Wang Fuxu, Guo Yu, Tang Yuru, Zhao Shuangmei, Xuan Kaige, Mao Zhi, Lu Ruogu, Hou Rongyao, Zhu Xiaoyan
Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Cardiovasc Diabetol. 2025 May 9;24(1):199. doi: 10.1186/s12933-025-02762-0.
Stress hyperglycemia ratio (SHR) and glycemic variability (GV) reflect acute glucose elevation and fluctuations, which correlate with adverse outcomes in patients with atherosclerotic cardiovascular disease (ASCVD). However, the prognostic significance of combined SHR-GV evaluation for ASCVD mortality remains unclear. This study examines associations of SHR, GV, and their synergistic effects with mortality in patients with ASCVD across different glucose metabolic states, incorporating machine learning (ML) to identify critical risk factors influencing mortality.
Patients with ASCVD were screened in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and stratified into normal glucose regulation (NGR), pre-diabetes mellitus (Pre-DM), and diabetes mellitus (DM) groups based on glucose metabolic status. The primary endpoint was 28-day mortality, with 90-day mortality as the secondary outcome. SHR and GV levels were categorized into tertiles. Associations with mortality were analyzed using Kaplan-Meier(KM) curves, Cox proportional hazards models, restricted cubic splines (RCS), receiver operating characteristic (ROC) curves, landmark analyses, and subgroup analyses. Five ML algorithms were employed for mortality risk prediction, with SHapley Additive exPlanations (SHAP) applied to identify critical predictors.
A total of 2807 patients were included, with a median age of 71 years, and 58.78% were male. Overall, 483 (23.14%) and 608 (29.13%) patients died within 28 and 90 days of ICU admission, respectively. In NGR and Pre-DM subgroups, combined SHR-GV assessment demonstrated superior predictive performance for 28-day mortality versus SHR alone [NGR: AUC 0.688 (0.636-0.739) vs. 0.623 (0.568-0.679), P = 0.028; Pre-DM: 0.712 (0.659-0.764) vs. 0.639 (0.582-0.696), P = 0.102] and GV alone [NGR: 0.688 vs. 0.578 (0.524-0.633), P < 0.001; Pre-DM: 0.712 vs. 0.593 (0.524-0.652), P < 0.001]. Consistent findings were observed for 90-day mortality prediction. However, in the DM subgroup, combined assessment improved prediction only for 90-day mortality vs. SHR alone [AUC 0.578 (0.541-0.616) vs. 0.560 (0.520-0.599), P = 0.027], without significant advantages in other comparisons.
Combined SHR and GV assessment serves as a critical prognostic tool for ASCVD mortality, providing enhanced predictive accuracy compared to individual metrics, particularly in NGR and Pre-DM patients. This integrated approach could inform personalized glycemic management strategies, potentially improving clinical outcomes.
应激性高血糖比率（SHR）和血糖变异性（GV）反映了急性血糖升高和波动情况，这与动脉粥样硬化性心血管疾病（ASCVD）患者的不良预后相关。然而，联合SHR-GV评估对ASCVD死亡率的预后意义仍不明确。本研究探讨了不同糖代谢状态下，SHR、GV及其协同效应对ASCVD患者死亡率的影响，并运用机器学习（ML）识别影响死亡率的关键危险因素。
在重症监护医学信息数据库IV（MIMIC-IV）中筛选ASCVD患者，并根据糖代谢状态将其分为血糖正常调节（NGR）、糖尿病前期（Pre-DM）和糖尿病（DM）组。主要终点为28天死亡率，次要终点为90天死亡率。SHR和GV水平分为三分位数。采用Kaplan-Meier（KM）曲线、Cox比例风险模型、限制性立方样条（RCS）、受试者工作特征（ROC）曲线、标志性分析和亚组分析来分析与死亡率的关联。使用五种ML算法进行死亡风险预测，并应用Shapley加性解释（SHAP）来识别关键预测因子。
共纳入2807例患者，中位年龄71岁，男性占58.78%。总体而言，分别有483例（23.14%）和608例（29.13%）患者在ICU入院后28天和90天内死亡。在NGR和Pre-DM亚组中，联合SHR-GV评估对28天死亡率的预测性能优于单独的SHR [NGR：曲线下面积（AUC）0.688（0.636-0.739）对0.623（0.568-0.679），P = 0.028；Pre-DM：0.712（0.659-0.764）对0.639（0.582-0.696），P = 0.102]和单独的GV [NGR：0.688对0.578（0.524-0.633），P < 0.001；Pre-DM：0.712对0.593（0.524-0.652），P < 0.001]。90天死亡率预测也观察到了一致的结果。然而，在DM亚组中，联合评估仅在90天死亡率预测方面优于单独的SHR [AUC 0.578（0.541-0.616）对0.560（0.520-0.599），P = 0.027]，在其他比较中没有显著优势。
联合SHR和GV评估是ASCVD死亡率的关键预后工具，与单独指标相比，具有更高的预测准确性，特别是在NGR和Pre-DM患者中。这种综合方法可为个性化血糖管理策略提供依据，可能改善临床结局。
