Evidence map›Paper›PMID 33682756›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2021

Prediction of hypertensive disorders in pregnancy based on placental growth factor.

Qi Xu, Ge Sun, Song Zhang, Guoli Liu, Lin Yang, Yu Meng, Aiqing Chen, Yimin Yang, Xuwen Li, Dongmei Hao and 2 more

Abstract read
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In one paragraph

Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Qi XuPeking University People's Hospital, Beijing 100044, China.
Ge SunFaculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Song ZhangFaculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Guoli LiuPeking University People's Hospital, Beijing 100044, China.
Lin YangFaculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Yu MengFaculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Aiqing ChenBeijing Yes Medical Devices Co. Ltd., Beijing 100152, China.
Yimin YangFaculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Xuwen LiFaculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Dongmei HaoFaculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Xiaohong LiuBeijing Yes Medical Devices Co. Ltd., Beijing 100152, China.
Jing ShaoBeijing Yes Medical Devices Co. Ltd., Beijing 100152, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prediction of hypertensive disorders in pregnancy (HDP) mainly involves various aspects such as maternal characteristics and biomarkers.

objectiveWe aimed to study the effect of the HDP prediction model with or without placental growth factor (PlGF).

methodsThis study used maternal factors and PlGF, and standardized the data uniformly. At 12-20 weeks, the comprehensive comparison of model quality with or without PlGF was conducted by logistic regression.

resultsThe area under curve and the model accuracy of the model with PlGF were higher than those of the model without PlGF. The accuracy of the model with PlGF was above 90%.

conclusionsAdding PlGF to the model for predicting HDP improved the accuracy and effectiveness of the model. This study confirmed the predictive performance of PlGF.

Indexed as

Hypertension, Pregnancy-InducedAdultBiomarkersFemaleHumansPlacenta Growth FactorPregnancyProspective StudiesBiomarkersPlacenta Growth Factorlogistic regressionModel accuracyplacental growth factor

Identifiers

PMID33682756
PMCPMC8150549

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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.