高血压性心脏病生物标志物的筛选:基于中国重庆市7家医疗机构的数据分析研究  

Screening biomarkers for hypertensive heart disease:Analysis based on data from 7 medical institutions

作　　者：张雪梅 钟小钢 龚军 田君 张谊 陈颖哲 崔婧[1] 汪曾子 冉淑琼 向天雨 谢友红 孙兴国 ZHANG Xue-mei;ZHONG Xiao-gang;GONG Jun;TIAN Jun;ZHANG Yi;CHEN Ying-zhe;CUI Jing;WANG Zeng-zi;RAN Shu-qiong;XIANG Tian-yu;XIE You-hong;SUN Xing-guo(Department of Medical and Nursing,The Affiliated Rehabilitation Hospital of Chongqing Medical University,Chongqing 400050;Medical Data Science Academy,Chongqing Medical University,Chongqing 400016;State Key Laboratory of Cardiovascular Disease,National Center of Cardiovascular Disease Fuwai Hospital,Chinese Academy Science and Peking Union Medical College,Beijing 100037;Beijing Hospital of Traditional Chinese Medicine,Capital Medical University,Beijing 100010,China)

机构地区：[1]重庆医科大学附属康复医院,重庆400050 [2]重庆医科大学医学数据研究院,重庆400016 [3]国家心血管病中心中国医学科学院阜外医院,北京100037 [4]首都医科大学附属北京中医院,北京100010

出　　处：《中国应用生理学杂志》2021年第2期142-146,168,共6页Chinese Journal of Applied Physiology

基　　金：国家高技术研究发展计划(863计划)课题资助项目(2012AA021009);国家自然科学基金医学科学部面上项目(81470204);中国康复医疗机构联合重大项目基金(20160102);中国医学科学院国家心血管病中心阜外医院科研开发启动基金(2012-YJR02);首都临床特色应用研究与成果推广(Z161100000516127);北京康复医院2019-2021科技发展专项(2019-003);北京协和医学院教学改革项目(2018E-JG07);北京协和医学院-国家外国专家局外国专家项目(2015,2016,T2017025,T2018046,G2019001660);重庆市卫计委医学科研计划项目(2017MSXM090);重庆市科委社会事业与民生保障科技创新专项项目(cstc2017shmsA130063)。

摘　　要：目的:筛选高血压性心脏病(HHD)的影响因素,建立HHD的预测模型,为HHD的发生提供预警。方法:选取中国重庆市某医科院校数据研究院平台2016年1月1日至2019年12月31日主要诊断为高血压性心脏病和高血压患者。通过单因素分析、多因素分析筛选HHD的影响因素,采用R语言分别构建Logistics模型、随机森林(RF)模型和极限梯度上升(XGBoost)模型。结果:单因素分析筛选出60项差异指标,多因素分析筛选出18项差异指标(P<0.05)。Logistics模型、RF模型、XGBoost模型曲线下面积(AUC)分别为0.979、0.983和0.990。结论:本文建立的3种HHD预测模型结果稳定,其中XGBoost模型对于HHD的发生具有良好的诊断效应。Objective:To screen the influencing factors of hypertensive heart disease(HHD),establish the predictive model of HHD,and provide early warning for the occurrence of HHD.Methods:Select the patients diagnosed as hypertensive heart disease or hypertensionfrom January 1,2016 to December 31,2019,in the medical data science academy of a medical school.Influencing factors were screened through single factor and multi-factor analysis,and R software was used to construct the logistics model,random forest(RF)model and extreme gradient boosting(XGBoost)model.Results:Univariate analysis screened 60 difference indicators,and multifactor analysis screened 18 difference indicators(P<0.05).The area under the curve(AUC)of Logistics model,RF model and XGBoost model are 0.979,0.983 and 0.990,respectively.Conclusion:The results of the three HHD prediction models established in this paper are stable,and the XGBoost prediction model has a good diagnostic effect on the occurrence of HHD.

关 键 词：高血压性心脏病 生物标志物 机器学习 随机森林 极限梯度上升 

分 类 号：R541.3[医药卫生—心血管疾病]