@article{ref1,
title="The Australian Traumatic Brain Injury Initiative: systematic review and consensus process to determine the predictive value of demographic, injury event and social characteristics on outcomes for people with moderate-severe traumatic brain injury",
journal="Journal of neurotrauma",
year="2023",
author="Gabbe, Belinda and Keeves, Jemma and McKimmie, Ancelin and Gadowski, Adelle and Holland, Andrew and Semple, Bridgette D. and Young, Jesse and Crowe, Louise Margaret and Ownsworth, Tamara and Bagg, Matthew and Antonic-Baker, Ana and Hicks, Amelia and Hill, Regina and Curtis, Kate and Romero, Lorena and Ponsford, Jennie and Lannin, Natasha A. and O'Brien, Terence J. and Cameron, Peter and Cooper, D. James and Rushworth, Nick and Fitzgerald, Melinda",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="BACKGROUND: The objective of the Australian Traumatic Brain Injury (AUS-TBI) Initiative is to develop a data dictionary to inform data collection and facilitate prediction of outcomes of people who experience moderate-severe TBI in Australia. The aim of this systematic review was to summarise the evidence of the association between demographic, injury event and social characteristics with outcomes, in people with moderate-severe TBI, to identify potentially predictive indicators. <br><br>METHODS: Standardised searches were implemented across bibliographic databases to 31st March 2022. English-language reports, excluding case-series, which evaluated the association between injury event, demographic and social characteristics, and any clinical outcome in at least ten patients with moderate-severe TBI were included. Abstracts, and full text records, were independently screened by at least 2 reviewers in Covidence. A pre-defined algorithm was used to assign a judgement of predictive value to each observed association. The review findings were discussed with an expert panel to determine the feasibility of incorporation of routine measurement into standard care. <br><br>FINDINGS: The search strategy retrieved 16,685 records; 867 full-length records were screened, and 111 studies included. Twenty-two predictors of 32 different outcomes were identified; 7 were classified as high-level (age, sex, ethnicity, employment, insurance, education and living situation at the time of injury). After discussion with an expert consensus group, 15 were recommended for inclusion in the data dictionary. <br><br>CONCLUSIONS: This review identified numerous predictors capable of enabling early identification of those at risk of poor outcomes and improved personalisation of care through inclusion in routine data collection.<p /> <p>Language: en</p>",
language="en",
issn="0897-7151",
doi="10.1089/neu.2023.0461",
url="http://dx.doi.org/10.1089/neu.2023.0461"
}