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Cited by 35Sepsis Diagnosis and TreatmentTrauma and Emergency Care StudiesTrauma Management and Diagnosis

Towards precision medicine: Accurate predictive modeling of infectious complications in combat casualties

Dente, Christopher J., Bradley, Matthew, Schobel, Seth, Gaucher, Beverly, Buchman, Timothy, Kirk, Allan D., Elster, Eric·2017·Journal of Trauma and Acute Care Surgery
DOI 10.1097/ta.0000000000001596
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Abstract

BACKGROUND: The biomarker profile of trauma patients may allow for the creation of models to assist bedside decision making and prediction of complications. We sought to determine the utility of modeling in the prediction of bacteremia and pneumonia in combat casualties. METHODS: This is a prospective, observational trial of patients with complex wounds treated at Walter Reed National Military Medical Center (2007-2012). Tissue, serum, and wound effluent samples were collected during operative interventions until wound closure. Clinical, biomarker, and outcome data were used in machine learning algorithms to develop models predicting bacteremia or pneumonia. Modeling was performed on the first operative washout to maximize predictive benefit. Variable selection of dataset variables was performed and the best-fitting Bayesian belief network (BBN), using Bayesian information criterion (BIC), was selected for predictive modeling. Random forest was performed using variables from BBN step. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) analysis. RESULTS: Seventy-three patients (mean age 23, mean Injury Severity Score 25) were enrolled. Patients required a median of 3 (2-13) operations. The incidence of bacteremia and pneumonia was 22% and 12%, respectively. Best-fitting variable selected BBNs were maximum-minimum parents and children (MMPC) for both bacteremia (BIC-24948) and pneumonia (BIC-17886). Full variable and MMPC random forest models AUC were 0.721 and 0.834, respectively, for bacteremia and 0.809 and 0.856, respectively, for pneumonia. CONCLUSIONS: We identified a profile predictive of bacteremia and pneumonia in combat casualties. This has important clinical implications and should be validated in the civilian trauma population. This and similar tools will allow for increasing precision in the management of critically ill and injured patients. LEVEL OF EVIDENCE: Prognostic, level III.

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  • Understanding microbiological niches (biomarkers) of trauma patients could hold the key to future wound infections and allow healthcare workers to prevent the worst outcome.

    “In a study published in the journal of trauma and acute care surgery, researchers think that by understanding the microbiological niches (biomarkers) of trauma patients, we could hold the key to future wound infections and therefore can allow healthcare workers to take the necessary arrangements to prevent the worst outcome [64].”

    From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [64]

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Authors
Dente, C. J., Bradley, M., Schobel, S., Gaucher, B., Buchman, T., Kirk, A. D., & Elster, E.
Affiliations
Grady Memorial Hospital (US)
ORCID
Venue
Journal of Trauma and Acute Care Surgery
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2017-10
Volume
83
Issue
4
Pages
609-616
ISSN
2163-0755 (ISSN-L)
DOI
10.1097/ta.0000000000001596
OpenAlex
W2619537995
Cited by
35
References
28
Citations by year
2026: 2 · 2025: 1 · 2024: 1 · 2023: 4 · 2022: 3 · 2021: 7 · 2020: 8 · 2019: 7 · 2018: 2
FWCI
3.66
Type
journal article
Language
en
Open access
closed
Locations
  • The Journal of Trauma: Injury, Infection, and Critical Care (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • Health Sciences › Medicine › Emergency Medicine › Trauma and Emergency Care Studies
  • Health Sciences › Medicine › Surgery › Trauma Management and Diagnosis
Keywords
combat casualties, bacteremia, pneumonia, biomarkers, Bayesian networks, random forest, infection prediction, complex wounds, precision medicine, military trauma
MeSH terms
Machine Learning, Algorithms, Bayes Theorem, Decision Making, Decision Support Techniques, Humans, Length of Stay, Military Medicine, Pneumonia, Postoperative Complications, Predictive Value of Tests, Prospective Studies, Wounds and Injuries, Biomarkers, Glasgow Coma Scale, Injury Severity Score, Polymerase Chain Reaction, Bacteremia, Risk Assessment, APACHE, Young Adult, Precision Medicine

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