Towards PErsonalised PRognosis for children with traumatic brain injury: the PEPR study protocol
Cece C Kooper1,2,3, Jaap Oosterlaan4,2, Hilgo Bruining2,3,5
1Department of Pediatrics, Emma Neuroscience Group, Emma Children's Hospital, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands c.c.kooper@amsterdamumc.nl.
Insights
This study develops predictive models for children with traumatic brain injury (TBI) to forecast motor, cognitive, and behavioral outcomes. Machine learning and neuroimaging will enhance personalized prognoses for pediatric TBI patients.
Area of Science:
- Pediatric neurology
- Neuroscience
- Medical imaging
Background:
- Traumatic brain injury (TBI) in children can lead to lasting deficits in motor, neurocognitive, and behavioral functions.
- Predicting individual outcomes for pediatric TBI is challenging due to complex interactions between patient factors and injury characteristics.
- Current methods are insufficient for accurate outcome prediction, leading to suboptimal follow-up and missed impairments.
Purpose of the Study:
- To develop prognostic models for predicting individual outcomes in children with TBI across key developmental domains.
- To assess the added value of advanced neuroimaging (MRI) and machine learning algorithms in improving prognostic accuracy.
- To enable personalized prognoses and identify children at risk for targeted interventions.
Main Methods:
- Prospective recruitment of 210 children (aged 4-18) with mild-to-severe TBI from a Dutch hospital network.
- Matching with 105 neurologically healthy controls (2:1 ratio).
- Inclusion of demographic, premorbid, clinical data, and MRI metrics (1 month post-injury) as predictors; assessment of motor, intelligence, behavioral, and school outcomes at 6 months post-injury.
Main Results:
- This section is to be filled once the study is completed and results are available.
Conclusions:
- This study aims to provide clinicians with tools to identify children with TBI who are at risk of poor outcomes.
- The findings will support the development of personalized prognosis strategies for pediatric TBI.
- The research will be disseminated through open-access, peer-reviewed publications.
Introduction:
Traumatic brain injury (TBI) in children can be associated with poor outcome in crucial functional domains, including motor, neurocognitive and behavioural functioning. However, outcome varies between patients and is mediated by complex interplay between demographic factors, premorbid functioning and (sub)acute clinical characteristics. At present, methods to understand let alone predict outcome on the basis of these variables are lacking, which contributes to unnecessary follow-up as well as undetected impairments in children. Therefore, this study aims to develop prognostic models for the individual outcome of children with TBI in a range of important developmental domains. In addition, the potential added value of advanced neuroimaging data and the use of machine learning algorithms in the development of prognostic models will be assessed.
Methods And Analysis:
210 children aged 4-18 years diagnosed with mild-to-severe TBI will be prospectively recruited from a research network of Dutch hospitals. They will be matched 2:1 to a control group of neurologically healthy children (n=105). Predictors in the model will include demographic, premorbid and clinical measures prospectively registered from the TBI hospital admission onwards as well as MRI metrics assessed at 1 month post-injury. Outcome measures of the prognostic models are (1) motor functioning, (2) intelligence, (3) behavioural functioning and (4) school performance, all assessed at 6 months post-injury.
Ethics And Dissemination:
Ethics has been obtained from the Medical Ethical Board of the Amsterdam UMC (location AMC). Findings of our multicentre prospective study will enable clinicians to identify TBI children at risk and aim towards a personalised prognosis. Lastly, findings will be submitted for publication in open access, international and peer-reviewed journals.
Trial Registration Number:
NL71283.018.19 and NL9051.
