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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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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. |
format | Online Article Text |
id | pubmed-5853156 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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