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

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.

Influence of trophectoderm biopsy for preimplantation genetic testing in the serum level of first trimester biomarkers

RESEARCH QUESTION

Does trophectoderm biopsy for preimplantation genetic testing for aneuploidies (PGT-A) affect maternal serum first-trimester pregnancy biomarkers (pregnancy-associated plasma protein A [PAPP-A], free β-HCG and placental growth factor [PIGF])?

DESIGN

Retrospective cohort study of all singleton pregnancies (n = 9794) after naturally conceived (n = 8005) IVF and fresh embryo transfers (n = 478), frozen embryo transfer of non PGT-A (FET) (n = 963) or PGT-A tested embryos (FET + PGT-A) (n = 348). Serum levels of free β-HCG and PAPP-A were measured in all women with a viable pregnancy at 8-13.6 weeks of pregnancy; PIGF was measured in 3784 women. Biomarkers were converted to a multiple of the expected normal median (MOM) for a pregnancy of the same gestational day. The medians for the multiple of the median were calculated and compared.

RESULTS

Free β-HCG did not differ according to mode of conception. The PAPP-A concentrations were significantly lower in IVF and fresh embryo transfers (-0.1 Log10 MOM raw PAPP-A) compared with FET + PGT-A (-0.04 Log 10 MOM raw PAPP-A, P = 0.009) and natural conceptions (-0.0187 Log 10 MOM raw PAPP-A) (P < 0.001). The PIGF levels were significantly lower in the FET + PGTA group versus natural conception (P = 0.001). Difference in means adjusted by crown rump length was 4.6 pg/ml (95% CI 2.7 to 6.6) for natural conceptions, 3.5 pg/ml (95% CI 0.34 to 6.6) for IVF and 2.2 pg/ml (95% CI 0.06 to 4.4) for FET.

CONCLUSION

Trophectoderm biopsy for PGT-A has a significant effect on first-trimester maternal serum PAPP-A and PIGF. This needs to be further validated, as it may mislead the estimation of the first-trimester risk of aneuploidies and pre-eclampsia.

Constructing small for gestational age prediction models: A retrospective machine learning study

OBJECTIVE

To develop machine learning prediction models for small for gestational age with baseline characteristics and biochemical tests of various pregnancy stages individually and collectively and compare predictive performance.

STUDY DESIGN

This retrospective study included singleton pregnancies with infants born between May 2018 and March 2023. Small for gestational age was defined as a birth weight below the 10th percentile according to the Intergrowth-21st fetal growth standards. The pregnancy data were categorized into four datasets at different gestational time points (14 and 28 weeks and admission). The LightGBM framework was utilized to assess the variable importance by employing a five-fold cross-validation. RandomizedSearchCV and sequential feature selection were applied to estimate the optimal number of features. Seven machine learning algorithms were used to develop prediction models, with an 8:2 ratio for training and testing. The model performance was evaluated using receiver operating characteristic curve analysis and sensitivity at a false positive rate of 10 %.

RESULTS

We included data of 4,394 women with singleton pregnancies, including 148 (3.4%) small for gestational age infants. Women delivering small for gestational age infants exhibited significantly shorter stature and lower fundal height and abdominal circumference at admission. Maternal height, age, and pre-pregnancy weight consistently ranked among the top 20 features in prediction models with any dataset. The models incorporated variables of admission stage have strong predictive performance with the area under the curves exceeding 0.8. The prediction model developed with variables of admission stage yielded the best performance, achieving an area under the curve of 0.85 and a sensitivity of 73% at the false positive rate of 10%.

CONCLUSIONS

By machine learning, various pregnancy stages' prediction models for small for gestational age showed good predictive performance, and the predictive value of variables at each pregnancy stage was fully explored. The prediction model with the best performance was established with variables of admission stage and emphasized the significance of prenatal physical examinations.

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.

The correlation between placental growth factor and small for gestational age infants: a matched case-control study

BACKGROUND

At present, research has not found easily accessible, accurate, and safe clinical biomarkers that can effectively predict the occurrence of infants born small for gestational age (SGA). The aim of this study is to explore the predictive role of maternal placental growth factor (PIGF) levels on the occurrence of SGA infants.

METHOD

We conducted a matched case-control study on 226 SGA infants and 226 non-SGA infants born in the Department of Obstetrics, Peking University People's Hospital, from January 2021 to December 2022, with regular monitoring of maternal serum PIGF levels in second trimester during pregnancy. Apply multiple logistic regression analysis and receiver operating characteristic (ROC) curve analysis to determine whether PIGF is an independent influencing factor for the occurrence of SGA in infants, and evaluate whether PIGF can predict the occurrence of SGA in infants.

RESULTS

Multiple logistic regression analysis found that multipara (HR = 0.484, 95% CI = 0.250-0.937, p = 0.031), maternal pre-pregnancy underweight (HR = 4.710, 95% CI = 1.881-11.792, p = 0.001), pre-eclampsia(HR = 2.291, 95% CI = 1.068-4.913, p = 0.033), low levels of PIGF (HR = 26.417, 95% CI = 12.850-54.311, p < 0.001) and oligohydramnios (HR = 4.764, 95% CI = 1.845-12.301, p = 0.001) were independent factors affecting the occurrence of infants born SGA. In addition, ROC curve analysis showed that the area under the curve (AUC) predicted by PIGF level and four other influencing factors for the occurrence of SGA infants were 0.834 and 0.723, respectively. In addition, the combination of PIGF and four other independent influencing factors improved the predictive value (AUC 0.902) for the birth of SGA infants, with enhanced sensitivity and specificity.

