Ultrasound‐based gestational‐age estimation in late pregnancy

作者：Aris T. Papageorghiou, B. Kemp, William Stones, Eric O. Ohuma, Stephen Kennedy, Manorama Purwar, L. J. Salomon, Douglas G. Altman, J. Alison Noble, Enrico Bertino, M. G. Gravett, Ran Pang, Leila Cheikh Ismail, Fernando C. Barros, Ann Lambert, Yasmin A. Jaffer, César G. Victora, Zulfiqar A Bhutta, José Villar · 发表于：Ultrasound in Obstetrics and Gynecology · 年份：2016 · DOI：10.1002/uog.15894 · 被引用次数：180 · 研究领域：Pregnancy and preeclampsia studies、Gestational Diabetes Research and Management、Preterm Birth and Chorioamnionitis

ABSTRACT Objective Accurate gestational‐age (GA) estimation, preferably by ultrasound measurement of fetal crown–rump length before 14 weeks' gestation, is an important component of high‐quality antenatal care. The objective of this study was to determine how GA can best be estimated by fetal ultrasound for women who present for the first time late in pregnancy with uncertain or unknown menstrual dates. Methods INTERGROWTH‐21 st was a large, prospective, multicenter, population‐based project performed in eight geographically defined urban populations. One of its principal components, the Fetal Growth Longitudinal Study, aimed to develop international fetal growth standards. Each participant had their certain menstrual dates confirmed by first‐trimester ultrasound examination. Fetal head circumference (HC), biparietal diameter (BPD), occipitofrontal diameter (OFD), abdominal circumference (AC) and femur length (FL) were measured every 5 weeks from 14 weeks' gestation until delivery. For each participant, a single, randomly selected ultrasound examination was used to explore all candidate biometric variables and permutations to build models to predict GA. Regression equations were ranked based upon minimization of the mean prediction error, goodness of fit and model complexity. An automated machine learning algorithm, the Genetic Algorithm, was adapted to evaluate > 64 000 potential polynomial equations as predictors. Results Of the 4607 eligible women, 4321 (94%) had a preg...