Massive Transfusions in a Resource Poor Setting: A

Background The reality of finite resources has a real-world impact on a patient’s ability to receive life-sav-ing care in resource-poor settings. Blood for transfusion is an example of a scarce resource. Very few studies have looked at predictors of survival in patients requiring massive transfu-sion. We used data from a rural hospital in Kenya to develop a prediction model of survival among patients receiving massive transfusion. Methods Patients who received five or more units of whole blood within 48 hours between 2004 and 2010 were identified from a blood registry in a rural hospital in Kenya. Presenting character-istics and in-hospital survival were collected from charts. Using stepwise selection, a logistic model was developed to predict who would survive with massive transfusion versus those who would die despite transfusion. An ROC curve was created from this model to quantify its predictive power. Result