Differential performance of first-trimester screening in predicting small-for-gestational-age neonate or fetal growth restriction

Efficacy of Cerebroplacental Doppler Ratio in Predicting Adverse Fetal Outcomes in Cases of Fetal Growth Restriction Multicenter Study

INTRODUCTION

Fetal growth restriction (FGR) is a significant cause of perinatal morbidity and mortality. Differentiating FGR from small-for-gestational-age fetuses is critical for risk assessment. This study investigates the cerebroplacental ratio (CPR) as a noninvasive predictor of adverse fetal outcomes, particularly neonatal intensive care unit (NICU) admissions, intrauterine fetal death (IUFD), and birth weight variability.

METHODS

This prospective, multicentre study included 60 pregnant women (gestational age 28-34 weeks) divided into normal and abnormal CPR groups. Doppler ultrasonography assessed umbilical artery (UA) and middle cerebral artery (MCA) pulsatility indices. Statistical analysis included receiver operating characteristic (ROC) curves for CPR, UA, and MCA indices to predict adverse outcomes.

RESULTS

Abnormal CPR correlated with increased NICU admissions (46.7%), IUFD (10%), and lower birth weight (mean: 2138 g). Elevated UA PI and reduced MCA PI were observed in the abnormal CPR group. Sensitivity and specificity analyses identified CPR (cutoff: 1.1) as a modestly accurate predictor of adverse outcomes.

DISCUSSION

CPR effectively stratifies risk in high-risk pregnancies but requires further validation. Abnormal Doppler findings highlight placental insufficiency and compromised cerebral perfusion. These findings could refine FGR management strategies.

New first trimester circulating angiogenic biomarkers in predicting early-onset and late-onset fetal growth restriction: a case-control study

BACKGROUND

First trimester prediction of fetal growth restriction (FGR) remain suboptimal. We aimed to search for new circulating angiogenic biomarkers for improvement.

METHODS

This case-control study compared 73 singleton pregnancies with early or late-onset FGR based on Delphi consensus and 73 matched normal controls. Their maternal serum samples stored during 11-13 weeks were retrieved for measurement of 36 angiogenic biomarkers by MILLIPLEX® human angiogenesis magnetic bead panels. Those biomarkers that showed significant differences between the study groups were further analysed with receiver operating characteristic (ROC) curve.

RESULTS

In the early-onset FGR group, log10MoM of soluble neuropilin-1 (sNRP-1: 0.08 ± 0.11 vs. 0.00 ± 0.09, P < 0.001) and log10MoM of soluble platelet and endothelial cell adhesion molecule 1 (sPECAM-1: 0.05 ± 0.06 vs. 0.00 ± 0.09, P < 0.05) were significantly higher than the control group, while log10MoM of platelet-derived growth factor AB/BB (PDGF-AB/BB: -0.08 ± 0.13 vs. 0.00 ± 0.16, P < 0.05) and PAPP-A (-0.15 ± 0.28 vs. 0.05 ± 0.23, P < 0.001) were lower. Their combination achieved the highest area under the ROC curve (AUC) of 0.83 (95% CI: 0.74-0.95) with a higher sensitivity than that of PAPP-A alone (61.5% vs. 30.8% at 10% false positive rate, P < 0.001). Concerning the late-onset FGR group, only log10MoMs of sFlt-1 (-0.12 vs. 0.00, P < 0.001) and PAPP-A (-0.07 vs. 0.05, P < 0.05) were lower than the control group, but their AUC was only 0.68 (95% CI:0.59-0.78).

CONCLUSIONS

Three new first trimester biomarkers, sNRP-1, sPECAM-1 and PDGF-AB/BB are predictive of subsequent development of early-onset FGR.

External validation and comparison of Fetal Medicine Foundation competing-risks model for small-for-gestational-age neonate in the first trimester: multicenter cohort study

OBJECTIVES

To examine the predictive performance of the Fetal Medicine Foundation (FMF) competing-risks model for the first-trimester prediction of a small-for-gestational-age (SGA) neonate in a large, independent, unselected European cohort and to compare the competing-risks algorithm with previously published logistic-regression models.

METHODS

This was a retrospective, non-interventional, multicenter cohort study including 35 170 women with a singleton pregnancy who underwent a first-trimester ultrasound assessment between 11 + 0 and 13 + 6 weeks' gestation. We used the default FMF competing-risks model for the prediction of SGA combining maternal factors, uterine artery pulsatility index (UtA-PI), pregnancy-associated plasma protein-A (PAPP-A) and placental growth factor (PlGF) to obtain risks for different cut-offs of birth-weight percentile and gestational age at delivery. We examined the predictive performance in terms of discrimination and calibration and compared it with the published data on the model's development population and with published logistic-regression equations.

RESULTS

At a 10% false-positive rate, maternal factors and UtA-PI predicted 42.2% and 51.5% of SGA < 10th percentile delivered < 37 weeks and < 32 weeks, respectively. The respective values for SGA < 3rd percentile were 44.7% and 51.7%. Also at a 10% false-positive rate, maternal factors, UtA-PI and PAPP-A predicted 42.2% and 51.5% of SGA < 10th percentile delivered < 37 weeks and < 32 weeks, respectively. The respective values for SGA < 3rd percentile were 46.2% and 51.7%. At a 10% false-positive rate, maternal factors, UtA-PI, PAPP-A and PlGF predicted 47.6% and 66.7% of SGA < 10th percentile delivered < 37 weeks and < 32 weeks, respectively. The respective values for SGA < 3rd percentile were 50.0% and 69.0%. These data were similar to those reported in the original model's development study and substantially better than those calculated using pre-existing logistic-regression models (McNemar's test, P < 0.001). The FMF competing-risks model was well calibrated.

CONCLUSIONS

The FMF competing-risks model for the first-trimester prediction of SGA is reproducible in an independent, unselected low-risk cohort and superior to logistic-regression approaches. © 2025 International Society of Ultrasound in Obstetrics and Gynecology.

Combined first-trimester screening for preterm small-for-gestational-age infants: Australian multicenter clinical feasibility study

OBJECTIVE

To assess the performance of the Fetal Medicine Foundation (FMF) first-trimester competing-risks screening model for small-for-gestational-age (SGA) fetuses requiring delivery at < 37 weeks' gestation, in a large cohort of women receiving maternity care in Australia.

METHODS

This was a retrospective analysis of prospectively collected data from a cohort of women attending one of two private multicenter fetal medicine practices for first-trimester screening for preterm pre-eclampsia (PE), defined as PE requiring delivery before 37 weeks' gestation. Risk for preterm SGA, defined as SGA requiring delivery before 37 weeks, was calculated but was not disclosed to the patient or referring physician. Screening data were matched to obstetric outcomes. The primary outcome was the efficacy of the FMF screening model in assessing the risk of preterm SGA. The potential effect on identifying other adverse pregnancy outcomes was also assessed.

RESULTS

During the study period, 22 841 women with a singleton pregnancy underwent combined first-trimester screening for preterm PE. These data were compared with those of 301 721 women in the state of Victoria with a singleton pregnancy who did not undergo screening during the study period. Calculation of the risk for preterm SGA identified 3030 (13.3%) pregnancies as high risk. The sensitivity of the model was 48.6% (95% CI, 41.0-56.2%), specificity was 87.0% (95% CI, 86.6-87.5%) and positive and negative predictive values were 2.9% (95% CI, 2.7-3.1%) and 99.5% (95% CI, 99.4-99.6%), respectively. Pregnancies at high risk for preterm SGA were also more likely to have preterm PE (risk ratio (RR), 2.28 (95% CI, 1.72-3.03)) and preterm birth (RR, 1.46 (95% CI, 1.32-1.63)), compared with unscreened pregnancies. Pregnancies at low risk for preterm SGA were less likely to result in a stillbirth (RR, 0.64 (95% CI, 0.47-0.86)) compared with unscreened pregnancies.

CONCLUSION

Combined first-trimester screening for preterm SGA shows moderate screening efficacy and therefore could help to inform pregnancy management and improve antenatal resource allocation. © 2025 International Society of Ultrasound in Obstetrics and Gynecology.

Plasma acute phase proteins as potential predictors of intra-amniotic inflammation and infection in preterm premature rupture of membranes

BACKGROUND

We aimed to investigate the potential of altered levels of various acute phase proteins (APPs) in the plasma, either used alone or in combination with ultrasound-, clinical-, and conventional blood-based tests, for predicting the risk of intra-amniotic inflammation (IAI), microbial invasion of the amniotic cavity (MIAC), histologic chorioamnionitis (HCA), and funisitis in women with preterm premature rupture of membranes (PPROM).

