Development and Validation of a Predictive Nomogram for Postpartum Hemorrhage in Pregnant Women: A Retrospective...

Postpartum hemorrhage (PPH) remains a major contributor to maternal morbidity and mortality worldwide. Early identification of high-risk women is crucial for implementing targeted interventions. This study aimed to develop and validate a predictive nomogram that integrates key maternal and perinatal risk factors to estimate the risk of PPH.

In this retrospective study, 336 pregnant women who delivered at our hospital from January 2022 to January 2024 were included. PPH was defined as blood loss >500 mL after vaginal delivery or >1000 mL after cesarean section. Independent predictors were identified using forward stepwise multivariable logistic regression and incorporated into a nomogram. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUC) and concordance index (C-index); calibration by the Hosmer-Lemeshow test and calibration curves; clinical utility by decision curve analysis (DCA). Internal validation used 1000 bootstrap resamples.

Participants were classified into non-PPH (n = 281) and PPH (n = 55) groups. Independent predictors of PPH included advanced maternal age (≥35 years), uterine atony, various placental abnormalities (previa, abruption, adhesion, and retained placenta), adverse obstetric history, cesarean delivery, higher prenatal weight, instrumental delivery, birth canal tears, multiple gestations, macrosomia, coagulopathy, uterine fibroids, and adverse psychological status (all p < 0.05). The nomogram achieved an AUC of 0.786 (95% CI: 0.695-0.865) and a corrected C-index of 0.796. Calibration was excellent (Hosmer-Lemeshow, p = 0.912), and DCA confirmed its clinical utility.

This internally validated nomogram showed strong predictive performance for PPH using routinely collected clinical and psychosocial data. Future studies should conduct external validation and prospective multicenter evaluation to confirm its generalizability and clinical applicability.