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山东大学学报(医学版) ›› 2017, Vol. 55 ›› Issue (12): 56-61.doi: 10.6040/j.issn.1671-7554.0.2017.425

• 公共卫生与管理学 • 上一篇    下一篇

健康管理人群支气管哮喘风险预测模型

柳晓涓1,2,丁荔洁1,2,康凤玲1,2,周苗1,2,薛付忠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
  • 收稿日期:2017-05-16 出版日期:2017-12-10 发布日期:2017-12-10
  • 通讯作者: 薛付忠. E-mail:xuefzh@sdu.edu.cn E-mail:xuefzh@sdu.edu.cn
  • 基金资助:
    国家自然科学基金(81573259);国家自然科学基金青年基金(81400072);山东省自然科学基金(2013HQ047)

A prediction model for bronchial asthma risk based on a health management population

LIU Xiaojuan1,2, DING Lijie1,2, KANG Fengling1,2, ZHOU Miao1,2, XUE Fuzhong1,2   

  1. 1. Department of Biostatistics, School of Public Health;
    2. Cheeloo Research Center for Biomedical Big Data, Shandong University, Jinan 250012, Shandong, China
  • Received:2017-05-16 Online:2017-12-10 Published:2017-12-10

摘要: 目的 构建健康管理人群支气管哮喘的风险预测模型。 方法 基于健康管理队列人群,针对队列基线中无支气管哮喘的77 493人健康体检对象随访,随访结局为支气管哮喘;采用单因素Cox回归模型筛选预测因子,单因素有意义的变量进入多因素Cox回归,采用向后消除法筛选变量,利用筛选出的预测因子构建Cox风险预测模型;采用ROC曲线下的面积评估模型判别准确度,十折交叉验证评估模型稳定性。 结果 随访9年期间134人被诊断为支气管哮喘。总发病密度48.28/10万人年。最终纳入模型的变量包括:年龄、嗜酸性粒细胞计数、低密度脂蛋白胆固醇、鼻炎史、气管/支气管炎史、慢性阻塞性肺疾病史。ROC曲线下面积(95%CI)为0.725(0.673~0.778),十折交叉验证ROC曲线下面积(95%CI)为0.707(0.647~0.767)。 结论 本研究构建的支气管哮喘风险预测模型可用于预测体检人群的支气管哮喘发病风险。

关键词: 风险预测模型, 队列研究, Cox回归, 支气管哮喘, 健康管理人群

Abstract: Objective To construct a prediction model for bronchial asthma risks based on a health management population. Methods A cohort consisting of 77,493 non-bronchial asthma individuals at baseline was followed up to detect the incidence of bronchial asthma. The risk factors were screened with a single-variable Cox regression model. The selected risk factors were analyzed with multivariate Cox regression model and backwards method. Then a Cox risk prediction model was constructed. The validation and predictive ability of the model were evaluated with the area under the receiver operator characteristic(ROC)curve. The stability of the model was tested with ten-fold cross validation method. Results During the 9-year follow-up, 134 new cases of bronchial asthma were observed. The variables finally included in the prediction model were age, eosinophil count(EOS), low density lipoprotein cholesterol(LDL-C), rhinitis history, trachea-bronchitis history and chronic obstructive pulmonary disease(COPD)history. The area under the ROC curve(95%CI)of the model was 0.725(0.673-0.778), and the area under the ROC curve(95%CI)of the ten-fold cross validation result was 0.707(0.647-0.767). Conclusion A prediction model for bronchial asthma risks has been constructed.

Key words: Bronchial asthma, Cohort study, Cox regression, Prediction model, Health management population

中图分类号: 

  • R562.2
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