BACKGROUND: The estimation of blood loss for a surgical procedure is both poorly reproducible and typically underestimated. Therefore, comparison of surgical transfusion outcomes such as blood loss and amount of blood transfused from one institution to another, or from one surgeon to another, is difficult. Recently, mathematical modeling has contributed to our understanding of transfusion strategies. STUDY DESIGN AND METHODS: A mathematical model of blood loss for a surgical hospitalization was developed on the basis of recently described mathematical principles for blood loss and hemodilution. The model was designed so that the calculation of blood loss would be based on easily measured factors such as the patient's blood volume, the number and type of red cell units transfused, the initial hematocrit, the discharge hematocrit, the transfusion trigger, the volume of intraoperatively salvaged blood transfused, and the amount of hemodilution performed. The calculated blood loss was then compared with the intraoperative blood loss actually estimated by the anesthesiologist in 250 consecutive patients who underwent radical retropubic prostatectomy. RESULTS: The mathematical equations were placed in a computer model to allow rapid calculation of a particular patient's blood loss. Figures were derived from the computer modelling to facilitate rapid manual calculation of the blood loss. There was a significant relation (p < 0.001) between the calculated blood loss for the hospitalization and the estimated intraoperative blood loss. However, the calculated blood loss was on average 2.1 times the intraoperative blood loss estimated by the anesthesiologist. CONCLUSION: The use of such mathematical modeling to rapidly estimate a patient's blood loss has the potential to allow ready, objective comparisons between sites and even surgeons. It also allows for a more judicial and informed decision as to what (if any) blood should be available or what blood-conservation techniques should be employed for a specific patient.
BACKGROUND: The estimation of blood loss for a surgical procedure is both poorly reproducible and typically underestimated. Therefore, comparison of surgical transfusion outcomes such as blood loss and amount of blood transfused from one institution to another, or from one surgeon to another, is difficult. Recently, mathematical modeling has contributed to our understanding of transfusion strategies. STUDY DESIGN AND METHODS: A mathematical model of blood loss for a surgical hospitalization was developed on the basis of recently described mathematical principles for blood loss and hemodilution. The model was designed so that the calculation of blood loss would be based on easily measured factors such as the patient's blood volume, the number and type of red cell units transfused, the initial hematocrit, the discharge hematocrit, the transfusion trigger, the volume of intraoperatively salvaged blood transfused, and the amount of hemodilution performed. The calculated blood loss was then compared with the intraoperative blood loss actually estimated by the anesthesiologist in 250 consecutive patients who underwent radical retropubic prostatectomy. RESULTS: The mathematical equations were placed in a computer model to allow rapid calculation of a particular patient's blood loss. Figures were derived from the computer modelling to facilitate rapid manual calculation of the blood loss. There was a significant relation (p < 0.001) between the calculated blood loss for the hospitalization and the estimated intraoperative blood loss. However, the calculated blood loss was on average 2.1 times the intraoperative blood loss estimated by the anesthesiologist. CONCLUSION: The use of such mathematical modeling to rapidly estimate a patient's blood loss has the potential to allow ready, objective comparisons between sites and even surgeons. It also allows for a more judicial and informed decision as to what (if any) blood should be available or what blood-conservation techniques should be employed for a specific patient.
Authors: Oliver M Theusinger; Stephanie L Kind; Burkhardt Seifert; Lain Borgeat; Christian Gerber; Donat R Spahn Journal: Blood Transfus Date: 2014-04 Impact factor: 3.443