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<p>The HDP prediction model was constructed and validated by using the demographic characteristics, blood routine and biochemical screening indicators in early pregnancy to reduce the incidence of HDP. 16,112 pregnant women admitted to Yuyao People’s Hospital from May 1, 2018 to April 30, 2022 were randomly divided into modeling group (<i>n</i> = 11279) and validation group (<i>n</i> = 4833) according to a ratio of 7:3. Demographic characteristics, blood routine and biochemical screening data of 8–12<sup>+ 6</sup> weeks gestation were obtained from Ningbo Health Records system. Univariate analysis and multivariate binary Logistic regression analysis were used to determine the independent risk factors of HDP, and the scoring system was established by using the nomogram. Univariate analysis and multivariate binary Logistic regression analysis showed that Age, BMI, previous medical history, HB, TG, HDL and ALB were independent risk factors for HDP (<i>P</i> &lt; 0.001). In the modeling group, AUC = 0.809, sensitivity = 74.30%, specificity = 73.10%, and in the validation group, AUC = 0.801, sensitivity = 77.60%, specificity = 68.90%. Hosmer-Lemeshow goodness of fit test showed that modeling group: <i>P</i> = 0.195 &gt; 0.05, validation group: <i>P</i> = 0.775 &gt; 0.05. The prediction model of early pregnancy Age, BMI, previous medical history, HB, TG, HDL and ALB can effectively predict the occurrence of HDP.</p>

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The construction and validation of a prediction model of hypertensive disease in pregnancy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月18日

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  • Yuanyuan Chen,
  • Jianting Ma

摘要

The HDP prediction model was constructed and validated by using the demographic characteristics, blood routine and biochemical screening indicators in early pregnancy to reduce the incidence of HDP. 16,112 pregnant women admitted to Yuyao People’s Hospital from May 1, 2018 to April 30, 2022 were randomly divided into modeling group (n = 11279) and validation group (n = 4833) according to a ratio of 7:3. Demographic characteristics, blood routine and biochemical screening data of 8–12+ 6 weeks gestation were obtained from Ningbo Health Records system. Univariate analysis and multivariate binary Logistic regression analysis were used to determine the independent risk factors of HDP, and the scoring system was established by using the nomogram. Univariate analysis and multivariate binary Logistic regression analysis showed that Age, BMI, previous medical history, HB, TG, HDL and ALB were independent risk factors for HDP (P < 0.001). In the modeling group, AUC = 0.809, sensitivity = 74.30%, specificity = 73.10%, and in the validation group, AUC = 0.801, sensitivity = 77.60%, specificity = 68.90%. Hosmer-Lemeshow goodness of fit test showed that modeling group: P = 0.195 > 0.05, validation group: P = 0.775 > 0.05. The prediction model of early pregnancy Age, BMI, previous medical history, HB, TG, HDL and ALB can effectively predict the occurrence of HDP.