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Mil Med. 2018 Aug 14. doi: 10.1093/milmed/usy182. [Epub ahead of print]

Combat-Related Invasive Fungal Infections: Development of a Clinically Applicable Clinical Decision Support System for Early Risk Stratification.

Author information

1
Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.
2
Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD.
3
Regenerative Medicine Department, Naval Medical Research Center, 503 Robert Grant Avenue, Silver Spring, MD.
4
DecisionQ Corporation, 2500 Wilson Blvd #325, Arlington, VA.
5
Infectious Disease Clinical Research Program, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD.
6
Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., 6720A Rockledge Drive #100, Bethesda, MD.
7
Veterans Affairs Medical Center, 50 Irving St NW, Washington, DC.

Abstract

Introduction:

Invasive fungal infections (IFI) are associated with high morbidity and mortality. A better method of risk stratifying trauma patients for combat-related IFI is needed to improve clinical outcomes while minimizing morbidity related to overtreatment. We sought to develop combat-related IFI clinical decision support (CDS) tools to assist providers to make treatment decisions both near the point of injury and subsequently at definitive treatment centers.

Materials and Methods:

We utilized a training dataset containing information from 227 combat-injured military personnel to build a Bayesian belief network (BBN) to predict the likelihood of developing IFI using information available at the point of initial resuscitation (THEATER model) and in the tertiary care setting (MEDCEN model). After selecting BBN models, external validation used a separate test dataset of 350 wounded warriors. Furthermore, the performance of the BBN models was compared with a "two-rule model" alone (based on physician experience) and combinations of the BBN models plus the two-rule model. The two-rule model contains plausible IFI criteria, but it has not been formally evaluated, and they are not currently actual clinical guidelines.

Results:

We found receiver operating characteristic areas under the curve (AUC) of 0.70 (95% CI: [0.62, 0.77]) and 0.68 (95% CI: [0.59, 0.76]) for the THEATER and MEDCEN BBN models, respectively, on cross-validation. External validation with the highest AUC BBN models produced THEATER AUC of 0.68 (95% CI: [0.58, 0.78]) and MEDCEN AUC of 0.67 (95% CI: [0.57, 0.78]). With the incorporation of two-rule model in low IFI-prevalence populations, external validation AUC increased to 0.77 (95% CI: [0.69, 0.84]) for the THEATER model and 0.76 (95% CI:[0.68, 0.85]) for the LRMC model. The two-rule model alone has an AUC of 0.72 (95% CI: [0.63, 0.81]).

Conclusions:

Overall, the IFI tools produced clinically useful, robust models. However, the clinical utility of these models is highly dependent upon the clinician's individual risk tolerance. The threshold probability for optimal clinical use of this CDS tool is currently being evaluated in an ongoing clinical utilization study. CDS tools, such as these, may facilitate early diagnosis of patients with or at risk for IFI, permitting early or prophylactic treatment with the aim of improving outcomes.

PMID:
30124943
DOI:
10.1093/milmed/usy182

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