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已发表论文
单胎足月肥胖孕妇引产失败后剖宫产结局预测模型的构建
Authors Feng Y , Luan Y, Zhou L, Yin C
Received 12 September 2025
Accepted for publication 24 November 2025
Published 9 December 2025 Volume 2025:17 Pages 5277—5286
DOI https://doi.org/10.2147/IJWH.S567166
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Matteo Frigerio
Yi Feng,1 Yingyi Luan,2 Li Zhou,1 Chenghong Yin2
1Department of Obstetrics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China; 2Department of Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China
Correspondence: Li Zhou, Email zhouliyz@ccmu.edu.cn Chenghong Yin, Email yinchh@ccmu.edu.cn
Background: Maternal obesity is a major global public health issue. Induction of labor (IoL) is common in obstetrics. Both the rate of IoL and induction failure rate are higher in obese women than in those with normal body mass index (BMI). This study aimed to construct a model to predict the failed IoL with a caesarean section (CS) as the outcome among term singleton obese pregnant women.
Methods: Electric health records of term singleton obese pregnant women were retrieved from Beijing Obstetrics and Gynecology Hospital from February 2018 to December 2022 (discovery cohort), and January to December 2024 (validation cohort). CS was defined as the outcome of failed IoL. Univariate logistic regression analysis was used to identify the risk factors, and multivariate logistic regression (stepwise and backward) was used to construct the prediction model. Performance was assessed using the area under the receiver operating characteristic curve (AUC) and internally validated with the validation cohort.
Results: Pre-pregnant BMI (OR 1.074, 95% CI 1.016– 1.135, p=0.011), gestational weight gain (OR 1.033, 95% CI 1.006– 1.062) and neonatal weight (OR 1.673, 95% CI 1.175– 2.380, p=0.004) were identified as the risk factors of a failed IoL, whereas gravidity (OR 0.706, 95% CI 0.592– 0.844, p< 0.001), parity (OR 0.105, 95% CI 0.055– 0.198, p< 0.001), height (OR 0.935, 95% CI 0.907– 0.963, p< 0.001) and Bishop score (OR 0.892, 95% CI 0.799– 0.996, p=0.042) as the protective factors. The final model included parity, height, pre-pregnant BMI, gestational weight gain, Bishop score, and neonatal weight, achieving an AUC of 0.752 (95% CI, 0.717– 0.788) in the discovery cohort and 0.826 (95% CI 0.757– 894) in the validation cohort.
Conclusion: This practical model predicts the failed IoL among term singleton obese women using routinely available variables. It may support obstetric decision-making, enhance counseling, and improve resource planning for women at increased risk of intra- and postpartum complications.
Keywords: induction of labor, cesarean section, obesity, prediction model
1Department of Obstetrics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China; 2Department of Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China
Correspondence: Li Zhou, Email zhouliyz@ccmu.edu.cn Chenghong Yin, Email yinchh@ccmu.edu.cn
Background: Maternal obesity is a major global public health issue. Induction of labor (IoL) is common in obstetrics. Both the rate of IoL and induction failure rate are higher in obese women than in those with normal body mass index (BMI). This study aimed to construct a model to predict the failed IoL with a caesarean section (CS) as the outcome among term singleton obese pregnant women.
Methods: Electric health records of term singleton obese pregnant women were retrieved from Beijing Obstetrics and Gynecology Hospital from February 2018 to December 2022 (discovery cohort), and January to December 2024 (validation cohort). CS was defined as the outcome of failed IoL. Univariate logistic regression analysis was used to identify the risk factors, and multivariate logistic regression (stepwise and backward) was used to construct the prediction model. Performance was assessed using the area under the receiver operating characteristic curve (AUC) and internally validated with the validation cohort.
Results: Pre-pregnant BMI (OR 1.074, 95% CI 1.016– 1.135, p=0.011), gestational weight gain (OR 1.033, 95% CI 1.006– 1.062) and neonatal weight (OR 1.673, 95% CI 1.175– 2.380, p=0.004) were identified as the risk factors of a failed IoL, whereas gravidity (OR 0.706, 95% CI 0.592– 0.844, p< 0.001), parity (OR 0.105, 95% CI 0.055– 0.198, p< 0.001), height (OR 0.935, 95% CI 0.907– 0.963, p< 0.001) and Bishop score (OR 0.892, 95% CI 0.799– 0.996, p=0.042) as the protective factors. The final model included parity, height, pre-pregnant BMI, gestational weight gain, Bishop score, and neonatal weight, achieving an AUC of 0.752 (95% CI, 0.717– 0.788) in the discovery cohort and 0.826 (95% CI 0.757– 894) in the validation cohort.
Conclusion: This practical model predicts the failed IoL among term singleton obese women using routinely available variables. It may support obstetric decision-making, enhance counseling, and improve resource planning for women at increased risk of intra- and postpartum complications.
Keywords: induction of labor, cesarean section, obesity, prediction model