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Prediction of labor outcome using serial transperineal ultrasound in the first stage of labor.

作者信息

Chor Chung Ming, Poon Liona Chiu Yee, Leung Tak Yeung

机构信息

a Department of Obstetrics and Gynaecology , Prince of Wales Hospital, The Chinese University of Hong Kong , New Territories , Hong Kong SAR.

出版信息

J Matern Fetal Neonatal Med. 2019 Jan;32(1):31-37. doi: 10.1080/14767058.2017.1369946. Epub 2017 Oct 3.

Abstract

OBJECTIVE

The objective of this study is to evaluate the feasibility of predicting labor outcome using serial transperineal ultrasound (TPU) in the early active phase of labor.

METHODS

This is a single center prospective observational study in a tertiary obstetrics unit in Hong Kong. Nulliparous women carrying singleton pregnancy at the onset of active phase of labor were recruited. Serial 3D volumes by TPU were acquired and then repeated after 1 and 2 h, which were subsequently analyzed for fetal head symphyseal distance (HSD), angle of progression (AoP), and fetal head progression distance (PD). The women were classified into two groups, according to whether they had vaginal delivery or cesarean section (CS) for reasons other than non-progressive labor (Group A) or CS for non-progressive labor (Group B). The TPU parameters were then compared between the groups.

RESULTS

Group A consisted of 74 (60.0%) women with vaginal delivery, 27 (21.8%) with instrumental delivery and 3 (2.4%) CS for reasons other than non-progressive labor, while Group B consisted of 20 (16.1%) women who had a CS for non-progressive labor. Group B had a significant slower hourly progression rate of AoP, HSD, and PD at 1-h and 2-h from the initial assessment, compared with Group A. Multivariate logistic regression analysis demonstrated that PD progression at 2-h and the use of oxytocin were significant independent predictors for CS for non-progressive labor.

CONCLUSION

It is feasible to predict CS for non-progressive labor in the early active phase of labor by a slower rate of fetal head descent determined by TPU.

摘要

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