OBJECTIVE: To develop a model based on maternal characteristics and medical history (maternal factors) for the prediction of delivery of large-for-gestational-age (LGA) neonates, and to examine the potential value of first-, second- and third-trimester fetal biometry and biomarkers in improving such a model. METHODS: This was a screening study in 76 300, 54 999, 25 727 and 6181 singleton pregnancies at 11-13, 19-24, 30-34 and 35-37 weeks' gestation, respectively. The a-priori risk for LGA with birth weight > 95(th) percentile (LGA > 95(th) ) was calculated using multivariable logistic regression analysis to determine which of the maternal factors had a significant contribution. Regression analysis was then used to determine whether screening by a combination of maternal factors, fetal biometry and various biophysical and biochemical markers had significant contribution in predicting delivery of LGA neonates. RESULTS: The likelihood of LGA > 95(th) increased with increasing maternal weight and height and was lower in women of Afro-Caribbean and South Asian racial origins, in cigarette smokers and in nulliparous women. The risk was higher in women with pre-existing diabetes mellitus Type I and lower in those with chronic hypertension. In parous women, the risk increased with birth-weight Z-score in previous pregnancy and prior history of gestational diabetes and decreased with interpregnancy interval. Screening by maternal factors at 11-13 weeks predicted 32%, 44% and 60% of LGA > 95(th) at false-positive rates (FPRs) of 5%, 10% and 20%, respectively. With the addition of fetal biometry, the detection rates improved to 37%, 51% and 68% at 19-24 weeks, 50%, 65% and 81% at 30-34 weeks and 60%, 73% and 85% at 35-37 weeks at FPRs of 5%, 10% and 20%, respectively. The addition of biomarkers did not improve the detection rates achieved when screening by a combination of maternal factors and fetal biometry. CONCLUSION: Combined screening by maternal factors and fetal biometry can predict a high proportion of pregnancies that will deliver LGA neonates.
OBJECTIVE: To develop a model based on maternal characteristics and medical history (maternal factors) for the prediction of delivery of large-for-gestational-age (LGA) neonates, and to examine the potential value of first-, second- and third-trimester fetal biometry and biomarkers in improving such a model. METHODS: This was a screening study in 76 300, 54 999, 25 727 and 6181 singleton pregnancies at 11-13, 19-24, 30-34 and 35-37 weeks' gestation, respectively. The a-priori risk for LGA with birth weight > 95(th) percentile (LGA > 95(th) ) was calculated using multivariable logistic regression analysis to determine which of the maternal factors had a significant contribution. Regression analysis was then used to determine whether screening by a combination of maternal factors, fetal biometry and various biophysical and biochemical markers had significant contribution in predicting delivery of LGA neonates. RESULTS: The likelihood of LGA > 95(th) increased with increasing maternal weight and height and was lower in women of Afro-Caribbean and South Asian racial origins, in cigarette smokers and in nulliparous women. The risk was higher in women with pre-existing diabetes mellitus Type I and lower in those with chronic hypertension. In parous women, the risk increased with birth-weight Z-score in previous pregnancy and prior history of gestational diabetes and decreased with interpregnancy interval. Screening by maternal factors at 11-13 weeks predicted 32%, 44% and 60% of LGA > 95(th) at false-positive rates (FPRs) of 5%, 10% and 20%, respectively. With the addition of fetal biometry, the detection rates improved to 37%, 51% and 68% at 19-24 weeks, 50%, 65% and 81% at 30-34 weeks and 60%, 73% and 85% at 35-37 weeks at FPRs of 5%, 10% and 20%, respectively. The addition of biomarkers did not improve the detection rates achieved when screening by a combination of maternal factors and fetal biometry. CONCLUSION: Combined screening by maternal factors and fetal biometry can predict a high proportion of pregnancies that will deliver LGA neonates.
Authors: Matias C Vieira; Lesley M E McCowan; Alexandra Gillett; Lucilla Poston; Elaine Fyfe; Gustaaf A Dekker; Philip N Baker; James J Walker; Louise C Kenny; Dharmintra Pasupathy Journal: PLoS One Date: 2017-06-01 Impact factor: 3.240
Authors: Lje Meertens; Ljm Smits; Smj van Kuijk; R Aardenburg; Ima van Dooren; J Langenveld; I M Zwaan; Mea Spaanderman; Hcj Scheepers Journal: BJOG Date: 2019-01-17 Impact factor: 6.531