JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES) ›› 2017, Vol. 55 ›› Issue (6): 72-76.doi: 10.6040/j.issn.1671-7554.0.2017.402

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A prediction model of hyperlipidemia risk based on the health management population

ZHANG Guang1, WANG Guangyin2, WU Hongyan1, ZHANG Hongyu1, WANG Tingting3,4, LI Jiqing3,4, LI Min3,4, KANG Fengling3,4, LIU Yanxun3,4, XUE Fuzhong3,4   

  1. 1. Health Management Center, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan 250014, Shandong, China;
    2. Health Management Section, Shengli Hospital of Shandong Dongying Shengli Petroleum Administration Bureau, Dongying 257055, Shandong, China;
    3. Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China;
    4. Cheeloo Research Center for Biomedical Big Data, Shandong University, Jinan 250012, Shandong, China
  • Received:2017-05-06 Online:2017-06-10 Published:2017-06-10

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Abstract: Objective To construct a risk prediction model of hyperlipidemia for people aged 20 years and over. Methods A total of 30 056 people without hyperlipidemia at baseline were included based on the Shandong Multi-center Longitudinal Cohort for Health Management. The prediction model was built on Cox proportional hazards regression model. The predictability was evaluated with the area under the receiver operating characteristic(ROC)curve(AUC). The predictive effect and distinguishing ability were verified with ten-fold cross-validation. Results During the follow-up of 3.53±2.65 years, there were 5 063 new hyperlipidemia cases, and the incidence was 47.78‰. The risk factors of hyperlipidemia included age, sex, drinking, smoking, total cholesterol, triglyceride, total bilirubin, high-density lipoprotein cholesterol, diabetes and hypertension. The AUC was 0.741(95%CI:0.731-0.752). Ten-fold cross-validation verified that the AUC was 0.741. Conclusion The prediction model of hyperlipidemia has good prediction ability in the health management population.

Key words: Longitudinal cohort, Hyperlipidemia, Risk prediction model, Health management population

CLC Number: 

  • R589.2
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