Prediction of vaginal birth after cesarean for labor dystocia by sonographic estimated fetal weight.

来自 PUBMED

作者:

Levin GTsur ATenenbaum LMor NZamir MMeyer R

展开

摘要:

To estimate the association of the weight difference between the index trial of labor after cesarean (TOLAC) sonographic estimated fetal weight (sEFW) and prior delivery birth weight with TOLAC success rate among women with previous labor dystocia and no prior vaginal delivery. A retrospective cohort study including all women with prior cesarean for labor dystocia and no prior vaginal delivery undergoing TOLAC during between March 2011 and June 2020 with a sEFW within 1 week from delivery. Overall, 168 women were included, of those 107 (63.7%) successfully delivered vaginally. The mean sEFW and mean birth weight were lower in the TOLAC success group (P = 0.010 and P = 0.013, respectively). The rate of higher sEFW in the current delivery compared with the previous delivery did not differ between study groups. The rate of higher TOLAC birth weight was lower in the TOLAC success group (odds ratio 0.30; 95% confidence interval 0.15-0.58). In multivariable regression analysis, maternal age older than 30 years, induction of labor, and higher birth weight were independently negatively associated with TOLAC success (adjusted odds ratio [95% confidence interval]: 0.27 [0.10-0.70], 0.27 [0.08-0.90], and 0.43 [0.19-0.94]; P = 0.008, P = 0.034, and P = 0.035, respectively). sEFW characteristics did not predict the success or failure of TOLAC among women with prior labor dystocia and no previous vaginal delivery.

收起

展开

DOI:

10.1002/ijgo.13946

被引量:

1

年份:

1970

SCI-Hub (全网免费下载) 发表链接

通过 文献互助 平台发起求助,成功后即可免费获取论文全文。

查看求助

求助方法1:

知识发现用户

每天可免费求助50篇

求助

求助方法1:

关注微信公众号

每天可免费求助2篇

求助方法2:

求助需要支付5个财富值

您现在财富值不足

您可以通过 应助全文 获取财富值

求助方法2:

完成求助需要支付5财富值

您目前有 1000 财富值

求助

我们已与文献出版商建立了直接购买合作。

你可以通过身份认证进行实名认证,认证成功后本次下载的费用将由您所在的图书馆支付

您可以直接购买此文献,1~5分钟即可下载全文,部分资源由于网络原因可能需要更长时间,请您耐心等待哦~

身份认证 全文购买

相似文献(156)

参考文献(0)

引证文献(1)

来源期刊

-

影响因子:暂无数据

JCR分区: 暂无

中科院分区:暂无

研究点推荐

关于我们

zlive学术集成海量学术资源,融合人工智能、深度学习、大数据分析等技术,为科研工作者提供全面快捷的学术服务。在这里我们不忘初心,砥砺前行。

友情链接

联系我们

合作与服务

©2024 zlive学术声明使用前必读