BACKGROUND: The risk of transfusion reactions (TR) and the cost of blood has led to efforts to reduce blood use. We changed our practice to transfuse just one instead of two units of red blood cells (RBC) when hemoglobin ≤8 g/dL due to patient blood management (PBM) recommendations. METHODS AND MATERIALS: We compared RBC utilization in patients receiving allogeneic HCT in the 10 months before (control arm) and 13 months after implementation of this new practice (intervention arm). We used regression models to estimate the independent effect of transfusion practice, length of hospitalization, the conditioning regimen, and donor type for patients who received at least one RBC unit. The outcome variable was total number of inpatient transfusions. In addition, a survey assessed the impact of this. RESULTS: Cohorts were matched for age, primary diagnosis, graft source, and conditioning regimen. The median number of RBC units transfused/patient was identical in both arms (4; interquartile range 19 units/patient). Using the regression model, only length of stay (relative increase of 1.035 units/day; 95%CI, 1.0271.043) was an independent predictor of the number of RBC units a patient received. When data were normalized/1000 patient days, the control arm received 240 units vs the intervention arm, which received 193 units, resulting in a reduction of 47 units transfused/1000-patient-days, which was not statistically significant (p-value = 0.32). The survey of RNs showed that it positively affected the workflow. CONCLUSIONS: There was a modest reduction in RBC utilization based on units transfused/1000-patient-days. There was a positive impact on RN workflow.
Bibliographical noteFunding Information:
NIH grants P01 CA065493-20 (C.G.B.) from the National Cancer Institute, P30 CA77598 utilizing the Biostatistics and Bioinfor-matics Core shared resource of the Masonic Cancer Center, University of Minnesota and by the National Center for Advancing Translational Sciences of the National Institutes of Health Award Number UL1TR000114.
Research reported in this publication was supported in part by NIH grants P01 CA065493-20 (C.G.B.) from the National Cancer Institute, P30 CA77598 utilizing the Biostatistics and Bioinformatics Core shared resource of the Masonic Cancer Center, University of Minnesota and by the National Center for Advancing Translational Sciences of the National Institutes of Health Award Number UL1TR002494. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.