Body surface area: a predictor of response to red blood cell transfusion.

J Blood Med 2016 21;7:199-204. Epub 2016 Sep 21.

Department of Internal Medicine, Eastern Virginia Medical School; Department of Pathology, Eastern Virginia Medical School; Department of Pathology, Sentara Norfolk General Hospital, Norfolk, VA, USA.

A current focus of transfusion medicine is a judicious strategy in transfusion of blood products. Unfortunately, our ability to predict hemoglobin (Hgb) response to transfusion has been limited. The objective of this study was to determine variability of response to red blood cell transfusion and to predict which patients will have an Hgb rise higher or lower than that predicted by the long-standing convention of "one and three". This was a retrospective chart review in a single hospital. Data for 167 consecutive patient encounters were reviewed. The dataset was randomly divided into derivation and validation subsets with no significant differences in characteristics. DeltaHgb was defined as posttransfusion Hgb minus pre-transfusion Hgb per red blood cell unit. We classified all the patients in both the subsets as "high responders" (DeltaHgb >1 g/dL) or as "low responders" (DeltaHgb ≤1 g/dL). In univariate analysis, age, sex, body weight, estimated blood volume, and body surface area were significantly associated with response category (<0.05). Different multivariate regression models were tested using the derivation subset. The probability of being a high responder was best calculated using the logarithmic formula / (1 + ), where H is B + (B × variable 1) + (B × variable 2). Bs are coefficients of the models. On validation, the model H=6.5-(3.3 × body surface area), with the cutoff probability of 0.5, was found to correctly classify patients into high and low responders in 69% of cases (sensitivity 84.6%, specificity 43.8%). This model may equip clinicians to make more appropriate transfusion decisions and serve as a springboard for further research in transfusion medicine.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036545PMC
http://dx.doi.org/10.2147/JBM.S105063DOI Listing
September 2016

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