区间：2.75-7.46；<0.001）、90天（HR=3.29，2.19-4.95；<0.001）和365天（HR=2.25，1.60-3.17；<0.001）时的死亡风险显著增加。在SHR四分位数升高的过程中，观察到死亡风险有显著的上升趋势（所有时间点的趋势<0.001）。受限立方样条分析表明，在28天和90天时，SHR与全因死亡率之间存在非线性关联（非线性<0.05），但总体趋势仍显著为正。机器学习模型将SHR确定为关键预测因子，曲线下面积（AUC）分别为0.771（28天）、0.778（90天）和0.778（365天）。
本研究揭示了出血性中风患者中SHR与短期和长期全因死亡率之间的阈值依赖性关联。SHR是出血性中风患者不良结局的可靠预测因子。
