A nomogram model for analyzing risk factors of postpartum hemorrhage in twin pregnancies with cesarean section

BACKGROUND: The rising prevalence of twin pregnancies and cesarean sections (CS) is a significant risk factor for postpartum hemorrhage (PPH), contributing to increased maternal morbidity and mortality globally. Establishing a reliable risk assessment model for PPH could facilitate timely identification and intervention, thereby improving maternal outcomes. This study aimed to develop an effective nomogram for predicting PPH in twin pregnancies undergoing CS. METHODS: This retrospective study analyzed data from 1704 women with twin pregnancies who delivered by CS between 2016 and 2022 at the Second Affiliated Hospital of Wenzhou Medical University, China. To identify risk factors for PPH after CS in twin pregnancies, univariate analysis and LASSO (least absolute shrinkage and selection operator) regression were employed. A nomogram was subsequently developed based on the results of binary logistic regression analysis and the Akaike information criterion (AIC). Model validation was performed using bootstrap resampling, with performance assessed through discrimination (receiver operating characteristic curve), calibration (calibration plot and Hosmer-Lemeshow test), and clinical utility (decision cur analysis, DCA). RESULTS: The nomogram for PPH was created using eight best predictor variables, namely, parity, assisted reproductive technology (ART), preeclampsia (PE), placenta previa, placenta accreta, anemia, the sum of the neonatal birthweight, and general anesthesia, which were all independent risk factors for PPH after CS in twin pregnancies. The nomogram demonstrated strong discrimination and calibration performance, with robust clinical utility as evidenced by area under the curve (AUC) analysis and DCA. Internal verification confirmed the consistency between predicted and actual values. CONCLUSIONS: Parity, the use of ART, PE, placenta previa, placenta accreta, degree of prenatal anemia, total neonatal birthweight, and the use of general anesthesia are significant risk factors affecting the incidence of postpartum hemorrhage after CS in patients. The prediction model based on the above factors provides an accurate tool for assessing PPH risk, offering potential to guide clinical decision-making and further reduce maternal mortality.