Lin Jingfang, Chen Yingjie, Xu Maokai, Chen Jianghu, Huang Yongxin, Chen Xiaohui, Tang Yanling, Chen Jiaxin, Jiang Jundan, Liao Yanling, Zheng Xiaochun
Department of Anesthesiology, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, No.134, Dongjie, Fuzhou, 350001, China.
Department of Anesthesiology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China.
Cardiovasc Diabetol. 2024 Dec 18;23(1):445. doi: 10.1186/s12933-024-02542-2.
The predictive importance of the stress hyperglycemia ratio (SHR), which is composed of admission blood glucose (ABG) and glycated hemoglobin (HbA1c), has not been fully established in noncardiac surgery. This study aims to evaluate the association and predictive capability the SHR for major perioperative adverse cardiovascular events (MACEs) in noncardiac surgery patients.
Individuals who underwent noncardiac surgical procedures between 2011 and 2020, including both diabetic and non-diabetic patients, were identified in the perioperative medicine database (INSPIRE 1.1) and classified into tertiles based on their SHR. The connection between the SHR and the risk of MACEs was studied using Cox proportional hazards regression analysis, then restricted cubic spline (RCS) was employed to assess the association's form. Additionally, the SHR's incremental predictive utility for MACEs was assessed by the C-statistic, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI), thereby quantifying the enhancement in predictive accuracy brought by incorporating the SHR into existing risk models. Feature importance and predictive models were generated utilizing the Boruta algorithm and machine learning approaches.
A total of 5609 patients were enrolled. With an upwards shift in SHR vertices, the rate of perioperative MACEs and cardiac death event steadily rose. The RCS analysis for perioperative MACEs and cardiac death event both indicated J-shaped associations. Inflection points occurred at SHR = 0.81 for MACEs and SHR = 0.97 for cardiac death. The model's fit improved significantly, with a continuous NRI of 0.067 (95% CI: 0.025-0.137, P < 0.001) and an IDI of 0.305 (95% CI: 0.155-0.430, P < 0.001). When SHR was added as a categorical variable (> 0.81), the C-statistic increased to 0.785 (95% CI: 0.756-0.814) with a ΔC-statistic of 0.035 (P = 0.009), a continuous NRI of 0.007 (95% CI: 0.000-0.021, P = 0.016), and an IDI of 0.076 (95% CI -0.024-0.142, P = 0.092). In the Boruta algorithm, variables identified as important features in the green area were incorporated into the machine learning models development.
The SHR was related with an increased risk of perioperative MACEs in patients following noncardiac surgery, highlighting its potential as a useful and reliable predictive tool for assessing the risk of perioperative MACEs.
由入院血糖（ABG）和糖化血红蛋白（HbA1c）组成的应激高血糖比值（SHR）在非心脏手术中的预测重要性尚未完全确立。本研究旨在评估SHR与非心脏手术患者围手术期主要不良心血管事件（MACE）的关联及预测能力。
在围手术期医学数据库（INSPIRE 1.1）中识别出2011年至2020年间接受非心脏手术的个体，包括糖尿病患者和非糖尿病患者，并根据其SHR分为三分位数。使用Cox比例风险回归分析研究SHR与MACE风险之间的联系，然后采用受限立方样条（RCS）评估关联的形式。此外，通过C统计量、连续净重新分类改善（NRI）和综合判别改善（IDI）评估SHR对MACE的增量预测效用，从而量化将SHR纳入现有风险模型所带来的预测准确性的提高。利用Boruta算法和机器学习方法生成特征重要性和预测模型。
共纳入5609例患者。随着SHR三分位数的升高，围手术期MACE和心源性死亡事件的发生率稳步上升。围手术期MACE和心源性死亡事件的RCS分析均显示为J形关联。MACE的拐点出现在SHR = 0.81时，心源性死亡的拐点出现在SHR = 0.97时。模型拟合显著改善，连续NRI为0.067（95%CI：0.025 - 0.137，P < 0.001），IDI为0.305（95%CI：0.155 - 0.430，P < 0.001）。当将SHR作为分类变量（> 0.81）添加时，C统计量增加到0.785（95%CI：0.756 - 0.814），ΔC统计量为0.035（P = 0.009），连续NRI为0.007（95%CI：0.000 - 0.021，P = 0.016），IDI为0.076（95%CI - 0.024 - 0.142，P = 0.092）。在Boruta算法中，被确定为绿色区域重要特征的变量被纳入机器学习模型开发。
SHR与非心脏手术后患者围手术期MACE风险增加相关，突出了其作为评估围手术期MACE风险的有用且可靠预测工具的潜力。
