Prediction Model in Paediatric Moderate to Severe TBI
Conditions: Traumatic Brain Injury (TBI)
Sponsor: Cambridge University Hospitals NHS Foundation Trust

 
i 
Final Version 1.1 March 2016- Qualitative Protocol Tool  
 
 
This protocol has regard for the HRA guidance and order of content; OR 
 
FULL/LONG TITLE OF THE STUDY: Prognostic model for predicting outcomes after moderate to 
severe Paediatric Traumatic Brain Injury 
 
SHORT STUDY TITLE / ACRONYM:  
Prognostic Model for Paediatric TBI 
 
PROTOCOL VERSION NUMBER AND DATE: Version 1.0, 21/03/2025 
 
RESEARCH REFERENCE NUMBERS: 
 
IRAS Number: 
354293 
SPONSORS Number: 
A097348 
FUNDERS Number: 
NA 
 
 

PROMPT v.1.0 21/03/2025 
ii 
Final Version 1.1 March 2016- Template & Guidance 
 
LIST of CONTENTS 
 
GENERAL INFORMATION 
Page No. 
HRA PROTOCOL COMPLIANCE DECLARATION  
i 
TITLE PAGE  
i 
RESEARCH REFERENCE NUMBERS 
 
i 
SIGNATURE PAGE 
ii 
LIST OF CONTENTS  
iii 
KEY STUDY CONTACTS 
iv 
STUDY SUMMARY 
iv 
FUNDING 
 
v 
ROLE OF SPONSOR AND FUNDER 
v 
ROLES & RESPONSIBILITIES OF STUDY STEERING GROUPS AND INDIVIDUALS   
v 
SECTION  
1. BACKGROUND 
 
1 
2. RATIONALE 
 
1 
3. THEORETICAL FRAMEWORK 
1 
4. RESEARCH QUESTION/AIM(S)  
1-2 
5. STUDY DESIGN/METHODS 
2 
6. STUDY SETTING 
2 
7. SAMPLE AND RECRUITMENT 
2-3 
8. ETHICAL AND REGULATORY COMPLIANCE 
4 
9. DISSEMINATION  POLICY 
4 
10. REFERENCES 
5 
 
 

PROMPT v.1.0 21/03/2025 
iii 
Final Version 1.1 March 2016- Template & Guidance 
 
KEY STUDY CONTACTS 
Insert full details of the key study contacts including the following 
Chief Investigator 
Dr Shruti Agrawal 
Consultant in Paediatric Intensive Care 
PICU, Level 3, Box 7 
Cambridge University Hospitals, Hills Road 
Cambridge CB2 0QQ 
Ph: +44(0) 1223 348 066 
Email: shruti.agrawal1@nhs.net, sa882@cam.ac.uk  
Study Co-ordinator 
NA 
Sponsor 
Dr Stephen Kelleher 
R&D Manager, Research and Development Department 
Cambridge University Hospitals 
Hills Road, Cambridge CB2 0QQ 
Ph: +44(0)1223 349 302 
Email: cuh.research@nhs.net 
Joint-sponsor(s)/co-sponsor(s)  
NA 
Funder(s) 
NA 
Key Protocol Contributors 
Dr Shruti Agrawal & Professor Ewout Steyerberg  
Committees 
NA  
 
STUDY SUMMARY 
Study Title 
Prognostic model for predicting outcomes after moderate to 
severe Paediatric Traumatic Brain Injury 
Internal ref. no. (or short title) 
PROMPT 
Study Design 
Retrospective combine database study 
Study Participants 
Children less than or equal to 18 years of age with moderated 
to severe traumatic brain injury  
Planned Size of Sample (if applicable) NA 
Follow up duration (if applicable) 
NA 
Planned Study Period 
01/06/2025- 30/06/2026 
Research Question/Aim(s) 
 
To create and validate a paediatric specific prognostic model 
for outcomes following moderate to severe TBI for use in both 
resource-limited and resource-rich environments. Our 
hypothesis is that the performance of the current adult TBI 
prognostic models in predicting outcomes following pTBI can 
be improved by introducing age-appropriate modifications to 
the existing models.   

