TY  - JOUR
PY  - 2020//
TI  - Predicting neurological recovery following traumatic brain injury in children: a systematic review of prognostic models
JO  - Journal of neurotrauma
A1  - Huth, Samuel Fergus
A1  - Slater, Anthony
A1  - Waak, Michaela
A1  - Barlow, Karen Maria
A1  - Raman, Sainath
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - Predictive modelling is foundational to treatment and long-term management of children with traumatic brain injury. Assessment of injury severity in the acute-care setting enables early stratification of patients based on their risk of death, lifelong disability or unfavourable outcome. This review evaluates predictive models which have been developed or validated for paediatric traumatic brain injury patients. The predictive accuracy of these models, the outcomes and timepoints predicted, and the variables and statistical methods utilised in model development were compared. EMBASE, Scopus, Medline and Web of Science were searched for studies that developed statistical models for predicting patient outcomes following paediatric traumatic brain injury. Studies were excluded if they focused on adults, non-traumatic brain injury, or did not assess classification accuracy. A total of 4538 entries were identified and screened, with 7 studies included for analysis. This included 5 studies where adult predictive models were validated for use in the paediatric setting, and 2 where new models were derived from a paediatric cohort. Trials of adult prediction tools in paediatric cohorts, including the IMPACT and CRASH-TBI models, showed comparable accuracy between classification of adults and children. Models derived from paediatric cohorts showed improved accuracy. Most studies solely focused on clinical variables, with two studies incorporating biochemical and imaging variables. Predictive models for paediatric traumatic brain injury are primarily based on methods and variables identified in adult studies. While adult models have proven effective in select paediatric cohorts, they may be suboptimal when compared to models derived or adjusted for children.<p />  <p>Language: en</p>
LA  - en
SN  - 0897-7151
UR  - http://dx.doi.org/10.1089/neu.2020.7158
ID  - ref1
ER  -