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Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room
Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS). Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS). Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
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2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 1, e0147670Article in journal (Refereed) Published
Abstract [en]

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69-0.99) and a specificity 0.84 (95% CI 0.58-0.94) with an AUC of 0.93 (95% CI 0.89-1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85-1.00) and specificity of 0.95 (95% CI 0.74-0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.

Place, publisher, year, edition, pages
2016. Vol. 11, no 1, e0147670
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Pharmaceutical Sciences Anesthesiology and Intensive Care
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URN: urn:nbn:se:umu:diva-130006DOI: 10.1371/journal.pone.0147670ISI: 000368655300138PubMedID: 26800189OAI: oai:DiVA.org:umu-130006DiVA: diva2:1063920
Available from: 2017-01-11 Created: 2017-01-11 Last updated: 2017-01-11Bibliographically approved

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Kauppi, Anna M.Edin, AliciaSjöstedt, AndersGylfe, ÅsaJohansson, Anders
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Clinical BacteriologyMolecular Infection Medicine Sweden (MIMS)Umeå Centre for Microbial Research (UCMR)
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