Validation of three models for prediction of blood transfusion during cesarean delivery admission

Obstetrics & Gynecology, University of Utah Health, Salt Lake City, United States. Obstetrics and Gynecology, Duke University School of Medicine, Durham, United States. The George Washington University Biostatistics Center, Rockville, United States. Obstetrics & Gynecology, University of Texas Medical Branch, Galveston, United States. OB-GYN, EVMS, Norfolk, United States. Obstetrics and Gynecology, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States. Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States. Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, United States. Columbia University, New York, United States. Ob-Gyn, Lyndon B. Johnson, Houston, United States. Obstetrics and Gynecology, University of Utah Health, Salt Lake City, United States. MFM, Ohio State Wexner Medical Center, Columbus, United States. Brown University, Providence, United States. MetroHealth Medical Center, Cleveland, United States. Obstetrics and Gynecology, The Ohio State University Wexner Medical Center, Columbus, United States. Ob/Gyn, University of Pittsburgh School of Medicine, Pittsburgh, United States.

American journal of perinatology. 2023

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Abstract
OBJECTIVE Prediction of blood transfusion during delivery admission allows for clinical preparedness and risk mitigation. Although prediction models have been developed and adopted into practice, their external validation is limited. We aimed to evaluate the performance of three blood transfusion prediction models in a U.S. cohort of individuals undergoing cesarean delivery. METHODS This was a secondary analysis of a multicenter randomized trial of tranexamic acid for prevention of hemorrhage at time of cesarean delivery. Three models were considered: a categorical risk tool (California Maternal Quality Care Collaborative (CMQCC)), and two regression models (Ahmadzia et al and Albright et al). The primary outcome was red blood cell transfusion. The CMQCC algorithm was applied to the cohort with frequency of risk category (low, medium, high) and associated transfusion rates reported. For the regression models, the area under the receiver-operating curve (AUC) was calculated and a calibration curve plotted to evaluate each model's capacity to predict receipt of transfusion. The regression model outputs were statistically compared. RESULTS Of 10,785 analyzed individuals, 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 (48.8%) as medium-risk, and 3,556 (33.0%) as high-risk with corresponding transfusion rates of 2.1% (95% CI 1.5-2.9%), 2.2% (95% CI 1.8-2.6%), and 7.5% (95% CI 6.6-8.4%), respectively. The AUC for prediction of blood transfusion using the Ahmadzia and Albright models was 0.78 (95% CI 0.76-0.81) and 0.79 (95% CI 0.77-0.82), respectively (p=0.38 for difference). Calibration curves demonstrated overall agreement between the predicted probability and observed likelihood of blood transfusion. CONCLUSION Three models were externally validated for prediction of blood transfusion during cesarean delivery admission in this U.S. COHORT Overall, performance was moderate; model selection should be based on ease of application until a specific model with superior predictive ability is developed.
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Language : eng
Credits : Bibliographic data from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine