基于CHARLS数据库的高血压风险预测模型

Hypertension Risk Prediction Model Based on the CHARLS Database

摘要: 背景：高血压是我国中老年人群常见慢性病之一，可显著增加心脑血管疾病、肾脏损害及死亡风险。基于大样本人群数据构建简便可用的高血压患病风险预测模型，有助于早期识别高风险个体并优化社区筛查。目的：基于中国健康与养老追踪调查(China Health and Retirement Longitudinal Study, CHARLS) 2020年随访数据，分析45岁及以上人群高血压相关因素，并构建高血压风险预测列线图模型。方法：提取中国健康与养老追踪调查(CHARLS)数据库2020年随访数据中45岁及以上中老年人群的社会人口学及健康相关数据。经数据清洗后共纳入19,218例，按7:3比例进行分层随机抽样，分为训练集与验证集。基于训练集进行单因素分析、多因素二元Logistic回归分析，筛选独立影响因素；采用R 4.2.1软件“rms”包构建列线图预测模型，并通过Bootstrap重抽样法进行内部验证，在验证集中进行外部验证。结果：多因素Logistic回归显示，年龄增加、女性、当前饮酒、当前吸烟、锻炼以及教育水平与高血压患病风险相关，婚姻状况在多因素模型中未达到统计学显著性。模型在训练集中的AUC/C指数为0.639 (95% CI: 0.629~0.648)，在验证集中的AUC/C指数为0.637 (95% CI: 0.622~0.651)；校准曲线显示预测风险与实际观察风险总体趋势一致，但部分风险区间仍存在偏差。结论：基于CHARLS 2020年数据构建的高血压风险预测模型具有一定区分度，可作为45岁及以上人群高血压风险初步筛查的参考工具。模型性能仍有限，未来需进一步纳入BMI、血脂、糖尿病、地区、饮食及体检指标等变量，并在独立人群中进行外部验证。

Abstract: Background: Hypertension is one of the common chronic diseases among the elderly in China, significantly increasing the risks of cardiovascular and cerebrovascular diseases, kidney damage, and death. Building a simple and applicable hypertension risk prediction model based on large sample population data is helpful for early identification of high-risk individuals and optimizing community screening. Objective: Based on the 2020 follow-up data from the China Health and Retirement Longitudinal Study (CHARLS), this study aims to analyze the factors related to hypertension among individuals aged 45 and above, and to construct a risk prediction nomogram model for hypertension. Method: Social demographic and health-related data of the elderly population aged 45 and above from the 2020 follow-up data of the Chinese Health and Retirement Longitudinal Study (CHARLS) database were extracted. After data cleaning, a total of 19,218 cases were included. Stratified random sampling was conducted at a ratio of 7:3 to divide them into a training set and a validation set. Univariate analysis and multivariate binary Logistic regression analysis were performed based on the training set to identify independent influencing factors. A nomogram prediction model was constructed using the “rms” package of R 4.2.1 software, and internal validation was conducted using the Bootstrap resampling method. External validation was performed in the validation set. Result: The multivariate Logistic regression analysis showed that age increase, female gender, current alcohol consumption, current smoking, exercise, and educational level were associated with the risk of hypertension. Marital status did not reach statistical significance in the multivariate model. The AUC/C index of the model in the training set was 0.639 (95% CI: 0.629~0.648), and in the validation set was 0.637 (95% CI: 0.622~0.651); The calibration curve showed that the predicted risk was consistent with the actual observed risk in general trend, but there was still deviation in some risk intervals. Conclusion: The hypertension risk prediction model constructed based on the 2020 data of CHARLS has a certain degree of discrimination and can be used as a reference tool for the preliminary screening of hypertension risk among people aged 45 and above. The model performance is still limited. In the future, variables such as BMI, blood lipids, diabetes, region, diet, and physical examination indicators need to be further included, and external validation should be conducted in independent populations.