CONCLUSION

Low levels of PIGF in second trimester during pregnancy are an independent risk factor for SGA infants. Compared with other indicators, PIGF levels PIGF in second trimester are a better predictor of SGA in infants.

Evaluating the predictive efficacy of first trimester biochemical markers (PAPP-A, fβ-hCG) in forecasting preterm delivery incidences

In this investigation, we explored the correlation between first-trimester biochemical markers and the incidence of preterm birth (PTB), irrespective of the cause, spontaneous preterm birth (sPTB), and preterm premature rupture of membranes (pPROM) within a cohort comprising 1164 patients. It was discovered that diminished levels of Pregnancy-Associated Plasma Protein-A (PAPP-A) between 11 and 13 + 6 weeks of gestation significantly contributed to the risk of preterm deliveries both before 35 and 37 weeks, as well as to pPROM instances. Furthermore, women experiencing sPTB before the 37th week of gestation also exhibited lower concentrations of PAPP-A. Moreover, reduced first-trimester concentrations of free beta-human chorionic gonadotropin (fb-HCG) were identified as a risk factor for deliveries preceding 37 weeks, pPROM, and sPTB before 35 weeks of gestation. Despite these correlations, the area under the curve for these biochemical markers did not surpass 0.7, indicating their limited diagnostic potential. The most significant discriminatory capability was noted for PAPP-A levels, with a threshold of < 0.71 multiples of the median (MoM) predicting PTB before 37 weeks, yielding an odds ratio of 3.11 (95% Confidence Interval [CI] 1.97-4.92). For sPTB, the greatest discriminatory potential was observed for PAPP-A < 0.688, providing an OR of 2.66 (95% CI 1.51-4.66). The cut-off points corresponded to accuracies of 76.05% and 79.1%, respectively. In regression analyses, the combined predictive models exhibited low explanatory power with R2 values of 9.2% for PTB and 7.7% for sPTB below 35 weeks of gestation. In conclusion, while certain biochemical markers demonstrated associations with outcomes of preterm birth, their individual and collective predictive efficacies for foreseeing such events were found to be suboptimal.

Validation of Fetal Medicine Foundation competing-risks model for small-for-gestational-age neonate in early third trimester

OBJECTIVE

To evaluate the new 36-week Fetal Medicine Foundation (FMF) competing-risks model for the prediction of small-for-gestational age (SGA) at an earlier gestation of 30 + 0 to 34 + 0 weeks.

METHODS

This was a retrospective multicenter cohort study of prospectively collected data on 3012 women with a singleton pregnancy undergoing ultrasound examination at 30 + 0 to 34 + 0 weeks' gestation as part of a universal screening program. We used the default FMF competing-risks model for prediction of SGA at 36 weeks' gestation combining maternal factors (age, obstetric and medical history, weight, height, smoking status, race, mode of conception), estimated fetal weight (EFW) and uterine artery pulsatility index (UtA-PI) to calculate risks for different cut-offs of birth-weight percentile and gestational age at delivery. We examined the accuracy of the model by means of discrimination and calibration.

RESULTS

The prediction of SGA < 3rd percentile improved with the addition of UtA-PI and with a shorter examination-to-delivery interval. For a 10% false-positive rate, maternal factors, EFW and UtA-PI predicted 88.0%, 74.4% and 72.8% of SGA < 3rd percentile delivered at < 37, < 40 and < 42 weeks' gestation, respectively. The respective values for SGA < 10th percentile were 86.1%, 69.3% and 66.2%. In terms of population stratification, if the biomarkers used are EFW and UtA-PI and the aim is to detect 90% of SGA < 10th percentile, then 10.8% of the population should be scanned within 2 weeks after the initial assessment, an additional 7.2% (total screen-positive rate (SPR), 18.0%) should be scanned within 2-4 weeks after the initial assessment and an additional 11.7% (total SPR, 29.7%) should be examined within 4-6 weeks after the initial assessment. The new model was well calibrated.

CONCLUSIONS

The 36-week FMF competing-risks model for SGA is also applicable and accurate at 30 + 0 to 34 + 0 weeks and provides effective risk stratification, especially for cases leading to delivery < 37 weeks of gestation. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Evaluation of screening performance of first-trimester competing-risks prediction model for small-for-gestational age in Asian population

OBJECTIVE

To examine the external validity of the Fetal Medicine Foundation (FMF) competing-risks model for the prediction of small-for-gestational age (SGA) at 11-14 weeks' gestation in an Asian population.

