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Epidemiology of uterine rupture among pregnant women in China and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study
OBJECTIVES: To describe the epidemiology of uterine rupture in China from 2015 to 2016 and to build a prediction model for uterine rupture in women with a scarred uterus. SETTING: A multicentre cross-sectional survey conducted in 96 hospitals across China in 2015–2016. PARTICIPANTS: Our survey initi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8634000/ https://www.ncbi.nlm.nih.gov/pubmed/34845076 http://dx.doi.org/10.1136/bmjopen-2021-054540 |
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author | Zhan, Wenqiang Zhu, Jing Hua, Xiaolin Ye, Jiangfeng Chen, Qian Zhang, Jun |
author_facet | Zhan, Wenqiang Zhu, Jing Hua, Xiaolin Ye, Jiangfeng Chen, Qian Zhang, Jun |
author_sort | Zhan, Wenqiang |
collection | PubMed |
description | OBJECTIVES: To describe the epidemiology of uterine rupture in China from 2015 to 2016 and to build a prediction model for uterine rupture in women with a scarred uterus. SETTING: A multicentre cross-sectional survey conducted in 96 hospitals across China in 2015–2016. PARTICIPANTS: Our survey initially included 77 789 birth records from hospitals with 1000 or more deliveries per year. We excluded 2567 births less than 24 gestational weeks or unknown and 1042 births with unknown status of uterine rupture, leaving 74 180 births for the final analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Complete and incomplete uterine rupture and the risk factors, and a prediction model for uterine rupture in women with scarred uterus (assigned each birth a weight based on the sampling frame). RESULTS: The weighted incidence of uterine rupture was 0.18% (95% CI 0.05% to 0.23%) in our study population during 2015 and 2016. The weighted incidence of uterine rupture in women with scarred and intact uterus was 0.79% (95% CI 0.63% to 0.91%) and 0.05% (95% CI 0.02% to 0.13%), respectively. Younger or older maternal age, prepregnancy diabetes, overweight or obesity, complications during pregnancy (hypertensive disorders in pregnancy and gestational diabetes), low education, repeat caesarean section (≥2), multiple abortions (≥2), assisted reproductive technology, placenta previa, induce labour, fetal malpresentation, multiple pregnancy, anaemia, high parity and antepartum stillbirth were associated with an increased risk of uterine rupture. The prediction model including eight variables (OR >1.5) yielded an area under the curve (AUC) of 0.812 (95% CI 0.793 to 0.836) in predicting uterine rupture in women with scarred uterus with sensitivity and specificity of 77.2% and 69.8%, respectively. CONCLUSIONS: The incidence of uterine rupture was 0.18% in this population in 2015–2016. The predictive model based on eight easily available variables had a moderate predictive value in predicting uterine rupture in women with scarred uterus. Strategies based on predictions may be considered to further reduce the burden of uterine rupture in China. |
format | Online Article Text |
id | pubmed-8634000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-86340002021-12-10 Epidemiology of uterine rupture among pregnant women in China and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study Zhan, Wenqiang Zhu, Jing Hua, Xiaolin Ye, Jiangfeng Chen, Qian Zhang, Jun BMJ Open Obstetrics and Gynaecology OBJECTIVES: To describe the epidemiology of uterine rupture in China from 2015 to 2016 and to build a prediction model for uterine rupture in women with a scarred uterus. SETTING: A multicentre cross-sectional survey conducted in 96 hospitals across China in 2015–2016. PARTICIPANTS: Our survey initially included 77 789 birth records from hospitals with 1000 or more deliveries per year. We excluded 2567 births less than 24 gestational weeks or unknown and 1042 births with unknown status of uterine rupture, leaving 74 180 births for the final analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Complete and incomplete uterine rupture and the risk factors, and a prediction model for uterine rupture in women with scarred uterus (assigned each birth a weight based on the sampling frame). RESULTS: The weighted incidence of uterine rupture was 0.18% (95% CI 0.05% to 0.23%) in our study population during 2015 and 2016. The weighted incidence of uterine rupture in women with scarred and intact uterus was 0.79% (95% CI 0.63% to 0.91%) and 0.05% (95% CI 0.02% to 0.13%), respectively. Younger or older maternal age, prepregnancy diabetes, overweight or obesity, complications during pregnancy (hypertensive disorders in pregnancy and gestational diabetes), low education, repeat caesarean section (≥2), multiple abortions (≥2), assisted reproductive technology, placenta previa, induce labour, fetal malpresentation, multiple pregnancy, anaemia, high parity and antepartum stillbirth were associated with an increased risk of uterine rupture. The prediction model including eight variables (OR >1.5) yielded an area under the curve (AUC) of 0.812 (95% CI 0.793 to 0.836) in predicting uterine rupture in women with scarred uterus with sensitivity and specificity of 77.2% and 69.8%, respectively. CONCLUSIONS: The incidence of uterine rupture was 0.18% in this population in 2015–2016. The predictive model based on eight easily available variables had a moderate predictive value in predicting uterine rupture in women with scarred uterus. Strategies based on predictions may be considered to further reduce the burden of uterine rupture in China. BMJ Publishing Group 2021-11-29 /pmc/articles/PMC8634000/ /pubmed/34845076 http://dx.doi.org/10.1136/bmjopen-2021-054540 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Obstetrics and Gynaecology Zhan, Wenqiang Zhu, Jing Hua, Xiaolin Ye, Jiangfeng Chen, Qian Zhang, Jun Epidemiology of uterine rupture among pregnant women in China and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study |
title | Epidemiology of uterine rupture among pregnant women in China and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study |
title_full | Epidemiology of uterine rupture among pregnant women in China and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study |
title_fullStr | Epidemiology of uterine rupture among pregnant women in China and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study |
title_full_unstemmed | Epidemiology of uterine rupture among pregnant women in China and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study |
title_short | Epidemiology of uterine rupture among pregnant women in China and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study |
title_sort | epidemiology of uterine rupture among pregnant women in china and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study |
topic | Obstetrics and Gynaecology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8634000/ https://www.ncbi.nlm.nih.gov/pubmed/34845076 http://dx.doi.org/10.1136/bmjopen-2021-054540 |
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