Development of a Risk-Scoring System for Prediction of Blood Transfusion During Hospitalization for Delivery

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OBJECTIVE: To develop and internally validate a practical and data-driven risk-scoring system to predict blood transfusion during hospitalization for delivery in a contemporary U.S. cohort. METHODS: This was a secondary analysis of a multicenter cohort of patients who delivered on randomly selected days at 17 U.S. hospitals (2019-2020). Patients with placenta accreta spectrum were excluded. The primary outcome was any blood transfusion during hospitalization for delivery. 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 that used 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 (OR) by the log of the OR 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 final model included thrombocytopenia, and placental abruption or significant antepartum bleeding. Risk score outputs among patients in the cohort ranged from 0 to 17 (maximum possible 26) with a corresponding predicted risk for transfusion from 1.0% to 84.4%. The AUC for prediction of transfusion in the validation subsample was 0.81 (95% CI, 0.76-0.85). CONCLUSION: We developed a clinically applicable numeric risk score to predict blood transfusion during hospitalization for delivery. Future work should externally validate this risk-scoring system.