Literature DB >> 27671013

The SaTH risk-assessment tool for the prediction of emergency cesarean section in women having induction of labor for all indications: a large-cohort based study.

Dimitrios Papoutsis1, Angeliki Antonakou2, Adam Gornall3, Chara Tzavara4, Michelle Mohajer3.   

Abstract

PURPOSE: To develop a risk-assessment model for the prediction of emergency cesarean section (CS) in women having induction of labor (IOL).
METHODS: This was an observational cohort study of women with IOL for any indication between 2007 and 2013. Women induced for stillbirths and with multiple pregnancies were excluded. The primary objective was to identify risk factors associated with CS delivery and to construct a risk-prediction tool.
RESULTS: 6169 women were identified with mean age of 28.9 years. Primiparity involved 47.1 %, CS rate was 13.3 % and post-date pregnancies were 32.4 %. Risk factors for CS were: age >30 years, BMI >25 kg/m2, primiparity, black-ethnicity, non post-date pregnancy, meconium-stained liquor, epidural analgesia, and male fetal gender. Each factor was assigned a score and with increasing scores the CS rate increased. The CS rate was 5.4 % for a score <11, while for a score ≥11 it increased to 25.0 %. The model had a sensitivity, specificity, negative predictive value and positive predictive value of 75.8, 65.1, 93.8 and 25.0 %, respectively.
CONCLUSION: We have constructed a risk-prediction tool for CS delivery in women with IOL. The risk-assessment tool for the prediction of emergency CS in induced labor has a high negative-predictive value and can provide reassurance to presumed low-risk women.

Entities:  

Keywords:  Cesarean section; Delivery; Ethnicity; Gender; Induced labor

Mesh:

Year:  2016        PMID: 27671013     DOI: 10.1007/s00404-016-4209-4

Source DB:  PubMed          Journal:  Arch Gynecol Obstet        ISSN: 0932-0067            Impact factor:   2.344


  3 in total

1.  Use of an artificial intelligence-based rule extraction approach to predict an emergency cesarean section.

Authors:  Yoko Nagayasu; Daisuke Fujita; Masahide Ohmichi; Yoichi Hayashi
Journal:  Int J Gynaecol Obstet       Date:  2021-09-06       Impact factor: 4.447

2.  Prediction of emergency cesarean section by measurable maternal and fetal characteristics.

Authors:  Ping Guan; Fei Tang; Guoqiang Sun; Wei Ren
Journal:  J Investig Med       Date:  2020-01-24       Impact factor: 2.895

3.  Maternal and fetal characteristics to predict c-section delivery: A scoring system for pregnant women.

Authors:  Rima Irwinda; Rabbania Hiksas; Angga Wiratama Lokeswara; Noroyono Wibowo
Journal:  Womens Health (Lond)       Date:  2021 Jan-Dec
  3 in total

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