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 < 10th 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 inci...

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Cervical length in the first trimester depends on maternal characteristics and a history of cervical surgery. The cervix exhibits minimal changes from 11 to 24 weeks for most women, although the shortening is more prominent in women with a history of cervical surgery or preterm delivery. First-trimester cervical length measurement can predict preterm delivery.

What are the novel findings of this work? This study presents a new competing-risks model for the prediction of a small-for-gestational-age (SGA) neonate by maternal factors and biomarkers at 11-13 weeks' gestation. This approach involves a joint prior distribution of gestational age (GA) at delivery and birth-weight Z-score, updated by the biomarkers' likelihood according to Bayes' theorem. The pattern of change, conditional to GA at delivery and birth-weight Z-score, is similar for all biomarkers and is captured by the same folded-plane regression modeling. The best biophysical predictor of preterm SGA was uterine artery pulsatility index and the best biochemical marker was placental growth factor. The prediction of SGA was consistently better for increasing degree of prematurity, greater severity of smallness, coexistence of pre-eclampsia and increasing number of biomarkers. What are the clinical implications of this work? A single continuous two-dimensional model provides early risk stratification, for any desired cutoffs 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.

Prediction for birthweight deviations is feasible using data available at the routine 11-14 weeks' examination. Delta CRL and delta nuchal translucency were significant independent predictors for both SGA and LGA.

A late third trimester ultrasound was superior in the prediction of SGA and LGA, and this improvement was more pronounced for the prediction of SGA.

This study describes a new competing-risks model based on a combination of maternal characteristics and medical history with serum pregnancy-associated plasma protein-A (PAPP-A) at 11-13 weeks' gestation for prediction of a small-for-gestational-age (SGA) neonate. PAPP-A likelihood was expressed as a continuous function of both gestational age at delivery and birth-weight Z-score in the same model. What are the clinical implications of this work? Addition of serum PAPP-A improves the performance of screening for a SGA neonate achieved by maternal factors alone and demonstrates the methodology for incorporation of further biomarkers into a single model that can be used numerous times during the course of pregnancy to predict SGA of any severity of smallness and degree of prematurity.

Obliteration of the CM appears to be the most consistent early sign of open neural tube defects. Attention should focus on either measuring the cisterna magna or simply observing the presence of four lines in the midsagittal view of the posterior brain. However, these early signs of brain herniation are not present in all abnormal cases.