BACKGROUND: Postpartum hemorrhage (PPH) remains a leading cause of maternal morbidity and mortality globally, particularly in women with antepartum hemorrhage (APH). Current risk assessment methods lack standardized predictive tools that are both simple and reliable for clinical application. MATERIAL AND METHODS: We conducted a secondary analysis of a prospectively collected cohort of 100 pregnant women presenting with APH at ≥28 weeks' gestation at a tertiary care centre in northern India. Multivariable logistic regression was used to identify significant predictors of PPH. A point-based clinical risk score was then developed based on the multivariable model and internally validated using bootstrap techniques with 1000 replicates. RESULTS: PPH occurred in 30% of patients (n=30). Multivariable analysis identified four independent predictors of PPH: maternal age (adjusted odds ratio OR 1.29 per year; 95% confidence interval CI 1.10-1.51; p=0.002), gravidity (OR 2.11 per unit; 95% CI 1.00-4.43; p=0.049), gestational age at delivery (OR 0.64 per week; 95% CI 0.44-0.94; p=0.021), and antepartum blood transfusion (OR 2.44; 95% CI 1.02-5.84; p=0.045). The prediction model demonstrated excellent discrimination with an area under the receiver operating characteristic (ROC) curve of 0.86 (95% CI 0.80-0.92) and good calibration (slope 0.95). Bootstrap internal validation yielded an optimism-corrected AUC of 0.84. The resulting four-factor risk score stratified patients into four risk categories with PPH rates ranging from 4% (low risk) to 100% (very high risk). CONCLUSION: The four-variable score provides an accurate, easily applicable tool with excellent predictive performance. The score is a promising tool that, pending external validation, may facilitate early identification of high-risk patients and improve maternal outcomes. Further research should focus on external validation of this tool in diverse populations and its integration into clinical practice.
Asati et al. (Thu,) studied this question.