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Can we improve the birth weight prediction? The effect of normal BMI using a multivariate model
R Vila-Candel1, J M Martin-Moreno2, S Alamar3
1PhD, Midwifery, Hospital Universitario de la Ribera, Spain. Director of Department of Nursing, Universidad Católica de Valencia.. rvila@hospital-ribera.com.
Objective:
The construction of a predictive model that improves the estimation of the fetal weight (EFW).
Study Design:
a comparative, descriptive study. One hundred forty pregnant women were recruited at two-stage sample in health department in Spain. They were classified in four groups depending on the pre-gestational BMI. Fetal weight at term was estimated by ultrasound at 33-35 weeks (EFW40w) by one gynecologist. A regression model was created with the variables that reacted to the newborn's weight, symphysis-fundal height (SFH), EFW40w, gestational age (GA), ferritin level and cigarettes smoked.
Results:
A multivariate model was created for the NW group to estimate the fetal weight (EFWme), resulting in R2=0.727 (p<0.001). The differences of the averages obtained between EFW40w and EFWme, with the newborn's weight were significant (p<0.001). EFWme underestimates birth weight by 0.07 g (mean error 0.53%), and EFW40w overestimates it by 300.89 g (mean error 10.12%). In order to evaluate the predictive model and verify the predictions we used the Bland-Altman analysis. The average error in estimating the birth weight with EFWme was 1.94% underestimating the result, whereas the ultrasound error overestimated the result 10.93%.
Conclusion:
The multivariate model created for the NW group improves the accuracy of the ultrasound.
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