Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach

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...

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Bibliographic Details
Published in:Pediatric research 2019-11, Vol.86 (5), p.641-645
Main Authors: Kayhanian, Saeed, Young, Adam M. H., Mangla, Chaitanya, Jalloh, Ibrahim, Fernandes, Helen M., Garnett, Matthew R., Hutchinson, Peter J., Agrawal, Shruti
Format: Article
Language:English
Subjects:
Brain Injuries - therapy
Child
Clinical Research Article
Female
Humans
Machine Learning
Male
Medical prognosis
Medical tests
Medicine
Medicine & Public Health
Patient Admission
Patient admissions
Pediatric Surgery
Pediatrics
Traumatic brain injury
Treatment Outcome
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Summary: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. 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. 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%). Conclusions Our results demonstrate the potential for highly accurate outcome prediction after severe paediatric TBI using admission laboratory data.
ISSN:0031-3998
1530-0447
DOI:10.1038/s41390-019-0510-9