METHODS

This was a secondary analysis of a multicenter prospective cohort study in 10 120 women with a singleton pregnancy undergoing routine assessment at 11-14 weeks' gestation. We applied the FMF competing-risks model for the first-trimester prediction of SGA, combining maternal characteristics and medical history with measurements of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI) and serum placental growth factor (PlGF) concentration. We calculated risks for different cut-offs of birth-weight percentile (< 10th , < 5th or < 3rd percentile) and gestational age at delivery (< 37 weeks (preterm SGA) or SGA at any gestational age). Predictive performance was examined in terms of discrimination and calibration.

RESULTS

The predictive performance of the competing-risks model for SGA was similar to that reported in the original FMF study. Specifically, the combination of maternal factors with MAP, UtA-PI and PlGF yielded the best performance for the prediction of preterm SGA with birth weight < 10th percentile (SGA < 10th ) and preterm SGA with birth weight < 5th percentile (SGA < 5th ), with areas under the receiver-operating-characteristics curve (AUCs) of 0.765 (95% CI, 0.720-0.809) and 0.789 (95% CI, 0.736-0.841), respectively. Combining maternal factors with MAP and PlGF yielded the best model for predicting preterm SGA with birth weight < 3rd percentile (SGA < 3rd ) (AUC, 0.797 (95% CI, 0.744-0.850)). After excluding cases with pre-eclampsia, the combination of maternal factors with MAP, UtA-PI and PlGF yielded the best performance for the prediction of preterm SGA < 10th and preterm SGA < 5th , with AUCs of 0.743 (95% CI, 0.691-0.795) and 0.762 (95% CI, 0.700-0.824), respectively. However, the best model for predicting preterm SGA < 3rd without pre-eclampsia was the combination of maternal factors and PlGF (AUC, 0.786 (95% CI, 0.723-0.849)). The FMF competing-risks model including maternal factors, MAP, UtA-PI and PlGF achieved detection rates of 42.2%, 47.3% and 48.1%, at a fixed false-positive rate of 10%, for the prediction of preterm SGA < 10th , preterm SGA < 5th and preterm SGA < 3rd , respectively. The calibration of the model was satisfactory.

CONCLUSION

The screening performance of the FMF first-trimester competing-risks model for SGA in a large, independent cohort of Asian women is comparable with that reported in the original FMF study in a mixed European population. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses

Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

The Role of Acetylsalicylic Acid in the Prevention of Pre-Eclampsia, Fetal Growth Restriction, and Preterm Birth

BACKGROUND

Recent studies suggest that low-dose acetylsalicylic acid (ASA) can lower pregnancy-associated morbidity.

METHODS

This review is based on pertinent publications that were retrieved by a selective search in PubMed, with special attention to systematic reviews, metaanalyses, and randomized controlled trials.

RESULTS

Current meta-analyses document a reduction of the risk of the occurrence of pre-eclampsia (RR 0.85, NNT 50), as well as beneficial effects on the rates of preterm birth (RR 0.80, NNT 37), fetal growth restriction (RR 0.82, NNT 77), and perinatal death (RR 0.79, NNT 167). Moreover, there is evidence that ASA raises the rate of live births after a prior spontaneous abortion, while also lowering the rate of spontaneous preterm births (RR 0.89, NNT 67). The prerequisites for therapeutic success are an adequate ASA dose, early initiation of ASA, and the identification of women at risk of pregnancy-associated morbidity. Side effects of treatment with ASA in this patient group are rare and mainly involve bleeding in connection with the pregnancy (RR 0.87, NNH 200).

CONCLUSION

ASA use during pregnancy has benefits beyond reducing the risk of pre-eclampsia. The indications for taking ASA during pregnancy may be extended at some point in the future; at present, in view of the available evidence, it is still restricted to high-risk pregnancies.

Role of First Trimester Screening Biochemical Markers to Predict Hypertensive Pregnancy Disorders and SGA Neonates-A Narrative Review

Early recognition of high-risk pregnancies through biochemical markers may promote antenatal surveillance, resulting in improved pregnancy outcomes. The goal of this study is to evaluate the possibilities of using biochemical markers during the first trimester of pregnancy in the prediction of hypertensive pregnancy disorders (HPD) and the delivery of small-for-gestational-age (SGA) neonates. A comprehensive search was conducted on key databases, including PubMed, Scopus, and Web of Science, for articles relating to the use of biochemical markers in the prediction of HPD and SGA. The findings show that changes in the levels of biomarkers in the early pregnancy phases could be an important indicator of adverse pregnancy outcomes. The literature shows that low PAPP-A (pregnancy-associated plasma protein A) and PlGF (placental growth factor) levels, low alkaline phosphatase (AP), higher sFlt-1 (soluble fms-like Tyrosine Kinase-1) levels, higher AFP (alfa fetoprotein) levels, and elevated levels of inflammatory markers such as β-HGC (free beta human chorionic gonadotropin), interferon-gamma (INF-γ), and tumor necrosis factor-α (TNF-α) may be associated with risks including the onset of HPD, fetal growth restriction (FGR), and delivery of SGA neonates. Comparatively, PAPP-A and PlGF appear to be the most important biochemical markers for the prediction of SGA and HPD.

External validation of Fetal Medicine Foundation competing-risks model for midgestation prediction of small-for-gestational-age neonates in Spanish population

OBJECTIVE

To examine the external validity of the new Fetal Medicine Foundation (FMF) competing-risks model for prediction in midgestation of small-for-gestational-age (SGA) neonates.

