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Department of Anesthesiology and Pain Medicine, University of Alberta, Edmonton, Alberta, Canada
Address correspondence to Saifudin Rashiq, MB, MSc, 3B2.32, 8440–112 Street, Edmonton AB Canada T6G 2B7. Address email to srashiq{at}ualberta.ca
Total joint arthroplasty (TJA) patients often receive allogeneic blood transfusion. In this study we sought to create and validate a clinical prediction rule for transfusion in TJA using data that are easily available when scheduling the procedure. Logistic regression modeling was applied to retrospective data from all TJA procedures performed in Edmonton, Alberta in 2000 (n = 1875). The area under the receiver operating curve for the resulting model in the training and validation data sets was 0.80 and 0.76 respectively. By assigning a simple score based on six independent predictors (age, gender, weight, hemoglobin, ASA operative risk classification and whether revision surgery was planned), it was possible to classify a given subject’s risk of receiving allogeneic transfusion. We conclude that accurate prediction of transfusion risk in TJA is possible using a rule based on simple preoperative clinical and laboratory data. Such prediction could allow transfusion prevention strategies to be applied selectively to those at greatest risk.
IMPLICATIONS: The use of allogeneic blood in patients undergoing joint replacement surgery was modeled statistically. A prediction rule was created from this model. It estimates a given patient’s risk of transfusion during total joint arthroplasty and can be used to target transfusion risk reduction measures more effectively.
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