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已发表论文
非初产妇阴道分娩产后出血预测模型的开发与验证
Received 28 April 2025
Accepted for publication 8 September 2025
Published 16 September 2025 Volume 2025:18 Pages 3079—3088
DOI https://doi.org/10.2147/RMHP.S537335
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Kyriakos Souliotis
Cuiping Zhou, Rongsheng Zhou
Department of Obstetrics and Gynecology, Hefei Third People’s Hospital, Hefei, Anhui, 230000, People’s Republic of China
Correspondence: Rongsheng Zhou, Department of Obstetrics and Gynecology, Hefei Third People’s Hospital, No. 204, Wangjiang East Road, Baohe District, Hefei, Anhui, People’s Republic of China, Email ZCP13956951013@163.com
Objective: To analyze the risk factors for postpartum hemorrhage in non-primary women giving birth naturally and construct a predictive model.
Methods: Retrospective analysis of the clinical data of 436 second-time mothers who underwent natural childbirth in the Department of Obstetrics, Hefei Third People’s Hospital. The cases were divided into a bleeding group (n=41) and a non-bleeding group (n=395) based on whether there was bleeding greater than 500 mL within 24 hours after delivery. Independent risk factors were established through univariate and multivariate analyses, a logistic regression model was established, and bootstrap resampling was used to internally verify and assess the calibration of the model.
Results: Among the 436 cases of maternal delivery included in the study, 41 (9.40%) were cases of postpartum hemorrhage. The results of the multifactor analysis indicated that in vitro fertilization, body mass index (BMI), episiotomy, placenta previa, newborn weight, and manual removal of the placenta were independent risk factors for postpartum hemorrhage (PPH) in non-primary mothers. Subsequently, a model was constructed, exhibiting an AUC value of 0.839 (95% CI: 0.758– 0.919). The Hosmer-Lemeshow test of the calibration curve yielded a chi-squared value of 8.1013 and a P-value of 0.4236, indicating an excellent performance of the DCA curve.
Conclusion: In vitro fertilization, body mass index (BMI), episiotomy, placenta previa, newborn weight, and manual removal of the placenta are identified as independent risk factors for postpartum hemorrhage (PPH) in non-primary mothers. The constructed logistic regression model is capable of more accurately identifying high-risk PPH mothers and providing a reference basis for individualized interventions.
Plain Language Summary: This study analyzed the risk factors for postpartum hemorrhage (PPH) in second-time mothers who gave birth naturally and developed a predictive model. Data from 436 cases were reviewed, identifying in vitro fertilization, BMI, episiotomy, placenta previa, newborn weight, and manual removal of the placenta as independent risk factors. A logistic regression model was constructed, showing good accuracy (AUC: 0.839) and calibration. The model can help identify high-risk PPH cases and guide personalized interventions.
Keywords: non-primary mother, postpartum hemorrhage, natural delivery, prediction model
Department of Obstetrics and Gynecology, Hefei Third People’s Hospital, Hefei, Anhui, 230000, People’s Republic of China
Correspondence: Rongsheng Zhou, Department of Obstetrics and Gynecology, Hefei Third People’s Hospital, No. 204, Wangjiang East Road, Baohe District, Hefei, Anhui, People’s Republic of China, Email ZCP13956951013@163.com
Objective: To analyze the risk factors for postpartum hemorrhage in non-primary women giving birth naturally and construct a predictive model.
Methods: Retrospective analysis of the clinical data of 436 second-time mothers who underwent natural childbirth in the Department of Obstetrics, Hefei Third People’s Hospital. The cases were divided into a bleeding group (n=41) and a non-bleeding group (n=395) based on whether there was bleeding greater than 500 mL within 24 hours after delivery. Independent risk factors were established through univariate and multivariate analyses, a logistic regression model was established, and bootstrap resampling was used to internally verify and assess the calibration of the model.
Results: Among the 436 cases of maternal delivery included in the study, 41 (9.40%) were cases of postpartum hemorrhage. The results of the multifactor analysis indicated that in vitro fertilization, body mass index (BMI), episiotomy, placenta previa, newborn weight, and manual removal of the placenta were independent risk factors for postpartum hemorrhage (PPH) in non-primary mothers. Subsequently, a model was constructed, exhibiting an AUC value of 0.839 (95% CI: 0.758– 0.919). The Hosmer-Lemeshow test of the calibration curve yielded a chi-squared value of 8.1013 and a P-value of 0.4236, indicating an excellent performance of the DCA curve.
Conclusion: In vitro fertilization, body mass index (BMI), episiotomy, placenta previa, newborn weight, and manual removal of the placenta are identified as independent risk factors for postpartum hemorrhage (PPH) in non-primary mothers. The constructed logistic regression model is capable of more accurately identifying high-risk PPH mothers and providing a reference basis for individualized interventions.
Plain Language Summary: This study analyzed the risk factors for postpartum hemorrhage (PPH) in second-time mothers who gave birth naturally and developed a predictive model. Data from 436 cases were reviewed, identifying in vitro fertilization, BMI, episiotomy, placenta previa, newborn weight, and manual removal of the placenta as independent risk factors. A logistic regression model was constructed, showing good accuracy (AUC: 0.839) and calibration. The model can help identify high-risk PPH cases and guide personalized interventions.
Keywords: non-primary mother, postpartum hemorrhage, natural delivery, prediction model