METHODS

This was a single-center prospective cohort study of 25 484 women with a singleton pregnancy undergoing routine ultrasound examination at 19 + 0 to 23 + 6 weeks' gestation. The FMF competing-risks model for the prediction of SGA combining maternal factors and midgestation estimated fetal weight by ultrasound scan (EFW) and uterine artery pulsatility index (UtA-PI) was used to calculate risks for different cut-offs of birth-weight percentile and gestational age at delivery. The predictive performance was evaluated in terms of discrimination and calibration.

RESULTS

The validation cohort was significantly different in composition compared with the FMF cohort in which the model was developed. In the validation cohort, at a 10% false-positive rate (FPR), maternal factors, EFW and UtA-PI yielded detection rates of 69.6%, 38.7% and 31.7% for SGA < 10th percentile with delivery at < 32, < 37 and ≥ 37 weeks' gestation, respectively. The respective values for SGA < 3rd percentile were 75.7%, 48.2% and 38.1%. Detection rates in the validation cohort were similar to those reported in the FMF study for SGA with delivery at < 32 weeks but lower for SGA with delivery at < 37 and ≥ 37 weeks. Predictive performance in the validation cohort was similar to that reported in a subgroup of the FMF cohort consisting of nulliparous and Caucasian women. Detection rates in the validation cohort at a 15% FPR were 77.4%, 50.0% and 41.5% for SGA < 10th percentile with delivery at < 32, < 37 and ≥ 37 weeks, respectively, which were similar to the respective values reported in the FMF study at a 10% FPR. The model had satisfactory calibration.

CONCLUSION

The new competing-risks model for midgestation prediction of SGA developed by the FMF performs well in a large independent Spanish population. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Personalized stratification of pregnancy care for small for gestational age neonates from biophysical markers at midgestation

BACKGROUND

Antenatal identification of pregnancies at high risk of delivering small for gestational age neonates may improve the management of the condition and reduce the associated adverse perinatal outcomes. In a series of publications, we have developed a new competing-risks model for small for gestational age prediction, and we demonstrated that the new approach has a superior performance to that of the traditional methods. The next step in shaping the appropriate management of small for gestational age is the timely assessment of these high-risk pregnancies according to an antenatal stratification plan.

OBJECTIVE

This study aimed to demonstrate the stratification of pregnancy care based on individual patient risk derived from the application of the competing-risks model for small for gestational age that combines maternal factors with sonographic estimated fetal weight and uterine artery pulsatility index at midgestation.

STUDY DESIGN

This was a prospective observational study of 96,678 singleton pregnancies undergoing routine ultrasound examination at 19 to 24 weeks of gestation, which included recording of estimated fetal weight and measurement of uterine artery pulsatility index. The competing-risks model for small for gestational age was used to create a patient-specific stratification curve capable to define a specific timing for a repeated ultrasound examination after 24 weeks. We examined different stratification plans with the intention of detecting approximately 80%, 85%, 90%, and 95% of small for gestational age neonates with birthweight <3rd and <10th percentiles at any gestational age at delivery until 36 weeks; all pregnancies would be offered a routine ultrasound examination at 36 weeks.

RESULTS

The stratification of pregnancy care for small for gestational age can be based on a patient-specific stratification curve. Factors from maternal history, low estimated fetal weight, and increased uterine artery pulsatility index shift the personalized risk curve toward higher risks. The degree of shifting defines the timing for assessment for each pregnancy. If the objective of our antenatal plan was to detect 80%, 85%, 90%, and 95% of small for gestational age neonates at any gestational age at delivery until 36 weeks, the median (range) proportions (percentages) of population examined per week would be 3.15 (1.9-3.7), 3.85 (2.7-4.5), 4.75 (4.0-5.4), and 6.45 (3.7-8.0) for small for gestational age <3rd percentile and 3.8 (2.5-4.6), 4.6 (3.6-5.4), 5.7 (3.8-6.4), and 7.35 (3.3-9.8) for small for gestational age <10th percentile, respectively.

CONCLUSION

The competing-risks model provides an effective personalized continuous stratification of pregnancy care for small for gestational age which is based on individual characteristics and biophysical marker levels recorded at the midgestation scan.

Maternal Serum Angiogenic Profile and Its Correlations with Ultrasound Parameters and Perinatal Results in Normotensive and Preeclamptic Pregnancies Complicated by Fetal Growth Restriction

FGR is a complication of pregnancy in which the fetus does not reach its programmed growth potential due to placental reasons and it is the single largest risk factor of stillbirth. Babies with FGR are at increased risk of mortality and morbidity not only in the perinatal period, but also in later life. FGR presents a huge challenge for obstetricians in terms of its detection and further monitoring of pregnancy. The ultrasound is the gold standard here; apart from assessing fetal weight, it is used to measure Doppler flows in maternal and fetal circulation. It seems that additional tests, like biochemical angiogenic factors measurement would be helpful in diagnosing FGR, identifying fetuses at risk and adjusting the surveillance model. The study aimed to assess the potential relationship between the concentration of sEng, sFlt-1, PlGF, and the sFlt-1/PlGF ratio in maternal serum at delivery and maternal and fetal Doppler flow measurements as well as perinatal outcomes in pregnancies complicated by FGR with and without PE, isolated PE cases and normal pregnancies. The use of angiogenic markers is promising not only in PE but also in FGR. Numerous correlations between ultrasound and Doppler studies, perinatal outcomes and disordered angiogenesis marker levels in maternal serum suggest that biochemical parameters have a great potential to be used as a complementary method to diagnose and monitor pregnancies with FGR. The, PlGF in particular, could play an outstanding role in this regard.

