Article ID Journal Published Year Pages File Type
6145748 American Journal of Obstetrics and Gynecology 2014 8 Pages PDF
Abstract

ObjectiveThe objective of the study was to combine early, direct assessment of the placenta with indirect markers of placental development to identify pregnancies at greatest risk of delivering small-for-gestational age infants (SGA10).Study DesignWe prospectively collected 3-dimensional ultrasound volume sets, uterine artery pulsatility index, and maternal serum of singleton pregnancies at 11-14 weeks. Placental volume (PV), quotient (placental quotient [PQ] = PV/gestational age), mean placental diameter (MPD) and chorionic diameters, and the placental morphology index (PMI = MPD/PQ and adjusts the lateral placental dimensions for quotient) were measured offline. Maternal serum was assayed for placental growth factor and placental protein-13. These variables were evaluated as predictors of SGA10.ResultsOf the 578 pregnancies included in the study, 56 (9.7%) delivered SGA10. SGA10 pregnancies had a significantly smaller PV, PQ, MPD, and mean placental diameter and higher PMI compared with normal pregnancies (P < .001 for each). Each placental measure remained significantly associated with SGA10 after adjusting for confounders and significantly improved the performance of the model using clinical variables alone (P < .04 for each) with adjusted areas under the curve ranging from 0.71 to 0.74. Uterine artery pulsatility index did not remain significantly associated with SGA10 after adjusting for confounders (P = .06). Placental growth factor was significantly lower in SGA10 pregnancies (P = .02) and remained significant in adjusted models but failed to significantly improve the predictive performance of the models as measured by area under the curve (P > .3). Placental protein-13 was not associated with SGA10 (P = .99).ConclusionDirect assessment of placental size and shape with 3-dimensional ultrasound can serve as the foundation upon which to build a multivariable model for the early prediction of SGA.

Related Topics
Health Sciences Medicine and Dentistry Medicine and Dentistry (General)
Authors
, , , ,