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Evaluation of factors that predict the success rate of trial of labor after the cesarean section

BACKGROUND: For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC a...

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Autores principales: Mi, Yang, Qu, Pengfei, Guo, Na, Bai, Ruimiao, Gao, Jiayi, Ma, Zhengfeei, He, Yiping, Wang, Caili, Luo, Xiaoqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305496/
https://www.ncbi.nlm.nih.gov/pubmed/34303355
http://dx.doi.org/10.1186/s12884-021-04004-z
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author Mi, Yang
Qu, Pengfei
Guo, Na
Bai, Ruimiao
Gao, Jiayi
Ma, Zhengfeei
He, Yiping
Wang, Caili
Luo, Xiaoqin
author_facet Mi, Yang
Qu, Pengfei
Guo, Na
Bai, Ruimiao
Gao, Jiayi
Ma, Zhengfeei
He, Yiping
Wang, Caili
Luo, Xiaoqin
author_sort Mi, Yang
collection PubMed
description BACKGROUND: For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). METHODS: All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. RESULTS: A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m(2), cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. CONCLUSION: The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-021-04004-z.
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spelling pubmed-83054962021-07-28 Evaluation of factors that predict the success rate of trial of labor after the cesarean section Mi, Yang Qu, Pengfei Guo, Na Bai, Ruimiao Gao, Jiayi Ma, Zhengfeei He, Yiping Wang, Caili Luo, Xiaoqin BMC Pregnancy Childbirth Research BACKGROUND: For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). METHODS: All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. RESULTS: A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m(2), cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. CONCLUSION: The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-021-04004-z. BioMed Central 2021-07-24 /pmc/articles/PMC8305496/ /pubmed/34303355 http://dx.doi.org/10.1186/s12884-021-04004-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Mi, Yang
Qu, Pengfei
Guo, Na
Bai, Ruimiao
Gao, Jiayi
Ma, Zhengfeei
He, Yiping
Wang, Caili
Luo, Xiaoqin
Evaluation of factors that predict the success rate of trial of labor after the cesarean section
title Evaluation of factors that predict the success rate of trial of labor after the cesarean section
title_full Evaluation of factors that predict the success rate of trial of labor after the cesarean section
title_fullStr Evaluation of factors that predict the success rate of trial of labor after the cesarean section
title_full_unstemmed Evaluation of factors that predict the success rate of trial of labor after the cesarean section
title_short Evaluation of factors that predict the success rate of trial of labor after the cesarean section
title_sort evaluation of factors that predict the success rate of trial of labor after the cesarean section
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305496/
https://www.ncbi.nlm.nih.gov/pubmed/34303355
http://dx.doi.org/10.1186/s12884-021-04004-z
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