基本公共卫生服务数据在我国中老年人群高血压发病预测模型中的应用研究

Research on the application of basic public health service database in the prediction model of hypertension among middle-aged and elderly people in China

目的建立针对我国中老年人群的高血压发病预测模型,使用基本公共卫生服务项目(以下简称基本公卫)体检数据外部验证模型效能。方法检索研究高血压发病的文献,采用Meta分析合并各影响因素效应值,遴选有统计学意义且属于基本公卫体检的条目作为预测项。将其效应值对数转换后作为参数β值构建Logit函数和风险评分两模型。将2017年1月1日至2022年1月1日参加成都市红光街道社区卫生服务中心基本公卫老年人健康体检项目、建档时未患高血压的人群队列作为验证人群,对两模型进行外部评价。结果15个研究的Meta分析结果显示,有统计学意义的高血压发病影响因素共11项[年龄、女性、收缩压、舒张压、身体质量指数(body mass index,BMI)、中心性肥胖、甘油三酯、吸烟、饮酒、糖尿病史、高血压家族史]。将以上影响因素纳入Logit函数和风险评分模型作为预测项。5年动态队列中合格验证对象共4997人,5年随访期内共684人新发高血压。外部验证Logit函数模型的受试者工作曲线下面积(area under the curve,AUC)为0.571,风险评分模型的AUC为0.657。结论本研究基于循证归纳的影响因素建立了两种高血压预测模型,使用基本公卫体检数据做外部验证。结果显示风险评分模型预测效能较好,利用该评分表可对中老年人群快速风险分层,优化血压分级管理工作。

Objective To establish a hypertension prediction model for middle-aged and elderly people in China and to use the basic public health service database for performance validation.Methods The literature related to hypertension was retrieved from the internet.Using meta-analysis to assess the effect value of influencing factors.Statistically significant factors,which were also combined in the database,were extracted as the predictors of the models.The predictors’effect values were logarithmarithm-transformed as the parameters of the Logit function model and the risk score model.Participants who were never diagnosed with hypertension at the physical examination of health service project of Hongguang Town Health Center in Pidu District of Chengdu from January 1,2017,to January 1,2022,were considered as the external validation group.Results A total of 15 original studies were involved in the meta-analysis and 11 statistically significant influencing factors for hypertension were identified,including age,female,systolic blood pressure,diastolic blood pressure,BMI,central obesity,triglyceride,smoking,drinking,history of diabetes and family history of hypertension.Of 4997 qualified participants,684 individuals were identified with hypertension during the fiveyears follow-up.External validation indicated an AUC of 0.571 for the Logit function model and an AUC of 0.657 for the risk score model.Conclusion In this study,we developed two different prediction models based on the results of metaanalysis.National basic public health service database is used to verify the models.The risk score model has a better prediction performance,which may help quickly stratify the risk class of the community crowd and strengthen the primary-level assistance system.