Abstract
In 2011 approximately 21 million blood components were transfused in the United States, with roughly 1 in 414 causing an adverse reaction [1]. Two adverse reactions in particular, transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO), accounted for 62% of reported transfusion-related fatalities in 2013 [2]. We describe newly developed models for predicting the likelihood of these adverse reactions, with a goal towards better informing the clinician prior to a transfusion decision. Our models include both traditional logistic regression as well as modern machine learning techniques, and incorporate over sampling methods to deal with severe class imbalance. We focus on a minimal set of predictors in order to maximize potential application. Results from 8 models demonstrate AUC's ranging from 0.72 to 0.84, with sensitivities tunable by threshold choice across ranges up to 0.93. Many of the models rank the same predictors amongst the most important, perhaps yielding insight into the mechanisms underlying TRALI and TACO. These models are currently being implemented in a Clinical Decision Support System [3] in perioperative environments at Mayo Clinic.