1.重庆市中医院神经内科,重庆 400010
2.陆军军医大学附属新桥医院心血管内科,重庆 400037
3.国药重庆西南铝医院心血管内科,重庆 400050
Received:09 April 2025,
Revised:2025-07-02,
Accepted:15 July 2025,
Published:28 October 2025
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杨伟业,高晓斐,肖寒,等.基于CHARLS数据构建高血压患者认知功能障碍风险预测列线图模型及验证[J].重庆医科大学学报,2025,50(10):1329-1337.
Yang Weiye,Gao Xiaofei,Xiao Han,et al.Establishment and validation of a nomogram prediction model for cognitive impairment in patients with hypertension based on the CHARLS database[J].Journal of Chongqing Medical University,2025,50(10):1329-1337.
杨伟业,高晓斐,肖寒,等.基于CHARLS数据构建高血压患者认知功能障碍风险预测列线图模型及验证[J].重庆医科大学学报,2025,50(10):1329-1337. DOI: 10.13406/j.cnki.cyxb.003917.
Yang Weiye,Gao Xiaofei,Xiao Han,et al.Establishment and validation of a nomogram prediction model for cognitive impairment in patients with hypertension based on the CHARLS database[J].Journal of Chongqing Medical University,2025,50(10):1329-1337. DOI: 10.13406/j.cnki.cyxb.003917.
目的
2
构建高血压患者认知功能障碍风险预测模型。
方法
2
采用中国健康与养老追踪调查(China Health and Retirement Longitudinal Study,CHARLS)数据,分析包括人口学特征、行为因素和健康状况在内的17项指标。研究队列按7∶3比例随机分为训练集(
n
=2 918)和内部验证集(
n
=1 249),并纳入了2024年1月至2024年12月重庆市中医院1 457例高血压患者作为外部验证集。通过对训练集进行最小绝对收缩和选择算子回归筛选预测变量,基于10折交叉验证选择最佳预测模型,采用逻辑回归模型探讨高血压患者认知功能障碍风险的相关因素并构建列线图预测模型,模型准确性通过
校准曲线评估,预测性能通过受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)和决策曲线分析评估。
结果
2
最终从CHARLS数据库中纳入4 167例45岁及以上高血压患者,其中668例出现认知功能障碍。多因素逻辑回归显示,年龄(
OR
=1.408,95%CI=1.040~1.056)、性别(
OR
=0.570,95%CI=0.492~0.660)、体质指数(
OR
=0.931,95%CI=0.914~0.948)、受教育程度(
OR
=0.235,95%CI=0.200~0.277)、居住地(
OR
=1.674,95%CI=1.447~1.936)、体育活动(
OR
=0.459,95%CI=0.373~0.562)、抑郁(
OR
=1.386,95%CI=1.198~1.604)和总胆固醇水平(
OR
=0.997,95%CI=0.995~0.999)为预测变量。ROC分析表明构建的列线图模型表现良好:训练集AUC为0.814(95%CI=0.802~0.826),内部验证集AUC为0.817(95%CI=0.788~0.846),外部验证集AUC为0.725(95%CI=0.699~0.752)。
结论
2
本研究构建的列线图模型可有效预测中国高血压患者认知功能障碍风险,具有良好应用价值。
Objective
2
To establish a predictive model for the risk of cognitive impairment in patients with hypertension.
Methods
2
Data were obtained from the China Health and Retirement Longitudinal Study(CHARLS),and 17 indicators were analyzed,including demographic features,behavioral factors,and health status. The study cohort was randomly divided into a training set(
n
=2 918) and an internal validation set(
n
=1 249) at a ratio of 7∶3,and 1 457 patients with hypertension who were treated in Chongqing Traditional Chinese Medicine Hospital from January to December 2024 were included as an external validation set. The least absolute shrinkage and selection operator regression analysis was used to identify predictive variables,and 10-fold cross-validation was used to determine the optimal model. A logistic regression model was used to investigate the risk factors for cognitive impairment in patients with hypertension,and then a nomogram prediction model was established. The calibration curve was used to assess the accuracy of the model,and the area under the ROC curve(AUC) and the decision cu
rve analysis were used to assess the predictive performance of the model.
Results
2
A total of 4167 hypertensive patients aged≥45 years were included from the CHARLS database,among whom 668 had cognitive impairment. The multivariate logistic regression analysis showed that age(odds ratio[
OR
]=1.408,95%CI=1.040-1.056),sex(
OR
=0.570,95%CI=0.492-0.660),body mass index(
OR
=0.931,95%CI=0.914-0.948),educational level(
OR
=0.235,95%CI=0.200-0.277),place of residence(
OR
=1.674,95%CI=1.447-1.936),physical activity(
OR
=0.459,95%CI=0.373-0.562),depression(
OR
=1.386,95%CI=1.198-1.604),and total cholesterol level(
OR
=0.997,95%CI=0.995-0.999) were predictive variables. The ROC curve analysis showed that the nomogram model had good performance,with an AUC of 0.814(95%CI=0.802-0.826) in the training set,0.817(95%CI=0.788-0.846) in the internal validation set,and 0.725(95%CI=0.699-0.752) in the external validation set.
Conclusion
2
The nomogram model developed in this study can effectively predict the risk of cognitive impairment in Chinese patients with hypertension and has a good application value.
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