The Australian and New Zealand brain injury lifespan cohort protocol: Leveraging common data elements to characterise longitudinal outcome and recovery

IntroductionCognitive, behavioural, academic, mental health and social impairments are common following paediatric traumatic brain injury (TBI). However, studies are often reliant on small samples of children drawn from narrow age bands, and employ highly variable methodologies, which make it challenging to generalise existing research findings and understand the lifetime history of TBI.Method and analysisThis study will synthesise common data sets from national (Victoria, New South Wales, Queensland) and international (New Zealand) collaborators, such that common data elements from multiple cohorts recruited from these four sites will be extracted and harmonised. Participant-level harmonised data will then be pooled to create a single integrated data set of participants including common cognitive, social, academic and mental health outcome variables. The large sample size (n=1816), consisting of participants with mild, moderate and severe TBI, will provide statistical power to answer important questions that cannot be addressed by small, individual cohorts. Complex statistical modelling, such as generalised estimation equation, multilevel and latent growth models, will be conducted.Ethics and disseminationEthics approval was granted by the Human Research Ethics Committee (HREC) of the Royal Children's Hospital (RCH), Melbourne (HREC Reference Number 2019.168). The approved study protocol will be used for all study-related procedures. Findings will be translated into clinical practice, inform policy decisions, guide the appropriate allocation of limited healthcare resources and support the implementation of individualised care.

Automated summary

This study aims to synthesize data to investigate the common consequences of pediatric traumatic brain injury (TBI). Multiple data sets from different regions will be merged to create an integrated data set with common outcome variables. Complex statistical modeling will be conducted to address important questions.