Application of a recursive partitioning decision tree algorithm for the prediction of massive transfusion in civilian trauma: the MTPitt prediction tool.
Transfusion
; 59(3): 953-964, 2019 03.
Article
em En
| MEDLINE
| ID: mdl-30548461
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Ferimentos e Lesões
/
Transfusão de Sangue
Tipo de estudo:
Health_economic_evaluation
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Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Aged
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Humans
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Middle aged
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article