Literature DB >> 29153659

A simple clinical method to identify women at higher risk of preeclampsia.

Effie Viguiliouk1, Alison L Park2, Howard Berger3, Michael P Geary4, Joel G Ray5.   

Abstract

An outstanding issue is how to efficiently identify women at high risk of preeclampsia. This retrospective cohort study included 8672 pregnancies at a single centre in Toronto. We tested our simple method - presence vs. absence of≥1 major (pre-pregnancy BMI>30kg/m2, chronic hypertension, pre-pregnancy diabetes mellitus and assisted reproductive therapy) or≥2 minor (prior stillbirth, age>40years, nulliparity, multifetal pregnancy, chronic kidney disease, and SLE) risk factors for PE. The RR of PE was 8.4 (95% CI 5.3-13.2) and the model C-statistic 0.74 (95% CI 0.69-0.79). Further testing of this method elsewhere is warranted.
Copyright © 2017 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  C-statistic; Hypertension; Prediction; Preeclampsia; Pregnancy; Preterm birth

Mesh:

Year:  2017        PMID: 29153659     DOI: 10.1016/j.preghy.2017.07.145

Source DB:  PubMed          Journal:  Pregnancy Hypertens        ISSN: 2210-7789            Impact factor:   2.899


  1 in total

1.  Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia.

Authors:  Herdiantri Sufriyana; Yu-Wei Wu; Emily Chia-Yu Su
Journal:  EBioMedicine       Date:  2020-04-10       Impact factor: 8.143

  1 in total

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