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dc.contributor.authorLamping, Florian
dc.contributor.authorJack, Thomas
dc.contributor.authorRübsamen, Nicole
dc.contributor.authorSasse, Michael
dc.contributor.authorBeerbaum, Philipp
dc.contributor.authorMikolajczyk, Rafael T
dc.contributor.authorBoehne, Martin
dc.contributor.authorKarch, André
dc.date.accessioned2019-04-17T13:00:23Z
dc.date.available2019-04-17T13:00:23Z
dc.date.issued2018-03-15
dc.identifier.citationBMC Pediatr. 2018 Mar 15;18(1):112. doi: 10.1186/s12887-018-1082-2en_US
dc.identifier.issn1471-2431
dc.identifier.pmid29544449
dc.identifier.doi10.1186/s12887-018-1082-2
dc.identifier.urihttp://hdl.handle.net/10033/621753
dc.description.abstractBACKGROUND: Since early antimicrobial therapy is mandatory in septic patients, immediate diagnosis and distinction from non-infectious SIRS is essential but hampered by the similarity of symptoms between both entities. We aimed to develop a diagnostic model for differentiation of sepsis and non-infectious SIRS in critically ill children based on routinely available parameters (baseline characteristics, clinical/laboratory parameters, technical/medical support). METHODS: This is a secondary analysis of a randomized controlled trial conducted at a German tertiary-care pediatric intensive care unit (PICU). Two hundred thirty-eight cases of non-infectious SIRS and 58 cases of sepsis (as defined by IPSCC criteria) were included. We applied a Random Forest approach to identify the best set of predictors out of 44 variables measured at the day of onset of the disease. The developed diagnostic model was validated in a temporal split-sample approach. RESULTS: A model including four clinical (length of PICU stay until onset of non-infectious SIRS/sepsis, central line, core temperature, number of non-infectious SIRS/sepsis episodes prior to diagnosis) and four laboratory parameters (interleukin-6, platelet count, procalcitonin, CRP) was identified in the training dataset. Validation in the test dataset revealed an AUC of 0.78 (95% CI: 0.70-0.87). Our model was superior to previously proposed biomarkers such as CRP, interleukin-6, procalcitonin or a combination of CRP and procalcitonin (maximum AUC = 0.63; 95% CI: 0.52-0.74). When aiming at a complete identification of sepsis cases (100%; 95% CI: 87-100%), 28% (95% CI: 20-38%) of non-infectious SIRS cases were assorted correctly. CONCLUSIONS: Our approach allows early recognition of sepsis with an accuracy superior to previously described biomarkers, and could potentially reduce antibiotic use by 30% in non-infectious SIRS cases. External validation studies are necessary to confirm the generalizability of our approach across populations and treatment practices.en_US
dc.language.isoenen_US
dc.publisherBioMedCentralen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectDiagnosisen_US
dc.subjectIntensive care uniten_US
dc.subjectPediatricen_US
dc.subjectRandom Foresten_US
dc.subjectSIRSen_US
dc.subjectSepsisen_US
dc.titleDevelopment and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children - a data-driven approach using machine-learning algorithms.en_US
dc.typeArticleen_US
dc.contributor.departmentHZI, Helmholtz Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7, 38124 Braunschweig Germany.en_US
dc.identifier.journalBMC Pediatricsen_US
refterms.dateFOA2019-04-17T13:00:24Z
dc.source.journaltitleBMC pediatrics


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