Revolutionizing Postpartum Hemorrhage Prediction with Insights from Iraq

Abstract

Background: Postpartum hemorrhage (PPH) is a severe complication affecting women with gestational hypertension or preeclampsia, yet predictive models for PPH in this population remain underdeveloped. Specific Background: Existing studies have not adequately addressed the combined influence of antepartum and intrapartum factors on the predictability of PPH. Knowledge Gap: There is a lack of comprehensive models integrating both antepartum and intrapartum variables to predict PPH risk in women with gestational hypertension or mild preeclampsia. Aims: The study investigates the predictability of preeclampsia (PPH) in women with gestational hypertension or preeclampsia at term using logistic regression models incorporating both antepartum and intrapartum factors. Methods: The study, conducted in Karbala, Iraq, involved 1252 women with hypertension or preeclampsia, developed two logistic regression models, and assessed their predictive efficacy using receiver operating characteristic analysis and calibration techniques. Results: A study found that 168 participants (10.4%) experienced preterm pregnancies, with antepartum predictors including maternal age, pre-pregnancy BMI, and preeclampsia, and intrapartum factors like gestational age and labor duration. Novelty: The study introduces a novel predictive model for Pregnancy-Positive Hypertension (PPH) that integrates antepartum and intrapartum variables for risk assessment in high-risk populations. Implications: The study suggests that combining antepartum and intrapartum variables can improve risk stratification and preventive measures, requiring further refinement for improved maternal care outcomes. Highlights: Antepartum and intrapartum variables enhance PPH prediction accuracy. Model B outperforms antepartum-only model in predicting PPH. Results stress need for improved risk stratification and prevention strategies. Keywords: postpartum hemorrhage, gestational hypertension, preeclampsia, predictive modeling, logistic regression