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

Autor: Nikouline A; Department of Emergency Medicine, London Health Sciences Centre, 800 Commissioners Road E, London, ON, N6A 5W9, Canada. anton.nikouline@mail.utoronto.ca.; Division of Critical Care and Emergency Medicine, Department of Medicine, Western University, London, ON, Canada. anton.nikouline@mail.utoronto.ca., Feng J; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Vector Institute for Artificial Intelligence, Toronto, ON, Canada., Rudzicz F; Vector Institute for Artificial Intelligence, Toronto, ON, Canada.; Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada., Nathens A; Department of Surgery, Sunnybrook Health Sciences Center, Toronto, ON, Canada.; American College of Surgeons, Chicago, IL, USA., Nolan B; Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, ON, Canada.; International Centre for Surgical Safety, St. Michael's Hospital, Toronto, ON, Canada.; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada.; Department of Emergency Medicine, St. Michael's Hospital, Toronto, ON, Canada.
Jazyk: angličtina
Zdroj: European journal of trauma and emergency surgery : official publication of the European Trauma Society [Eur J Trauma Emerg Surg] 2024 Jun; Vol. 50 (3), pp. 1073-1081. Date of Electronic Publication: 2024 Jan 24.
DOI: 10.1007/s00068-023-02423-5
Abstrakt: 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.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)
Databáze: MEDLINE
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