Development and Validation of a Nomogram for Predicting Postpartum Hemorrhage Following Vaginal Delivery: a Retrospective Cohort Study

Background Postpartum hemorrhage (PPH) is a leading cause of maternal morbidity and mortality. Early risk stratification is vital, especially for vaginal deliveries without specific predictive tools. This study aimed to develop and validate a user-friendly nomogram to predict PPH risk in this group. The goal is to enhance clinical decision-making. Methods We conducted a retrospective cohort study of 6,386 women who had vaginal deliveries at a single center in China from January 2024 to June 2025. The cohort was randomly divided into a training set (n = 4470) and an external validation set (n = 1916). We used multivariate logistic regression to identify independent risk factors. We then created a predictive nomogram. The model was validated internally with bootstrap resampling and externally. We assessed its discrimination, calibration, and clinical utility. Results The nomogram included twelve independent risk factors, such as placental abruption, battledore or velamentous placenta, and history of uterine surgery. The model had excellent discrimination, with AUCs of 0.878 (95% CI: 0.850–0.905) for the training set and 0.882 (95% CI: 0.841–0.923) for the validation set. Calibration was good. Decision curve analysis showed high net clinical benefit. Conclusions We developed and validated a highly accurate, practical nomogram to predict PPH after vaginal delivery. This tool helps clinicians and midwives identify high-risk women early. It enables timely interventions and supports proactive management to improve maternal safety.