Paper Info
Title
Predicting Adverse Reactions to Blood Transfusion.
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.
Year
DOI
Venue
2015
10.1109/ICHI.2015.17
ICHI
Keywords
Field
DocType
Transfusion, Risk prediction, adverse reaction, Transfusion-Associated Circulatory Overload, Transfusion-Related Acute Lung Injury, machine learning, logistic regression
Transfusion-related acute lung injury,Blood transfusion,Adverse effect,Intensive care medicine,Perioperative,Clinical decision support system,Transfusion associated circulatory overload,Logistic regression,Medicine
Conference
Citations 
PageRank 
References 
1
0.51
7
Authors
8
Name
Order
Citations
PageRank
Dennis H. Murphree121.92
Leanne Clifford220.91
Yaxiong Lin320.91
Nagesh Madde420.91
Che Ngufor5179.73
Sudhindra Upadhyaya651.98
Jyotishman Pathak767776.52
Daryl J Kor854.01