A prognostic model for the preoperative identification of patients at risk for receiving transfusion of packed red blood cells in cardiac surgery

Background Patients undergoing cardiothoracic surgery are at substantial risk for blood transfusion. Increased awareness and patient blood management have resulted in a significant reduction over the past years. The next step is preoperative treatment of patients at high risk for packed red blood cells (RBC) transfusion, with the ultimate goal to eventually prevent RBC transfusion. A prediction model was developed to select patients at high risk for RBC transfusion. Materials and methods Data of all patients that underwent cardiac surgery in our center between 2008 and 2013 (n = 2951) were used for model development, and between 2014 and 2016 for validation (n = 1136). Only preoperative characteristics were included in a multinomial regression model with three outcome categories (no, RBC, other transfusion). The accuracy of the estimated risks and discriminative ability of the model were assessed. Clinical usefulness was explored. Results Risk factors included are sex, type of surgery, redo surgery, age, height, body mass index, preoperative hemoglobin level, and preoperative platelet count. The model has excellent discriminative ability for predicting RBC transfusion versus no transfusion (area under the curve [AUC] = 94%) and good discriminative ability for RBC transfusion versus other transfusion (AUC = 84%). With a cut-off value of RBC risk of 16.8% and higher, the model is well able to identify a high proportion of patients at risk for RBC transfusion (sensitivity = 87.1%, specificity = 82.3%). Conclusion In the current study, a prediction tool was developed to be used for risk stratification of patients undergoing elective cardiac surgery at risk for blood transfusions.