Comparison of fetal size standards obtained with conventional methods and individualized assessment: the effect of adjusting for differences in growth potential

Abstract

Abstract Objective: Detection of fetal growth restriction depends on the biometric standard definitions of normal variability. We examined the impact of correcting for differences in fetal growth potential on the variability of third-trimester size standards. Methods: Size standards, corrected differences in growth potential using Individualized Growth Assessment [IGA], were obtained in 119 pregnancies with normal neonatal growth outcomes. Using the same cohort, a second set of size standards, without these corrections, were determined with mixed modeling [IGA Cross-sectional]. An independent set of size standards, obtained by quantile regression in a population-based sample of 1387 pregnant women [World Health Organization (WHO)], was also evaluated. The anatomical parameters studied included BPD, HC, AC, FDL, THC, HDL, ArmC and EWT whenever possible. The variability measures compared were percent deviation reference range [IGA] or twice the coefficient of variation [IGA Cross-sectional, WHO] at weekly time points between 28 and 38 weeks, menstrual age. Results: All six IGA variabilities were significantly smaller [range: 19–60%] when IGA and IGA-cross-sectional size standards were compared. Similar IGA-WHO comparisons showed that the IGA variabilities for 5 of 6 anatomical parameters were significantly smaller [range: 26–32%; exception: FDL (5.8%)]. Comparisons of cross-sectional size standards gave variable results depending on the anatomical parameter studied. Conclusions: Third-trimester variability measures, based on IGA, were consistently lower than those obtained with conventional cross-sectional methods in normal pregnancies. These results were found when the identical sample was used in both IGA and cross-sectional analyses. Decreased variability can improve the sensitivity of IGA for detecting restricted growth and may be partly responsible for its ability to identify different types of growth abnormalities earlier in pregnancy.