F Crovetto1,2, S Triunfo1, F Crispi1, V Rodriguez-Sureda3, E Roma4, C Dominguez3, E Gratacos1, F Figueras1. 1. BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain. 2. Department of Obstetrics and Gynecology, Fondazione Ca' Granda, Ospedale Maggiore Policlinico, Università degli Studi di Milano, Milan, Italy. 3. Biochemistry and Molecular Biology Research Centre for Nanomedicine, Hospital Universitari Vall d'Hebron, and Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain. 4. Obstetrics and Gynecology Department, Althaia, Network Healthcare Manresa Foundation, Barcelona, Spain.
Abstract
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 < 10(th) percentile plus Doppler abnormalities or a birth weight < 3(rd) 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.
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 < 10(th) percentile plus Doppler abnormalities or a birth weight < 3(rd) 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.
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