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Ultrasonography. 2019 Jan;38(1):50-57. English. Original Article. https://doi.org/10.14366/usg.18004
Hiwale S , Misra H , Ulman S .
Philips Research India, Philips Innovation Campus, Bengaluru, India. sujit_hiwale@outlook.com
Abstract

Purpose

Existing ultrasound-based fetal weight estimation models have been shown to have high errors when used in the Indian population. Therefore, the primary objective of this study was to develop Indian population-based models for fetal weight estimation, and the secondary objective was to compare their performance against established models.

Methods

Retrospectively collected data from 173 cases were used in this study. The inclusion criteria were a live singleton pregnancy and an interval from the ultrasound scan to delivery of ≤7 days. Multiple stepwise regression (MSR) and lasso regression methods were used to derive fetal weight estimation models using a randomly selected training group (n=137) with cross-products of abdominal circumference (AC), biparietal diameter (BPD), head circumference (HC), and femur length (FL) as independent variables. In the validation group (n=36), the bootstrap method was used to compare the performance of the new models against 12 existing models.

Results

The equations for the best-fit models obtained using the MSR and lasso methods were as follows: log₁₀(EFW)=2.7843700+0.0004197(HC×AC)+0.0008545(AC×FL) and log₁₀(EFW)=2.38 70211110+0.0074323216(HC)+0.0186555940(AC)+0.0013463735(BPD×FL)+0.0004519715 (HC×FL), respectively. In the training group, both models had very low systematic errors of 0.01% (±7.74%) and −0.03% (±7.70%), respectively. In the validation group, the performance of these models was found to be significantly better than that of the existing models.

Conclusion

The models presented in this study were found to be superior to existing models of ultrasound-based fetal weight estimation in the Indian population. We recommend a thorough evaluation of these models in independent studies.

Copyright © 2019. Korean Association of Medical Journal Editors.