蒋红清, 陈寒, 宋慧颖, 钱年凤, 张松, 王安然. 妊娠期高血压疾病发病危险预测模型的建立分析[J]. 解放军医学院学报, 2018, 39(10): 844-849. DOI: 10.3969/j.issn.2095-5227.2018.10.003
引用本文: 蒋红清, 陈寒, 宋慧颖, 钱年凤, 张松, 王安然. 妊娠期高血压疾病发病危险预测模型的建立分析[J]. 解放军医学院学报, 2018, 39(10): 844-849. DOI: 10.3969/j.issn.2095-5227.2018.10.003
JIANG Hongqing, CHEN Han, SONG Huiying, QIAN Nianfeng, ZHANG Song, WANG Anran. Prediction model of risk factors for hypertensive disorder complicating pregnancy[J]. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL, 2018, 39(10): 844-849. DOI: 10.3969/j.issn.2095-5227.2018.10.003
Citation: JIANG Hongqing, CHEN Han, SONG Huiying, QIAN Nianfeng, ZHANG Song, WANG Anran. Prediction model of risk factors for hypertensive disorder complicating pregnancy[J]. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL, 2018, 39(10): 844-849. DOI: 10.3969/j.issn.2095-5227.2018.10.003

妊娠期高血压疾病发病危险预测模型的建立分析

Prediction model of risk factors for hypertensive disorder complicating pregnancy

  • 摘要: 目的 探讨妊娠期高血压疾病相关危险因素,建立随孕周变化的妊娠期高血压疾病发病危险预测模型。 方法 收集2016年7月- 2017年8月在北京海淀妇幼保健院规律产检和分娩的妊娠期高血压疾病孕妇300例(疾病组)和正常孕妇390例(正常组),以两组临床流行病学、血流动力学和血液生化中有统计学意义的相关参数建立妊娠高血压危险预测模型。 结果 疾病组和正常组孕妇的临床流行病学参数(年龄、多胎妊娠、自然流产史、妊娠期高血压病史、孕前BMI、孕期BMI)、血流动力学参数(收缩压、舒张压、脉压、平均动脉压、波形面积参数K值、心脏指数、外周阻力)和血液生化参数(红细胞比容、血小板计数、平均血小板体积、尿酸、肌酐)在不同妊娠阶段均有统计学差异(P<0.05)。联合流行病学、血流动力学和血液生化因素进行妊娠期高血压疾病预测。早期妊娠(孕≤13周)妊娠期高血压疾病预测模型的准确率为77.78%,灵敏度为44.44%;中期妊娠(孕21~27周)预测模型的准确率为83.5%,灵敏度为71.34%;晚期妊娠(≥28周)预测模型的准确率达91%以上,灵敏度和特异度分别达87%和94%以上,阳性预测率和阴性预测率达90%和92%以上。 结论 联合应用妊娠期高血压疾病相关危险因素建立随孕周变化的疾病危险动态预测模型,便于对妊娠期高血压疾病进行早期预测和干预,有助于改善母儿不良妊娠结局。

     

    Abstract: Objective To investigate the risk factors of hypertensive disorder complicating pregnancy (HDCP), and establish a model to predict the risk of developing HDCP over gestational weeks. Methods From July 2016 to August 2017, we recruited 300 pregnant women patients with HDCP (disease group) and 390 normal pregnant women (control group) who were routinely examined and delivered in Beijing Haidian Maternal and Child Health Hospital. Univariate and multivariate analysis were performed to compare the epidemiologic, hemodynamics, and laboratory variables between the two groups. Results There were significant differences in epidemiologic variables (age, multiple pregnancy, previous spontaneous abortion, HDCP history, pre-pregnancy BMI, BMI during pregnancy), hemodynamics variables (systolic blood pressure, diastolic blood pressure, pulse pressure, mean arterial pressure, K value, Cardiac index, total peripheral resistance) and laboratory variables (hematocrit, platelet, mean platelet volume, uric acid, creatinine) between the disease group and the control group at different stages of pregnancy, which facilitated the early identification of HDCP. The combination of epidemiologic, hemodynamics and laboratory variables led to great improvement in the accuracy(84.88%), sensitivity (79.75%) and specificity (89.25%) of the risk prediction model for HDCP. The prediction model was more reliable over gestational weeks, with an accuracy rate of 77.78%, lowest sensitivity of 44.44% in early pregnancy (pregnancy≤13 weeks), an accuracy rate of 83.5%, sensitivity of 71.34% in mid-pregnancy (21-27 weeks of pregnancy), while an accuracy rate of more than 91%, sensitivity of 87% and specificity of more than 94% in late pregnancy (≥28 weeks). Conclusion The establishment of a dynamic model for HDCP risk prediction model based on the associated risk factors may facilitate the early identification and intervention of HDCP, finally improve the adverse pregnancy outcome.

     

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