Development and Validation of a Predictive Model for Postpartum Hemorrhage in Non-Primiparous Women Who Deliver...

To analyze the risk factors for postpartum hemorrhage in non-primary women giving birth naturally and construct a predictive model.

Retrospective analysis of the clinical data of 436 second-time mothers who underwent natural childbirth in the Department of Obstetrics, Hefei Third People's Hospital. The cases were divided into a bleeding group (n=41) and a non-bleeding group (n=395) based on whether there was bleeding greater than 500 mL within 24 hours after delivery. Independent risk factors were established through univariate and multivariate analyses, a logistic regression model was established, and bootstrap resampling was used to internally verify and assess the calibration of the model.

Among the 436 cases of maternal delivery included in the study, 41 (9.40%) were cases of postpartum hemorrhage. The results of the multifactor analysis indicated that in vitro fertilization, body mass index (BMI), episiotomy, placenta previa, newborn weight, and manual removal of the placenta were independent risk factors for postpartum hemorrhage (PPH) in non-primary mothers. Subsequently, a model was constructed, exhibiting an AUC value of 0.839 (95% CI: 0.758-0.919). The Hosmer-Lemeshow test of the calibration curve yielded a chi-squared value of 8.1013 and a P-value of 0.4236, indicating an excellent performance of the DCA curve.

In vitro fertilization, body mass index (BMI), episiotomy, placenta previa, newborn weight, and manual removal of the placenta are identified as independent risk factors for postpartum hemorrhage (PPH) in non-primary mothers. The constructed logistic regression model is capable of more accurately identifying high-risk PPH mothers and providing a reference basis for individualized interventions.