Literature DB >> 28165141

A prospective study on first trimester prediction of ischemic placental diseases.

Gulnar Nuriyeva1, Semir Kose2, Gamze Tuna3, Melis Kant4, Merve Akis4, Sabahattin Altunyurt2, Gül Huray Islekel4, Omer Erbil Dogan1.   

Abstract

OBJECTIVE: The objective of the study is to assess the predictive power of mean uterine artery pulsatility index (UtA PI), maternal serum placental growth factor (PlGF) and placenta associated plasma protein A levels for the development of ischemic placental diseases (IPD) in a cohort of unselected singleton pregnancies during the first trimester combined test period.
MATERIALS AND METHODS: A sample of 880 pregnancies was registered between September 2014 and January 2016. After routine examination for first trimester combined test, UtA PI was measured, and maternal serum was obtained and stored at -80 °C for PlGF assessment.
RESULTS: Early-onset preeclampsia, late-onset preeclampsia and placental dysfunction-related fetal growth restriction were observed in 6 (0.7%), 17 (2.0%) and 27 (3.2%) cases, respectively. IPD requiring delivery before 34 weeks of gestation could be predicted with a sensitivity, specificity, positive predictive value and negative predictive value of 76.2%, 90.2%, 20.2% and 99.1%, respectively.
CONCLUSION: A combination of UtA PI, placenta associated plasma protein A and PlGF was proven to be successful in the first trimester prediction of IPD, with the highest sensitivity in the subgroup who required delivery before 34 weeks of gestation. In reducing the number of pregnancies that should be followed-up, further studies for new biomarkers are needed.
© 2017 John Wiley & Sons, Ltd. © 2017 John Wiley & Sons, Ltd.

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Year:  2017        PMID: 28165141     DOI: 10.1002/pd.5017

Source DB:  PubMed          Journal:  Prenat Diagn        ISSN: 0197-3851            Impact factor:   3.050


  1 in total

1.  Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort.

Authors:  Herdiantri Sufriyana; Yu-Wei Wu; Emily Chia-Yu Su
Journal:  JMIR Med Inform       Date:  2020-05-18
  1 in total

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