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Development of a Bayesian model to estimate health care outcomes in the severely wounded

Authors Stojadinovic A, Eberhardt J, Brown TS, Hawksworth JS, Gage F, Tadaki DK, Forsberg JA, Davis TA, Potter BK, Dunne JR, Elster EA

Published 16 August 2010 Volume 2010:3 Pages 125—135


Review by Single-blind

Peer reviewer comments 2

Alexander Stojadinovic1, John Eberhardt2, Trevor S Brown3, Jason S Hawksworth4, Frederick Gage3, Douglas K Tadaki3, Jonathan A Forsberg5, Thomas A Davis3, Benjamin K Potter5, James R Dunne6, E A Elster3

1Combat Wound Initiative Program, 4Department of Surgery, Walter Reed Army Medical Center, Washington, DC, USA; 2DecisionQ Corporation, Washington, DC, USA; 3Regenerative Medicine Department, Combat Casualty Care, Naval Medical Research Center, Silver Spring, MD, USA; 5Integrated Department of Orthopaedics and Rehabilitation, 6Department of Surgery, National Naval Medical Center, Bethesda, MD, USA

Background: Graphical probabilistic models have the ability to provide insights as to how clinical factors are conditionally related. These models can be used to help us understand factors influencing health care outcomes and resource utilization, and to estimate morbidity and clinical outcomes in trauma patient populations.

Study design: Thirty-two combat casualties with severe extremity injuries enrolled in a prospective observational study were analyzed using step-wise machine-learned Bayesian belief network (BBN) and step-wise logistic regression (LR). Models were evaluated using 10-fold cross-validation to calculate area-under-the-curve (AUC) from receiver operating characteristics (ROC) curves.

Results: Our BBN showed important associations between various factors in our data set that could not be developed using standard regression methods. Cross-validated ROC curve analysis showed that our BBN model was a robust representation of our data domain and that LR models trained on these findings were also robust: hospital-acquired infection (AUC: LR, 0.81; BBN, 0.79), intensive care unit length of stay (AUC: LR, 0.97; BBN, 0.81), and wound healing (AUC: LR, 0.91; BBN, 0.72) showed strong AUC.

Conclusions: A BBN model can effectively represent clinical outcomes and biomarkers in patients hospitalized after severe wounding, and is confirmed by 10-fold ­cross-validation and further confirmed through logistic regression modeling. The method warrants further development and independent validation in other, more diverse patient populations.

Keywords: combat, wounds, probabilistic model, Bayesian belief network, outcomes

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