PROMPT v.1.0 21/03/2025 
iv 
Final Version 1.1 March 2016- Template & Guidance 
 
 
FUNDING AND SUPPORT IN KIND: NA 
ROLE OF STUDY SPONSOR AND FUNDER 
Aim: To clarify the potential influence of sponsor and funders over the study 
The sponsor has no direct influence in study design, conduct, data analysis and interpretation 
including writing manuscripts and dissemination of results. There is no funding/funder for the project. 
 
ROLES AND RESPONSIBILITIES OF STUDY MANAGEMENT COMMITEES/GROUPS & 
INDIVIDUALS- NA 
  
KEY WORDS: 
Prognostic model, Paediatric, TBI, head injury  

PROMPT v.1.0 21/03/2025 
1 
Final Version 1.1 March 2016- Template & Guidance 
 
 
STUDY PROTOCOL: Prognostic model for predicting outcomes after 
moderate to severe Paediatric Traumatic Brain Injury (PROMPT) 
 
1. Background  
The healthcare, societal and economic impact from paediatric traumatic brain injury (pTBI) is one of 
the greatest unmet needs with estimates suggesting upto 2/3rd of survivors developing life-long 
neurological deficits1. Due to ethical and logistical challenges related to paediatric research, there is 
limited age-appropriate evidence for children which perpetuates the funding gaps further confounding 
the lack of evidence2. However, extrapolation of adult TBI research evidence to children is inaccurate 
and inappropriate given the differences in mechanisms and patterns of injury, pathophysiological 
responses and neurological recovery from it, in the context of developmental trajectory3,4; hence, 
robust evidence is required to improve outcomes from pTBI as well as facilitate research 
collaborations.  
The prognostic models used for adult TBI research (IMPACT, CRASH)5,6 and imaging criteria 
(Marshall and Rotterdam scores)7,8, have been derived and validated from analysis of large 
international datasets which have undergone further validation in multiple prospective studies9; the 
wide use of these prognostic models across neurotrauma research highlights the relative simplicity 
and the variables used for prediction making them applicable to both low and high resource set-ups. 
This has facilitated international collaborative research in adult TBI; however, no such models exist for 
pTBI with most pTBI research continuing to use adult prognostic models10,11. Though the variables 
used for these models show association with outcome in pTBI as well, there are multiple issues with 
this approach with the key difficulty being the age variable. In the adult models, the younger age is 
expected to be associated with better outcome; for CRASH, the model is likely weighed heavily 
towards non-age variables as the younger patients are expected to do better with patients between 
18-40 lumped as <40 years and for IMPACT, slightly better which accounts for 14 years and above. 
However, the balance between neuroplasticity and neurodevelopmental trajectory in paediatric age 
group is difficult to predict with evidence suggesting worse neuro-developmental outcomes after TBI in 
younger children. Hence the adult models can either over- or under-predict neurological outcomes in 
pTBI and have never been validated in pTBI datasets.  
 
2. Rationale & Theoretical Framework 
  
A validated predictive model for pTBI would be the first step towards collaborative research and 
identifying age-appropriate benchmarks for pTBI research studies as well as to complement an 
individual child’s clinical assessment, treatment decisions, informing families and resource 
allocation. Given that the amount of data required to create pTBI predictive model is difficult to 
collect and the reasonable validity of adult prognostic models in pTBI, albeit in small single-centre 
studies, we propose to create paediatric modification to the adult models and identify a robust 
pTBI predictive model for improved classification of injury severity to predict disease trajectory and 
outcome as well as stratification of patients for interventional research and benchmarking in pTBI 
to help with appropriate resource allocation for neuro-interventions towards improved outcomes.  
 
3. Research Question/Aims and Objectives:  
To create and validate a paediatric specific prognostic model for outcomes following moderate to 
severe TBI for use in both resource-limited and resource-rich environments. Our hypothesis is that the 
performance of the current adult TBI prognostic models in predicting outcomes following pTBI can be 
improved by introducing age-appropriate modifications to the existing models.   

PROMPT v.1.0 21/03/2025 
2 
Final Version 1.1 March 2016- Template & Guidance 
 
 
4. Study Design/ Methods:  
We will use combined retrospective pTBI datasets (moderate to severe TBI in children < 18 years); 
Table 1 shows the approximate number and outcome distribution of the datasets currently ready for 
this collaboration with ongoing discussions for combining more datasets.  
 
