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Do Mild Traumatic Brain Injury Severity Sub-classification Systems Help to Identify People Who Go on to Experience Long-Term Symptoms?

Published online by Cambridge University Press:  06 July 2017

Alice Theadom ,
Suzanne Barker-Collo ,
Andrea Greenwood ,
Priya Parmar ,
Kelly Jones ,
Nicola Starkey ,
Kathryn McPherson  and
Valery L. Feigin
Alice Theadom*
Affiliation:
National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand
Suzanne Barker-Collo
Affiliation:
School of Psychology, University of Auckland, Auckland, New Zealand
Andrea Greenwood
Affiliation:
School of Psychology, University of Auckland, Auckland, New Zealand
Priya Parmar
Affiliation:
National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand
Kelly Jones
Affiliation:
National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand
Nicola Starkey
Affiliation:
School of Psychology, University of Waikato, Hamilton, New Zealand
Kathryn McPherson
Affiliation:
Health Research Council of New Zealand, Auckland, New Zealand
Valery L. Feigin
Affiliation:
National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand
*
Address for correspondence: Alice Theadom, PhD, National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, AUT North Shore Campus, AR417, 90 Akoranga Dr, Northcote 0627, Private Bag 92006, Auckland 1142, New Zealand. E-mail: alice.theadom@aut.ac.nz
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Abstract

Objective: To identify the systems available to sub-classify mild traumatic brain injury (TBI) and to determine their utility in predicting 1-year outcome.

Methods: A systematic review to identify mild-TBI sub-classification systems was conducted until March 2016. The identified systems were applied to a cohort of N = 290 adults who had experienced a mild-TBI, and who had been assessed for post-concussion symptoms 1-year post injury. ANOVAs and regression models were used to determine whether each sub-classification system could distinguish between outcomes and to explore their contribution to explaining variance in post-concussion symptoms 1-year post injury.

Results: Nineteen sub-classification systems for mild-TBI met the inclusion criteria for this review. The Saal (1991) classification system significantly differentiated the experience of post-concussion symptoms in our cohort 1-year post injury (F = 2.39, p = 0.05). However, the findings did not remain significant following correction for multiple comparisons and inclusion of socio-demographic and contextual factors in the regression model.

Conclusions: Current sub-classification systems fail to explain much of the variance in post-concussion symptoms 1 year following mild-TBI. Further research is needed to identify the factors (including socio-demographic and contextual factors) to determine, who may be at risk of developing persistent post-concussion symptoms.

Keywords

mild traumatic brain injuryconcussionclassificationpost-concussion symptomsadults
Type
Articles
Information
Brain Impairment , Volume 19 , Special Issue 2: Mild Traumatic Brain Injury , September 2018 , pp. 119 - 132
Copyright
Copyright © Australasian Society for the Study of Brain Impairment 2017 

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