JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES) ›› 2017, Vol. 55 ›› Issue (6): 98-103.doi: 10.6040/j.issn.1671-7554.0.2017.359

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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

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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

CLC Number: 

  • R692
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