METHODS

A total of 195 consecutive pregnancies involving singleton women with PPROM (at 23 + 0-34 + 0 weeks) who underwent amniocentesis and from whom plasma samples were obtained at amniocentesis were retrospectively included in this study. Amniotic fluid (AF) was cultured to assess the MIAC and analyzed for interleukin (IL)-6 levels to define IAI (AF IL-6 level of ≥2.6 ng/mL). The plasma concentrations of hepcidin, mannose-binding lectin (MBL), pentraxin-2, retinol-binding protein 4 (RBP4), serum amyloid A1 (SAA1), and serpin A1 were determined using ELISA. Ultrasonographic cervical length (CL), neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein levels were measured. IAI/MIAC was defined as IAI, MIAC, or both.

RESULTS

Multivariate logistic regression analyses showed the following: (1) elevated plasma levels of hepcidin and SAA1 and decreased levels of RBP4 in the plasma were independently associated with IAI/MIAC and (2) decreased plasma RBP4 levels were independently associated with funisitis; however, (3) none of the plasma APPs investigated were associated with acute HCA when adjusted for baseline covariates. Using stepwise regression analysis, noninvasive prediction models comprising plasma RBP4 levels, CL, NLR, and gestational age at sampling were proposed, which provided a good prediction of IAI/MIAC and funisitis (area under the curve: 0.80 and 0.72, respectively).

CONCLUSIONS

Hepcidin, RBP4, and SAA1 were identified as potential APP biomarkers in the plasma predictive of IAI/MIAC or funisitis in patients with PPROM. In particular, combination of these APP biomarkers with ultrasound-, clinical-, and conventional blood-based markers can significantly support the diagnosis of IAI/MIAC and funisitis.

Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms

BACKGROUND

This study aimed to develop a clinical model to predict late-onset fetal growth restriction (FGR).

METHODS

This retrospective study included seven hospitals and was conducted between January 2009 and December 2020. Two sets of variables from the first trimester until 13 weeks (E1) and the early third trimester until 28 weeks (T1) were used to develop the FGR prediction models using a machine learning algorithm. The dataset was randomly divided into training and test sets (7:3 ratio). A simplified prediction model using variables with XGBoost's embedded feature selection was developed and validated.

RESULTS

Precisely 32,301 patients met the eligibility criteria. In the prediction model for the whole cohort, the area under the curve (AUC) was 0.73 at E1 and 0.78 at T1 and the area under the precision-recall curve (AUPR) was 0.23 at E1 and 0.31 at T1 in the training set, while an AUC of 0.62 at E1 and 0.73 at T1 and an AUPR if 0.13 at E1, and 0.24 at T1 were obtained in the test set. The simplified prediction model performed similarly to the original model.

CONCLUSIONS

A simplified machine learning model for predicting late FGR may be useful for evaluating individual risks in the early third trimester.

Can maternal inflammatory and nutritional status, evaluated by the hemoglobin, albumin, lymphocyte, and platelet (HALP) score and the prognostic nutritional index (PNI) in the first trimester, predict late-onset fetal growth restriction?

OBJECTIVE

The aim of this study was to evaluate the potential of immunonutritional markers, specifically the hemoglobin, albumin, lymphocyte, and platelet (HALP) score and the prognostic nutritional index (PNI), in predicting late-onset fetal growth restriction (LO-FGR) during the first trimester.

MATERIALS AND METHODS

This retrospective study was conducted at a tertiary care center between October 2022 and August 2023. The study included a total of 213 singleton pregnancies, with 99 women in the LO-FGR group and 114 in the healthy control group, matched by maternal age and gestational age at delivery. All blood samples were collected between 11 and 14 weeks of gestation (during the first-trimester screening test). We analyzed first-trimester laboratory parameters, specifically focusing on hemoglobin levels, white blood cells (WBCs), lymphocytes, platelets, and albumin levels. Afterwards, we calculated the HALP score and PNI, and then compared the values of both groups.

RESULTS

Both HALP score (3.58 ± 1.31 vs. 4.19 ± 1.8, p = 0.012) and PNI (36.75 ± 2.9 vs. 39.37 ± 3.96, p < 0.001) were significantly lower in the FGR group than in the control group. The HALP score cut-off value of < 3.43 in predicting FGR had a sensitivity of 62.3% and specificity of 54.5% (AUC = 0.600, 95% CI: 0.528-0.672, p = 0.012). The PNI cut-off value of < 37.9 in predicting FGR had a sensitivity of 65.8% and specificity of 62.9% (AUC = 0.707, 95% CI: 0.632-0.778, p < 0.001). While the HALP score was not a significant predictor of composite adverse neonatal outcomes in the FGR group, PNI showed a cut-off value of < 37.7 with a sensitivity of 60.9% and specificity of 59.7% (AUC = 0.657, 95% CI: 0.581-0.733, p < 0.001).

CONCLUSION

The HALP score and PNI are valuable prognostic tools for predicting the risk of FGR in the first trimester. Low PNI values are also associated with composite adverse neonatal outcomes in pregnancies complicated by FGR.

Prediction of fetal growth restriction and small for gestational age by ultrasound cardiac parameters

OBJECTIVE

Prediction of fetal growth restriction (FGR) and small of gestational age (SGA) infants by using various ultrasound cardiac parameters in a logistic regression model.

METHODS

In this retrospective study we obtained standardized ultrasound images of 357 fetuses between the 20th and 39th week of gestation, 99 of these fetuses were between the 3rd and 10th growth percentile, 61 smaller than 3rd percentile and 197- appropriate for gestational age over the 10th percentile (control group). Several cardiac parameters were studied. The cardiothoracic ratio and sphericity of the ventricles was calculated. A binary logistic regression model was developed for prediction of growth restriction using the cardiac and biometric parameters.

RESULTS

There were noticeable differences between the control and study group in the sphericity of the right ventricle (p = 0.000), left and right longitudinal ventricle length (pright = 0.000, pleft = 0.000), left ventricle transverse length (p = 0.000), heart diameter (p = 0.002), heart circumference (p = 0.000), heart area (p = 0.000), and thoracic diameter limited by the ribs (p = 0.002). There was no difference of the cardiothoracic ratio between groups. The logistic regression model achieved a prediction rate of 79.4 % with a sensitivity of 74.5 % and specificity of 83.2 %.

CONCLUSION

The heart of growth restricted infants is characterized by a more globular right ventricle, shorter ventricle length and smaller thorax diameter. These parameters could improve prediction of FGR and SGA.

Construction of prediction model for fetal growth restriction during first trimester in an Asian population

OBJECTIVE

To construct a prediction model for fetal growth restriction (FGR) during the first trimester of pregnancy and evaluate its screening performance.

METHODS

This was a prospective cohort study of singleton pregnancies that underwent routine ultrasound screening at 11 to 13 + 6 weeks at the Affiliated Suzhou Hospital of Nanjing Medical University between January 2019 and April 2022. Basic clinical information, ultrasound indicators and serum biomarkers of pregnant women were collected. Fetal weight assessment was based on the fetal growth curve for the Southern Chinese population. FGR was diagnosed according to Delphi consensus criteria. Least absolute shrinkage and selection operator (lasso) regression was used to select variables for inclusion in the model. Discrimination, calibration and clinical effectiveness of the model were evaluated in training and validation cohorts.

RESULTS

A total of 1188 pregnant women were included, of whom 108 had FGR. Lasso regression identified seven predictive features, including history of maternal hypertension, maternal smoking or passive smoking, gravidity, uterine artery pulsatility index, ductus venosus pulsatility index and multiples of the median values of placental growth factor and soluble fms-like tyrosine kinase-1. The nomogram prediction model constructed from these seven variables accurately predicted FGR, and the area under the receiver-operating-characteristics curve in the validation cohort was 0.82 (95% CI, 0.74-0.90). The calibration curve and Hosmer-Lemeshow test demonstrated good calibration, and the clinical decision curve and clinical impact curve supported its practical value in a clinical setting.

