Abstract
Postpartum hemorrhage (PPH) is one of the leading causes of maternal morbidity worldwide. This study aimed to develop and validate a predictive model for PPH after vaginal deliveries, based on routinely available clinical and biological data. The derivation monocentric cohort included pregnant women with vaginal delivery at Brest University Hospital (France) between April 2013 and May 2015. Immediate PPH was defined as a blood loss of ≥500 mL in the first 24 h after delivery and measured with a graduated collector bag. A logistic model, using a combination of multiple imputation and variable selection with bootstrap, was used to construct a predictive model and a score for PPH. An external validation was performed on a prospective cohort of women who delivered between 2015 and 2019 at Brest University Hospital. Among 2742 deliveries, PPH occurred in 141 (5.1%) women. Eight factors were independently associated with PPH: pre-eclampsia (aOR 6.25, 95% CI 2.35−16.65), antepartum bleeding (aOR 2.36, 95% CI 1.43−3.91), multiple pregnancy (aOR 3.24, 95% CI 1.52−6.92), labor duration ≥ 8 h (aOR 1.81, 95% CI 1.20−2.73), macrosomia (aOR 2.33, 95% CI 1.36−4.00), episiotomy (aOR 2.02, 95% CI 1.40−2.93), platelet count < 150 Giga/L (aOR 2.59, 95% CI 1.47−4.55) and aPTT ratio ≥ 1.1 (aOR 2.01, 95% CI 1.25−3.23). The derived predictive score, ranging from 0 to 10 (woman at risk if score ≥ 1), demonstrated a good discriminant power (AUROC 0.69; 95% CI 0.65−0.74) and calibration. The external validation cohort was composed of 3061 vaginal deliveries. The predictive score on this independent cohort showed an acceptable ability to discriminate (AUROC 0.66; 95% CI 0.62−0.70). We derived and validated a robust predictive model identifying women at risk for PPH using in-depth statistical methodology. This score has the potential to improve the care of pregnant women and to take preventive actions on them.