ArticleIndian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion2021
Massive transfusion prediction in patients with multiple trauma by decision tree: a retrospective analysis.
Article in Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.Scientific reports · 2026Pooled it
- Severe trauma care: advances and future directions in diagnostic and therapeutic techniques and information technology support.Medical review (2021) · 2026Article
- Association of Serum Glucose/Potassium Ratio with Injury Severity and Transfusion Requirements in Traumatic Pelvic Fractures: A Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Artificial intelligence and machine learning for hemorrhagic trauma care.Military Medical Research · 2023Review
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Authors and funding
4 authors at 1 institution in 1 country.
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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.
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