Abstract: Objective To develop and validate a nomogram model for predicting the risk of postpartum hemorrhage (PPH) within 24 hours after cesarean section, thus supporting timely targeted preventive measures and preoperative blood preparation. Methods In this retrospective study, we analyzed 1 000 cesarean section cases from Yijishan Hospital of Wannan Medical College. Based on postpartum blood loss, the training cohort (n=1 000) was divided into a PPH group and a non-PPH group. An additional independent validation cohort (n=207) was collected. Independent risk factors for PPH were screened using Least Absolute Shrinkage and Selection Operator (LASSO) regression and further assessed by multivariable logistic regression analysis. A nomogram prediction model was then constructed based on the identified factors. The model's performance was evaluated via both internal and external validation, including discrimination assessed by the area under the receiver operating characteristic curve (AUC), calibration evaluated using the Hosmer-Lemeshow test, and clinical utility examined by decision curve analysis (DCA). Results (1) Multivariable logistic regression analysis identified five independent predictors of PPH after cesarean section: placental abnormalities (OR=5.75), preterm birth (OR=4.41), preeclampsia (OR=2.11), gestational diabetes mellitus (GDM) (OR=2.07) and gestational hypertension (OR=1.78) (all P<0.05). These factors were incorporated into the nomogram. (2) The nomogram demonstrated an AUC of 0.80 (95%CI: 0.80~0.85) in the training cohort, with improved performance in the validation cohort (AUC: 0.87, 95%CI: 0.81~0.93). The calibration curves showed good agreement between predicted and observed probabilities. Decision curve analysis (DCA) confirmed the model's clinical utility across a wide range of risk thresholds. Conclusion This study successfully constructed a nomogram model with good predictive performance based on five factors: placenta accreta spectrum disorders, preterm birth, preeclampsia, gestational diabetes mellitus, and gestational hypertension. This model helps identify high-risk populations for postpartum hemorrhage after cesarean section, providing a tool for clinical preoperative decision-making and blood transfusion preparedness.

Key words: Postpartum hemorrhage, Cesarean section, Nomogram, Risk factors, Prediction model