Advanced Modeling to Predict Pneumonia in Combat Trauma Patients.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Bradley, M; Dente, C; Khatri, V; Schobel, S; Lisboa, F; Shi, A; Hensman, H; Kirk, A; Buchman, TG; Elster, E

Published Date

  • July 2020

Published In

Volume / Issue

  • 44 / 7

Start / End Page

  • 2255 - 2262

PubMed ID

  • 31748888

Electronic International Standard Serial Number (EISSN)

  • 1432-2323

Digital Object Identifier (DOI)

  • 10.1007/s00268-019-05294-3


  • eng

Conference Location

  • United States