Differential performance of first-trimester screening in predicting small-for-gestational-age neonate or fetal growth restriction
ObjectiveTo assess the ability of integrated first-trimester screening, combining maternal characteristics and biophysical and biochemical markers, to predict delivery of a small-for-gestational-age (SGA) neonate, and compare this with its ability to predict fetal growth restriction (FGR). MethodsTh...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Fundació Sant Joan de Déu |
| Repositorio: | r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu |
| OAI Identifier: | oai:fsjd.fundanetsuite.com:p9704 |
| Acceso en línea: | https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9704 |
| Access Level: | acceso abierto |
| Palabra clave: | fetal growth restriction first-trimester screening placental growth factor small-for-gestational age soluble fms-like tyrosine kinase-1 |
| Sumario: | ObjectiveTo assess the ability of integrated first-trimester screening, combining maternal characteristics and biophysical and biochemical markers, to predict delivery of a small-for-gestational-age (SGA) neonate, and compare this with its ability to predict fetal growth restriction (FGR). MethodsThis was a prospective cohort study of singleton pregnancies undergoing routine first-trimester screening. SGA was defined as birth weight (BW) < 10(th) percentile and FGR was defined as an ultrasound estimated fetal weight < 10(th) percentile plus Doppler abnormalities, or BW < 3(rd) percentile. Logistic regression-based predictive models were developed for predicting SGA and FGR. Models incorporated the a-priori risk from maternal characteristics, and mean arterial pressure, uterine artery Doppler, placental growth factor and soluble fms-like tyrosine kinase-1. ResultsIn total, 9150 births were included. Of these, 979 (10.7%) qualified for a postnatal diagnosis of SGA and 462 (5.0%) for a prenatal diagnosis of FGR. For predicting SGA, the model achieved a detection rate of 35% for a false-positive rate (FPR) of 5% and 42% for a 10% FPR. The model's performance was significantly higher for predicting FGR (P < 0.001), with detection rates of 59% and 67%, for a FPR of 5% and 10%, respectively. ConclusionThe predictive performance of first-trimester screening for cases with growth impairment by a combination of maternal characteristics and biophysical and biochemical markers is improved significantly when a prenatal and strict definition of FGR is used rather than a postnatal definition based on BW. Copyright (c) 2016 ISUOG. Published by John Wiley & Sons Ltd. |
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