Dataset 
Number of moderate to severe 
pTBI 
Outcome: 
Non-
survivors 
STARSHIP-extended 
 
724 
89 
ADAPT 
1000 
129 
CENTER-TBI 
177 
7 
Total 
2001 
225 
Table 1: Existing datasets for analyses 
5. Study setting: We will collect the following variables for children affected with moderate to severe 
TBI from the pre-existing datasets 
Variables 
Essential 
Desirable 
Admission 
• 
Age 
• 
Sex 
• 
GCS 
• 
Motor Score 
• 
Pupils  
• 
AIS and ISS 
• 
Mechanism of Injury 
 
• 
Pre-hospital hypoxia and/or hypotension 
• 
Pre-hospital cardiac arrest 
• 
Major extra cranial injuries 
• 
Blood Glucose 
• 
Full blood count, coagulation profile 
• 
Electrolytes 
• 
Lactate  
• 
CT variables* 
Outcome 
6 months GOS or PCPC 
 
• 
2 weeks GOS or mortality 
• 
6 mo GOS Extended Pediatrics 
(GOSEP) 
 *CT variables: Blood (ICH, SDH, EDH, SAH, IVH, contusions), pressure (Cisterns, midline shift) 
Table 2: Variables to be collected 
6. Sample & Statistical Methods:  
There is no defined sample size for the study. We will aim to collect datasets which contain the 
variables mentioned in table 2 from children with moderate to severe TBI upto the age of 18 years. 
There are no exclusion criteria for the study. All the datasets will either be from a prospective study 
(ethics approved with valid consent) and/or datasets created for audit/institutional purposes with 
appropriate approvals from local/national research committees and contain anonymised non-
identifiable information.  

PROMPT v.1.0 21/03/2025 
3 
Final Version 1.1 March 2016- Template & Guidance 
 
The analysis will include specific and important considerations for pTBI listed as below:  
• 
Representation across the age range (pTBI has two peaks, under one year and then 
adolescent age group). 
• 
Lower mortality as compared to adult TBI, important that model predicts poor neurological 
outcome. 
• 
Non-accidental injury as unlikely to be a single injury, but equally an important group in pTBI 
who are traditionally omitted from research and consider including mechanism as a variable for 
analysis if adequate data. 
• 
Imputing for missing data.  
Model development and performance: 
• 
Assess performance of the available models, namely, CRASH (basic and CT), IMPACT (core, 
extended and lab) and Marshall, Rotterdam and Helsinki CT criteria. Performance includes 
assessments of discrimination and calibration.  
• 
Create a stratified pTBI prediction model (for use in low vs high resource settings), with basic 
data (where limited variables are available) and extended (which may become better with 
additional variables where available) using logistic regression, internal and external validation, 
considering predictors from existing models and recent literature as a basis 
• 
Compare the performance of different models in predicting outcomes from pTBI, dropping one 
cohort at a time (internal – external cross-validation). 
Statistical Analysis:  
Classic regression analysis approaches will be followed for prediction model development; 
including modern estimation with L1 and L2 penalties in LASSO and Ridge regression models. 
• 
We will also explore application of available Machine learning (ML) tools: neural 
networks/support vector machine, SVM and random forest as examples of tools which have 
been used.  
• 
For internal – external cross-validation, sample size in validation cohorts should preferably 
have at least 100 events; smaller sample sizes may be used with caution, and attempts will be 
made to combine smaller cohorts with similar cohorts where possible. 
• 
Standard performance measures will be used for discrimination (area under the ROC curve) 
and calibration (baseline risk miscalibration with a model intercept; overall miscalibration with a 
slope parameter)12,13. 
 
