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Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit.

Garcia-Simon M, Morales JM, Modesto-Alapont V, Gonzalez-Marrachelli V, Vento-Rehues R, Jorda-Miñana A, Blanquer-Olivas J, Monleon D - PLoS ONE (2015)

Bottom Line: In addition, we compared the prediction power of metabolomics data respect the Sequential Organ Failure Assessment (SOFA) score.Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage.A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA.

View Article: PubMed Central - PubMed

Affiliation: Department of Critical Care, Clinical University Hospital of Valencia, Valencia, Spain.

ABSTRACT
Early diagnosis and patient stratification may improve sepsis outcome by a timely start of the proper specific treatment. We aimed to identify metabolomic biomarkers of sepsis in urine by (1)H-NMR spectroscopy to assess the severity and to predict outcomes. Urine samples were collected from 64 patients with severe sepsis or septic shock in the ICU for a (1)H NMR spectra acquisition. A supervised analysis was performed on the processed spectra, and a predictive model for prognosis (30-days mortality/survival) of sepsis was constructed using partial least-squares discriminant analysis (PLS-DA). In addition, we compared the prediction power of metabolomics data respect the Sequential Organ Failure Assessment (SOFA) score. Supervised multivariate analysis afforded a good predictive model to distinguish the patient groups and detect specific metabolic patterns. Negative prognosis patients presented higher values of ethanol, glucose and hippurate, and on the contrary, lower levels of methionine, glutamine, arginine and phenylalanine. These metabolites could be part of a composite biopattern of the human metabolic response to sepsis shock and its mortality in ICU patients. The internal cross-validation showed robustness of the metabolic predictive model obtained and a better predictive ability in comparison with SOFA values. Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage. A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA.

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ROC curves constructed with the logistic regression model and frequency table for discrimination between survivor and non-survivor patients in predicting 30-days mortality.(A) ROC curve based on the metabolomics scores evolution values (AUC 0.85; p<0.05, 95% CI 0.68–1). (B) ROC curve based on the SOFA evolution values (AUC 0.78; p<0.05, 95% CI 0.6–0.9). (C) Comparison of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC) and area under (AUC) the ROC curve of SOFA and metabolomic predictive models. The numbers in parenthesis represent the confidence interval in 95%.
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pone.0140993.g003: ROC curves constructed with the logistic regression model and frequency table for discrimination between survivor and non-survivor patients in predicting 30-days mortality.(A) ROC curve based on the metabolomics scores evolution values (AUC 0.85; p<0.05, 95% CI 0.68–1). (B) ROC curve based on the SOFA evolution values (AUC 0.78; p<0.05, 95% CI 0.6–0.9). (C) Comparison of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC) and area under (AUC) the ROC curve of SOFA and metabolomic predictive models. The numbers in parenthesis represent the confidence interval in 95%.

Mentions: In order to make comparable the death prediction value of metabolomic model and SOFA score a multi-step statistical analysis was performed. The logistic regression ROC curve obtained with the metabolomic values (Fig 3A) has an AUC of 0.85 (CI 0.69–1)). Whereas the ROC curve obtained with the logistic regression SOFA values (Fig 3B) has an AUC of 0.78 (CI 0.70–0.95). Sensitivity, specificity, positive and negative predictive values and its corresponding confidence interval of 95% for both ROC curves are summarized in Fig 3C. The urine-based metabolomic score obtained from the logistic regression performed better discrimination between positive and negative prognosis than with SOFA scale. As the number of samples in the survivor and non-survivor groups was not well balanced, we performed a new model with a subset of samples from survivals matched with respect gender, age, SOFA and Bacteremia of the non-survival group (see S3 Fig). This model did not exhibit better discriminating results than the obtained with non-matched samples.


Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit.

Garcia-Simon M, Morales JM, Modesto-Alapont V, Gonzalez-Marrachelli V, Vento-Rehues R, Jorda-Miñana A, Blanquer-Olivas J, Monleon D - PLoS ONE (2015)

ROC curves constructed with the logistic regression model and frequency table for discrimination between survivor and non-survivor patients in predicting 30-days mortality.(A) ROC curve based on the metabolomics scores evolution values (AUC 0.85; p<0.05, 95% CI 0.68–1). (B) ROC curve based on the SOFA evolution values (AUC 0.78; p<0.05, 95% CI 0.6–0.9). (C) Comparison of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC) and area under (AUC) the ROC curve of SOFA and metabolomic predictive models. The numbers in parenthesis represent the confidence interval in 95%.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4643898&req=5

pone.0140993.g003: ROC curves constructed with the logistic regression model and frequency table for discrimination between survivor and non-survivor patients in predicting 30-days mortality.(A) ROC curve based on the metabolomics scores evolution values (AUC 0.85; p<0.05, 95% CI 0.68–1). (B) ROC curve based on the SOFA evolution values (AUC 0.78; p<0.05, 95% CI 0.6–0.9). (C) Comparison of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC) and area under (AUC) the ROC curve of SOFA and metabolomic predictive models. The numbers in parenthesis represent the confidence interval in 95%.
Mentions: In order to make comparable the death prediction value of metabolomic model and SOFA score a multi-step statistical analysis was performed. The logistic regression ROC curve obtained with the metabolomic values (Fig 3A) has an AUC of 0.85 (CI 0.69–1)). Whereas the ROC curve obtained with the logistic regression SOFA values (Fig 3B) has an AUC of 0.78 (CI 0.70–0.95). Sensitivity, specificity, positive and negative predictive values and its corresponding confidence interval of 95% for both ROC curves are summarized in Fig 3C. The urine-based metabolomic score obtained from the logistic regression performed better discrimination between positive and negative prognosis than with SOFA scale. As the number of samples in the survivor and non-survivor groups was not well balanced, we performed a new model with a subset of samples from survivals matched with respect gender, age, SOFA and Bacteremia of the non-survival group (see S3 Fig). This model did not exhibit better discriminating results than the obtained with non-matched samples.

Bottom Line: In addition, we compared the prediction power of metabolomics data respect the Sequential Organ Failure Assessment (SOFA) score.Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage.A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA.

View Article: PubMed Central - PubMed

Affiliation: Department of Critical Care, Clinical University Hospital of Valencia, Valencia, Spain.

ABSTRACT
Early diagnosis and patient stratification may improve sepsis outcome by a timely start of the proper specific treatment. We aimed to identify metabolomic biomarkers of sepsis in urine by (1)H-NMR spectroscopy to assess the severity and to predict outcomes. Urine samples were collected from 64 patients with severe sepsis or septic shock in the ICU for a (1)H NMR spectra acquisition. A supervised analysis was performed on the processed spectra, and a predictive model for prognosis (30-days mortality/survival) of sepsis was constructed using partial least-squares discriminant analysis (PLS-DA). In addition, we compared the prediction power of metabolomics data respect the Sequential Organ Failure Assessment (SOFA) score. Supervised multivariate analysis afforded a good predictive model to distinguish the patient groups and detect specific metabolic patterns. Negative prognosis patients presented higher values of ethanol, glucose and hippurate, and on the contrary, lower levels of methionine, glutamine, arginine and phenylalanine. These metabolites could be part of a composite biopattern of the human metabolic response to sepsis shock and its mortality in ICU patients. The internal cross-validation showed robustness of the metabolic predictive model obtained and a better predictive ability in comparison with SOFA values. Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage. A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA.

Show MeSH
Related in: MedlinePlus