1. Massive transfusion prediction in patients with multiple trauma by decision tree: a retrospective analysis
- Author
-
Liu Wei, Zou Juan, Wu Chenggao, and Le Aiping
- Subjects
medicine.medical_specialty ,Blood transfusion ,business.industry ,Decision tree learning ,medicine.medical_treatment ,Decision tree ,Hematology ,030204 cardiovascular system & hematology ,Massive transfusion ,03 medical and health sciences ,0302 clinical medicine ,Trauma management ,Emergency medicine ,Retrospective analysis ,Medicine ,Injury Severity Score ,In patient ,Original Article ,business ,030215 immunology - Abstract
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.
- Published
- 2019