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EBioMedicine. 2015 Jul 17;2(9):1235-42. doi: 10.1016/j.ebiom.2015.07.022. eCollection 2015 Sep.

Lessons of War: Turning Data Into Decisions.

Author information

1
Department of Surgery at the Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD USA ; Regenerative Medicine Department, Naval Medical Research Center, Silver Spring, MD USA ; Surgical Critical Care Initiative (SC2i), Bethesda, MD, USA.
2
Department of Surgery at the Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD USA ; Surgical Critical Care Initiative (SC2i), Bethesda, MD, USA.
3
Department of Epidemiology and Biostatistics Memorial Sloan-Kettering Cancer Center, New York, NY USA.
4
Department of Surgery, Emory University, Atlanta, GA USA ; Surgical Critical Care Initiative (SC2i), Bethesda, MD, USA.
5
Department of Surgery, Duke University Medical Center, Durham, NC USA ; Surgical Critical Care Initiative (SC2i), Bethesda, MD, USA.

Abstract

BACKGROUND:

Recent conflicts in Afghanistan and Iraq produced a substantial number of critically wounded service-members. We collected biomarker and clinical information from 73 patients who sustained 116 life-threatening combat wounds, and sought to determine if the data could be used to predict the likelihood of wound failure.

METHODS:

From each patient, we collected clinical information, serum, wound effluent, and tissue prior to and at each surgical débridement. Inflammatory cytokines were quantified in both the serum and effluent, as were gene expression targets. The primary outcome was successful wound healing. Computer intensive methods were used to derive prognostic models that were internally validated using target shuffling and cross-validation methods. A second cohort of eighteen critically injured civilian patients was evaluated to determine if similar inflammatory responses were observed.

FINDINGS:

The best-performing models enhanced clinical observation with biomarker data from the serum and wound effluent, an indicator that systemic inflammatory conditions contribute to local wound failure. A Random Forest model containing ten variables demonstrated the highest accuracy (AUC 0.79). Decision Curve Analysis indicated that the use of this model would improve clinical outcomes and reduce unnecessary surgical procedures. Civilian trauma patients demonstrated similar inflammatory responses and an equivalent wound failure rate, indicating that the model may be generalizable to civilian settings.

INTERPRETATION:

Using advanced analytics, we successfully codified clinical and biomarker data from combat patients into a potentially generalizable decision support tool. Analysis of inflammatory data from critically ill patients with acute injury may inform decision-making to improve clinical outcomes and reduce healthcare costs.

FUNDING:

United States Department of Defense Health Programs.

KEYWORDS:

Clinical decision support; Combat trauma; Decision analysis; Inflammation; Wound healing

PMID:
26501123
PMCID:
PMC4588374
DOI:
10.1016/j.ebiom.2015.07.022
[Indexed for MEDLINE]
Free PMC Article

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