Validation of Three Models for Prediction of Blood Transfusion during Cesarean Delivery Admission
Year: 2023
Type: article
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. STUDY DESIGN: 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 intrapartum or postpartum 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: = 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. KEY POINTS: · A total of 3.9% of individuals received a blood transfusion during cesarean delivery admission.. · Three models used in clinical practice are externally valid for blood transfusion prediction.. · Institutional model selection should be based on ease of application until further research identifies the optimal approach..
Source: American Journal of Perinatology
DOI: 10.1055/a-2234-8171
Authors: Ann M. Bruno, Jerome J. Federspiel, Paula McGee, Luis D. Pacheco, George R. Saade, Samuel Parry, Monica Longo, Alan T.N. Tita, Cynthia Gyamfi-Bannerman, Suneet P. Chauhan, Brett D. Einerson, Kara Rood, Dwight J. Rouse, Jennifer Bailit, William A. Grobman, Hyagriv N. Simhan, for the Eunice Kennedy Shriver National Institute of Child Health Human Development Maternal-Fetal Medicine Units Network
Institutions: University of Utah, University of Utah Health Care, Duke University, Duke Medical Center, George Washington University, The University of Texas Medical Branch at Galveston, University of Pennsylvania, Eunice Kennedy Shriver National Institute of Child Health and Human Development, University of Alabama at Birmingham, UAB Medicine, Columbia University Irving Medical Center, Columbia University, Memorial Hermann, The University of Texas Health Science Center at Houston, The Ohio State University, Brown University, MetroHealth, MetroHealth Medical Center, Case Western Reserve University, Northwestern University, University of Pittsburgh
Language: English
Subfield: Biochemistry
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G124993864, G1641658650, G2454580142, G265911422, G3409724790, G3775748390, G3910300655, G444025864, G4640344014, G4908100860, G6300134082, G6438784790, G670772918, NICHD Maternal-Fetal Medicine Units (MFMU) Network: Data CoordinatingCenter, Maternal Fetal Medicine Units Network - Northwestern Study Center, Duke Women's Reproductive Health Research Scholars
OpenAlex ID: W4390099580