Competing-risks model for prediction of small-for-gestational-age neonate at 36 weeks' gestation

OBJECTIVES

To develop further a competing-risks model for the prediction of a small-for-gestational-age (SGA) neonate by including sonographically estimated fetal weight (EFW) and biomarkers of impaired placentation at 36 weeks' gestation, and to compare the performance of the new model with that of the traditional EFW < 10^th percentile cut-off.

METHODS

This was a prospective observational study in 29 035 women with a singleton pregnancy undergoing routine ultrasound examination at 35 + 0 to 36 + 6 weeks' gestation. A competing-risks model for the prediction of a SGA neonate was used. The parameters included in the prior-history model were provided in previous studies. An interaction continuous model was used for the EFW likelihood. A folded plane regression model was fitted to describe likelihoods of biomarkers of impaired placentation. Stratification plans were also developed. The new model was evaluated and compared with EFW percentile cut-offs.

RESULTS

The performance of the model was better for predicting SGA neonates delivered closer to the point of assessment. The prediction provided by maternal factors alone was improved significantly by the addition of EFW, uterine artery pulsatility index (UtA-PI) and placental growth factor (PlGF) but not by mean arterial pressure or soluble fms-like tyrosine kinase-1. At a 10% false-positive rate, maternal factors and EFW predicted 77.6% and 65.8% of SGA neonates < 10^th percentile delivered before 38 and 42 weeks, respectively. The respective figures for SGA < 3^rd percentile were 85.5% and 74.2%. Addition of UtA-PI and PlGF resulted in marginal improvement in prediction of SGA < 3^rd percentile requiring imminent delivery. A competing-risks approach that combines maternal factors and EFW performed better when compared with fixed EFW percentile cut-offs at predicting a SGA neonate, especially with increasing time interval between assessment and delivery. The new model was well-calibrated.

CONCLUSIONS

A competing-risks model provides effective risk stratification for a SGA neonate at 35 + 0 to 36 + 6 weeks' gestation and is superior to EFW percentile cut-offs. The use of biomarkers of impaired placentation in addition to maternal factors and fetal biometry results in small improvement of the predictive performance for a neonate with severe SGA. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Placental protein levels in maternal serum are associated with adverse pregnancy outcomes in nulliparous patients

BACKGROUND

The Eunice Kennedy Shriver National Institute of Child Health and Human Development Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be was established to investigate the underlying causes and pathophysiological pathways associated with adverse pregnancy outcomes in nulliparous gravidas.

OBJECTIVE

This study aimed to study placental physiology and identify novel biomarkers concerning adverse pregnancy outcomes, including preterm birth (medically indicated and spontaneous), preeclampsia, small-for-gestational-age neonates, and stillbirth. We measured levels of placental proteins in the maternal circulation in the first 2 trimesters of pregnancy.

STUDY DESIGN

Maternal serum samples were collected at 2 study visits (6-13 weeks and 16-21 weeks), and levels of 9 analytes were measured. The analytes we measured were vascular endothelial growth factor, placental growth factor, endoglin, soluble fms-like tyrosine kinase-1, A disintegrin and metalloproteinase domain-containing protein 12, pregnancy-associated plasma protein A, free beta-human chorionic gonadotropin, inhibin A, and alpha-fetoprotein. The primary outcome was preterm birth between 20 0/7 and 36 6/7 weeks of gestation. The secondary outcomes were spontaneous preterm births, medically indicated preterm births, preeclampsia, small-for-gestational-age neonates, and stillbirth.

RESULTS

A total of 10,038 eligible gravidas were enrolled in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be cohort, from which a nested case-control study was performed comparing 800 cases with preterm birth (466 spontaneous preterm births, 330 medically indicated preterm births, and 4 unclassified preterm births), 568 with preeclampsia, 406 with small-for-gestational-age birth, and 49 with stillbirth with 911 controls who delivered at term without complications. Although levels of each analyte generally differed between cases and controls at 1 or 2 visits, the odds ratios revealed a <2-fold difference between cases and controls in all comparisons. Receiver operating characteristic curves, generated to determine the relationship between analyte levels and preterm birth and the other adverse pregnancy outcomes, resulted in areas under the receiver operating characteristic curves that were relatively low (range, 0.50-0.64) for each analyte. Logistic regression modeling demonstrated that areas under the receiver operating characteristic curves for predicting adverse pregnancy outcomes were greater using baseline clinical characteristics and combinations of analytes than baseline characteristics alone, but areas under the receiver operating characteristic curves remained relatively low for each outcome (range, 0.65-0.78).

CONCLUSION

We have found significant associations between maternal serum levels of analytes evaluated early in pregnancy and subsequent adverse pregnancy outcomes in nulliparous gravidas. However, the test characteristics for these analytes do not support their use as clinical biomarkers to predict adverse pregnancy outcomes, either alone or in combination with maternal clinical characteristics.

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.

Interpreting the role of nuchal fold for fetal growth restriction prediction using machine learning

The objective of the study is to investigate the effect of Nuchal Fold (NF) in predicting Fetal Growth Restriction (FGR) using machine learning (ML), to explain the model's results using model-agnostic interpretable techniques, and to compare the results with clinical guidelines. This study used second-trimester ultrasound biometry and Doppler velocimetry were used to construct six FGR (birthweight < 3rd centile) ML models. Interpretability analysis was conducted using Accumulated Local Effects (ALE) and Shapley Additive Explanations (SHAP). The results were compared with clinical guidelines based on the most optimal model. Support Vector Machine (SVM) exhibited the most consistent performance in FGR prediction. SHAP showed that the top contributors to identify FGR were Abdominal Circumference (AC), NF, Uterine RI (Ut RI), and Uterine PI (Ut PI). ALE showed that the cutoff values of Ut RI, Ut PI, and AC in differentiating FGR from normal were comparable with clinical guidelines (Errors between model and clinical; Ut RI: 15%, Ut PI: 8%, and AC: 11%). The cutoff value for NF to differentiate between healthy and FGR is 5.4 mm, where low NF may indicate FGR. The SVM model is the most stable in FGR prediction. ALE can be a potential tool to identify a cutoff value for novel parameters to differentiate between healthy and FGR.

First Trimester Prediction of Adverse Pregnancy Outcomes—Identifying Pregnancies at Risk from as Early as 11–13 Weeks

There is consistent evidence that many of the pregnancy complications that occur late in the second and third trimester can be predicted from an integrated 11–13 weeks visit, where a maternal and fetal assessment are comprehensively performed. The traditional aims of the 11–13 weeks visit have been: establishing fetal viability, chorionicity and dating of the pregnancy, and performing the combined screening test for common chromosomal abnormalities. Recent studies have shown that the first trimester provides important information that may help to predict pregnancy complications, such as preeclampsia and fetal growth restriction, stillbirth, preterm birth, gestational diabetes mellitus and placenta accreta spectrum disorder. The aim of this manuscript is to review the methods available to identify pregnancies at risk for adverse outcomes after screening at 11–13 weeks. Effective screening in the first trimester improves pregnancy outcomes by allowing specific interventions such as administering aspirin and directing patients to specialist clinics for regular monitoring.

Second-trimester contingent screening for small-for-gestational-age neonate

OBJECTIVES

First, to investigate the additive value of second-trimester placental growth factor (PlGF) for the prediction of a small-for-gestational-age (SGA) neonate. Second, to examine second-trimester contingent screening strategies.

METHODS

This was a prospective observational study in women with singleton pregnancy undergoing routine ultrasound examination at 19-24 weeks' gestation. We used the competing-risks model for prediction of SGA. The parameters for the prior model and the likelihoods for estimated fetal weight (EFW) and uterine artery pulsatility index (UtA-PI) were those presented in previous studies. A folded-plane regression model was fitted in the dataset of this study to describe the likelihood of PlGF. We compared the prediction of screening by maternal risk factors against the prediction provided by a combination of maternal risk factors, EFW, UtA-PI and PlGF. We also examined the additive value of PlGF in a policy that uses maternal risk factors, EFW and UtA-PI.

RESULTS

The study population included 40 241 singleton pregnancies. Overall, the prediction of SGA improved with increasing degree of prematurity, with increasing severity of smallness and in the presence of coexisting pre-eclampsia. The combination of maternal risk factors, EFW, UtA-PI and PlGF improved significantly the prediction provided by maternal risk factors alone for all the examined cut-offs of birth weight and gestational age at delivery. Screening by a combination of maternal risk factors and serum PlGF improved the prediction of SGA when compared to screening by maternal risk factors alone. However, the incremental improvement in prediction was decreased when PlGF was added to screening by a combination of maternal risk factors, EFW and UtA-PI. If first-line screening for a SGA neonate with birth weight < 10^th percentile delivered at < 37 weeks' gestation was by maternal risk factors and EFW, the same detection rate of 90%, at an overall false-positive rate (FPR) of 50%, as that achieved by screening with maternal risk factors, EFW, UtA-PI and PlGF in the whole population can be achieved by reserving measurements of UtA-PI and PlGF for only 80% of the population. Similarly, in screening for a SGA neonate with birth weight < 10^th percentile delivered at < 30 weeks, the same detection rate of 90%, at an overall FPR of 14%, as that achieved by screening with maternal risk factors, EFW, UtA-PI and PlGF in the whole population can be achieved by reserving measurements of UtA-PI and PlGF for only 70% of the population. The additive value of PlGF in reducing the FPR to about 10% with a simultaneous detection rate of 90% for a SGA neonate with birth weight < 3^rd percentile born < 30 weeks, is gained by measuring PlGF in only 50% of the population when first-line screening is by maternal factors, EFW and UtA-PI.

