• Belinda J Gabbe, Jemma Keeves, Ancelin McKimmie, Adelle M Gadowski, Andrew J Holland, Bridgette D Semple, Jesse T Young, Louise Crowe, Tamara Ownsworth, Matthew K Bagg, Ana Antonic-Baker, Amelia J Hicks, Regina Hill, Kate Curtis, Lorena Romero, Jennie L Ponsford, Natasha A Lannin, Terence J O'Brien, Peter A Cameron, D Jamie Cooper, Nick Rushworth, and Melinda Fitzgerald.
• School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
• J. Neurotrauma. 2024 Apr 1.

AbstractThe 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 summarize 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. Standardized searches were implemented across bibliographic databases to March 31, 2022. English-language reports, excluding case series, which evaluated the association between demographic, injury event, and social characteristics, and any clinical outcome in at least 10 patients with moderate-severe TBI were included. Abstracts and full text records were independently screened by at least two 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. 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. This review identified numerous predictors capable of enabling early identification of those at risk for poor outcomes and improved personalization of care through inclusion in routine data collection.

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