Ultrasound-based gestational-age estimation in late pregnancy

A. T. Papageorghiou, B. Kemp, W. Stones, E. O. Ohuma, S. H. Kennedy, M. Purwar, L. J. Salomon, D. G. Altman, J. A. Noble, E. Bertino, M. G. Gravett, R. Pang, L. Cheikh Ismail, F. C. Barros, A. Lambert, Y. A. Jaffer, C. G. Victora, Z. A. Bhutta, J. Villar, M. KatzM. K. Bhan, C. Garza, S. Zaidi, A. Langer, P. M. Rothwell, D. Weatherall, F. Burton, M. Carvalho, W. C. Chumlea, P. Lumbiganon, J. Rivera, M. Shorten, L. Hoch, H. E. Knight, C. Cosgrove, I. Blakey, F. Roseman, N. Kunnawar, S. H. Gu, J. H. Wang, M. H. Wu, M. Domingues, P. Gilli, L. Juodvirsiene, N. Musee, H. Al-Jabri, S. Waller, D. Muninzwa, D. Yellappan, A. Carter, for the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st)

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

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-21st 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 loge(GA) = 0.03243 × (loge(HC))2 + 0.001644 × FL × loge(HC) + 3.813. When FL was not available, the best equation based on HC alone was loge(GA) = 0.05970 × (loge(HC))2 + 0.000000006409 × (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.

Original languageEnglish (US)
Pages (from-to)719-726
Number of pages8
JournalUltrasound in Obstetrics and Gynecology
Volume48
Issue number6
DOIs
StatePublished - Dec 1 2016
Externally publishedYes

Keywords

  • dating
  • fetal growth
  • gestational age

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Reproductive Medicine
  • Radiology Nuclear Medicine and imaging
  • Obstetrics and Gynecology

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