Evidence map›Paper›PMID 38926668›Full record

ArticleBMC pregnancy and childbirth2024

A dynamic prediction model for preeclampsia using the sFlt-1/PLGF ratio combined with multiple factors.

Guili Chen, Yuanyuan Chen, Yao Shi, Zhoufen Mao, Jiaqi Lou, Jianting Ma

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Article in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Guili Chen *The People's Hospital of Yuyao, Zhejiang, 315400, China.
Yuanyuan Chen *Ningbo University Medical Department, Zhejiang, 315000, China.
Yao ShiThe People's Hospital of Yuyao, Zhejiang, 315400, China.
Zhoufen MaoThe People's Hospital of Yuyao, Zhejiang, 315400, China.
Jiaqi LouHealth Science Center, Ningbo University, Zhejiang, 315000, China.
Jianting MaThe People's Hospital of Yuyao, Zhejiang, 315400, China. mjt9977@126.com.

Funding

Project for Zhejiang Medical Technology and Hygiene Program 2021KY1073
6 · The paper itself

Abstract

objectivePreeclampsia (PE) is a pregnancy-related multi-organ disease and a significant cause of incidence rate and mortality of pregnant women and newborns worldwide. Delivery remains the only available treatment for PE. This study aims to establish a dynamic prediction model for PE.

methodsA total of 737 patients who visited our hospital from January 2021 to June 2022 were identified according to the inclusion and exclusion criteria, forming the primary dataset. Additionally, 176 singleton pregnant women who visited our hospital from July 2022 to November 2022 comprised the verification set. We investigated different gestational weeks of sFlt-1/PLGF (soluble FMS-like tyrosine kinase-1, placental growth factor) ratio combined with maternal characteristics and routine prenatal laboratory results in order to predict PE in each trimester. Multivariate logistic regression was used to establish the prediction model for PE at different gestational weeks. The discrimination, calibration, and clinical validity were utilized to evaluate predictive models as well as models in external validation queues.

resultsAt 20-24 weeks, the obtained prediction model for PE yielded an area under the curve of 0.568 (95% confidence interval, 0.479-0.657). At 25-29 weeks, the obtained prediction model for PE yielded an area under the curve of 0.773 (95% confidence interval, 0.703-0.842)and 0.731 (95% confidence interval, 0.653-0.809) at 30-34 weeks. After adding maternal factors, uterine artery pulsation index(Ut-IP), and other laboratory indicators to the sFlt-1/PLGF ratio, the predicted performance of PE improved. It found that the AUC improved to 0.826(95% confidence interval, 0.748 ∼ 0.904) at 20-24 weeks, 0.879 (95% confidence interval, 0.823 ∼ 0.935) at 25-29 weeks, and 0.862(95% confidence interval, 0.799 ∼ 0.925) at 30-34 weeks.The calibration plot of the prediction model indicates good predictive accuracy between the predicted probability of PE and the observed probability. Furthermore, decision-curve analysis showed an excellent clinical application value of the models.

conclusionUsing the sFlt-1/PLGF ratio combined with multiple factors at 25-29 weeks can effectively predict PE, but the significance of re-examination in late pregnancy is not significant.

Indexed as

BiomarkersPlacenta Growth FactorPre-EclampsiaVascular Endothelial Growth Factor Receptor-1AdultFemaleGestational AgeHumansLogistic ModelsPredictive Value of TestsPregnancyRetrospective StudiesBiomarkersFLT1 protein, humanPGF protein, humanPlacenta Growth FactorVascular Endothelial Growth Factor Receptor-1Dynamic prediction modelLogistic modelsPreeclampsia(PE)SFlt-1/PLGF ratio

Identifiers

PMID38926668
PMCPMC11202363

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.