CONCLUSIONS

The combination of maternal risk factors, EFW, UtA-PI and PlGF provides effective second-trimester prediction of SGA. Serum PlGF is useful for predicting a SGA neonate with birth weight < 3^rd percentile born < 30 weeks after an inclusive assessment by maternal risk factors and biophysical markers. Similar detection rates and FPRs can be achieved by application of contingent screening strategies. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Competing risks model for prediction of small-for-gestational-age neonates from biophysical markers at 19 to 24 weeks' gestation

BACKGROUND

Antenatal identification of women at high risk to deliver small-for-gestational-age neonates may improve the management of the condition. The traditional but ineffective methods for small-for-gestational-age screening are the use of risk scoring systems based on maternal demographic characteristics and medical history and the measurement of the symphysial-fundal height. Another approach is to use logistic regression models that have higher performance and provide patient-specific risks for different prespecified cutoffs of birthweight percentile and gestational age at delivery. However, such models have led to an arbitrary dichotomization of the condition; different models for different small-for-gestational-age definitions are required and adding new biomarkers or examining other cutoffs requires refitting of the whole model. An alternative approach for the prediction of small-for-gestational-age neonates is to consider small for gestational age as a spectrum disorder whose severity is continuously reflected in both the gestational age at delivery and z score in birthweight for gestational age.

OBJECTIVE

This study aimed to develop a new competing risks model for the prediction of small-for-gestational-age neonates based on a combination of maternal demographic characteristics and medical history with sonographic estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure at 19 to 24 weeks' gestation.

STUDY DESIGN

This was a prospective observational study of 96,678 women with singleton pregnancies undergoing routine ultrasound examination at 19 to 24 weeks' gestation, which included recording of estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure. The competing risks model for small for gestational age is based on a previous joint distribution of gestational age at delivery and birthweight z score, according to maternal demographic characteristics and medical history. The likelihoods of the estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure were fitted conditionally to both gestational age at delivery and birthweight z score and modified the previous distribution, according to the Bayes theorem, to obtain an individualized posterior distribution for gestational age at delivery and birthweight z score and therefore patient-specific risks for any desired cutoffs for birthweight z score and gestational age at delivery. The model was internally validated by randomly dividing the data into a training data set, to obtain the parameters of the model, and a test data set, to evaluate the model. The discrimination and calibration of the model were also examined.

RESULTS

The estimated fetal weight was described using a regression model with an interaction term between gestational age at delivery and birthweight z score. Folded plane regression models were fitted for uterine artery pulsatility index and mean arterial pressure. The prediction of small for gestational age by maternal factors was improved by adding biomarkers for increasing degree of prematurity, higher severity of smallness, and coexistence of preeclampsia. Screening by maternal factors with estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure, predicted 41%, 56%, and 70% of small-for-gestational-age neonates with birthweights of <10th percentile delivered at ≥37, <37, and <32 weeks' gestation, at a 10% false-positive rate. The respective rates for a birthweight of <3rd percentile were 47%, 65%, and 77%. The rates in the presence of preeclampsia were 41%, 72%, and 91% for small-for-gestational-age neonates with birthweights of <10th percentile and 50%, 75%, and 92% for small-for-gestational-age neonates with birthweights of <3rd percentile. Overall, the model was well calibrated. The detection rates and calibration indices were similar in the training and test data sets, demonstrating the internal validity of the model.

CONCLUSION

The performance of screening for small-for-gestational-age neonates by a competing risks model that combines maternal factors with estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure was superior to that of screening by maternal characteristics and medical history alone.

Competing Risks Model for Prediction of Small for Gestational Age Neonates and the Role of Second Trimester Soluble Fms-like Tyrosine Kinase-1

Small for gestational age (SGA) fetuses/neonates are characterized by the increased risk for adverse outcomes that can be reduced if the condition is identified antenatally. We have recently developed a new approach in SGA prediction that considers SGA a spectrum condition that is reflected in two dimensions: gestational age at delivery and Z score in birth weight for gestational age. The new method has a better predictive ability than the traditionally used risk-scoring systems and logistic regression models. In this prospective study in 40241 singleton pregnancies, at 19–24 weeks’ gestation, we examined the potential value of the antiangiogenic soluble fms-like tyrosine kinase-1 (sFlt-1) and the ratio of sFlt-1 to the angiogenic placental growth factor (PlGF) in the prediction of SGA. We found that first, sFlt-1 did not improve the performance of screening by maternal risk factors, and second, the ratio of sFlt-1/PlGF had a worse performance than PlGF alone in the prediction of SGA. Consequently, second trimester sFlt-1 and sFlt-1/PlGF are not useful in screening for SGA.

