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Integration of metabolic and inflammatory mediator profiles as a potential prognostic approach for septic shock in the intensive care unit.

Mickiewicz B, Tam P, Jenne CN, Leger C, Wong J, Winston BW, Doig C, Kubes P, Vogel HJ, Alberta Sepsis Netwo - Crit Care (2015)

Bottom Line: Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients.The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits.By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality.

View Article: PubMed Central - PubMed

Affiliation: Bio-NMR-Centre, Department of Biological Sciences, University of Calgary, 2500 University Drive Northwest, Calgary, AB, T2N 1N4, Canada. bmmickie@ucalgary.ca.

ABSTRACT

Introduction: Septic shock is a major life-threatening condition in critically ill patients and it is well known that early recognition of septic shock and expedient initiation of appropriate treatment improves patient outcome. Unfortunately, to date no single compound has shown sufficient sensitivity and specificity to be used as a routine biomarker for early diagnosis and prognosis of septic shock in the intensive care unit (ICU). Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients. In this study, we have evaluated whether a combined nuclear magnetic resonance spectroscopy-based metabolomics and a multiplex cytokine/chemokine profiling approach could be used for diagnosis and prognostic evaluation of septic shock patients in the ICU.

Methods: Serum and plasma samples were collected from septic shock patients and ICU controls (ICU patients with the systemic inflammatory response syndrome but not suspected of having an infection). (1)H Nuclear magnetic resonance spectra were analyzed and quantified using the targeted profiling methodology. The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits.

Results: By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality. These metabolites and cytokines/chemokines represent candidate biomarkers of the human response to septic shock and have the potential to improve early diagnosis and prognosis of septic shock.

Conclusions: Our findings show that integration of quantitative metabolic and inflammatory mediator data can be utilized for the diagnosis and prognosis of septic shock in the ICU.

No MeSH data available.


Related in: MedlinePlus

Mortality model. The OPLS-DA score scatter plot (A) and the ‘Predicted vs. Observed’ plot (B) for septic shock nonsurvivors (black dots) and age-sex-matched survivors (black circles) based on the combined metabolomics and cytokine/chemokine dataset. Both groups are well separated along the first PLS component and none of the nonsurvivors were predicted as a survivor. In figure 2B only seven dots are visible instead of eight because two samples had a very similar predicted value and their symbols overlap. OPLS-DA, orthogonal partial least squares discriminant analysis; PLS, partial least squares.
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Fig2: Mortality model. The OPLS-DA score scatter plot (A) and the ‘Predicted vs. Observed’ plot (B) for septic shock nonsurvivors (black dots) and age-sex-matched survivors (black circles) based on the combined metabolomics and cytokine/chemokine dataset. Both groups are well separated along the first PLS component and none of the nonsurvivors were predicted as a survivor. In figure 2B only seven dots are visible instead of eight because two samples had a very similar predicted value and their symbols overlap. OPLS-DA, orthogonal partial least squares discriminant analysis; PLS, partial least squares.

Mentions: Additionally, we applied OPLS-DA for the prediction of ICU patient outcome. From the available septic shock samples we selected eight nonsurvivors and eight age-sex-matched survivors. The median age of these patients was 63 (59.8 to 77), admission APACHE II score: 25.5 (17.5 to 31.3), admission SOFA score: 10.5 (7 to 12.5) and the length of ICU stay: 6.4 days (3.5 to 9.6 days). The score scatter plot (Figure 2A) and ‘Predicted vs. Observed’ plot (Figure 2B) reveals that septic shock survivors are very well separated from the nonsurvivors. The R2Y, Q2 metric and AUROC have very high values: 0.94, 0.74 and 1.0 respectively. A summary of the quantitative model evaluation results for the various OPLS-DA models that were constructed is presented in Table 2.Figure 2


Integration of metabolic and inflammatory mediator profiles as a potential prognostic approach for septic shock in the intensive care unit.

Mickiewicz B, Tam P, Jenne CN, Leger C, Wong J, Winston BW, Doig C, Kubes P, Vogel HJ, Alberta Sepsis Netwo - Crit Care (2015)

Mortality model. The OPLS-DA score scatter plot (A) and the ‘Predicted vs. Observed’ plot (B) for septic shock nonsurvivors (black dots) and age-sex-matched survivors (black circles) based on the combined metabolomics and cytokine/chemokine dataset. Both groups are well separated along the first PLS component and none of the nonsurvivors were predicted as a survivor. In figure 2B only seven dots are visible instead of eight because two samples had a very similar predicted value and their symbols overlap. OPLS-DA, orthogonal partial least squares discriminant analysis; PLS, partial least squares.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4340832&req=5

Fig2: Mortality model. The OPLS-DA score scatter plot (A) and the ‘Predicted vs. Observed’ plot (B) for septic shock nonsurvivors (black dots) and age-sex-matched survivors (black circles) based on the combined metabolomics and cytokine/chemokine dataset. Both groups are well separated along the first PLS component and none of the nonsurvivors were predicted as a survivor. In figure 2B only seven dots are visible instead of eight because two samples had a very similar predicted value and their symbols overlap. OPLS-DA, orthogonal partial least squares discriminant analysis; PLS, partial least squares.
Mentions: Additionally, we applied OPLS-DA for the prediction of ICU patient outcome. From the available septic shock samples we selected eight nonsurvivors and eight age-sex-matched survivors. The median age of these patients was 63 (59.8 to 77), admission APACHE II score: 25.5 (17.5 to 31.3), admission SOFA score: 10.5 (7 to 12.5) and the length of ICU stay: 6.4 days (3.5 to 9.6 days). The score scatter plot (Figure 2A) and ‘Predicted vs. Observed’ plot (Figure 2B) reveals that septic shock survivors are very well separated from the nonsurvivors. The R2Y, Q2 metric and AUROC have very high values: 0.94, 0.74 and 1.0 respectively. A summary of the quantitative model evaluation results for the various OPLS-DA models that were constructed is presented in Table 2.Figure 2

Bottom Line: Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients.The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits.By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality.

View Article: PubMed Central - PubMed

Affiliation: Bio-NMR-Centre, Department of Biological Sciences, University of Calgary, 2500 University Drive Northwest, Calgary, AB, T2N 1N4, Canada. bmmickie@ucalgary.ca.

ABSTRACT

Introduction: Septic shock is a major life-threatening condition in critically ill patients and it is well known that early recognition of septic shock and expedient initiation of appropriate treatment improves patient outcome. Unfortunately, to date no single compound has shown sufficient sensitivity and specificity to be used as a routine biomarker for early diagnosis and prognosis of septic shock in the intensive care unit (ICU). Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients. In this study, we have evaluated whether a combined nuclear magnetic resonance spectroscopy-based metabolomics and a multiplex cytokine/chemokine profiling approach could be used for diagnosis and prognostic evaluation of septic shock patients in the ICU.

Methods: Serum and plasma samples were collected from septic shock patients and ICU controls (ICU patients with the systemic inflammatory response syndrome but not suspected of having an infection). (1)H Nuclear magnetic resonance spectra were analyzed and quantified using the targeted profiling methodology. The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits.

Results: By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality. These metabolites and cytokines/chemokines represent candidate biomarkers of the human response to septic shock and have the potential to improve early diagnosis and prognosis of septic shock.

Conclusions: Our findings show that integration of quantitative metabolic and inflammatory mediator data can be utilized for the diagnosis and prognosis of septic shock in the ICU.

No MeSH data available.


Related in: MedlinePlus