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Purpose <p>We conduct this study to develop and validate a predictive nomogram for preeclampsia (PE) to inform the development of early intervention strategies in clinical practice.</p> Methods <p>In this analysis, we collected data from women with medium or high risk for PE who underwent placental growth factor (PlGF)-based testing between December 20, 2021 and December 31, 2022. The gestational age at the time of taking the PlGF-based test for the PE and non-PE groups was 20.0&#xa0;weeks (range 16.1–26.1&#xa0;weeks) and 22.2&#xa0;weeks (range 16.2–27.3&#xa0;weeks), respectively. The independent risk factors for PE were identified through both univariate and multivariate analyses. Based on these independent risk factors, a logistic regression model for risk prediction was developed. The model was validated using five-fold cross-validation. Moreover, the efficacy of the model was appraised using the area under the receiver operating characteristic curve (AUROC), while the calibration of the model was assessed through calibration curves. Additionally, decision curves and clinical impact curves were leveraged to evaluate the clinical applicability of the model.</p> Results <p>In total, 2063 women were included. Of these, 108 had PE. Body mass index, mean arterial pressure, a ratio of soluble fms-like tyrosine kinase-1/PlGF, history of adverse pregnancy, family history of PE, previous history of PE, chronic hypertension, autoimmune disease, and polycystic ovary syndrome were independent risk factors for PE. The model constructed based on independent risk factors demonstrated that the AUROC in the training set was 0.883 (95% confidence interval [CI] 0.838–0.928), with a sensitivity of 0.827 and specificity of 0.816. In the validation set, the AUROC was 0.862 (95% CI 0.774–0.951), with a sensitivity of 0.815 and specificity of 0.772. The decision curve revealed that the model had a large probability interval for the net benefit threshold.</p> Conclusion <p>The predictive nomogram for PE constructed based on common interpretable features has desirable efficacy, which informs the development of specialized preventive protocols in clinical practice.</p>

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Development and validation of a predictive model for preeclampsia: a retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月1日

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  • Changxiu Wang,
  • Tao Zeng,
  • Xiangyu Zhao,
  • Cuiping You,
  • Yucheng Lu,
  • Guanqing Kong,
  • Lingling Hu,
  • Jinyan Huang,
  • Yanxin Zhang

摘要

Purpose

We conduct this study to develop and validate a predictive nomogram for preeclampsia (PE) to inform the development of early intervention strategies in clinical practice.

Methods

In this analysis, we collected data from women with medium or high risk for PE who underwent placental growth factor (PlGF)-based testing between December 20, 2021 and December 31, 2022. The gestational age at the time of taking the PlGF-based test for the PE and non-PE groups was 20.0 weeks (range 16.1–26.1 weeks) and 22.2 weeks (range 16.2–27.3 weeks), respectively. The independent risk factors for PE were identified through both univariate and multivariate analyses. Based on these independent risk factors, a logistic regression model for risk prediction was developed. The model was validated using five-fold cross-validation. Moreover, the efficacy of the model was appraised using the area under the receiver operating characteristic curve (AUROC), while the calibration of the model was assessed through calibration curves. Additionally, decision curves and clinical impact curves were leveraged to evaluate the clinical applicability of the model.

Results

In total, 2063 women were included. Of these, 108 had PE. Body mass index, mean arterial pressure, a ratio of soluble fms-like tyrosine kinase-1/PlGF, history of adverse pregnancy, family history of PE, previous history of PE, chronic hypertension, autoimmune disease, and polycystic ovary syndrome were independent risk factors for PE. The model constructed based on independent risk factors demonstrated that the AUROC in the training set was 0.883 (95% confidence interval [CI] 0.838–0.928), with a sensitivity of 0.827 and specificity of 0.816. In the validation set, the AUROC was 0.862 (95% CI 0.774–0.951), with a sensitivity of 0.815 and specificity of 0.772. The decision curve revealed that the model had a large probability interval for the net benefit threshold.

Conclusion

The predictive nomogram for PE constructed based on common interpretable features has desirable efficacy, which informs the development of specialized preventive protocols in clinical practice.