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山东大学学报（医学版） ›› 2017, Vol. 55 ›› Issue (6): 104-107.doi: 10.6040/j.issn.1671-7554.0.2017.401

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健康管理队列白内障发病风险预测模型

于媛媛1,2,王春霞3,苏萍1,2,孙苑潆1,2,薛付忠1,2,刘言训1,2   

  1. 1.山东大学公共卫生学院生物统计学系, 山东 济南 250012;2.山东大学齐鲁生物医学大数据研究中心, 山东 济南 250012;3.济宁医学院附属医院健康管理中心, 山东 济宁 272000
  • 收稿日期:2017-05-06 出版日期:2017-06-10 发布日期:2017-06-10
  • 通讯作者: 薛付忠. E-mail:xuefzh@sdu.edu.cn刘言训. E-mail:liu-yx@sdu.edu.cn E-mail:xuefzh@sdu.edu.cn
  • 基金资助:
    国家国际科技合作专项项目(2014DFA32830)

A prediction model to estimate risks of cataract based on health management cohort

YU Yuanyuan1,2, WANG Chunxia3, SU Ping1,2, SUN Yuanying1,2, XUE Fuzhong1,2, LIU Yanxun1,2   

  1. 1. Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China;
    2. Cheeloo Research Center for Biomedical Big Data, Shandong University, Jinan 250012, Shandong, China;
    3. Health Management Center, Affiliated Hospital of Jining Medical University, Jining 272000, Shandong, China
  • Received:2017-05-06 Online:2017-06-10 Published:2017-06-10

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摘要： 目的 构建50岁以上健康管理队列的白内障发病风险预测模型。 方法 依托山东多中心健康管理纵向观察数据库,采用Cox比例风险回归构建白内障发病风险预测模型,通过ROC曲线下面积(AUC)评价模型的预测效果,并利用十折交叉验证来检验模型的稳定性。 结果 随访期间共新发白内障病例1 010例,发病密度为24.76‰。预测模型最终纳入年龄、性别、吸烟、高黏稠血症、鼓膜疾患、屈光不正、糖尿病、总胆固醇和收缩压9个变量。白内障发病风险预测模型的AUC为0.712(95%CI:0.693~0.732)。十折交叉验证的平均AUC为0.714。 结论 研究构建的白内障发病风险预测模型有较好的预测效果,为白内障高危人群的早期筛查提供了依据。

关键词: 风险预测模型, 队列研究, 白内障, Cox比例风险回归

Abstract: Objective To establish a prediction model to estimate risks of cataract among health management population aged above 50 years. Methods Based on the Shandong Multi-center Longitudinal Cohort for Health Management, a prediction model for cataract was constructed using Coxs proportional hazards regression model. The predictability was evaluated with the area under the receiver operating characteristic(ROC)curve(AUC). The stability was tested with ten-fold cross-validation. Results During the follow-up period, there were 1010 new cataract cases, and the incidence density was 24.76‰. The risk factors included in prediction model were age, sex, smoking habit, hyperviscosemia, tympanic diseases, ametropia, diabetes, total cholesterol and systolic blood pressure(SBP). The AUC of the prediction model was 0.712(95% CI: 0.693-0.732). The ten-fold cross-validation showed that the AUC was 0.714. Conclusion The prediction model of cataract has high predictability and reliability. It can provide scientific basis for identifying high-risk groups of cataract.

Key words: Cohort study, Cataract, Coxs proportional hazards regression, Risk prediction model

中图分类号: 

  • R776.1
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