Application of a recursive partitioning decision tree algorithm for the prediction of massive transfusion in civilian trauma: the MTPitt prediction tool

Abstract

BACKGROUND A supervised machine learning algorithm was used to generate decision trees for the prediction of massive transfusion at a Level 1 trauma center. METHODS Trauma patients who received at least one unit of RBCs and/or low‐titer group O whole blood between January 1, 2015, and December 31, 2017, were included. Massive transfusion was defined as the transfusion of 10 or more units of RBCs and/or low‐titer group O whole blood in the first 24 hours of admission. A recursive partitioning algorithm was used to generate two decision trees for prediction of massive transfusion using a training data set (n = 550): the first, MTPitt, was based on demographic and clinical parameters, and the second, MTPitt+Labs, also included laboratory data. Decision tree performance was compared with the Assessment of Blood Consumption score and the Trauma Associated Severe Hemorrhage score. RESULTS The incidence of massive transfusion in the validation data set (n = 199) was 7.5%. The MTPitt decision tree had a higher balanced accuracy (81.4%) and sensitivity (86.7%) compared to an Assessment of Blood Consumption Score of 2 or higher (77.9% and 66.7%, respectively) and a Trauma Associated Severe Hemorrhage score of 9 or higher (75.0% and 73.3%, respectively), although the 95% confidence intervals overlapped. Addition of laboratory data to the MTPitt decision tree (MTPitt+Labs) resulted in a higher specificity and balanced accuracy compared to MTPitt without an increase in sensitivity. CONCLUSIONS The MTPitt decisions trees are highly sensitive tools for identifying patients who received a massive transfusion and do not require computational resources to be implemented in the trauma setting.

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