TY - JOUR
T1 - Validation of three models for prediction of blood transfusion during cesarean delivery admission
AU - Bruno, Ann
AU - Federspiel, Jerome J.
AU - McGee, Paula
AU - Pacheco, Luis
AU - Saade, George
AU - Parry, Samuel
AU - Longo, Monica
AU - Tita, Alan
AU - Gyamfi-Bannerman, Cynthia
AU - Chauhan, Suneet
AU - Einerson, Brett D.
AU - Rood, Kara
AU - Rouse, Dwight J.
AU - Bailit, Jennifer
AU - Grobman, William A.
AU - Simhan, Hyagriv
N1 - Publisher Copyright:
© 2023 Thieme Medical Publishers, Inc.. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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 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: 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.
AB - 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 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: 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.
KW - Blood preparedness
KW - California Maternal Quality Care Collaborative risk tool
KW - cesarean morbidity
KW - external validation
KW - prediction models
KW - transfusion prediction
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U2 - 10.1055/a-2234-8171
DO - 10.1055/a-2234-8171
M3 - Article
C2 - 38134939
AN - SCOPUS:85181046295
SN - 0735-1631
JO - American Journal of Perinatology
JF - American Journal of Perinatology
ER -