PT  - JOURNAL ARTICLE
AU  - Li, Jinke
AU  - Zhang, Dandan
AU  - Lin, Hong
AU  - Shao, Mengyuan
AU  - Wang, Xiaoxue
AU  - Chen, Xueting
AU  - Zhou, Yangzi
AU  - Song, Zixuan
TI  - Postpartum haemorrhage following vaginal delivery: a comprehensive analysis and development of predictive models for aetiological subgroups in Chinese women
AID  - 10.1136/bmjopen-2024-089734
DP  - 2025 Jan 01
TA  - BMJ Open
PG  - e089734
VI  - 15
IP  - 1
4099  - http://bmjopen.bmj.com/content/15/1/e089734.short
4100  - http://bmjopen.bmj.com/content/15/1/e089734.full
SO  - BMJ Open2025 Jan 01; 15
AB  - Objectives This study aimed to dissect the aetiological subgroups of postpartum haemorrhage (PPH) that occur after vaginal delivery in women with full-term singleton pregnancies. Our goal was to craft and validate predictive models to guide clinical decision-making and optimise resource allocation.Design A retrospective cohort study.Setting Shengjing Hospital of China Medical University, Liaoning Maternal and Child Health Hospital, and Shenyang Women’s and Children’s Hospital.Participants 29 842 women who underwent vaginal delivery were enrolled in the study across three hospitals from 2016 to 2022.Primary outcome measures PPH, categorised into uterine atony (UA), placental factors (PF), cervical trauma (CT), and coagulation abnormalities (CA) by aetiology.Results The logistic regression for overall PPH and UA-PPH showcased high discrimination (AUCs of 0.807 and 0.794, respectively), coupled with commendable calibration and DCA-assessed clinical utility, culminating in the development of a nomogram for risk prediction. The PF-PPH model exhibited a modest AUC of 0.739, while the CT-PPH and CA-PPH models demonstrated suboptimal clinical utility and calibration.Conclusion The study identified factors associated with PPH and developed models with good performance for overall PPH and UA-PPH. The nomogram offers a valuable tool for risk prediction. However, models for PF-PPH, CT-PPH, and CA-PPH require further refinement. Future research should focus on larger samples and multicentre validation for enhanced model generalisability.Data are available upon reasonable request. The data that support the findings of this study are available from the corresponding author upon reasonable request.