Predicting risk of peripartum blood transfusion during vaginal and cesarean delivery: A risk prediction model

Document Type

Journal Article

Publication Date



Journal of neonatal-perinatal medicine




Blood transfusion; Consortium on Safe Labor; maternal mortality; maternal morbidity; postpartum hemorrhage; risk prediction model


OBJECTIVE: The objective of this study is to develop a model that will help predict the risk of blood transfusion using information available prior to delivery. STUDY DESIGN: The study is a secondary analysis of the Consortium on Safe Labor registry. Women who had a delivery from 2002 to 2008 were included. Pre-delivery variables that had significant associations with transfusion were included in a multivariable logistic regression model predicting transfusion. The prediction model was internally validated using randomly selected samples from the same population of women. RESULTS: Of 156,572 deliveries, 5,463 deliveries (3.5%) required transfusion. Women who had deliveries requiring transfusion were more likely to have a number of comorbidities such as preeclampsia (6.3% versus 4.1%, OR 1.21, 95% CI 1.08-1.36), placenta previa (1.8% versus 0.4%, OR 4.11, 95% CI 3.25-5.21) and anemia (10.6% versus 5.4%, OR 1.30, 95% CI 1.21-1.41). Transfusion was least likely to occur in university teaching hospitals compared to community hospitals. The c statistic was 0.71 (95% CI 0.70-0.72) in the derivation sample. The most salient predictors of transfusion included type of hospital, placenta previa, multiple gestations, diabetes mellitus, anemia, asthma, previous births, preeclampsia, type of insurance, age, gestational age, and vertex presentation. The model was well-calibrated and showed strong internal validation. CONCLUSION: The model identified independent risk factors that can help predict the risk of transfusion prior to delivery. If externally validated in another dataset, this model can assist health care professionals counsel patients and prepare facilities/resources to reduce maternal morbidity.


Anesthesiology and Critical Care Medicine