Development of a Novel Prediction Model for Red Blood Cell Transfusion Risk in Cardiac Surgery.

Abstract

Cardiac surgery is a complex and invasive procedure that often requires blood transfusions to replace the blood lost during surgery. Blood products are a scarce and expensive resource. Therefore, it is essential to develop a standardized approach to determine the need for blood transfusions in cardiac surgery. The main objective of our study is to develop a simple prediction model for determining the risk of red blood cell transfusion in cardiac surgery. Retrospective cohorts of adult patients who underwent cardiac surgery between 2017 and 2019 were studied to identify hypothetical predictors of blood transfusion. Finally, a multivariable logistic regression model was developed to predict the risk of transfusion in cardiac surgery using the AUC and the Hosmer-Lemeshow goodness-of-fit test. We included 1234 patients who underwent cardiac surgery. Of the entire cohort, 875 patients underwent a cardiac procedure 69.4% [CI 95% (66.8%; 72.0%)]; 119 patients 9.6% [CI 95% (8.1%; 11.4%)] underwent a combined procedure, and 258 patients 20.9% [CI 95% (18.7; 23.2)] underwent other cardiac procedures. The median perioperative hemoglobin was 13.0 mg/dL IQR (11.7; 14.2). The factors associated with the risk of transfusion were age > 60 years OR 1.37 CI 95% (1.02; 1.83); sex female OR 1.67 CI 95% (1.24; 2.24); BMI > 30 OR 1.46 (1.10; 1.93); perioperative hemoglobin < 14 OR 2.11 to 51.41 and combined surgery OR 3.97 CI 95% (2.19; 7.17). The final model shows an AUC of 80.9% for the transfusion risk prediction [IC 95% (78.5-83.3%)]; p < 0.001]. We have developed a model with good discriminatory ability, which is more parsimonious and efficient than other models.