Application of a recursive partitioning decision tree algorithm for the prediction of massive transfusion in civilian trauma: the MTPitt prediction tool. Issue 3 (12th December 2018)

• Record Type: Journal Article
• Authors: Seheult, Jansen N.; Anto, Vincent P.; Farhat, Nadim; Stram, Michelle N.; Spinella, Philip C.; Alarcon, Louis; Sperry, Jason; Triulzi, Darrell J.; Yazer, Mark H.
• Is Part Of: Transfusion. Volume 59:Issue 3(2019)
• Page Start: 953
• Page End: 964
• Publication Date: 2018-12-12
• Subjects: Hematology -- Periodicals; Blood -- Transfusion -- Periodicals; Blood Group Antigens -- Periodicals; Blood Preservation -- Periodicals; Blood Transfusion -- Periodicals; 615
• DOI: 10.1111/trf.15078
• Languages: English
• ISSNs: 0041-1132

Abstract

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.