CONCLUSION

The multi-index prediction model for FGR has good predictive value during the first trimester. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Prevalence of Preterm Birth and Perinatal Outcomes in a Tertiary Hospital in Malaysia

Background Preterm birth (PTB) is defined as neonates that are born alive >22 weeks of gestation and <37 weeks of gestation. Because of the immaturity of different organ systems, 14.84 million newborns worldwide are born prematurely, which is the largest contributing factor to mortality and morbidity. Although studies have been conducted in this field, the magnitude of PTB is a major issue in most developing countries including Malaysia. Objective To assess the prevalence of PTB and the perinatal outcome among women delivered in a tertiary university hospital in Malaysia.  Methods This was a cross-sectional study evaluating all singleton live births weighing>500g and delivered at >22+1 weeks of gestation between January 2015 and December 2019 in Universiti Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia. Data were collected from the hospital's recorded birth registry. The primary outcome was the PTB rate. Data were entered and analysed using Statistical Product and Service Solutions (SPSS) (version 28.0; IBM SPSS Statistics for Windows, Armonk, NY). Results A total of 26,022 singleton live births were reported for the period 2015-2019. PTB rates showed a sharp 6% decrease from 2015 to 2016, after which the trend was inconsistent until 2019. The risk of preterm babies being admitted to the neonatal intensive care unit (NICU) or the ward compared to the risk of neonatal mortality increases for babies of identified sex, delivered via caesarean, and with a birth weight between 2 and 3 kgs. Babies born at a gestational age between 22+1 and 33+6 have a higher risk of neonatal mortality compared to late preterm babies. Conclusions The PTB incidence trend was inconsistent from 2015 to 2019 in a tertiary university hospital in Malaysia, with a far higher prevalence compared to national data. The high NICU admission and mortality rates among preterm infants mean urgent strategies and policies are needed to improve perinatal outcomes.

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

BACKGROUND

IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR.

METHODS

We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures.

RESULTS

We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80-0.88), specificity = 0.87 (95% CI 0.83-0.90), positive predictive value = 0.78 (95% CI 0.68-0.86), negative predictive value = 0.91 (95% CI 0.86-0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34-49.59). In detail, the RF-SVM (Random Forest-Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG.

CONCLUSIONS

our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.

Circulating biomarkers associated with placental dysfunction and their utility for predicting fetal growth restriction

Fetal growth restriction (FGR) leading to low birth weight (LBW) is a major cause of neonatal morbidity and mortality worldwide. Normal placental development involves a series of highly regulated processes involving a multitude of hormones, transcription factors, and cell lineages. Failure to achieve this leads to placental dysfunction and related placental diseases such as pre-clampsia and FGR. Early recognition of at-risk pregnancies is important because careful maternal and fetal surveillance can potentially prevent adverse maternal and perinatal outcomes by judicious pregnancy surveillance and careful timing of birth. Given the association between a variety of circulating maternal biomarkers, adverse pregnancy, and perinatal outcomes, screening tests based on these biomarkers, incorporating maternal characteristics, fetal biophysical or circulatory variables have been developed. However, their clinical utility has yet to be proven. Of the current biomarkers, placental growth factor and soluble fms-like tyrosine kinase 1 appear to have the most promise for placental dysfunction and predictive utility for FGR.

Impaired in vivo feto-placental development is associated with neonatal neurobehavioral outcomes

BACKGROUND

Fetal growth restriction (FGR) is a risk factor for neurodevelopmental problems, yet remains poorly understood. We sought to examine the relationship between intrauterine development and neonatal neurobehavior in pregnancies diagnosed with antenatal FGR.

METHODS

We recruited women with singleton pregnancies diagnosed with FGR and measured placental and fetal brain volumes using MRI. NICU Network Neurobehavioral Scale (NNNS) assessments were performed at term equivalent age. Associations between intrauterine volumes and neurobehavioral outcomes were assessed using generalized estimating equation models.

RESULTS

We enrolled 44 women diagnosed with FGR who underwent fetal MRI and 28 infants underwent NNNS assessments. Placental volumes were associated with increased self-regulation and decreased excitability; total brain, brainstem, cortical and subcortical gray matter (SCGM) volumes were positively associated with higher self-regulation; SCGM also was positively associated with higher quality of movement; increasing cerebellar volumes were positively associated with attention, decreased lethargy, non-optimal reflexes and need for special handling; brainstem volumes also were associated with decreased lethargy and non-optimal reflexes; cerebral and cortical white matter volumes were positively associated with hypotonicity.

CONCLUSION

Disrupted intrauterine growth in pregnancies complicated by antenatally diagnosed FGR is associated with altered neonatal neurobehavior. Further work to determine long-term neurodevelopmental impacts is warranted.

IMPACT

Fetal growth restriction is a risk factor for adverse neurodevelopment, but remains difficult to accurately identify. Intrauterine brain volumes are associated with infant neurobehavior. The antenatal diagnosis of fetal growth restriction is a risk factor for abnormal infant neurobehavior.

Does the Blood-Brain Barrier Integrity Change in Regard to the Onset of Fetal Growth Restriction?

The aim of the study was to determine whether early-onset and late-onset fetal growth restriction (FGR) differentially affects the blood-brain barrier integrity. Furthermore, the purpose of the study was to investigate the relationship between the blood-brain barrier breakdown and neurological disorders in FGR newborns. To evaluate the serum tight junction (TJ) proteins and the placental TJ proteins expression, an ELISA method was used. A significant difference in serum OCLN concentrations was noticed in pregnancies complicated by the early-onset FGR, in relation to the intraventricular hemorrhage (IVH) occurrence in newborns. No significant differences in concentrations of the NR1 subunit of the N-methyl-d-aspartate receptor (NR1), nucleoside diphosphate kinase A (NME1), S100 calcium-binding protein B (S100B), occludin (OCLN), claudin-5 (CLN5), zonula occludens-1 (zo-1), the CLN5/zo-1 ratio, and the placental expression of OCLN, CLN5, claudin-4 (CLN4), zo-1 were noticed between groups. The early-onset FGR was associated with a higher release of NME1 into the maternal circulation in relation to the brain-sparing effect and premature delivery. Additionally, in late-onset FGR, the higher release of the S100B into the maternal serum in regard to fetal distress was observed. Furthermore, there was a higher release of zo-1 into the maternal circulation in relation to newborns' moderate acidosis in late-onset FGR. Blood-brain barrier disintegration is not dependent on pregnancy advancement at the time of FGR diagnosis. NME1 may serve as a biomarker useful in the prediction of fetal circulatory centralization and extremely low birth weight in pregnancies complicated by the early-onset FGR. Moreover, the serum zo-1 concentration may have prognostic value for moderate neonatal acidosis in late-onset FGR pregnancies.

Fetal growth restriction and stillbirth: Biomarkers for identifying at risk fetuses

Fetal growth restriction (FGR) is a major cause of stillbirth, prematurity and impaired neurodevelopment. Its etiology is multifactorial, but many cases are related to impaired placental development and dysfunction, with reduced nutrient and oxygen supply. The fetus has a remarkable ability to respond to hypoxic challenges and mounts protective adaptations to match growth to reduced nutrient availability. However, with progressive placental dysfunction, chronic hypoxia may progress to a level where fetus can no longer adapt, or there may be superimposed acute hypoxic events. Improving detection and effective monitoring of progression is critical for the management of complicated pregnancies to balance the risk of worsening fetal oxygen deprivation in utero, against the consequences of iatrogenic preterm birth. Current surveillance modalities include frequent fetal Doppler ultrasound, and fetal heart rate monitoring. However, nearly half of FGR cases are not detected in utero, and conventional surveillance does not prevent a high proportion of stillbirths. We review diagnostic challenges and limitations in current screening and monitoring practices and discuss potential ways to better identify FGR, and, critically, to identify the “tipping point” when a chronically hypoxic fetus is at risk of progressive acidosis and stillbirth.

