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Ultrasound‐based gestational‐age estimation in late pregnancy
Author(s) -
Papageorghiou A. T.,
Kemp B.,
Stones W.,
Ohuma E. O.,
Kennedy S. H.,
Purwar M.,
Salomon L. J.,
Altman D. G.,
Noble J. A.,
Bertino E.,
Gravett M. G.,
Pang R.,
Cheikh Ismail L.,
Barros F. C.,
Lambert A.,
Jaffer Y. A.,
Victora C. G.,
Bhutta Z. A.,
Villar J.
Publication year - 2016
Publication title -
ultrasound in obstetrics and gynecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.202
H-Index - 141
eISSN - 1469-0705
pISSN - 0960-7692
DOI - 10.1002/uog.15894
Subject(s) - medicine , ultrasound , obstetrics , gestational age , pregnancy , population , gestation , concordance , fetus , crown rump length , gynecology , radiology , first trimester , environmental health , biology , genetics
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 pregnancy without major complications and delivered a live singleton without congenital malformations. After other exclusions (missing measurements in GA window and outliers), the final sample comprised 4229 women. Two skeletal measures, HC and FL, produced the best GA prediction, given by the equation log e (GA) = 0.03243 × (log e (HC)) 2 + 0.001644 × FL × log e (HC) + 3.813. When FL was not available, the best equation based on HC alone was log e (GA) = 0.05970 × (log e (HC)) 2 + 0.6409 × (HC) 3 + 3.3258. The estimated uncertainty of GA prediction (half width 95% interval) was 6–7 days at 14 weeks' gestation, 12–14 days at 26 weeks' gestation and > 14 days in the third trimester. The addition of FL to the HC model led to improved prediction intervals compared with using HC alone, but no further improvement in prediction was afforded by adding AC, BPD or OFD. Equations that included other measurements (BPD, OFD and AC) did not perform better. Conclusions Among women initiating antenatal care late in pregnancy, a single set of ultrasound measurements combining HC and FL in the second trimester can be used to estimate GA with reasonable accuracy. We recommend this tool for underserved populations but considerable efforts should be implemented to improve early initiation of antenatal care worldwide. © 2016 Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.