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

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. 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.

journal_name

Pediatr Res

journal_title

Pediatric research

authors

Kayhanian S,Young AMH,Mangla C,Jalloh I,Fernandes HM,Garnett MR,Hutchinson PJ,Agrawal S

doi

10.1038/s41390-019-0510-9

subject

Has Abstract

pub_date

2019-11-01 00:00:00

pages

641-645

issue

5

eissn

0031-3998

issn

1530-0447

pii

10.1038/s41390-019-0510-9

journal_volume

86

pub_type

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