Vitamin D Deficiency, Excessive Gestational Weight Gain, and Oxidative Stress Predict Small for Gestational Age Newborns Using an Artificial Neural Network Model

(1) Background: Size at birth is an important early determinant of health later in life. The prevalence of small for gestational age (SGA) newborns is high worldwide and may be associated with maternal nutritional and metabolic factors. Thus, estimation of fetal growth is warranted. (2) Methods: In this work, we developed an artificial neural network (ANN) model based on first-trimester maternal body fat composition, biochemical and oxidative stress biomarkers, and gestational weight gain (GWG) to predict an SGA newborn in pregnancies with or without obesity. A sensibility analysis to classify maternal features was conducted, and a simulator based on the ANN algorithm was constructed to predict the SGA outcome. Several predictions were performed by varying the most critical maternal features attained by the model to obtain different scenarios leading to SGA. (3) Results: The ANN model showed good performance between the actual and simulated data (R2 = 0.938) and an AUROC of 0.8 on an independent dataset. The top-five maternal predictors in the first trimester were protein and lipid oxidation biomarkers (carbonylated proteins and malondialdehyde), GWG, vitamin D, and total antioxidant capacity. Finally, excessive GWG and redox imbalance predicted SGA newborns in the implemented simulator. Significantly, vitamin D deficiency also predicted simulated SGA independently of GWG or redox status. (4) Conclusions: The study provided a computational model for the early prediction of SGA, in addition to a promising simulator that facilitates hypothesis-driven constructions, to be further validated as an application.

Prediction of late-onset fetal growth restriction using a combined first- and second-trimester screening model

BACKGROUND

Prediction models for early fetal growth restriction (FGR) have been exhibited in many researches. However, prediction models for late FGR are limited. Late-onset FGR is easy to miss clinically because of its insidious onset. This study aimed to develop a simple combined first- and second-trimester prediction model for screening late-onset FGR in fetuses.

METHODS

This retrospective study included 2746 women who had singleton pregnancies and received routine ultrasound scans as training dataset. Late FGR is that diagnosed >32 weeks. Multivariate logistic regression was used to develop a prediction model.

RESULTS

One hundred and twenty-nine fetuses were identified as late-onset FGR. The significant predictors for late-onset FGR were maternal height, weight, and medical history; the first-trimester mean arterial pressure, the second-trimester head circumference/ abdominal circumference ratio; and the second-trimester estimated fetal weight. This model achieved a detection rate (DR........) of 51.6% for late-onset FGR at a 10% false positive rate (FPR) (area under the curve (AUC): 0.80, 95%CI 0.76-0.84).

CONCLUSIONS

A multivariate model combining first- and second-trimester default tests can detect 51.6% of cases of late-onset FGR at a 10% FPR. Further studies with more screening markers are needed to improve the detection rate.

FIGO (international Federation of Gynecology and obstetrics) initiative on fetal growth: best practice advice for screening, diagnosis, and management of fetal growth restriction

Fetal growth restriction (FGR) is defined as the failure of the fetus to meet its growth potential due to a pathological factor, most commonly placental dysfunction. Worldwide, FGR is a leading cause of stillbirth, neonatal mortality, and short- and long-term morbidity. Ongoing advances in clinical care, especially in definitions, diagnosis, and management of FGR, require efforts to effectively translate these changes to the wide range of obstetric care providers. This article highlights agreements based on current research in the diagnosis and management of FGR, and the areas that need more research to provide further clarification of recommendations. The purpose of this article is to provide a comprehensive summary of available evidence along with practical recommendations concerning the care of pregnancies at risk of or complicated by FGR, with the overall goal to decrease the risk of stillbirth and neonatal mortality and morbidity associated with this condition. To achieve these goals, FIGO (the International Federation of Gynecology and Obstetrics) brought together international experts to review and summarize current knowledge of FGR. This summary is directed at multiple stakeholders, including healthcare providers, healthcare delivery organizations and providers, FIGO member societies, and professional organizations. Recognizing the variation in the resources and expertise available for the management of FGR in different countries or regions, this article attempts to take into consideration the unique aspects of antenatal care in low-resource settings (labelled “LRS” in the recommendations). This was achieved by collaboration with authors and FIGO member societies from low-resource settings such as India, Sub-Saharan Africa, the Middle East, and Latin America.

Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics, serum pregnancy-associated plasma protein-A and placental growth factor at 11-13 weeks' gestation

OBJECTIVES

To expand a new competing-risks model for prediction of a small-for-gestational-age (SGA) neonate, by the addition of pregnancy-associated plasma protein-A (PAPP-A) and placental growth factor (PlGF), and to evaluate and compare PAPP-A and PlGF in predicting SGA.

METHODS

This was a prospective observational study of 60 875 women with singleton pregnancy undergoing routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation. We fitted a folded-plane regression model for the PAPP-A and PlGF likelihoods. A previously developed maternal history model and the likelihood models were combined, according to Bayes' theorem, to obtain individualized distributions for gestational age (GA) at delivery and birth-weight Z-score. We assessed the discrimination and calibration of the model. McNemar's test was used to compare the detection rates for SGA with, without or independently of pre-eclampsia (PE) occurrence, of different combinations of maternal history, PAPP-A and PlGF, for a fixed false-positive rate.

RESULTS

The distributions of PAPP-A and PlGF depend on both GA at delivery and birth-weight Z-score, in the same continuous likelihood, according to a folded-plane regression model. The new approach offers the capability for risk computation for any desired birth-weight Z-score and GA at delivery cut-off. PlGF was consistently and significantly better than PAPP-A in predicting SGA delivered before 37 weeks, especially in cases with co-existence of PE. PAPP-A had similar performance to PlGF for the prediction of SGA without PE. At a fixed false-positive rate of 10%, the combination of maternal history, PlGF and PAPP-A predicted 33.8%, 43.8% and 48.4% of all cases of a SGA neonate with birth weight < 10^th percentile delivered at ≥ 37, < 37 and < 32 weeks' gestation, respectively. The respective values for birth weight < 3^rd percentile were 38.6%, 48.7% and 51.0%. The new model performed well in terms of risk calibration.

CONCLUSIONS

The combination of PAPP-A and PlGF values with maternal characteristics, according to Bayes' theorem, improves prediction of SGA. PlGF is a better predictor of SGA than PAPP-A, especially when PE is present. The new competing-risks model for SGA can be tailored to each pregnancy and to the relevant clinical requirements. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.

Competing-risks model for prediction of small-for-gestational-age neonate from biophysical and biochemical markers at 11-13 weeks' gestation

OBJECTIVE

To develop a new competing-risks model for the prediction of a small-for-gestational-age (SGA) neonate, based on maternal factors and biophysical and biochemical markers at 11-13 weeks' gestation.

METHODS

This was a prospective observational study in 60 875 women with singleton pregnancy undergoing routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation. All pregnancies had pregnancy-associated plasma protein-A and placental growth factor (PlGF) measurements, 59 001 had uterine artery pulsatility index (UtA-PI) measurements and 58 479 had mean arterial pressure measurements; 57 131 cases had complete data for all biomarkers. We used a previously developed competing-risks model for the joint distribution of gestational age (GA) at delivery and birth-weight Z-score, according to maternal demographic characteristics and medical history. The likelihoods of the biophysical markers were developed by fitting folded-plane regression models, a technique that has already been used in previous studies for the likelihoods of biochemical markers. The next step was to modify the prior distribution by the likelihood, according to Bayes' theorem, to obtain individualized distributions for GA at delivery and birth-weight Z-score. We used the 57 131 cases with complete data to assess the discrimination and calibration of the model for predicting SGA with, without or independently of pre-eclampsia, by different combinations of maternal factors and biomarkers.

RESULTS

The distribution of biomarkers, conditional to both GA at delivery and birth-weight Z-score, was best described by folded-plane regression models. These continuous two-dimensional likelihoods update the joint distribution of birth-weight Z-score and GA at delivery that has resulted from a competing-risks approach; this method allows application of user-defined cut-offs. The best biophysical predictor of preterm SGA was UtA-PI and the best biochemical marker was PlGF. The prediction of SGA was consistently better for increasing degree of prematurity, greater severity of smallness, coexistence of PE and increasing number of biomarkers. The combination of maternal factors with all biomarkers predicted 34.3%, 48.6% and 59.1% of all cases of a SGA neonate with birth weight < 10^th percentile delivered at ≥ 37, < 37 and < 32 weeks' gestation, at a 10% false-positive rate. The respective values for birth weight < 3^rd percentile were 39.9%, 53.2% and 64.4%, and for birth weight < 3^rd percentile with pre-eclampsia they were 46.3%, 66.8% and 80.4%. The new model was well calibrated.

CONCLUSIONS

This study has presented a single continuous two-dimensional model for prediction of SGA for any desired cut-offs of smallness and GA at delivery, laying the ground for a personalized antenatal plan for predicting and managing SGA, in the milieu of a new inverted pyramid of prenatal care. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.

