王炳源, 高莉, 秦露伟, 潘盼, 冯化飞, 邢天放, 底秀娟, 李少芳, 李卉, 杨文杰, 康锴. 河南省心脑血管疾病发病预测模型的建立与评估[J]. 疾病监测, 2023, 38(10): 1239-1246. DOI: 10.3784/jbjc.202303130099
引用本文: 王炳源, 高莉, 秦露伟, 潘盼, 冯化飞, 邢天放, 底秀娟, 李少芳, 李卉, 杨文杰, 康锴. 河南省心脑血管疾病发病预测模型的建立与评估[J]. 疾病监测, 2023, 38(10): 1239-1246. DOI: 10.3784/jbjc.202303130099
Wang Bingyuan, Gao Li, Qin Luwei, Pan Pan, Feng Huafei, Xing Tianfang, Di Xiujuan, Li Shaofang, Li Hui, Yang Wenjie, Kang Kai. Establishment and evaluation of cardio-cerebrovascular disease prediction model in Henan[J]. Disease Surveillance, 2023, 38(10): 1239-1246. DOI: 10.3784/jbjc.202303130099
Citation: Wang Bingyuan, Gao Li, Qin Luwei, Pan Pan, Feng Huafei, Xing Tianfang, Di Xiujuan, Li Shaofang, Li Hui, Yang Wenjie, Kang Kai. Establishment and evaluation of cardio-cerebrovascular disease prediction model in Henan[J]. Disease Surveillance, 2023, 38(10): 1239-1246. DOI: 10.3784/jbjc.202303130099

河南省心脑血管疾病发病预测模型的建立与评估

Establishment and evaluation of cardio-cerebrovascular disease prediction model in Henan

  • 摘要:
      目的  构建河南省心脑血管疾病发病预测模型,为河南省心脑血管疾病的防控提供科学依据。
      方法  采用队列研究设计,选取2013—2014年参加中国慢性病及危险因素监测的18岁以上河南省常住居民,将基线无心脑血管疾病、无失访、无关键变量缺失的5 757人作为随访队列。 采用加权的Cox回归分析构建模型,以C统计量和校准曲线评估模型的区分度和校准度,并采用Bootstrap法进行内部验证。
      结果  平均随访时间7.01±1.02年,心脑血管疾病累积发病率为9.23%。 纳入预测模型的变量有年龄[风险率(HR)= 1.05,95%置信区间(CI):1.04~1.06,P<0.001]、收缩压(HR =1.01,95%CI: 1.01~1.02,P=0.001)、吸烟(HR= 1.44,95%CI:1.19~1.74,P=0.002)、高血压服药(HR= 1.81,95%CI:1.32~2.50,P=0.002)和糖尿病(HR= 1.49,95%CI:1.11~2.00,P=0.014)。 模型的C统计量为0.76(0.74~0.78),校准曲线显示预测值与观察值有较好的一致性。 采用Bootstrap法验证结果显示,C统计量均值的偏差为0.000 16。
      结论  该模型具有较好的可信度和准确性,可为河南省心脑血管疾病高危人群的筛查提供科学依据。

     

    Abstract:
      Objective  To develop a cardio-cerebrovascular disease prediction model and provide scientific evidence for the prevention and control of cardio-cerebrovascular disease in Henan province.
      Methods  Using a cohort study design, 5 757 permanent residents aged ≥18 years without baseline cardio-erebrovascular diseases, loss to follow up and missing data in China chronic diseases and risk factor surveillance in Henan during 2013−2014 were selected as the follow-up cohort. Weighted Cox regression analysis was used to construct the model, and C-statistics and calibration curve were used to evaluate the differentiation and calibration of the model, and Bootstrap method was used for internal validation.
      Results  The mean follow-up time was 7.01±1.02 years, and the cumulative incidence of cardio-cerebrovascular disease was 9.23%. Variables included in the prediction model were age hazard rate (HR) : 1.05, 95% confidence interval (CI): 1.04–1.00, P<0.001, systolic blood pressure (HR: 1.01, 95%CI: 1.01–1.02, P=0.001), smoking (HR: 1.44, 95%CI: 1.19–1.74, P=0.002), anti-hypertension treatment (HR: 1.81, 95%CI: 1.32–2.50, P=0.002), and diabetes (HR: 1.49, 95%CI: 1.11–2.00, P=0.014). The C statistic of the model was 0.76 (0.74–0.78), and the calibration curve showed that the predicted value was highly consistent with the observed value. The results of Bootstrap method showed that the bias of the mean value of C statistic was 0.000 16.
      Conclusion  The model can be used to identify individuals at high risk for cardio-cerebrovascular disease in Henan with good reliability and accuracy.

     

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