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

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Abstract

Purpose

Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various prediction tools have been developed for the initiation of MHP, but no single tool has demonstrated strong prediction with early clinical data. We sought to develop a massive transfusion prediction model using machine learning and early clinical data.

Methods

Using the National Trauma Data Bank from 2013 to 2018, we included severely injured trauma patients and extracted clinical features available from the pre-hospital and emergency department. We subsequently balanced our dataset and used the Boruta algorithm to determine feature selection. Massive transfusion was defined as five units at 4 h and ten units at 24 h. Six machine learning models were trained on the balanced dataset and tested on the original.

Results

A total of 326,758 patients met our inclusion with 18,871 (5.8%) requiring massive transfusion. Emergency department models demonstrated strong performance characteristics with mean areas under the receiver-operating characteristic curve of 0.83. Extreme gradient boost modeling slightly outperformed and demonstrated adequate predictive performance with pre-hospital data only, as well as 4-h transfusion thresholds.

Conclusions

We demonstrate the use of machine learning in developing an accurate prediction model for massive transfusion in trauma patients using early clinical data. This research demonstrates the potential utility of artificial intelligence as a clinical decision support tool.

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Data availability

The anonymized data collected for this study is available via the National Trauma Data Bank participant use file online data repository: https://www.facs.org/quality-programs/trauma/quality/national-trauma-data-bank/datasets/

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Acknowledgements

We would like to acknowledge Dr. David Gomez for his expertise in the NTDB and assisting in our methods, the Li Ka Shing Institute for providing the computers necessary to run our models, and Physician Services Incorporated and the St. Michael’s Hospital Association Alternate Funds Plan for their financial support for this study.

Funding

BN was supported by Physician Services Incorporated and the St. Michael’s Hospital Association Alternate Funds Plan. The funders did not have a role in study design, data analysis, interpretation of the results, writing the manuscript, or submitting the article for publication.

Author information

Authors and Affiliations

  1. Department of Emergency Medicine, London Health Sciences Centre, 800 Commissioners Road E, London, ON, N6A 5W9, Canada

    Anton Nikouline

  2. Division of Critical Care and Emergency Medicine, Department of Medicine, Western University, London, ON, Canada

    Anton Nikouline

  3. Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, ON, Canada

    Brodie Nolan

  4. International Centre for Surgical Safety, St. Michael’s Hospital, Toronto, ON, Canada

    Brodie Nolan

  5. Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada

    Brodie Nolan

  6. Department of Computer Science, University of Toronto, Toronto, ON, Canada

    Jinyue Feng

  7. Vector Institute for Artificial Intelligence, Toronto, ON, Canada

    Jinyue Feng & Frank Rudzicz

  8. Department of Surgery, Sunnybrook Health Sciences Center, Toronto, ON, Canada

    Avery Nathens

  9. American College of Surgeons, Chicago, IL, USA

    Avery Nathens

  10. Department of Emergency Medicine, St. Michael’s Hospital, Toronto, ON, Canada

    Brodie Nolan

  11. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada

    Frank Rudzicz

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  1. Anton Nikouline
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  5. Brodie Nolan
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Corresponding author

Correspondence to Anton Nikouline.

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Conflict of interest

Dr Nathens works for the American College of Surgeons as the director of the Trauma Quality Improvement Program and has oversight of the database used for this analysis. No other authors have any disclosures to report.

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Nikouline, A., Feng, J., Rudzicz, F. et al. Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept. Eur J Trauma Emerg Surg (2024). https://doi.org/10.1007/s00068-023-02423-5

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