JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES) ›› 2017, Vol. 55 ›› Issue (6): 93-97.doi: 10.6040/j.issn.1671-7554.0.2017.349

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A stroke prediction model for the health management population

LI Min1,2, WANG Chunxia3, XIA Bing3, ZHU Qian1,2, SUN Yuanying1,2, WANG Shukang1,2, XUE Fuzhong1,2, JIA Hongying4   

  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;
    4. Evidence Based Medicine Center, The Second Hospital of Shandong University, Jinan 250033, Shandong, China
  • Received:2017-04-24 Online:2017-06-10 Published:2017-06-10

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Abstract: Objective To construct a stroke prediction model for the health management population aged above 20 years. Methods A total of 74,326 cohort members without stroke at baseline were included based on the Shandong Multi-center Longitudinal Cohort for Health Management. Fine-Gray model was used to construct a stroke risk prediction model for females and males respectively. Results During the average follow-up of 3.9 years, 1,299(male: 829, female: 470)new stroke occurred, and the incidence density was 4.51‰. The risk factors for males included age, hypertension, coronary heart disease, diabetes mellitus, smoking, body mass index, triglyceride, white blood cell count, platelet count, high-density lipoprotein, and total cholesterol. The risk factors for females included age, hypertension, coronary heart disease, red blood cell count, hemoglobin, and body mass index. The estimated area under the receiver-operating characteristic curve(AUC)for the male model and female model was 0.846(95%CI: 0.828-0.864), and 0.878(95%CI: 0.858-0.898). Conclusion The stroke risk prediction model we constructed is effective in identifying individuals at high risk of stroke in the health management population.

Key words: Stroke, Prediction model, Longitudinal cohort, Health management population

CLC Number: 

  • R743.3
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