| 子痫前期风险评估模型建立与验证 |
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| 引用本文： | 杜文琼,赵枫,郭玲玲,申嘉欣,王科科,王颖,张萍,冯永亮,杨海澜,王素萍,邬惟为,张亚玮.子痫前期风险评估模型建立与验证[J].中华疾病控制杂志,2019,23(8):981-986. |
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| 作者姓名： | 杜文琼 赵枫 郭玲玲 申嘉欣 王科科 王颖 张萍 冯永亮 杨海澜 王素萍 邬惟为 张亚玮 |
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| 作者单位： | 山西医科大学公共卫生学院流行病学教研室,太原,030001;山西医科大学第一医院妇产科,太原,030001;030001太原,山西医科大学公共卫生学院流行病学教研室;06511纽黑文,耶鲁大学公共卫生学院环境健康科学系 |
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| 基金项目： | 国家自然科学基金81473061国家自然科学基金81703314山西省青年科技研究基金2013021033-2 |
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| 摘 要： | 目的 分析影响子痫前期发病的危险因素，建立子痫前期风险评估模型，以期早期评估子痫前期的发生风险。 方法 以山西医科大学第一医院产科于2012年3月-2016年9月分娩的所有产妇为调查对象，进行面对面问卷调查。共收集合格问卷10 319份，排除其他妊娠期相关高血压疾病后，共纳入9 623例，随机抽取其中70%的对象作为训练样本，分析子痫前期的影响因素，建立Logistic回归分析模型，其余30%的对象作为测试样本，验证模型效果。 结果 用训练样本建立Logistic回归分析模型，Logit P=-2.517-0.696×孕前偏瘦+0.200×孕前超重+0.944×孕前肥胖-1.295×居住在城市-0.409×孕前补充过叶酸+1.323×双胎及多胎妊娠+1.708×既往妊娠期高血压病史。Homer-Lemeshow拟合优度检验P=0.377。模型AUC=0.767（95%CI:0.747~0.786，P < 0.001）。用测试样本进行验证模型，得到模型灵敏度81.68%，特异度75.05%，阳性似然比为3.27，阴性似然比为0.24。测试样本模型AUC=0.771（95%CI:0.763~0.790，P < 0.001）。 结论 建立了一个纳入可控因素，且简单、高效的子痫前期风险评估模型，模型拟合度好，灵敏度和特异度较高。
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| 关 键 词： | 子痫前期 叶酸 Logistic回归分析模型 ROC曲线 |
| 收稿时间： | 2019-02-20 |
Establishment and verification of preeclampsia risk assessment model |
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| Affiliation: | 1.Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China2.Department of Obstetrics and Gynecology, the First Hospital of Shanxi Medical University, Taiyuan 030001, China3.Department of Environmental Health Sciences, School of Public Health, Yale University, New Haven 06511, American |
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| Abstract: | Objective To analyze the risk factors affecting pre-eclampsia, to establish a pre-eclampsia risk assessment model, and to assess the risk of pre-eclampsia early. Methods A face-to-face questionnaire survey was conducted for all women who gave birth in the Department of Obstetrics, the First Hospital of Shanxi Medical University from March 2012 to September 2016. A total of 10 319 qualified questionnaires were collected to exclude 9 623 cases of other hypertensive diseases related to pregnancy. A total of 70% of the subjects were randomly selected as training samples to analyze the influencing factors of pre-eclampsia, and a Logistic regression model was established. The remaining 30% of the objects are used as test samples to verify the effect of the model. Results Logistic regression model was established with training samples. Logit P=-2.517-0.696×Pre-pregnancy lean +0.200×Pre-pregnancy overweight +0.944×Pre-pregnancy obesity -1.995×Residential in city -0.409×Folic acid supplemented before pregnancy +1.323×Twin and multiple pregnancy +1.708×History of previous pregnancy hypertension. Homer-Lemeshow test P=0.377. Model AUC=0.767 (95%CI: 0.747-0.786, P < 0.001). Using the test sample to verify the model, the model sensitivity was 81.68%, the specificity was 75.05%, the positive likelihood ratio was 3.27, and the negative likelihood ratio was 0.24. The test sample model AUC=0.771 (95%CI=0.763-0.790, P < 0.001). Conclusion This study establishes a simple and effective pre-eclampsia risk assessment model with controllable factors. The model has good fit and sensitivity and specificity. |
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