Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jun 1.
doi: 10.1007/s00404-025-08076-6. Online ahead of print.

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

Changxiu Wang #  1 , Tao Zeng #  2 , Xiangyu Zhao  3 , Cuiping You  4 , Yucheng Lu  5 , Guanqing Kong  6 , Lingling Hu  1 , Jinyan Huang  2 , Yanxin Zhang  7
Affiliations

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

Changxiu Wang et al. Arch Gynecol Obstet. .

Abstract

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.

Keywords: Nomogram; Placental growth factor; Preeclampsia; Soluble fms-like tyrosine kinase-1.

PubMed Disclaimer

Conflict of interest statement

Declarations. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical approval: The dataset of this retrospective study was from patient’s electronic medical records. Written consent was obtained from all the participants. The study protocol was approved by the Science Research Ethics Committee, Linyi People’s Hospital (YX200683). Declaration of generative AI in scientific writing: The authors did not use generative AI or AI-assisted technologies in the development of this manuscript.

Similar articles

See all similar articles

References

    1. Kassebaum NJ, Barber RM, Bhutta ZA, Dandona L, Gething PW, Hay SI, Kinfu Y, Larson HJ, Liang X, Lim SS, Lopez AD (2016) Global, regional, and national levels of maternal mortality, 1990–2015: a systematic analysis for the global burden of disease study 2015. Lancet 388(10053):1775–812. https://doi.org/10.1016/s0140-6736(16)31470-2 - DOI
    1. Poon LC, Shennan A, Hyett JA, Kapur A, Hadar E, Divakar H et al (2019) The international federation of gynecology and obstetrics (FIGO) initiative on pre-eclampsia: a pragmatic guide for first-trimester screening and prevention. Int J Gynaecol Obstet 145(Suppl 1):1–33. https://doi.org/10.1002/ijgo.12802 - DOI - PubMed - PMC
    1. Magee LA, Nicolaides KH, von Dadelszen P (2022) Preeclampsia. N Engl J Med 386(19):1817–32. https://doi.org/10.1056/NEJMra2109523 - DOI - PubMed
    1. Chappell LC, Cluver CA, Kingdom J, Tong S (2021) Pre-eclampsia. Lancet 398(10297):341–54. https://doi.org/10.1016/s0140-6736(20)32335-7 - DOI - PubMed
    1. Scott G, Gillon TE, Pels A, von Dadelszen P, Magee LA (2022) Guidelines-similarities and dissimilarities: a systematic review of international clinical practice guidelines for pregnancy hypertension. Am J Obstet Gynecol 226(2s):S1222-s36. https://doi.org/10.1016/j.ajog.2020.08.018 - DOI - PubMed

LinkOut - more resources