• Daniel Lammers, James Williams, Jeff Conner, Andrew Francis, Beau Prey, Christopher Marenco, Kaitlin Morte, John Horton, Meade Barlow, Mauricio Escobar, Jason Bingham, and Matthew Eckert.
• Department of General Surgery, Madigan Army Medical Center, Tacoma, WA 98431, USA.
• Mil Med. 2024 Jan 23; 189 (1-2): 345351345-351.

BackgroundIdentification of pediatric trauma patients at the highest risk for death may promote optimization of care. This becomes increasingly important in austere settings with constrained medical capabilities. This study aimed to develop and validate predictive models using supervised machine learning (ML) techniques to identify pediatric warzone trauma patients at the highest risk for mortality.MethodsSupervised learning approaches using logistic regression (LR), support vector machine (SVM), neural network (NN), and random forest (RF) models were generated from the Department of Defense Trauma Registry, 2008-2016. Models were tested and compared to determine the optimal algorithm for mortality.ResultsA total of 2,007 patients (79% male, median age range 7-12 years old, 62.5% sustaining penetrating injury) met the inclusion criteria. Severe injury (Injury Severity Score > 15) was noted in 32.4% of patients, while overall mortality was 7.13%. The RF and SVM models displayed recall values of .9507 and .9150, while LR and NN displayed values of .8912 and .8895, respectively. Random forest (RF) outperformed LR, SVM, and NN on receiver operating curve (ROC) analysis demonstrating an area under the ROC of .9752 versus .9252, .9383, and .8748, respectively.ConclusionMachine learning (ML) techniques may prove useful in identifying those at the highest risk for mortality within pediatric trauma patients from combat zones. Incorporation of advanced computational algorithms should be further explored to optimize and supplement the diagnostic and therapeutic decision-making process.Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2022. This work is written by (a) US Government employee(s) and is in the public domain in the US.

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