作者：王阳1，2 ，李扬1 ，矫树生2 ，马浩源2 ，刘之荣1

单位：

1.710032陕西省西安市，空军军医大学第一附属医院神经内科　2.050000河北省石家庄市，中国人民解放 军联勤保障部队第九八〇医院 白求恩国际和平医院神经内科

单位（英文）：

1.Department of Neurology, First Affiliated Hospital of Air Force Medical University, Xi'an 710032, China 2.Department of Neurology, the 980th Hospital of the Joint Logistics Support Force of PLA/Bethune International Peace Hospital, Shijiazhuang 050000, China

关键词：

大脑小血管疾病；认知障碍；三酰甘油-葡萄糖指数；影响因素分析；列线图

关键词（英文）：

Cerebral small vessel diseases; Cognition disorders; Triglyceride-glucose index; Root cause analysis; Nomograms

中图分类号：

R 743

DOI：

10.12114/j.issn.1008-5971.2024.00.020

基金项目：

国家重点研发计划项目（2017YFC0907703）

摘要：

目的　探讨脑小血管病（CSVD）患者发生认知障碍的影响因素，构建其风险预测列线图模型并进行 验证。方法　回顾性收集2016年12月—2022年12月空军军医大学第一附属医院收治的CSVD患者415例。收集患者的 临床资料，根据认知障碍发生情况将患者分为认知障碍组和认知功能正常组。采用多因素Logistic回归分析探讨CSVD 患者发生认知障碍的影响因素；采用R 4.3.0软件中rms包构建CSVD患者发生认知障碍的风险预测列线图模型；采用 Hosmer-Lemeshow拟合优度检验及校准曲线评估该列线图模型的拟合情况；采用ROC曲线分析载脂蛋白A（ApoA）、 胱抑素C（CysC）、同型半胱氨酸（Hcy）、三酰甘油-葡萄糖（TyG）指数、CSVD高负荷及该列线图模型对CSVD患 者发生认知障碍的预测价值。结果　415例CSVD患者发生认知障碍206例，发生率为49.6%。认知障碍组年龄大于认知 功能正常组，高血压发生率、饮酒率、吸烟率、FBG、TG、载脂蛋白B（ApoB）、尿酸（UA）、CysC、Hcy、TyG指 数、CSVD高负荷发生率高于认知功能正常组，HDL-C、ApoA低于认知功能正常组（P＜0.05）。多因素Logistic回归 分析结果显示，ApoA、CysC、Hcy、TyG指数、CSVD高负荷为CSVD患者发生认知障碍的独立影响因素（P＜0.05）。 ROC曲线分析结果显示，ApoA、CysC、Hcy、TyG指数、CSVD高负荷预测CSVD患者发生认知障碍的AUC分别为 0.641、0.649、0.676、0.734、0.795。基于多因素Logistic回归分析结果，构建CSVD 患者发生认知障碍的风险预测列 线图模型。Hosmer-Lemeshow拟合优度检验结果显示，该列线图模型拟合较好（χ 2 =54.853 ，P=0.860）。ROC曲线分 析结果显示，该列线图模型预测CSVD患者发生认知障碍的AUC为0.890〔95%CI（0.859～0.921）〕。结论　ApoA、 CysC、Hcy、TyG指数、CSVD高负荷为CSVD患者发生认知障碍的独立影响因素，其中TyG指数、CSVD高负荷对CSVD 患者发生认知障碍具有一定预测价值，基于上述因素构建的列线图模型具有较好的校准度和区分度，且对CSVD患者 发生认知障碍具有一定预测价值。

英文摘要：

Objective To explore the influencing factors of cognitive impairment in patients with cerebral small vessel disease (CSVD) , and to construct and validate a nomogram model for predicting its risk. Methods A retrospective study was conducted on 415 patients with CSVD admitted to First Affiliated Hospital of Air Force Medical University from December 2016 to December 2022. The clinical data of the patients were collected, and the patients were divided into normal cognitive function group and cognitive impairment group according to the occurrence of cognitive impairment. Multivariate Logistic regression analysis was used to explore the influencing factors of cognitive impairment in patients with CSVD. The nomogram model for predicting the risk of cognitive impairment in patients with CSVD was constructed by using the rms package of R 4.3.0 software. Hosmer-Lemeshow goodness of fit test and calibration curve were used to evaluate the fit of the nomogram model. ROC curve was used to explore the predictive value of the apolipoprotein A (ApoA) , cystatin C (CysC) , homocysteine (Hcy) , triglyceride-glucose (TyG) index, high CSVD burden and nomogram model for cognitive impairment in patients with CSVD. Results Among 415 patients with CSVD, 206 (49.6%) developed cognitive impairment. The age in the cognitive impairment group was higher than that in the normal cognitive function group, the incidence of hypertension, drinking rate, smoking rate, FBG , TG , apolipoprotein B (ApoB) , uric acid (UA) , CysC, Hcy, TyG index, incidence of high CSVD burden were higher than those in the normal cognitive function group, HDL-C, ApoA were lower than those in the normal cognitive function group (P < 0.05) . Multivariate Logistic regression analysis showed that ApoA, CysC, Hcy, TyG index, high CSVD burden were the independent influencing factors of cognitive impairment in patients with CSVD (P < 0.05) . ROC curve analysis showed that the AUC of the ApoA, CysC, Hcy, TyG index, high CSVD burden for predicting cognitive impairment in patients with CSVD were 0.641, 0.649, 0.676, 0.734, 0.795 respectively. The nomogram model for predicting cognitive impairment in patients with CSVD was constructed based on the results of multivariate Logistic regression analysis. The results of Hosmer-Lemeshow goodness of fit test showed that the nomogram model fit well (χ 2 =54.853, P=0.860) . The results of ROC curve analysis showed that the AUC of the nomogram model for predicting cognitive impairment in patients with CSVD was 0.890 [95%CI (0.859-0.921) ] . Conclusion ApoA, CysC, Hcy, TyG index, high CSVD burden are the independent influencing factors of cognitive impairment in patients with CSVD, and TyG index, high CSVD burden have certain value in the prediction of cognitive impairment in patients with CSVD. The nomogram model constructed based on the above factors has a high calibration and discrimination, and has certain predictive value for cognitive impairment in patients with CSVD.

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