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山东大学学报(医学版) ›› 2017, Vol. 55 ›› Issue (6): 98-103.doi: 10.6040/j.issn.1671-7554.0.2017.359

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健康管理人群慢性肾脏病风险预测模型

周苗1,2,夏同耀3,孙爱玲4,李明5,申振伟1,2,卞伟玮1,2,蒋正1,2,康凤玲1,2,柳晓涓1,2,薛付忠1,2,刘静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.北大医疗淄博医院, 山东 淄博 255069;4.北大医疗淄博医院社会工作部, 山东 淄博 255069;5.北大医疗淄博医院体检中心, 山东 淄博 255069
  • 收稿日期:2017-04-26 出版日期:2017-06-10 发布日期:2017-06-10
  • 通讯作者: 夏同耀. E-mail:xiatongyao@126.com E-mail:xiatongyao@126.com
  • 基金资助:
    国家自然科学基金(81273177)

Risk prediction model of chronic kidney disease in health management population

ZHOU Miao1,2, XIA Tongyao3, SUN Ailing4, LI Ming5, SHEN Zhenwei1,2, BIAN Weiwei1,2, JIANG Zheng1,2, KANG Fengling1,2, LIU Xiaojuan1,2, XUE Fuzhong1,2, LIU Jing1,2   

  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. PKUCare Zibo Hospital, Zibo 255069, Shandong, China;
    4. Social Work Department, PKUCare Zibo Hospital, Zibo 255069, Shandong, China;
    5. Health Examination Center, PKUCare Zibo Hospital, Zibo 255069, Shandong, China
  • Received:2017-04-26 Online:2017-06-10 Published:2017-06-10

摘要: 目的 构建健康管理人群慢性肾脏病(CKD)发病风险预测模型。 方法 从山东多中心健康管理纵向观察队列中选取年龄20岁以上、至少有两次纵向观察结果、初次观察结果未患CKD的健康管理对象,共17 654人,随访观察结局为CKD。应用Cox比例风险回归模型建立风险预测模型,以受试者工作特征曲线下面积(AUC)评价模型的拟合效果,应用十折交叉验证法验证模型的稳定性。 结果 观察期间共有770例新发CKD病例,发病密度为17.69/1 000人年。最终纳入模型的预测因子有年龄、性别、高血压、糖尿病、血肌酐、血尿素氮、血尿酸、嗜碱性粒细胞百分比。模型AUC为0.685(95%CI:0.678~0.692),且稳定性较好。 结论 建立的CKD风险预测模型在健康管理人群中有较好的预测能力。

关键词: 风险预测模型, 慢性肾脏病, 健康管理, 队列

Abstract: Objective To establish a risk prediction model of chronic kidney disease(CKD). Methods The data were obtained from Shandong Multi-center Longitudinal Cohort for Health Management. A total of 17 654 subjects with age of 20 years or older were included who had no CKD at baseline and accepted health examination at least twice during the study period. The follow-up outcome was CKD. Cox proportional hazards regression was applied to establish the model and the predictive performance of the model was evaluated by AUC. Ten-fold cross validation was used to verify the stability of the model. Results A total of 770 cases were observed during the follow-up. The incidence density of CKD was 17.69 per thousand person-years. The predictive factors in the final model included age, sex, hypertension, diabetes, creatinine, blood urea nitrogen, uric acid and basophils percentage. The AUC of the model was 0.685(95%CI: 0.678-0.692). Conclusion We have constructed a risk model that could be useful for identifying individuals at high risk of CKD in health management population.

Key words: Chronic kidney disease, Risk prediction model, Health management, Cohort

中图分类号: 

  • R692
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