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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. [ABSTRACT FROM AUTHOR]

This article discusses a study that evaluated the performance of a machine learning model for predicting pre-eclampsia (PE) in a large Asian population. The study used data from 10,935 women with singleton pregnancies and applied the machine learning model for first trimester screening. The results showed that after adjusting for biochemical marker analyzers, the predictive performance of the model was comparable to that of the Fetal Medicine Foundation competing risk model in Asian populations. This study suggests that the machine learning model could be a useful tool for predicting PE in Asian populations. [Extracted from the article]

A new hybrid spectral/finite difference method can analyze three-dimensional unsteady viscous flows between concentric, transversely oscillating cylinders placed in a laminar flow. The method estimates the fluid-dynamic forces by adding the circumferential shear stresses with the unsteady pressure acting on the cylinder. The new approach incorporates a partial spectral collocation framework using Chebyshev polynomials. A semi-analytical method for validating the hybrid spectral method is discussed.