First-trimester screening with specific algorithms for early- and late-onset fetal growth restriction

Objective To develop optimal first-trimester algorithms for the prediction of early and late fetal growth restriction (FGR). Methods This was a prospective cohort study of singleton pregnancies undergoing first-trimester screening. FGR was defined as an ultrasound estimated fetal weight <10th per...

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Detalles Bibliográficos
Autores: Crovetto F, Triunfo S, Crispi F, Rodriguez-Sureda V, Roma E, Dominguez C, Gratacos E, Figueras F
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
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:p9721
Acceso en línea:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9721
Access Level:acceso abierto
Palabra clave:early fetal growth restriction
fetal growth restriction
first-trimester screening
late fetal growth restriction
placental growth factor
soluble fms-like tyrosine kinase-1
Descripción
Sumario:Objective To develop optimal first-trimester algorithms for the prediction of early and late fetal growth restriction (FGR). Methods This was a prospective cohort study of singleton pregnancies undergoing first-trimester screening. FGR was defined as an ultrasound estimated fetal weight <10th percentile plus Doppler abnormalities or a birth weight <3rd percentile. Logistic regression-based predictive models were developed for predicting early and late FGR (cut-off: delivery at 34 weeks). The model included the a-priori risk (maternal characteristics), mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1). Results Of the 9150 pregnancies included, 462 (5%) fetuses were growth restricted: 59 (0.6%) early and 403 (4.4%) late. Significant contributions to the prediction of early FGR were provided by black ethnicity, chronic hypertension, previous FGR, MAP, UtA-PI, PlGF and sFlt-1. The model achieved an overall detection rate (DR) of 86.4% for a 10% false-positive rate (area under the receiver-operating characteristics curve (AUC): 0.93 (95% CI, 0.87-0.98)). The DR was 94.7% for FGR with pre-eclampsia (PE) (64% of cases) and 71.4% for FGR without PE (36% of cases). For late FGR, significant contributions were provided by chronic hypertension, autoimmune disease, previous FGR, smoking status, nulliparity, MAP, UtA-PI, PlGF and sFlt-1. The model achieved a DR of 65.8% for a 10% false-positive rate (AUC: 0.76 (95% CI, 0.73-0.80)). The DR was 70.2% for FGR with PE (12% of cases) and 63.5% for FGR without PE (88% of cases). Conclusions The optimal screening algorithm was different for early vs late FGR, supporting the concept that screening for FGR is better performed separately for the two clinical forms. Copyright (C) 2016 ISUOG. Published by John Wiley & Sons Ltd.