Back to Search Start Over

Diagnostic accuracy of the Gaussian first-trimester combined screening for pre-eclampsia to predict small-for-gestational-age neonates.

Authors :
Mendoza, Manel
Serrano, Berta
Bonacina, Erika
Capote, Sira
Garcia‐Manau, Pablo
Regincós, Laia
Murcia, Maria Teresa
Barberan, Lidia
Míguez, Marta
Carreras, Elena
Garcia-Manau, Pablo
Source :
International Journal of Gynecology & Obstetrics. Feb2022, Vol. 156 Issue 2, p322-330. 9p.
Publication Year :
2022

Abstract

<bold>Objective: </bold>Pre-eclampsia and delivery of small-for-gestational-age (SGA) neonates can be predicted from the first trimester. A Gaussian model for prediction of PE has recently been described, although its capacity to predict SGA is still unknown.<bold>Methods: </bold>This was a secondary analysis of a prospective cohort study conducted at Vall d'Hebron University Hospital (Barcelona) in 2483 single pregnancies from October 2015 to September 2017. Mean arterial blood pressure and mean uterine artery pulsatility index were recorded at the first-trimester scan. Serum concentrations of placental growth factor and pregnancy-associated plasma protein-A were assessed between 8+0 and 13+6  weeks. The predictive capacities of early (<32 weeks) and preterm (<37 weeks) SGA were tested.<bold>Results: </bold>For SGA without pre-eclampsia, detection rates of 25.0% (95% confidence interval [CI] 0-75.0) for early SGA and 14.3% (95% CI 3.6-28.6) for preterm SGA were achieved. For SGA with pre-eclampsia, the algorithm showed detection rates of 100.0% (95% CI 25.0-100.0) for early SGA and 56.3% (95% CI 31.3-81.3) for preterm SGA.<bold>Conclusion: </bold>This algorithm identifies 62.5% of early SGA and 27.3% of preterm SGA. Combined screening for predicting both pre-eclampsia and SGA by using the Gaussian algorithm is feasible and would simplify clinical practice. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00207292
Volume :
156
Issue :
2
Database :
Academic Search Index
Journal :
International Journal of Gynecology & Obstetrics
Publication Type :
Academic Journal
Accession number :
154564807
Full Text :
https://doi.org/10.1002/ijgo.13673