Development and validation of a clinical prediction model for postpartum hemorrhage after elective cesarean section

Predicting postpartum hemorrhage risk can be useful in clinical settings. We aimed to develop and validate a clinical prediction model for postpartum hemorrhage in patients who undergo elective cesarean section.

This retrospective observational study included patients who underwent elective cesarean section between January 2008 and September 2021. The primary outcome to be predicted was postpartum hemorrhage, defined as blood loss of ≥ 1500 mL during surgery. We used data prior to January 2018 for the development cohort and after for the validation cohort. We then constructed a multivariate logistic regression model. The model performance, including discrimination and calibration, was evaluated and its diagnostic ability was assessed.

Of the 4070 patients, 860 (21.0%) had postpartum hemorrhage. The predictors were twin pregnancy, benign uterine disease, assisted reproduction, gestational diabetes mellitus, placenta previa, nulliparity, and neonatal weight > 3000 g. In the validation cohort, the prediction model C-statistic was 0.780 (95% confidence interval [CI] 0.745-0.816). We developed a simple scoring system to divide the patients into three risk groups (low, moderate, and high). If the cut-off risk score was set to moderate, the negative likelihood ratio was low (0.221, 95% CI 0.108-0.374); conversely, if the cut-off risk score was set to high, the positive likelihood ratio was high (5.882, 95% CI 3.750-9.333).

The model we developed can stratify the risk of postpartum hemorrhage and assist in clinical decision-making. Future studies are required to validate the performance of our model.