Predictive value of PAPP-A for ectopic pregnancy and analysis of related factors

The present study was designed to analyze the expression of pregnancy-associated plasma protein-A (PAPP-A) in the serum of patients with ectopic pregnancy (EP) and related factors inducing this condition. Seventy-five patients with EP admitted to the Affiliated Hospital of Jining Medical University from January 2018 to February 2019 were selected as the research group, and another 59 healthy pregnant women of the corresponding age, gravidity and gestational week were enrolled in the control group. ELISA was employed to detect the serum expression levels of PAPP-A and inflammatory factors such as interleukin-8 (IL-8) and tumor necrosis factor-α (TNF-α). ROC was adopted to evaluate the diagnostic value of serum PAPP-A in patients with EP, and Pearson correlation coefficient was applied to analyze the correlation of PAPP-A with inflammatory factors IL-8 and TNF-α. Serum PAPP-A expression was significantly lower in EP patients than those in the control group. The area under the curve (AUC) of serum PAPP-A in diagnosing EP patients was 0.812, and the PAPP-A value in the control group was significantly higher than that of the research group at 7-8 weeks and ≥9 weeks. With regard to the expression of inflammatory factors, the research group presented markedly higher IL-8 and TNF-α levels than the control group. PAPP-A was negatively related to inflammatory factors IL-8 and TNF-α in the research group. In addition, it was revealed that patients with a history of genital surgery, salpingotomy, pelvic infection, EP or low PAPP-A expression were at high risk of EP. In conclusion, PAPP-A was revealed to be lowly expressed in the serum of EP patients, and to negatively be correlated with inflammatory factors IL-8 and TNF-α, which may serve as a useful marker for the diagnosis and prognosis of EP.

Evaluation of the RCOG guideline for the prediction of neonates that are small for gestational age and comparison with the competing risks model

OBJECTIVE

To examine the predictive performance of the relevant guideline by the Royal College of Obstetricians and Gynaecologists (RCOG) for neonates that are small for gestational age (SGA), and to compare the performance of the RCOG guideline with that of our competing risks model for SGA.

DESIGN

Prospective observational study.

SETTING

Obstetric ultrasound departments in two UK maternity hospitals.

POPULATION

A total of 96 678 women with singleton pregnancies attending for routine ultrasound examination at 19-24 weeks of gestation.

METHODS

Risks for SGA for different thresholds were computed, according to the competing risks model using maternal history, second-trimester estimated fetal weight, uterine artery pulsatility index and mean arterial pressure. The detection rates by the RCOG guideline scoring system and the competing risks model for SGA were compared, at the screen positive rate (SPR) derived from the RCOG guideline.

MAIN OUTCOME MEASURES

Small for gestational age (SGA), <10th or <3rd percentile, for different gestational age thresholds.

RESULTS

At an SPR of 22.5%, as defined by the RCOG guideline, the competing risks model predicted 56, 72 and 81% of cases of neonates that are SGA, with birthweights of <10th percentile, delivered at ≥37, <37 and <32 weeks of gestation, respectively, which were significantly higher than the respective figures of 36, 44 and 45% achieved by the application of the RCOG guideline. The respective figures for neonates that were SGA with birthweights of <3rd percentile were 66, 79, 85 and 41, 45, 44%.

CONCLUSION

The detection rate for neonates that were SGA with the competing risk approach is almost double than that obtained with the RCOG guideline.

TWEETABLE ABSTRACT

The competing risks approach for the prediction of SGA performs better than the existing RCOG guideline.

Competing-risks model for prediction of small-for-gestational-age neonate from estimated fetal weight at 19-24 weeks' gestation

OBJECTIVE

To develop further a new competing-risks model for the prediction of a small-for-gestational-age (SGA) neonate, by including second-trimester ultrasonographic estimated fetal weight (EFW).

METHODS

This was a prospective observational study in 96 678 women with singleton pregnancy undergoing routine ultrasound examination at 19-24 weeks' gestation. All pregnancies had ultrasound biometry assessment, and EFW was calculated according to the Hadlock formula. We refitted in this large dataset a previously described 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, to obtain the prior distribution. The continuous likelihood of the EFW was fitted conditionally to GA at delivery and birth-weight Z-score and modified the prior distribution, according to Bayes' theorem, to obtain individualized distributions for GA at delivery and birth-weight Z-score and therefore patient-specific risks for any cut-offs for GA at delivery and birth-weight Z-score. We assessed the discriminative ability of the model for predicting SGA with, without or independently of pre-eclampsia occurrence. A calibration study was carried out. Performance of screening was evaluated for SGA defined according to the Fetal Medicine Foundation birth-weight charts.

RESULTS

The distribution of EFW, conditional to both GA at delivery and birth-weight Z-score, was best described by a regression model. For earlier gestations, the association between EFW and birth weight was steeper. The prediction of SGA by maternal factors and EFW improved for increasing degree of prematurity and greater severity of smallness but not for coexistence of pre-eclampsia. Screening by maternal factors predicted 31%, 34% and 39% of SGA neonates with birth weight < 10^th percentile delivered at ≥ 37, < 37 and < 30 weeks' gestation, respectively, at a 10% false-positive rate, and, after addition of EFW, these rates increased to 38%, 43% and 59%, respectively; the respective rates for birth weight < 3^rd percentile were 43%, 50% and 64%. The addition of EFW improved the calibration of the model.

CONCLUSION

In the competing-risks model for prediction of SGA, the performance of screening by maternal characteristics and medical history is improved by the addition of second-trimester EFW. © 2021 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.