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A Pediatric Concussion Model in Mice: Closed Head Injury with Long-Term Disorders (CHILD)
Published on: February 7, 2025
The application of adult traumatic brain injury models in a pediatric cohort
Adam M H Young1, Mathew R Guilfoyle1, Helen Fernandes1
1Division of Academic Neurosurgery, Department of Clinical Neurosciences, and.
Insights
Adult head injury models accurately predict outcomes in pediatric traumatic brain injury (TBI). The Corticosteroid Randomisation After Significant Head Injury (CRASH) and International Mission on Prognosis and Analysis of randomized Controlled Trials in TBI (IMPACT) models showed good accuracy in a pediatric cohort.
Area of Science:
- Neuroscience
- Pediatric Critical Care
- Medical Informatics
Background:
- Predictive models for adult head injury outcomes, such as the CRASH and IMPACT models, are established.
- These models have not been validated for pediatric populations with severe traumatic brain injury (TBI).
Purpose of the Study:
- To evaluate the accuracy and applicability of adult-based CRASH and IMPACT models for predicting mortality and morbidity in pediatric severe TBI patients.
Main Methods:
- Retrospective analysis of 94 pediatric patients with severe TBI admitted to a PICU.
- Calculated 14-day mortality risk using CRASH-Basic and -CT models.
- Calculated 6-month mortality risk using IMPACT-Core and -Extended models.
- Assessed model accuracy using standardized mortality ratio (SMtR), discrimination (AUROC), and calibration (Hosmer-Lemeshow test).
Main Results:
- The CRASH-Basic model demonstrated good calibration (p=0.64) and discrimination (AUROC=0.92) for mortality.
- The IMPACT-Core model showed good calibration (p=0.34) and discrimination (AUROC=0.85) for mortality.
- Both models showed varying degrees of accuracy in predicting poor outcomes (morbidity).
Conclusions:
- Adult head injury predictive models (CRASH and IMPACT) can be applied to pediatric severe TBI.
- These models demonstrate sufficient accuracy in identifying predictors of morbidity and mortality in pediatric TBI patients.
Abstract:
OBJECTIVE There is increasing interest in the use of predictive models of outcome in adult head injury. Two international models have been identified to be reliable modalities for predicting outcome: the Corticosteroid Randomisation After Significant Head Injury (CRASH) model, and the International Mission on Prognosis and Analysis of randomized Controlled Trials in TBI (IMPACT) model. However, these models are designed only to identify outcomes in adult populations. METHODS A retrospective analysis was performed on pediatric patients with severe traumatic brain injury (TBI) admitted to the pediatric intensive care unit (PICU) of Addenbrooke's Hospital between January 2009 and December 2013. The individual risk of 14-day mortality was calculated using the CRASH-Basic and -CT models, and the risk of 6-month mortality calculated using the IMPACT-Core and -Extended (including CT findings) models. Model accuracy was determined by standardized mortality ratio (SMtR; observed/expected deaths), discrimination was evaluated as the area under the receiver operating curve (AUROC), and calibration assessed using the Hosmer-Lemeshow χ2 test. RESULTS Ninety-four patients with an average age of 7.3 years were admitted to the PICU with a TBI. The mortality rate was 12.7% at 14 days and at 6 months. For the CRASH-Basic model, the SMtR was 1.42 and both calibration (χ2 = 6.1, p = 0.64) and discrimination (AUROC = 0.92) were good. For the IMPACT-Core model, the SMtR was 1.03 and the model was also well calibrated (χ2 = 8.99, p = 0.34) and had good discrimination (AUROC = 0.85). Poor outcome was observed in 17% of the cohort and identified with the CRASH-Basic and IMPACT-Core models to varying degrees: standardized morbidity ratio = 0.89 vs 0.67, respectively; calibration = 6.5 (χ2) and 0.59 (p value) versus 8.52 (χ2) and 0.38 (p value), respectively; and discrimination (AUROC) = 0.92 versus 0.83, respectively. CONCLUSIONS Adult head injury models may be applied with sufficient accuracy to identify predictors of morbidity and mortality in pediatric TBI.
