Open AccessMassive transfusion prediction in patients with multiple trauma by decision tree: a retrospective analysis
Author(s) -
Wei Liu,
Canrong Wu,
Juan Zou,
Le Aiping
Publication year - 2020
Publication title -
indian journal of hematology and blood transfusion/indian journal of hematology and blood transfusion
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.213
H-Index - 15
eISSN - 0974-0449
pISSN - 0971-4502
DOI - 10.1007/s12288-020-01348-y
Subject(s) - medicine , decision tree , pelvis , injury severity score , retrospective cohort study , blood transfusion , emergency medicine , protocol (science) , decision tree learning , intensive care medicine , surgery , data mining , computer science , poison control , injury prevention , pathology , alternative medicine
Early initial massive transfusion protocol and blood transfusion can reduce patient mortality, however accurately identifying the risk of massive transfusion (MT) remains a major challenge in severe trauma patient therapy. We retrospectively analyzed clinical data of severe trauma patients with and without MT. Based on analysis results, we established a MT prediction model of clinical and laboratory data by using the decision tree algorithm in patients with multiple trauma. Our results demonstrate that shock index, injury severity score, international normalized ratio, and pelvis fracture were the most significant risk factors of MT. These four indexes were incorporated into the prediction model, and the model was validated by using the testing dataset. Moreover, the sensitivity, specificity, accuracy and area under curve values of prediction model for MT risk prediction were 60%, 92%, 90% and 0.85. Our study provides an easy and understandable classification rules for identifying risk factors associated with MT that may be useful for promoting trauma management.