First-trimester prediction of small-for-gestational age in pregnancies at false-positive high or intermediate risk for fetal aneuploidy

OBJECTIVES

To explore the risk of small-for-gestational age (SGA) and fetal growth restriction (FGR) and to test the performance of first-trimester screening for SGA and FGR in women with a false-positive high or intermediate risk for aneuploidy.

METHODS

This was a prospective cohort study of women with a singleton pregnancy attending for a routine first-trimester scan. The risks of aneuploidy and preterm SGA (defined as birth weight < 10^th percentile with delivery before 37 weeks) were determined according to Fetal Medicine Foundation algorithms. In non-malformed euploid pregnancies, the predictive performance of both the aneuploidy and preterm SGA risks was evaluated for SGA, FGR (defined as birth weight < 3^rd centile), preterm SGA and early SGA (delivery before 34 weeks), using receiver-operating-characteristics (ROC) curve analysis, in those with a high or intermediate risk of aneuploidy and in the overall population.

RESULTS

A total of 2053 pregnancies were included in the analysis, of which 191 (9.3%) were at high or intermediate risk for aneuploidy (≥ 1/1000) and 304 (14.8%) were at high risk for preterm SGA (≥ 1/100). In total, there were 140 (6.8%) cases of SGA, 61 (3.0%) of FGR, 44 (2.1%) of preterm SGA and 33 (1.6%) of early SGA. Among women with a false-positive high or intermediate risk for aneuploidy, the rates of SGA, FGR, preterm SGA and early SGA were 13.6% (26/191), 7.9 % (15/191), 6.8% (13/191) and 5.8% (11/191), respectively. Compared with women with a first-trimester low risk for preterm SGA, regardless of aneuploidy risk, those with a high risk for preterm SGA and a high or intermediate risk for aneuploidy had relative risks for SGA, FGR, preterm SGA and early SGA of 6 (95% CI, 3.9-9), 9.2 (95% CI, 5.1-16.5), 13.4 (95% CI, 6.9-26.1) and 17.6 (95% CI, 8.1-38.2), respectively. The predictive performance for SGA of the preterm SGA algorithm was higher in women at high or intermediate risk for aneuploidy than in the overall population (area under the ROC curve (AUC), 0.8 vs 0.7; P < 0.001). Among women at high or intermediate risk for aneuploidy, the predictive performance of the preterm SGA algorithm was better than that of the aneuploidy algorithm for SGA (AUC, 0.80 vs 0.58; P = 0.003), preterm SGA (AUC, 0.85 vs 0.65; P = 0.013) and early SGA (AUC, 0.86 vs 0.60; P = 0.002).

CONCLUSION

In women with a first-trimester false-positive high or intermediate risk of aneuploidy, further screening for SGA allows stratification of the risk for fetal growth disorders. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.

Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics and serum pregnancy-associated plasma protein-A at 11-13 weeks' gestation

OBJECTIVES

To develop a continuous likelihood model for pregnancy-associated plasma protein-A (PAPP-A), in the context of a new competing-risks model for prediction of a small-for-gestational-age (SGA) neonate, and to compare the predictive performance of the new model for SGA to that of previous methods.

METHODS

This was a prospective observational study of 60 875 women with singleton pregnancy undergoing routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation. The dataset was divided randomly into a training dataset and a test dataset. The training dataset was used for PAPP-A likelihood model development. We used Bayes' theorem to combine the previously developed prior model for the joint Gaussian distribution of gestational age (GA) at delivery and birth-weight Z-score with the PAPP-A likelihood to obtain a posterior distribution. This patient-specific posterior joint Gaussian distribution of GA at delivery and birth-weight Z-score allows risk calculation for SGA defined in terms of different birth-weight percentiles and GA. The new model was validated internally in the test dataset and we compared its predictive performance to that of the risk-scoring system of the UK National Institute for Health and Care Excellence (NICE) and that of logistic regression models for different SGA definitions.

RESULTS

PAPP-A has a continuous association with both birth-weight Z-score and GA at delivery according to a folded-plane regression. The new model, with the addition of PAPP-A, was equal or superior to several logistic regression models. The new model performed well in terms of risk calibration and consistency across different GAs and birth-weight percentiles. In the test dataset, at a false-positive rate of about 30% using the criteria defined by NICE, the new model predicted 62.7%, 66.5%, 68.1% and 75.3% of cases of a SGA neonate with birth weight < 10^th percentile delivered at < 42, < 37, < 34 and < 30 weeks' gestation, respectively, which were significantly higher than the respective values of 46.7%, 55.0%, 55.9% and 52.8% achieved by application of the NICE guidelines.

CONCLUSIONS

Using Bayes' theorem to combine PAPP-A measurement data with maternal characteristics improves the prediction of SGA and performs better than logistic regression or NICE guidelines, in the context of a new competing-risks model for the joint distribution of birth-weight Z-score and GA at delivery. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.

Combining early (<11 weeks' gestation) ultrasound features and maternal factors to predict small-for-gestational age neonates

Objectives

Placental related adverse pregnancy outcomes such as fetal growth restriction have significant short- and long-term implications for both mother and fetus. This study aimed to determine if conventional and novel early first trimester ultrasound measures are associated with small for gestational age (SGA) neonates. In addition, we aimed to assess whether a combination of ultrasound measures, maternal characteristics and biochemistry improved the prediction of this adverse pregnancy outcome.

Methods

This was a prospective cohort study including ultrasound measurements: trophoblast thickness (TT), trophoblast volume (TV), mean uterine artery pulsatility index, crown-rump length, fetal heart rate, mean sac diameter (MSD) and yolk sac diameter. Biochemical markers considered in the analysis were placental growth factor (PIGF), pregnancy - associated plasma protein A (PAPP-A), beta human chorionic gonadotropin and alpha fetoprotein. Regression models were fitted for ultrasound parameters using multiples of the median (MoM). All measures were compared with normal birthweight (BW) ≥10^th centile and SGA (BW < 10^th centile). Logistic regression analysis was used to create a clinical prediction model for SGA based on maternal characteristics, ultrasound measurements at <11 weeks gestational age and maternal biochemistry collected at 10-14 weeks.

Results

As compared to pregnancies delivered of babies with normal BW (n = 1068), MoM values for TT, TV, MSD, PAPP-A and PIGF were significantly reduced (P < 0.05) in pregnancies delivered of SGA babies (n = 73). The proposed logistic regression model includes maternal height, TV and PIGF resulting in an area under the receiver operator curve 0.70 (95% CI 0.63-0.76) for the prediction of SGA.

Conclusion

A significantly decreased TV, measured <11 weeks gestation, is predictive of BW < 10^th centile. With addition of maternal height and PIGF, this three-marker algorithm provided a reasonable predictive value for the development of SGA later in pregnancy.

Placental Histopathology and Pregnancy Outcomes in "Early" vs. "Late" Placental Abruption

Placenta-associated pregnancy complications (fetal growth restriction and preeclampsia) are traditionally classified as "early" and "late" due to their different pathophysiology, histopathology, and pregnancy outcomes. As placental abruption (PA) represents another placenta-associated complication, we aimed to study if this categorization can be applied to PA as well. Pregnancy and placental reports of all pregnancies complicated by PA between November 2008 and January 2019 were reviewed. Maternal background, pregnancy outcomes, and placental histopathology were compared between cases of PA < 34 weeks (early PA group) vs. > 34 weeks (late PA group). Placental lesions were classified according to the "Amsterdam" criteria. The primary outcome was severe neonatal morbidity (≥ 1 severe neonatal complications: seizures, IVH, HIE, PVL, blood transfusion, NEC, or death). Included were 305 cases of PA, 71 (23.3%) in the early group and 234 (76.7%) in the late group. The early PA group was characterized by higher rates of vaginal bleeding upon presentation (p = 0.003), DIC (p = 0.018), and severe neonatal morbidity (p < 0.001). The late PA group was characterized by a higher rate of urgent Cesarean deliveries (p < 0.001). The early PA group was characterized by higher rates of placental maternal vascular malperfusion (MVM) lesions (p < 0.001), maternal inflammatory response (MIR) lesions (p < 0.001), placental hemorrhage (p < 0.001), and a lower feto-placental ratio (p < 0.001). Using regression analysis, we found that severe neonatal morbidity was independently associated with early abruption (aOR = 5.3, 95% CI = 3.9-7.6), placental MVM (aOR = 1.5, 95% CI = 1.2-1.9), placental MIR (aOR = 1.9, 95% CI = 1.4-2.3), and inversely associated with antenatal corticosteroids (aOR = 0.9, 95% CI = 0.6-0.98). "Early" and "late" PA significantly differ in their presentation, placental pathology, and pregnancy outcomes.

Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics and medical history

BACKGROUND

The established method of identifying a group of women at high risk of delivering a small-for-gestational-age (SGA) neonate, requiring increased surveillance, is use of risk scoring systems based on maternal demographic characteristics and medical history. Although this approach is relatively simple to perform, it does not provide patient-specific risks and has an uncertain performance in predicting SGA. Another approach to predict delivery of a SGA neonate is to use logistic regression models that combine maternal factors with first-trimester biomarkers. These models provide patient-specific risks for different prespecified cut-offs of birth-weight percentile and gestational age (GA) at delivery.

OBJECTIVES

First, to develop a competing-risks model for prediction of SGA based on maternal demographic characteristics and medical history, in which GA at the time of delivery and birth-weight Z-score are treated as continuous variables. Second, to compare the predictive performance of the new model for SGA neonates to that of previous methods.

METHODS

This was a prospective observational study in 124 443 women with singleton pregnancy undergoing routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation. The dataset was divided randomly into a training and a test dataset. The training dataset was used to develop a model for the joint distribution of GA at delivery and birth-weight Z-score from variables of maternal characteristics and medical history. This patient-specific joint Gaussian distribution of GA at delivery and birth-weight Z-score allows risk calculation for SGA defined in terms of different birth-weight percentiles and GA. The new model was then validated in the test dataset to assess performance of screening and we compared its predictive performance to that of logistic regression models for different SGA definitions.

RESULTS

In the new model, the joint Gaussian distribution of GA at delivery and birth-weight Z-score is shifted to lower GA at delivery and birth-weight Z-score values, resulting in an increased risk for SGA, by lower maternal weight and height, black, East Asian, South Asian and mixed racial origin, medical history of chronic hypertension, diabetes mellitus and systemic lupus erythematosus and/or antiphospholipid syndrome, conception by in-vitro fertilization and smoking. In parous women, variables from the last pregnancy that increased the risk for SGA were history of pre-eclampsia or stillbirth, decreasing birth-weight Z-score and decreasing GA at delivery of the last pregnancy and interpregnancy interval < 0.5 years. In the test dataset, at a false-positive rate of 10%, the new model predicted 30.1%, 32.1%, 32.2% and 37.8% of cases of a SGA neonate with birth weight < 10^th percentile delivered at < 42, < 37, < 34 and < 30 weeks' gestation, respectively, which were similar or higher than the respective values achieved by a series of logistic regression models. The calibration study demonstrated good agreement between the predicted risks and the observed incidence of SGA in both the training and test datasets.

CONCLUSIONS

A new competing-risks model, based on maternal characteristics and medical history, provides estimation of patient-specific risks for SGA in which GA at delivery and birth-weight Z-score are treated as continuous variables. Such estimation of the a-priori risk for SGA is an essential first step in the use of Bayes' theorem to combine maternal factors with biomarkers for the continuing development of more effective methods of screening for SGA. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.

Can Inflammatory Hematological Parameters be a Guide to Late-onset Fetal Growth Restriction?

PURPOSE

To compare the rates obtained from hematological parameters in cases of late-onset idiopathic fetal growth restriction (FGR) with healthy pregnancies and to evaluate the effect on neonatal outcomes.

METHODS

The study group consisted of 63 pregnant women with late-onset idiopathic FGR and the control group consisted of 91 healthy pregnant women. The determined rates were calculated from the control hemograms of patients at 28 weeks. Both groups were compared for neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and other parameters.

RESULTS

NLR, leukocyte and neutrophil levels were significantly higher in the FGR group (p<0.05). There was no significant difference in PLR, platelet and lymphocyte levels between the groups (p>0.05). To predict FGR, the best cut-off value of NLR was determined to be 4.11 with 56% sensitivity and 88% specificity values.

CONCLUSION

Neutrophil, lymphocyte and platelet interactions have an important role in FGR development. Inflammation can be involved in the etiopathogenesis in late-onset FGR.

Non-invasive prediction of fetal growth restriction by whole-genome promoter profiling of maternal plasma DNA: a nested case-control study

Objective

To predict fetal growth restriction (FGR) by whole‐genome promoter profiling of maternal plasma.

Design

Nested case–control study.

Setting

Hospital‐based.

Population or Sample

810 pregnancies: 162 FGR cases and 648 controls.

Methods

We identified gene promoters with a nucleosome footprint that differed between FGR cases and controls based on maternal plasma cell‐free DNA (cfDNA) nucleosome profiling. Optimal classifiers were developed using support vector machine (SVM) and logistic regression (LR) models.

Main outcome measures

Genes with differential coverages in promoter regions through the low‐coverage whole‐genome sequencing data analysis among FGR cases and controls. Receiver operating characteristic (ROC) analysis (area under the curve [AUC], accuracy, sensitivity and specificity) was used to evaluate the performance of classifiers.

Results

Through the low‐coverage whole‐genome sequencing data analysis of FGR cases and controls, genes with significantly differential DNA coverage at promoter regions (−1000 to +1000 bp of transcription start sites) were identified. The non‐invasive ‘FGR classifier 1’ (C_FGR1) had the highest classification performance (AUC, 0.803; 95% CI 0.767–0.839; accuracy, 83.2%) was developed based on 14 genes with differential promoter coverage using a support vector machine.

Conclusions

A promising FGR prediction method was successfully developed for assessing the risk of FGR at an early gestational age based on maternal plasma cfDNA nucleosome profiling.

Tweetable abstract

A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling.

A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling.

Early sonographic evaluation of the placenta in cases with IUGR: a pilot study

PURPOSE

The objective of this study was to evaluate the feasibility and value of measuring early placental echogenicity to predict fetal intrauterine growth restriction (IUGR).

METHODS

This is a single center, retrospective cohort study. Early ultrasound examination (6 + o to 8 + 6 weeks of gestation in singleton pregnancies) was used to measure placental dimensions and placental echogenicity. A ratio between placental echogenicity and myometrial echogenicity (PE/ME-ratio) was calculated for each patient. Study population was assigned to either the IUGR group or the control group based on clinical data.

RESULTS

184 eligible pregnancies were analysed. 49 patients were included in our study. Of those, 9 (18.37%) cases were affected by IUGR and 40 (81.63%) were controls. Measuring the placental echogenicity was feasible in all cases. IUGR neonates had a significant lower placental echogenicity (1.20 (± 0.24) vs. 1.64 (± 0.60), p = 0.033), but no significant differences in the other placental outcomes were observed.

CONCLUSION

Our results showed that measuring placental echogenicity is feasible in the early first trimester and demonstrated a significantly lower placental echogenicity in fetuses with subsequent IUGR. Further prospective studies are needed to validate those results.

First Trimester Protein Biomarkers for Risk of Spontaneous Preterm Birth: Identifying a Critical Need for More Rigorous Approaches to Biomarker Identification and Validation

BACKGROUND

Spontaneous preterm birth is the leading cause of perinatal morbidity and mortality worldwide and continues to present a major clinical dilemma. We previously reported that a number of protein species were dysregulated in maternal serum collected at 11-13+6 weeks' gestation from pregnancies that continued to labour spontaneously and deliver preterm.

OBJECTIVES AND METHODS

In this study, we aimed to validate changes seen in 4 candidate protein species: alpha-1-antitrypsin, vitamin D-binding protein (VDBP), alpha-1beta-glycoprotein and apolipoprotein A-1 in a larger cohort of women using a western blot approach.

RESULTS

Serum levels of all 4 proteins were reduced in women who laboured spontaneously and delivered preterm. This reduction was significant for VDBP (p = 0.04), which has been shown to be involved in a plethora of essential biological functions, including actin scavenging, fatty acid transport, macrophage activation and chemotaxis.

CONCLUSIONS

The decrease in select proteoforms of VDBP may result in an imbalance in the optimal intrauterine environment for the developing foetus as well as to a successful uncomplicated pregnancy. Thus, certain (phosphorylated) species of VDBP may be of value in developing a targeted approach to the early prediction of spontaneous preterm labour. Importantly, this study raises the importance of a focus on proteoforms and the need for any biomarker validation process to most effectively take these into account rather than the more widespread practice of simply focussing on the primary amino acid sequence of a protein.

