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*

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。

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

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.