 
 

PROMPT v.1.0 21/03/2025 
4 
Final Version 1.1 March 2016- Template & Guidance 
 
7. Ethical and Regulatory Consideration 
Consent and Confidentiality: The data collection part of this study is limited to using variables required 
for the model development from anonymized existing datasets. There is no additional data collection 
for the study purposes beyond what is already available in the ethics approved/consented datasets 
either as a part of a multicentre research study or approved institutional data collection. Only the de-
identified dataset consisting of the study variables will be shared for the study purposes.  
Declaration of Helsinki and ICH Good Clinical Practice: The project is to be carried out in conformation 
with the spirit and the letter of the declaration of Helsinki, and in accord with the ICH Good Clinical 
Practice Guidelines. 
IT security and access to database: The data will be stored in a secure server located on a private 
network behind a tight firewall. It will only be accessible to the study personnel as outlined above.  
 
8. Dissemination and Possible Linkage to other databases/registries:  
The results will be disseminated with presentations and publications at national/international 
conferences and peer-reviewed journals. The data extracts from the different studies will be uploaded 
to TBI-REPORTER platform to facilitate integration of combined second-stage analysis of datasets. In 
future, based upon the analysis and the data collected in this project, we will endeavour to identify 
further funding and national and international collaboration to help scientific understanding and 
improve patient outcomes.  
 
9. Information Governance 
The application and the database management and monitoring have full support from the patient 
safety department at CUH and the protocol has been approved by them. The entire project will be 
conducted under close scrutiny of data custodian and the patient safety department.  
 
 
 

PROMPT v.1.0 21/03/2025 
5 
Final Version 1.1 March 2016- Template & Guidance 
 
10. References:  
1. 
Dewan MC, Mummareddy N, Wellons JC, 3rd, Bonfield CM. Epidemiology of Global Pediatric 
Traumatic Brain Injury: Qualitative Review. World Neurosurg 2016; 91: 497-509 e1. 
2. 
Speer EM, Lee LK, Bourgeois FT, et al. The state and future of pediatric research-an 
introductory overview : The state and future of pediatric research series. Pediatr Res 2023: 1-5. 
3. 
Sta Maria NS, Sargolzaei S, Prins ML, et al. Bridging the gap: Mechanisms of plasticity and 
repair after pediatric TBI. Exp Neurol 2019; 318: 78-91. 
4. 
Figaji A. An update on pediatric traumatic brain injury. Childs Nerv Syst 2023; 39(11): 3071-81. 
5. 
Steyerberg EW, Mushkudiani N, Perel P, et al. Predicting outcome after traumatic brain injury: 
development and international validation of prognostic scores based on admission characteristics. 
PLoS Med 2008; 5(8): e165; discussion e. 
6. 
Collaborators MCT, Perel P, Arango M, et al. Predicting outcome after traumatic brain injury: 
practical prognostic models based on large cohort of international patients. BMJ 2008; 336(7641): 
425-9. 
7. 
Marshall LF, Marshall SB, Klauber MR, et al. A New Classification of Head-Injury Based on 
Computerized-Tomography. Journal of Neurosurgery 1991; 75: S14-S20. 
8. 
Maas AIR, Hukkelhoven CWPM, Marshall LF, Steyerberg EW. Prediction of outcome in 
traumatic brain injury with computed tomographic characteristics: A comparison between the 
computed tomographic classification and combinations of computed tomographic predictors. 
Neurosurgery 2005; 57(6): 1173-81. 
9. 
Yue JK, Lee YM, Sun X, et al. Performance of the IMPACT and CRASH prognostic models for 
traumatic brain injury in a contemporary multicenter cohort: a TRACK-TBI study. J Neurosurg 2024; 
141(2): 417-29. 
10. 
Young AM, Guilfoyle MR, Fernandes H, Garnett MR, Agrawal S, Hutchinson PJ. The 
application of adult traumatic brain injury models in a pediatric cohort. J Neurosurg Pediatr 2016; 
18(5): 558-64. 
11. 
Huth SF, Slater A, Waak M, Barlow K, Raman S. Predicting Neurological Recovery after 
Traumatic Brain Injury in Children: A Systematic Review of Prognostic Models. J Neurotrauma 2020; 
37(20): 2141-9. 
12. 
Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: 
a framework for traditional and novel measures. Epidemiology 2010; 21(1): 128-38. 
13. 
Steyerberg EW, Harrell FE, Jr. Prediction models need appropriate internal, internal-external, 
and external validation. J Clin Epidemiol 2016; 69: 245-7.