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*

doi:10.12047/j.cjap.0093.2021.117

中国应用生理学杂志 ›› 2021, Vol. 37 ›› Issue (2): 142-146.doi: 10.12047/j.cjap.0093.2021.117

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^*

张雪梅¹, 钟小钢^1,2, 龚军², 田君¹, 张谊¹, 陈颖哲^3,4, 崔婧¹, 汪曾子¹, 冉淑琼¹, 向天雨², 谢友红¹, 孙兴国^1,3△

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

收稿日期:2020-08-12 修回日期:2021-03-10 出版日期:2021-03-28 发布日期:2021-10-20
通讯作者: ^△Tel：010-88398300;E-mail: xgsun@lundquist.org
基金资助:
^*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）

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

ZHANG Xue-mei¹, ZHONG Xiao-gang^1,2, GONG Jun², TIAN Jun¹, ZHANG Yi¹, CHEN Ying-zhe^3,4, CUI Jing¹, WANG Zeng-zi¹, RAN Shu-qiong¹, XIANG Tian-yu², XIE You-hong¹, SUN Xing-guo^1,3△

1. Department of Medical and Nursing, The Affiliated Rehabilitation Hospital of Chongqing Medical University, Chongqing 400050;
2. Medical Data Science Academy, Chongqing Medical University, Chongqing 400016;
3. State Key Laboratory of Cardiovascular Disease, National Center of Cardiovascular Disease Fuwai Hospital, Chinese Academy Science and Peking Union Medical College, Beijing 100037;
4. Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China

Received:2020-08-12 Revised:2021-03-10 Online:2021-03-28 Published:2021-10-20

摘要/Abstract

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。

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

Abstract: 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.

Key words: hypertensive heart disease, biomarkers, machine learning, random forest, XGBoost

中图分类号:

R541.3

张雪梅, 钟小钢, 龚军, 田君, 张谊, 陈颖哲, 崔婧, 汪曾子, 冉淑琼, 向天雨, 谢友红, 孙兴国. 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^*[J]. 中国应用生理学杂志, 2021, 37(2): 142-146.

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. Screening biomarkers for hypertensive heart disease: Analysis based on data from 7 medical institutions[J]. CJAP, 2021, 37(2): 142-146.

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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

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