University of Utah Health Salt Lake City, UT, United States
Objective: The most widely used tool for prediction of blood transfusion during admission for delivery is informed by expert opinion rather than data. We aimed to develop and internally validate a practical and data-driven risk scoring system to predict blood transfusion during delivery admission in a contemporary U.S. cohort.
Study Design: This was a secondary analysis of a multicenter cohort of patients delivering on randomly selected days at 17 U.S. hospitals (2019-20). Patients with placenta accreta spectrum were excluded (given known high risk for transfusion). The primary outcome was any blood transfusion during delivery admission. Candidate risk factors for transfusion were selected based on relevant literature. A multivariable logistic regression model was developed and internally validated using stratified k-fold (k=5) cross validation with stepwise backward elimination using significance level of 0.05. Each risk factor included in the final model was assigned a point value by dividing the log of the odds ratio by the log of the odds ratio of the factor with the lowest value. The summed points for an individual generate a numeric risk score predictive of transfusion. Performance of the risk score to predict transfusion was assessed using the area under the receiver operating curve (AUC).
Results: Of 21,780 included individuals, 2.5% (N=545) received a blood transfusion. Factors associated with the highest risk for transfusion in the adjusted model included thrombocytopenia and placental abruption or significant antepartum bleeding (Table). Risk score outputs among patients in the cohort ranged from 0 to 18 (maximum possible 26) with a corresponding predicted risk for transfusion from 1.0% to 87.8%. The AUC for prediction of transfusion in the validation sub-sample was 0.81. Using the full cohort, the AUC was 0.76 (95% CI 0.74-0.78).
Conclusion: We developed a clinically applicable numeric risk score to predict blood transfusion during delivery admission. Future work should externally validate this risk scoring system.
