Objective: To develop an integrated model with the best performing criteria for predicting adverse outcome in small‐for‐gestational‐age (SGA) pregnancies.Methods: A cohort of 509 pregnancies with a suspected SGA fetus, eligible for trial of labor, was recruited prospectively and data on perinatal outcome were recorded. A predictive model for emergency Cesarean delivery because of non‐reassuring fetal status or neonatal acidosis was constructed using a decision tree analysis algorithm, with predictors: maternal age, body mass index, smoking, nulliparity, gestational age at delivery, onset of labor (induced vs spontaneous), estimated fetal weight (EFW), umbilical artery pulsatility index (PI), mean uterine artery (UtA) PI, fetal middle cerebral artery PI and cerebroplacental ratio (CPR).Results: An adverse outcome occurred in 134 (26.3%) cases. The best performing predictors for defining a high risk for adverse outcome in SGA fetuses was the presence of a CPR < 10^th centile, a mean UtA‐PI > 95^th centile or an EFW < 3^rd centile. The algorithm showed a sensitivity, specificity and positive and negative predictive values for adverse outcome of 82.8% (95% CI, 75.1–88.6%), 47.7% (95% CI, 42.6–52.9%), 36.2% (95% CI, 30.8–41.8%) and 88.6% (95% CI, 83.2–92.5%), respectively. Positive and negative likelihood ratios were 1.58 and 0.36.Conclusions: Our model could be used as a diagnostic tool for discriminating SGA pregnancies at risk of adverse perinatal outcome. Copyright © 2014 ISUOG. Published by John Wiley & Sons Ltd.

To assess the usefulness of circulating microRNAs (miRNAs) as non-invasive molecular biomarkers for early prediction of preeclampsia, a differential miRNA profiling analysis was performed in first-trimester pooled sera from 31 early preeclampsia patients, requiring delivery before 34 weeks of gestation, and 44 uncomplicated pregnancies using microfluidic arrays. Among a total of 754 miRNAs analyzed, the presence of 63 miRNAs (8%) was consistently documented in the sera from preeclampsia and control samples. Nevertheless, only 15 amplified miRNAs (2%) seemed to be differentially, although modestly, represented (fold change range: 0.4-1.4). After stem loop RT-qPCR from individual samples, the statistical analysis confirmed that none of the most consistent and differentially represented miRNAs (3 overrepresented and 4 underrepresented) were differentially abundant in serum from preeclamptic pregnancies compared with serum from normal pregnancies. Therefore, maternal serum miRNA assessment at first-trimester of pregnancy does not appear to have any predictive value for early preeclampsia.

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. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.