Risk Factors and a Preliminary Clinical Prediction Model for Postpartum Hemorrhage or the Need for Hemostatic Intervention in Twin Cesarean Deliveries.

Objective: Postpartum hemorrhage (PPH) remains the leading cause of maternal mortality globally. Twin pregnancies demonstrate significantly higher PPH risk compared to singleton gestations. The predictive capacity for PPH in twin pregnancies requires improvement. This study conducted a retrospective analysis of clinical cases involving twin pregnancies delivered by cesarean section to determine risk factors for PPH or required hemostatic interventions and to construct a preliminary prediction model. The findings aim to enhance clinical anticipation and management of PPH in twin cesarean deliveries. Methods: This retrospective study analyzed twin pregnancies delivered by cesarean section with live births at Mianyang Central Hospital from March 6, 2020 to March 6, 2025. The primary outcome was postpartum hemorrhage, defined as a blood loss of≥1000 mL within 24 hours after delivery, or the requirement for hemostatic intervention. Risk factors were identified using logistic regression analysis. Based on these factors, a preliminary clinical prediction model was developed, visualized as a nomogram, and internally validated. Results: Multivariate analysis revealed six independent risk factors (all P<0.05): first twin birth weight (P=0.018), second twin amniotic fluid depth (P=0.042), prolonged operative time (P=0.008), employed status (P=0.016), placenta implantation (P=0.009), and general anesthesia (P=0.019). We developed a clinical nomogram incorporating these six independent risk factors. The prediction model demonstrated moderate discriminative ability in the training set (AUC=0.722, 95% CI: 0.656-0.789) and clinically relevant predictive value in the validation set (AUC=0.613, 95% CI: 0.503-0.722). Conclusion: This study successfully identified independent risk factors for postpartum hemorrhage or the need for hemostatic intervention in twin cesarean deliveries and preliminarily established a clinical nomogram prediction model, aiming to facilitate early detection and management of high-risk populations for bleeding in twin cesarean sections.