R A Metcalf1,2, M B Pagano3, J R Hess3,4, J Reyes5, J D Perkins5, M I Montenovo5. 1. Division of Clinical Pathology, Department of Pathology, University of Utah, Salt Lake City, UT, USA. 2. ARUP Laboratories, Salt Lake City, UT, USA. 3. Division of Transfusion Medicine, Department of Laboratory Medicine, University of Washington, Seattle, WA, USA. 4. Division of Hematology, Department of Medicine, University of Washington, Seattle, WA, USA. 5. Division of Transplantation, Department of Surgery, University of Washington, Seattle, WA, USA.
Abstract
BACKGROUND AND OBJECTIVES: Blood utilization during liver transplant has decreased, but remains highly variable due to many complex surgical and physiologic factors. Previous models attempted to predict utilization using preoperative variables to stratify cases into two usage groups, usually using entire blood units for measurement. We sought to develop a practical predictive model using specific transfusion volumes (in ml) to develop a data-driven patient blood management strategy. MATERIALS AND METHODS: This is a retrospective evaluation of primary liver transplants at a single institution from 2013 to 2015. Multivariable analysis of preoperative recipient and donor factors was used to develop a model predictive of intraoperative red-blood-cell (pRBC) use. RESULTS: Of 256 adult liver transplants, 207 patients had complete transfusion volume data for analysis. The median intraoperative allogeneic pRBC transfusion volume was 1250 ml, and the average was 1563 ± 1543 ml. Preoperative haemoglobin, spontaneous bacterial peritonitis, preoperative haemodialysis and preoperative international normalized ratio together yielded the strongest model predicting pRBC usage. When it predicted <1250 ml of pRBCs, all cases with 0 ml transfused were captured and only 8·6% of the time >1250 ml were used. This prediction had a sensitivity of 0·91 and a specificity of 0·89. If predicted usage was >2000 ml, 75% of the time blood loss exceeded 2000 ml. CONCLUSION: Patients likely to require low or high pRBC transfusion volumes were identified with excellent accuracy using this predictive model at our institution. This model may help predict bleeding risk for each patient and facilitate optimized blood ordering.
BACKGROUND AND OBJECTIVES: Blood utilization during liver transplant has decreased, but remains highly variable due to many complex surgical and physiologic factors. Previous models attempted to predict utilization using preoperative variables to stratify cases into two usage groups, usually using entire blood units for measurement. We sought to develop a practical predictive model using specific transfusion volumes (in ml) to develop a data-driven patient blood management strategy. MATERIALS AND METHODS: This is a retrospective evaluation of primary liver transplants at a single institution from 2013 to 2015. Multivariable analysis of preoperative recipient and donor factors was used to develop a model predictive of intraoperative red-blood-cell (pRBC) use. RESULTS: Of 256 adult liver transplants, 207 patients had complete transfusion volume data for analysis. The median intraoperative allogeneic pRBC transfusion volume was 1250 ml, and the average was 1563 ± 1543 ml. Preoperative haemoglobin, spontaneous bacterial peritonitis, preoperative haemodialysis and preoperative international normalized ratio together yielded the strongest model predicting pRBC usage. When it predicted <1250 ml of pRBCs, all cases with 0 ml transfused were captured and only 8·6% of the time >1250 ml were used. This prediction had a sensitivity of 0·91 and a specificity of 0·89. If predicted usage was >2000 ml, 75% of the time blood loss exceeded 2000 ml. CONCLUSION:Patients likely to require low or high pRBC transfusion volumes were identified with excellent accuracy using this predictive model at our institution. This model may help predict bleeding risk for each patient and facilitate optimized blood ordering.
Authors: Diego Costanzo; Maria Bindi; Davide Ghinolfi; Massimo Esposito; Francesco Corradi; Francesco Forfori; Paolo De Simone; Andrea De Gasperi; Gianni Biancofiore Journal: BMC Anesthesiol Date: 2020-01-30 Impact factor: 2.217