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Validation of Three Models for Prediction of Blood Transfusion during Cesarean Delivery Admission

April 9, 2024

In collaboration with colleagues, Hyagriv Simhan, MD, professor, UPMC Department of Obstetrics, Gynecology, and Reproductive Sciences, executive vice chair of Obstetrical Services, University of Pittsburgh School of Medicine, and director of Clinical Innovation, UPMC Women’s Health Service Line, evaluated the performance of three blood transfusion prediction models in a cohort of individuals undergoing cesarean delivery.

Published online ahead of print in the American Journal of Perinatology in January 2024, the research provided a secondary analysis of a multicenter randomized trial of tranexamic acid, a medication that slows the breakdown of blood clots, to help prevent hemorrhage at time of cesarean delivery.

Participants included individuals aged 18 years or older with singleton or twin gestation undergoing cesarean delivery at 31 hospitals across the United States between 2018 and 2021. Individuals were randomized to receive 1g of intravenous tranexamic acid versus placebo after umbilical cord clamping.

Specifically, three prediction models of blood transfusion were evaluated

The primary outcome evaluated was intrapartum or postpartum red blood cell transfusion. The CMQCC algorithm was applied to the cohort with the frequency of risk category (low, medium, high), and associated transfusion rates were reported. 

For the two regression models, the area under the receiver-operating curve (AUC) was calculated and a calibration curve was plotted to evaluate each model's capacity to predict the necessity of red blood cell transfusion. The regression model outputs were then statistically compared.

Results showed that out of 10,785 participants, 3.9% received a red blood cell transfusion during delivery admission. The CMQCC risk tool categorized 1,970 (18.3%) individuals as low risk, 5,259 individuals (48.8%) as medium risk, and 3,556 individuals (33.0%) as high risk with corresponding transfusion rates of 2.1%.

The AUC for prediction of blood transfusion using the Albright and Ahmadzia models was 0.78. Calibration curves showed correlation between the predicted probability and observed likelihood of a blood transfusion.

In conclusion, the three models showed validity in the ability to predict blood transfusion during cesarean delivery admission in the United States. For additional details, please visit PubMed to view the full research analysis.


1. Bruno AM, Federspiel JJ, McGee P, Pacheco LD, Saade GR, Parry S, Longo M, Tita ATN, Gyamfi-Bannerman C, Chauhan SP, Einerson BD, Rood K, Rouse DJ, Bailit J, Grobman WA, Simhan HN; Eunice Kennedy Shriver National Institute of Child Health Human Development Maternal-Fetal Medicine Units Network. Validation of Three Models for Prediction of Blood Transfusion during Cesarean Delivery Admission. Am J Perinatol. 2024 Jan 16. doi: 10.1055/a-2234-8171. Epub ahead of print. PMID: 38134939.

2, Albright CM, Spillane TE, Hughes BL, Rouse DJ. A regression model for prediction of cesarean-associated blood transfusion. Am J Perinatol 2019; 36 (09) 879-885

3. Ahmadzia HK, Phillips JM, James AH, Rice MM, Amdur RL. Predicting peripartum blood transfusion in women undergoing cesarean delivery: a risk prediction model. PLoS One 2018; 13 (12) e0208417