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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 Bayesian Belief Network can be represented graphically, as demonstrated in Panel A. Receiver Operator Characteristic Analysis and Decision Curve Analysis are depicted in Panels B and C, respectively.
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f0010: The Bayesian Belief Network can be represented graphically, as demonstrated in Panel A. Receiver Operator Characteristic Analysis and Decision Curve Analysis are depicted in Panels B and C, respectively.

Mentions: Each modeling method yielded prognostic information. The BBN, represented graphically (Fig. 2a), revealed seven features that are most closely related to the wound outcome, Serum IL7, Effluent IL4, Serum IL1a, Serum MCP-1, Effluent IL-6, Genitourinary trauma, and the transfusion requirement after admission. After measuring accuracy (Fig. 2b), the RF model designed to select the 10 important features was most accurate (AUC 0.79; 95% C.I. 0.57–0.91) followed by the BBN (12 features) (AUC 0.74; 95% C.I. 0.53–0.90) and the RF model designed to consider all 157 features contained within each record (AUC 0.72; 95% C.I. 0.47–0.83). The LASSO model (AUC 0.62; 95% C.I. 0.53–0.8) performed worst of all models and required 8 variables. Throughout the modeling process, we sought to mitigate the risk of overfitting whenever possible. Our efforts to do so are described in the Statistics, Data Modeling, and Cost Analysis section, above.


Lessons of War: Turning Data Into Decisions.

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

The Bayesian Belief Network can be represented graphically, as demonstrated in Panel A. Receiver Operator Characteristic Analysis and Decision Curve Analysis are depicted in Panels B and C, respectively.
© Copyright Policy
Related In: Results  -  Collection

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

f0010: The Bayesian Belief Network can be represented graphically, as demonstrated in Panel A. Receiver Operator Characteristic Analysis and Decision Curve Analysis are depicted in Panels B and C, respectively.
Mentions: Each modeling method yielded prognostic information. The BBN, represented graphically (Fig. 2a), revealed seven features that are most closely related to the wound outcome, Serum IL7, Effluent IL4, Serum IL1a, Serum MCP-1, Effluent IL-6, Genitourinary trauma, and the transfusion requirement after admission. After measuring accuracy (Fig. 2b), the RF model designed to select the 10 important features was most accurate (AUC 0.79; 95% C.I. 0.57–0.91) followed by the BBN (12 features) (AUC 0.74; 95% C.I. 0.53–0.90) and the RF model designed to consider all 157 features contained within each record (AUC 0.72; 95% C.I. 0.47–0.83). The LASSO model (AUC 0.62; 95% C.I. 0.53–0.8) performed worst of all models and required 8 variables. Throughout the modeling process, we sought to mitigate the risk of overfitting whenever possible. Our efforts to do so are described in the Statistics, Data Modeling, and Cost Analysis section, above.

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