Red blood cells are often overprepared for surgery, leading to waste and increased costs, despite the need for cross-matching tests. This study aimed to develop a model for predicting the number of red blood cell units required during cardiovascular surgery using patient characteristics.

This retrospective study included patients who underwent cardiovascular surgery at our hospital from April 2022 to October 2022. Multiple regression analysis was performed using preoperative patient attributes and blood data, with red blood cell units used during surgery as the objective variable. Models were developed using patient demographics and blood data, with additional models incorporating specific surgical procedures to assess predictive accuracy.

Model 1 included age, sex, weight, hematocrit, prothrombin time-international normalized ratio, serum creatinine, volume of preoperatively donated diluted autologous blood, and history of cardiovascular surgery. Model 2 included the same variables as Model 1, plus aortic aneurysm resection as a surgical procedure. The predictive equations of the study showed superior accuracy for both Model 1 and Model 2 compared to the conventional red blood cell units requested by physicians or those predicted using the surgical blood order equation based on correlation coefficients. Model 2 outperformed both Model 1 and conventional methods in predictive utility.

This study developed a clinically useful formula for predicting the number of red blood cell units required during surgery based on preoperative patient-specific data, without restricting the analysis to specific procedures. This formula may improve blood product inventory management and reduce medical costs.