Role of sFlt-1 and PlGF in the screening of small-for-gestational age neonates during pregnancy: A systematic review

Background

Fetal growth restriction, i.e. the restriction of genetically predetermined growth potential due to placental dysfunction, is a major cause of neonatal morbidity and mortality. The consequences of inadequate fetal growth can be life-long, but the risks can be reduced substantially if the condition is identified prenatally. Currently, screening strategies are based on ultrasound detection of a small-for-gestational age fetus and do not take into account the underlying vascular pathology in the placenta. Measurement of maternal circulating angiogenic biomarkers placental growth factor, sFlt-1 (soluble FMS-like tyrosine kinase-1) are increasingly used in studies on fetal growth restriction as they reflect the pathophysiological process in the placenta. However, interpretation of the role of angiogenic biomarkers in prediction of fetal growth restriction is hampered by the varying design, population, timing, assay technique and cut-off values used in these studies.

Methods

We conducted a systematic-review in PubMed (MEDLINE), EMBASE (Ovid) and Cochrane to explore the predictive performance of maternal concentrations of placental growth factor, sFlt-1 and their ratio for fetal growth restriction and small-for-gestational age, at different gestational ages, and describe the longitudinal changes in biomarker concentrations and optimal discriminatory cut-off values.

Results

We included 26 studies with 2514 cases with small-for-gestational age, 27 cases of fetal growth restriction, 582 cases mixed small-for-gestational age/fetal growth restriction and 29,374 reference. The largest mean differences for the two biomarkers and their ratio were found after 26 weeks of gestational age and not in the first trimester. The ROC-AUC varied between 0.60 and 0.89 with sensitivity and specificity matching the different cut-off values or a preset false-positive rate of 10%.

Conclusions

Most of the studies did not make a distinction between small-for-gestational age and fetal growth restriction, and therefore the small-for-gestational age group consists of fetuses with growth restriction and fetuses that are constitutionally normal. The biomarkers can be a valuable screening tool for small-for-gestational age pregnancies, but unfortunately, there is not yet a clear cut-off value to use for screening. More research is needed to see if these biomarkers are sufficiently able to differentiate growth restriction on their own and how these biomarkers in combination with other relevant clinical and ultrasound parameters can be used in clinical routine diagnostics.

Comparison of Placental Three-Dimensional Power Doppler Vascular Indices and Placental Volume in Pregnancies with Small for Gestational Age Neonates

This prospective observational study aimed to compare the changes in placental vascular indices and placental volume using three-dimensional power Doppler (3DPD) ultrasound in pregnancies with small for gestational age (SGA) neonates. We enrolled 396 women with singleton pregnancies from September 2013 to June 2016. Placental vascular indices, including the vascularization index (VI), flow index (FI), and vascularization flow index (VFI), and placental volume were obtained using 3DPD ultrasound in the first and second trimesters. Of the enrolled women, 21 delivered SGA neonates and 375 did not. In the first trimester, the SGA group had a significantly lower mean FI (25.10 ± 7.51 versus 33.10 ± 10.97, p < 0.001) and VFI (4.59 ± 1.95 versus 6.28 ± 2.35, p = 0.001) than the non-SGA group. However, there was no significant difference in the placental volume between the two groups during the first trimester. In the second trimester, the SGA group also had a significantly lower mean FI (27.08 ± 7.97 versus 31.54 ± 11.01, p = 0.022) and VFI (6.68 ± 1.71 versus 8.68 ± 3.09, p < 0.001) than the non-SGA group. In addition, a significantly smaller placental volume was noted in the SGA group (104.80 ± 24.23 cm³ versus 122.67 ± 26.35 cm³, p = 0.003) than in the non-SGA group during the second trimester. The results showed that a decreased placental VFI occurred earlier than a decreased placental volume in SGA pregnancies.

The Role of Early Pregnancy Maternal Selenium Levels on the Risk for Small-for-Gestational Age Newborns

It has not yet been established, whether or not the maternal serum selenium (Se) in early pregnancy may be a risk marker of small-for-gestational age (SGA) birth weight. Selenium is important for human health and is involved in oxidative balance, a key element in the development of the placenta and fetus. This innovative study was nested in a prospective cohort of 750 women recruited in the 10–14th week of a single pregnancy, all of whom were healthy during recruitment. We examined mothers delivering SGA infants (with birth weight <10th percentile) (n = 48) and matched mothers delivering appropriate-for-gestational age (AGA) infants (between 10–90th percentile) (n = 192). We measured the maternal microelement concentrations in the serum from the 10–14th gestational week, using the inductively coupled plasma mass spectrometry (ICP-MS). The odds ratios of SGA (and 95% confidence intervals) were assessed in logistic regression. The mean maternal Se concentrations were lower in mothers in the SGA group compared to the AGA group (59.60 vs. 62.54 µg/L; p = 0.020). Women in the lowest Q₁ quartile of Se (≤56.60 µg/L) have about three times higher risk of SGA compared to women in the higher quartiles (Q₂ or Q₄); the odds ratio of SGA was OR = 3.02 (p = 0.019) for Q₁ vs. Q₂ quartile. The risk profile graph confirms the results. We found that excessive pre-pregnancy BMI (body mass index) affected the estimated SGA odds ratios. Early pregnancy maternal serum selenium status can be a risk marker of SGA newborns and more research is needed in larger groups.

Could masking gestational age estimation during scanning improve detection of small-for-gestational-age fetuses? A controlled pre-post evaluation

BACKGROUND

Antenatal detection of small-for-gestational-age fetuses improves outcomes and reduces perinatal mortality rates. However, ultrasonographic estimation of fetal weight is subject to several potential sources of error. One potential source of error is subconscious operator bias towards "normal" measurement values for gestational age (observer-expectancy bias).

OBJECTIVE

We aimed to determine whether the sensitivity of small-for-gestational-age detection is improved by removing real-time display of estimated gestational age during measurement of the abdominal circumference in the third trimester.

STUDY DESIGN

This retrospective evaluation (November 2014-May 2018 inclusive) included all singleton infants liveborn at ≥28 weeks gestation in a single United Kingdom obstetrics center. In the preintervention phase, real-time estimated gestational age was displayed to sonographers as they measured fetal abdominal circumference (the key determinant of estimated fetal weight with the use of the INTERGROWTH 21st fetal weight equation) in the third trimester. In the postintervention phase, real-time gestational age information was removed on selected ultrasound machines. Accuracy of birthweight percentile estimation was assessed before and after intervention, both in the full cohort comprising all eligible scans and in a subcohort that was scanned within 4 weeks of delivery. We assessed the accuracy of small-for-gestational-age detection using the sensitivity, positive likelihood ratio, and area under the receiver-operator curve.

RESULTS

Of the 18,342 eligible pregnancies, 9342 (51%) had a third-trimester growth scan. The sensitivity of ultrasonographic estimation of fetal weight for antenatal detection of small-for-gestational-age babies did not change significantly between the before and after intervention phases (31.5% confidence interval, 27.1-36.2 vs 31.7% confidence interval, 20.2-45.0). Although the sensitivity for small-for-gestational-age detection was higher in the subcohort that was scanned within 4 weeks of delivery than in the full cohort (P<.001), there was no significant difference between the before and after intervention phases (58% confidence interval, 50-66 vs 65% confidence interval, 43-84). With the use of an estimation of the abdominal circumference percentile rather than estimated fetal weight percentile significantly decreased the sensitivity for small-for-gestational-age detection in all groups (P<.01), but there was no difference between the before and after intervention phases.

CONCLUSION

Blinding operators to the estimated gestation of the fetus during abdominal circumference measurement does not significantly alter the antenatal detection rate of small-for-gestational-age babies. The observer-expectancy effect is therefore unlikely to be a significant contributor to the error that is associated with ultrasonographic estimation of fetal weight.

Clinical evaluation of a first trimester pregnancy algorithm predicting the risk of small for gestational age neonates

BACKGROUND

The Fetal Medicine Foundation developed a multiple logistic regression algorithm for risk prediction of delivering a small for gestational age neonate.

AIM

To validate this algorithm in an Australian population.

METHODS

At the combined first trimester screen participants' medical histories, demographic data, mean arterial pressure, uterine artery pulsatility index and pregnancy-associated plasma protein-A were assessed. After delivery, risk of delivering a small for gestational age neonate at <37 or ≥37 weeks gestation was retrospectively calculated using the Fetal Medicine Foundation algorithm.

