@article{ref1,
title="Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach",
journal="Pediatric research",
year="2019",
author="Kayhanian, Saeed and Young, Adam M. H. and Mangla, Chaitanya and Jalloh, Ibrahim and Fernandes, Helen M. and Garnett, Matthew R. and Hutchinson, Peter J. and Agrawal, Shruti",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="BACKGROUND: Severe traumatic brain injury (TBI) is a leading cause of mortality in children, but the accurate prediction of outcomes at the point of admission remains very challenging. Admission laboratory results are a promising potential source of prognostic data, but have not been widely explored in paediatric cohorts. Herein, we use machine-learning methods to analyse 14 different serum parameters together and develop a prognostic model to predict 6-month outcomes in children with severe TBI. <br><br>METHODS: A retrospective review of patients admitted to Cambridge University Hospital's Paediatric Intensive Care Unit between 2009 and 2013 with a TBI. The data for 14 admission serum parameters were recorded. Logistic regression and a support vector machine (SVM) were trained with these data against dichotimised outcomes from the recorded 6-month Glasgow Outcome Scale. <br><br>RESULTS: Ninety-four patients were identified. Admission levels of lactate, H+, and glucose were identified as being the most informative of 6-month outcomes. Four different models were produced. The SVM using just the three most informative parameters was the best able to predict favourable outcomes at 6 months (sensitivity = 80%, specificity = 99%). <br><br>CONCLUSIONS: Our results demonstrate the potential for highly accurate outcome prediction after severe paediatric TBI using admission laboratory data.<p /> <p>Language: en</p>",
language="en",
issn="0031-3998",
doi="10.1038/s41390-019-0510-9",
url="http://dx.doi.org/10.1038/s41390-019-0510-9"
}