Blood transfusions could have serious consequences for patients. A reduction in the transfusion rate could be accomplished by an optimized blood management. Clear guidelines and awareness among all employees at a single institution have resulted in a reduction in transfusion rates in recent years. Identification of the group of patients who still received a blood transfusion in recent years could result in a further reduction. This study enrolled 4022 patients undergoing cardiothoracic surgery between 2008 and 2013. Patients were divided into three groups: "no blood transfusion", "transfusion of packed red cells only" and "any other combinations of blood transfusion". In total, 16 variables were tested for their association with the administration of homologous blood. The variables associated with blood transfusion were included in a stepwise multinomial logistic regression analysis to find the variables with the strongest association.For the transfusion of packed red cells only and any other combinations of blood transfusion, the following predictors are found: gender, age, weight, type of surgery, reoperation, unstable angina pectoris, endocarditis, recent myocardial infarction, preoperative creatinine level, preoperative hemoglobin level and preoperative platelet count. The best predictor for the transfusion of packed red cells is preoperative hemoglobin level (4.1 to 7.8 mmol/l). For other blood products, the strongest association was found with type of surgery (aortic surgery, ventricular septal rupture and intracardiac tumour).

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.