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Development 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

BACKGROUND: 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-infe...

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Autores principales: Lamping, Florian, Jack, Thomas, Rübsamen, Nicole, Sasse, Michael, Beerbaum, Philipp, Mikolajczyk, Rafael T., Boehne, Martin, Karch, André
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5853156/
https://www.ncbi.nlm.nih.gov/pubmed/29544449
http://dx.doi.org/10.1186/s12887-018-1082-2
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author Lamping, Florian
Jack, Thomas
Rübsamen, Nicole
Sasse, Michael
Beerbaum, Philipp
Mikolajczyk, Rafael T.
Boehne, Martin
Karch, André
author_facet Lamping, Florian
Jack, Thomas
Rübsamen, Nicole
Sasse, Michael
Beerbaum, Philipp
Mikolajczyk, Rafael T.
Boehne, Martin
Karch, André
author_sort Lamping, Florian
collection PubMed
description BACKGROUND: 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. TRIAL REGISTRATION: ClinicalTrials.gov number: NCT00209768; registration date: September 21, 2005. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12887-018-1082-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-58531562018-03-22 Development 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 Lamping, Florian Jack, Thomas Rübsamen, Nicole Sasse, Michael Beerbaum, Philipp Mikolajczyk, Rafael T. Boehne, Martin Karch, André BMC Pediatr Research Article BACKGROUND: 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. TRIAL REGISTRATION: ClinicalTrials.gov number: NCT00209768; registration date: September 21, 2005. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12887-018-1082-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-03-15 /pmc/articles/PMC5853156/ /pubmed/29544449 http://dx.doi.org/10.1186/s12887-018-1082-2 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Lamping, Florian
Jack, Thomas
Rübsamen, Nicole
Sasse, Michael
Beerbaum, Philipp
Mikolajczyk, Rafael T.
Boehne, Martin
Karch, André
Development 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
title Development 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
title_full Development 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
title_fullStr Development 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
title_full_unstemmed Development 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
title_short Development 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
title_sort development 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5853156/
https://www.ncbi.nlm.nih.gov/pubmed/29544449
http://dx.doi.org/10.1186/s12887-018-1082-2
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