RESULTS

Three thousand and eight women underwent prediction of risk for delivering a small for gestational age neonate. The algorithm detected 15.0% (95% CI: 3.2-37.9) of small for gestational age neonates delivered <37 weeks gestation at a fixed 10% false positive rate (or 35.0% (95% CI: 15.4-59.2) at a fixed 20% false positive rate). It detected 23.4% (95% CI: 16.1-30.7) of small for gestational age neonates delivered ≥37 weeks gestation at a fixed 10% false positive rate (or 39.1% (95% CI: 30.7-47.5) at a fixed 20% false positive rate). The algorithm performed significantly better than individual parameters (P < 0.05). The area under the receiver operating characteristic curve was 0.68 (95% CI: 0.56-0.80) and 0.70 (95% CI: 0.65-0.74) for small for gestational age neonates at <37 and ≥37 weeks gestation, respectively.

CONCLUSIONS

The Fetal Medicine Foundation algorithm for first trimester prediction of small for gestational age neonates does not perform as well in an Australian population as in the original United Kingdom cohort. However, it performs significantly better than any individual test parameter in both preterm and term neonates. Incorporation of further variables may help improve screening efficacy.

External validation and clinical usefulness of first-trimester prediction models for small- and large-for-gestational-age infants: a prospective cohort study

Objective

To assess the external validity of all published first‐trimester prediction models based on routinely collected maternal predictors for the risk of small‐ and large‐for‐gestational‐age (SGA and LGA) infants. Furthermore, the clinical potential of the best‐performing models was evaluated.

Design

Multicentre prospective cohort.

Setting

Thirty‐six midwifery practices and six hospitals (in the Netherlands).

Population

Pregnant women were recruited at <16 weeks of gestation between 1 July 2013 and 31 December 2015.

Methods

Prediction models were systematically selected from the literature. Information on predictors was obtained by a web‐based questionnaire. Birthweight centiles were corrected for gestational age, parity, fetal sex, and ethnicity.

Main outcome measures

Predictive performance was assessed by means of discrimination (C‐statistic) and calibration.

Results

The validation cohort consisted of 2582 pregnant women. The outcomes of SGA <10th percentile and LGA >90th percentile occurred in 203 and 224 women, respectively. The C‐statistics of the included models ranged from 0.52 to 0.64 for SGA (n = 6), and from 0.60 to 0.69 for LGA (n = 6). All models yielded higher C‐statistics for more severe cases of SGA (<5th percentile) and LGA (>95th percentile). Initial calibration showed poor‐to‐moderate agreement between the predicted probabilities and the observed outcomes, but this improved substantially after recalibration.

Conclusion

The clinical relevance of the models is limited because of their moderate predictive performance, and because the definitions of SGA and LGA do not exclude constitutionally small or large infants. As most clinically relevant fetal growth deviations are related to ‘vascular’ or ‘metabolic’ factors, models predicting hypertensive disorders and gestational diabetes are likely to be more specific.

Tweetable abstract

The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited.

The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited.

Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study

BACKGROUND

While there is increasing interest in identifying pregnancies at risk for adverse outcome, existing prediction models have not adequately assessed population-based risks, and have been based on conventional regression methods. The objective of the current study was to identify predictors of fetal growth abnormalities using logistic regression and machine learning methods, and compare diagnostic properties in a population-based sample of infants.

METHODS

Data for 30,705 singleton infants born between 2009 and 2014 to mothers resident in Nova Scotia, Canada was obtained from the Nova Scotia Atlee Perinatal Database. Primary outcomes were small (SGA) and large for gestational age (LGA). Maternal characteristics pre-pregnancy and at 26 weeks were studied as predictors. Logistic regression and select machine learning methods were used to build the models, stratified by parity. Area under the curve was used to compare the models; relative importance of predictors was compared qualitatively.

RESULTS

7.9% and 13.5% of infants were SGA and LGA, respectively; 48.6% of births were to primiparous women and 51.4% were to multiparous women. Prediction of SGA and LGA was poor to fair (area under the curve 60-75%) and improved with increasing parity and pregnancy information. Smoking, previous low birthweight infant, and gestational weight gain were important predictors for SGA; pre-pregnancy body mass index, gestational weight gain, and previous macrosomic infant were the strongest predictors for LGA.

CONCLUSIONS

The machine learning methods used in this study did not offer any advantage over logistic regression in the prediction of fetal growth abnormalities. Prediction accuracy for SGA and LGA based on maternal information is poor for primiparous women and fair for multiparous women.

Screening for fetal growth restriction and placental insufficiency

Fetal growth restriction (FGR) continues to be a leading cause of preventable stillbirth and poor neurodevelopmental outcomes in offspring, and furthermore is strongly associated with the obstetrical complications of iatrogenic preterm birth and pre-eclampsia. The terms small for gestational age (SGA) and FGR have, for too long, been considered equivalent and therefore used interchangeably. However, the delivery of improved clinical outcomes requires that clinicians effectively distinguish fetuses that are pathologically growth-restricted from those that are constitutively small. A greater understanding of the multifactorial pathogenesis of both early- and late-onset FGR, especially the role of underlying placental pathologies, may offer insight into targeted treatment strategies that preserve placental function. The new maternal blood biomarker placenta growth factor offers much potential in this context. This review highlights new approaches to effective screening for FGR based on a comprehensive review of: etiology, diagnosis, antenatal surveillance and management. Recent advances in novel imaging methods provide the basis for stepwise multi-parametric testing that may deliver cost-effective screening within existing antenatal care systems.

The impact of IUGR on pancreatic islet development and β-cell function

Placental insufficiency is a primary cause of intrauterine growth restriction (IUGR). IUGR increases the risk of developing type 2 diabetes mellitus (T2DM) throughout life, which indicates that insults from placental insufficiency impair β-cell development during the perinatal period because β-cells have a central role in the regulation of glucose tolerance. The severely IUGR fetal pancreas is characterized by smaller islets, less β-cells, and lower insulin secretion. Because of the important associations among impaired islet growth, β-cell dysfunction, impaired fetal growth, and the propensity for T2DM, significant progress has been made in understanding the pathophysiology of IUGR and programing events in the fetal endocrine pancreas. Animal models of IUGR replicate many of the observations in severe cases of human IUGR and allow us to refine our understanding of the pathophysiology of developmental and functional defects in islet from IUGR fetuses. Almost all models demonstrate a phenotype of progressive loss of β-cell mass and impaired β-cell function. This review will first provide evidence of impaired human islet development and β-cell function associated with IUGR and the impact on glucose homeostasis including the development of glucose intolerance and diabetes in adulthood. We then discuss evidence for the mechanisms regulating β-cell mass and insulin secretion in the IUGR fetus, including the role of hypoxia, catecholamines, nutrients, growth factors, and pancreatic vascularity. We focus on recent evidence from experimental interventions in established models of IUGR to understand better the pathophysiological mechanisms linking placental insufficiency with impaired islet development and β-cell function.

Fetal growth restriction: current knowledge

BACKGROUND

Fetal growth restriction (FGR) is a condition that affects 5-10% of pregnancies and is the second most common cause of perinatal mortality. This review presents the most recent knowledge on FGR and focuses on the etiology, classification, prediction, diagnosis, and management of the condition, as well as on its neurological complications.

METHODS

The Pubmed, SCOPUS, and Embase databases were searched using the term "fetal growth restriction".

RESULTS

Fetal growth restriction (FGR) may be classified as early or late depending on the time of diagnosis. Early FGR (<32 weeks) is associated with substantial alterations in placental implantation with elevated hypoxia, which requires cardiovascular adaptation. Perinatal morbidity and mortality rates are high. Late FGR (≥32 weeks) presents with slight deficiencies in placentation, which leads to mild hypoxia and requires little cardiovascular adaptation. Perinatal morbidity and mortality rates are lower. The diagnosis of FGR may be clinical; however, an arterial and venous Doppler ultrasound examination is essential for diagnosis and follow-up. There are currently no treatments to control FGR; the time at which pregnancy is interrupted is of vital importance for protecting both the mother and fetus.

CONCLUSION

Early diagnosis of FGR is very important, because it enables the identification of the etiology of the condition and adequate monitoring of the fetal status, thereby minimizing risks of premature birth and intrauterine hypoxia.

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 < 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.