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Title: Sepsis Diagnostics: Intensive Care Scoring Systems Superior to MicroRNA Biomarker Testing
Author(s): Schumann, Julia
Link, Fabian
Krohn, Knut
Burgdorff, Anna-Maria
Christel, Annett
Granting Institution: Martin-Luther-Universität Halle-Wittenberg
Issue Date: 2020-09-16
Type: Article
Language: English
Publisher: Universitäts- und Landesbibliothek Sachsen-Anhalt
Subjects: sepsis
sequential organ failure assessment (SOFA) score
Abstract: Sepsis represents a serious medical problem accounting for numerous deaths of critically ill patients in intensive care units (ICUs). An early, sensitive, and specific diagnosis is considered a key element for improving the outcome of sepsis patients. In addition to classical laboratory markers, ICU scoring systems and serum miRNAs are discussed as potential sepsis biomarkers. In the present prospective observational study, the suitability of miRNAs in sepsis diagnosis was tested based on proper validated and normalized data (i.e., absolute quantification by means of Droplet Digital PCR (ddPCR)) in direct comparison to classical sepsis markers and ICU scores within the same patient cohort. Therefore, blood samples of septic intensive care patients (n = 12) taken at day of admission at ICU were compared to non-septic intensive care patients (n = 12) and a healthy control group (n = 12). Our analysis indicates that all tested biomarkers have only a moderate informative power and do not allow an unequivocal differentiation between septic and non-septic ICU patients. In conclusion, there is no standalone laboratory parameter that enables a reliable diagnosis of sepsis. miRNAs are not superior to classical parameters in this respect. It seems recommendable to measure multiple parameters and scores and to interpret them with regard to the clinical presentation.
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Original Publication: 10.3390/diagnostics10090701
Appears in Collections:Medizinische Fakultät MLU

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