Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept

Nikouline, Anton; Feng, Jinyue; Rudzicz, Frank; Nathens, Avery; Nolan, Brodie

doi:10.1007/s00068-023-02423-5

Eur J Trauma Emerg Surg

2024

DOI: 10.1007/s00068-023-02423-5

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Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept

Anton Nikouline,

Jinyue Feng,

Frank Rudzicz

et al.

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2024

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Cited by 2 publications

References 39 publications

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Evaluation of factors effecting survival of patients following massive transfusion and analyzing characteristics of massive transfusion protocol

Ram,

Ganvir,

Pathinangil

et al. 2024

International Journal of Surgery: Global Health

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Objective: To examine the impact of the time elapsed between a traumatic event and the implementation of a massive transfusion protocol on the patient outcomes and to analyze the characteristics of the protocol depending on the type of injury sustained. Methods: A total of 50 polytrauma patients who received blood transfusion by massive transfusion were included in the study. Demographic details, mechanism of injury, duration of time between injury, and other vital data were collected for analysis. Results: Our study revealed that the mean duration between the occurrence of injury and massive transfusion protocol activation (185 min) was highest among individuals who succumbed to their injuries within 1 hour and shortest among individuals who survived beyond the 24 hours following trauma (20.3 min) (P < 0.005). Blunt trauma had a lesser mortality rate (35.1%) than the penetrating type of trauma (61.5%). The average amount of fresh frozen plasma administered to patients with penetrating trauma (3.15) was significantly higher than that of blunt trauma (2.35) (P < 0.05). Conclusion: Delaying massive transfusion increases trauma patient mortality. Swift initiation of massive transfusion stabilizes severe blood loss cases. Varied blood product ratios and initiation times highlight the necessity for a standardized protocol.

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Evaluation of factors effecting survival of patients following massive transfusion and analyzing characteristics of massive transfusion protocol

Ram,

Ganvir,

Pathinangil

et al. 2024

International Journal of Surgery: Global Health

View full text Add to dashboard Cite

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Enhancing Trauma Care: A Machine Learning Approach with XGBoost for Predicting Urgent Hemorrhage Interventions Using NTDB Data

Zhang,

Jin,

Tang

et al. 2024

Bioengineering

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Objective: Trauma is a leading cause of death worldwide, with many incidents resulting in hemorrhage before the patient reaches the hospital. Despite advances in trauma care, the majority of deaths occur within the first three hours of hospital admission, offering a very limited window for effective intervention. Unfortunately, a significant increase in mortality from hemorrhagic trauma is primarily due to delays in hemorrhage control. Therefore, we propose a machine learning model to predict the need for urgent hemorrhage intervention. Methods: This study developed and validated an XGBoost-based machine learning model using data from the National Trauma Data Bank (NTDB) from 2017 to 2019. It focuses on demographic and clinical data from the initial hours following trauma for model training and validation, aiming to predict whether trauma patients require urgent hemorrhage intervention. Results: The XGBoost model demonstrated superior performance across multiple datasets, achieving an AUROC of 0.872 on the training set, 0.869 on the internal validation set, and 0.875 on the external validation set. The model also showed high sensitivity (77.8% on the external validation set) and specificity (82.1% on the external validation set), with an accuracy exceeding 81% across all datasets, highlighting its high reliability for clinical applications. Conclusions: Our study shows that the XGBoost model effectively predicts urgent hemorrhage interventions using data from the National Trauma Data Bank (NTDB). It outperforms other machine learning algorithms in accuracy and robustness across various datasets. These results highlight machine learning’s potential to improve emergency responses and decision-making in trauma care.

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Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept

Cited by 2 publications

References 39 publications

Evaluation of factors effecting survival of patients following massive transfusion and analyzing characteristics of massive transfusion protocol

Evaluation of factors effecting survival of patients following massive transfusion and analyzing characteristics of massive transfusion protocol

Enhancing Trauma Care: A Machine Learning Approach with XGBoost for Predicting Urgent Hemorrhage Interventions Using NTDB Data

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