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Lessons of War: Turning Data Into Decisions.

Forsberg JA, Potter BK, Wagner MB, Vickers A, Dente CJ, Kirk AD, Elster EA - EBioMedicine (2015)

Bottom Line: The primary outcome was successful wound healing.Decision Curve Analysis indicated that the use of this model would improve clinical outcomes and reduce unnecessary surgical procedures.Analysis of inflammatory data from critically ill patients with acute injury may inform decision-making to improve clinical outcomes and reduce healthcare costs.

View Article: PubMed Central - PubMed

Affiliation: 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.

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.

No MeSH data available.


Related in: MedlinePlus

The comparison of inflammatory mediators in the serum and effluent of military and civilian patients demonstrates similar distributions. We observed more variability, however, in the concentrations of these proteins in the military patients.
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f0015: The comparison of inflammatory mediators in the serum and effluent of military and civilian patients demonstrates similar distributions. We observed more variability, however, in the concentrations of these proteins in the military patients.

Mentions: To determine if the biologic responses seen in our military cohort apply to civilian trauma patients, we analyzed a second set of 18 subjects with 27 significant extremity wounds who underwent 49 surgical débridements. Most of these injuries were due to blunt trauma; none were related to blast. Four (15%) of the closed wounds failed. Of note, civilian patients with wound failure exhibit similar distributions in HLOS as those derived from our military sample. Fig. 3 depicts the similarities between the concentrations of the majority of serum and effluent biomarkers in military and civilian patients (Fig. 3). Due to differences in sample analysis platform, direct external model validation was not possible; however, these preliminary findings suggest that future validation of our existing model using civilian patients will be feasible and such tools will be relevant for both populations.


Lessons of War: Turning Data Into Decisions.

Forsberg JA, Potter BK, Wagner MB, Vickers A, Dente CJ, Kirk AD, Elster EA - EBioMedicine (2015)

The comparison of inflammatory mediators in the serum and effluent of military and civilian patients demonstrates similar distributions. We observed more variability, however, in the concentrations of these proteins in the military patients.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4588374&req=5

f0015: The comparison of inflammatory mediators in the serum and effluent of military and civilian patients demonstrates similar distributions. We observed more variability, however, in the concentrations of these proteins in the military patients.
Mentions: To determine if the biologic responses seen in our military cohort apply to civilian trauma patients, we analyzed a second set of 18 subjects with 27 significant extremity wounds who underwent 49 surgical débridements. Most of these injuries were due to blunt trauma; none were related to blast. Four (15%) of the closed wounds failed. Of note, civilian patients with wound failure exhibit similar distributions in HLOS as those derived from our military sample. Fig. 3 depicts the similarities between the concentrations of the majority of serum and effluent biomarkers in military and civilian patients (Fig. 3). Due to differences in sample analysis platform, direct external model validation was not possible; however, these preliminary findings suggest that future validation of our existing model using civilian patients will be feasible and such tools will be relevant for both populations.

Bottom Line: The primary outcome was successful wound healing.Decision Curve Analysis indicated that the use of this model would improve clinical outcomes and reduce unnecessary surgical procedures.Analysis of inflammatory data from critically ill patients with acute injury may inform decision-making to improve clinical outcomes and reduce healthcare costs.

View Article: PubMed Central - PubMed

Affiliation: 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.

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.

No MeSH data available.


Related in: MedlinePlus