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Advanced Modeling to Predict Pneumonia in Combat Trauma Patients

  • Matthew Bradley
  • Christopher Dente
  • Vivek Khatri
  • Seth Schobel
  • Felipe Lisboa
  • Audrey Shi
  • Hannah Hensman
  • Allan Kirk
  • Timothy G. Buchman
  • Eric ElsterEmail author
Original Scientific Report

Abstract

Background

Tools to assist clinicians in predicting pneumonia could lead to a significant decline in morbidity. Therefore, we sought to develop a model in combat trauma patients for identifying those at highest risk of pneumonia.

Methods

This was a retrospective study of 73 primarily blast-injured casualties with combat extremity wounds. Binary classification models for pneumonia prediction were developed with measurements of injury severity from the Abbreviated Injury Scale (AIS), transfusion blood products received before arrival at Walter Reed National Military Medical Center (WRNMMC), and serum protein levels. Predictive models were generated with leave-one-out-cross-validation using the variable selection method of backward elimination (BE) and the machine learning algorithms of random forests (RF) and logistic regression (LR). BE was attempted with two predictor sets: (1) all variables and (2) serum proteins alone.

Results

Incidence of pneumonia was 12% (n = 9). Different variable sets were produced by BE when considering all variables and just serum proteins alone. BE selected the variables ISS, AIS chest, and cryoprecipitate within the first 24 h following injury for the first predictor set 1 and FGF-basic, IL-2R, and IL-6 for predictor set 2. Using both variable sets, a RF was generated with AUCs of 0.95 and 0.87—both higher than LR algorithms.

Conclusion

Advanced modeling allowed for the identification of clinical and biomarker data predictive of pneumonia in a cohort of predominantly blast-injured combat trauma patients. The generalizability of the models developed here will require an external validation dataset.

Notes

Acknowledgements

Funding was provided by Surgical Critical Care Initiative (Grant No. HU0001-15-2-0001).

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Copyright information

© Société Internationale de Chirurgie 2019

Authors and Affiliations

  • Matthew Bradley
    • 1
    • 2
    • 6
  • Christopher Dente
    • 3
    • 4
    • 6
  • Vivek Khatri
    • 1
    • 6
    • 8
  • Seth Schobel
    • 1
    • 6
    • 8
  • Felipe Lisboa
    • 1
    • 6
    • 8
  • Audrey Shi
    • 6
    • 7
  • Hannah Hensman
    • 6
    • 7
  • Allan Kirk
    • 5
    • 6
  • Timothy G. Buchman
    • 3
    • 7
  • Eric Elster
    • 1
    • 6
    Email author
  1. 1.Department of SurgeryUniformed Services University of the Health Sciences and Walter Reed National Military Medical CenterBethesdaUSA
  2. 2.Department of Regenerative MedicineNaval Medical Research CenterSilver SpringUSA
  3. 3.Emory UniversityAtlantaUSA
  4. 4.Grady Memorial HospitalAtlantaUSA
  5. 5.Duke UniversityDurhamUSA
  6. 6.Surgical Critical Care Initiative (SC2i)BethesdaUSA
  7. 7.DecisionQ CorporationArlingtonUSA
  8. 8.Henry M. Jackson Foundation for the Advancement of Military SciencesBethesdaUSA

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