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BMC Pediatrics
Published in: BMC Pediatrics 1/2018

Open Access 01-12-2018 | Research article

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

Authors: Florian Lamping, Thomas Jack, Nicole Rübsamen, Michael Sasse, Philipp Beerbaum, Rafael T. Mikolajczyk, Martin Boehne, André Karch

Published in: BMC Pediatrics | Issue 1/2018

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Abstract

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.
Appendix
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Metadata
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
Authors
Florian Lamping
Thomas Jack
Nicole Rübsamen
Michael Sasse
Philipp Beerbaum
Rafael T. Mikolajczyk
Martin Boehne
André Karch
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Pediatrics / Issue 1/2018
Electronic ISSN: 1471-2431
DOI
https://doi.org/10.1186/s12887-018-1082-2

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