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A Pediatric Concussion Model in Mice: Closed Head Injury with Long-Term Disorders (CHILD)
Published on: February 7, 2025
Applicability of the CATCH, CHALICE and PECARN paediatric head injury clinical decision rules: pilot data from a
Mark D Lyttle1, John A Cheek, Carol Blackburn
1Emergency Department, Royal Children's Hospital, Melbourne, Victoria, Australia.
Insights
The Children's head injury algorithm for the prediction of important clinical events (CHALICE) rule was applicable to the most pediatric head injury patients, while the Canadian assessment of tomography for childhood head injury (CATCH) rule was applicable to the fewest. These clinical decision rules (CDRs) vary in applicability.
Area of Science:
- Pediatric Emergency Medicine
- Clinical Decision Science
- Trauma Care
Background:
- Clinical decision rules (CDRs) aid in identifying pediatric head injury (HI) patients at risk for traumatic brain injury.
- Key CDRs include Canadian assessment of tomography for childhood head injury (CATCH), Children's head injury algorithm for the prediction of important clinical events (CHALICE), and Pediatric Emergency Care Applied Research Network (PECARN).
- These CDRs target different pediatric HI cohorts and have not been directly compared in the same clinical setting.
Purpose of the Study:
- To quantify the proportion of pediatric head injury patients to whom each of the three major CDRs (CATCH, CHALICE, PECARN) is applicable.
- To provide data for informed selection and implementation of CDRs in pediatric emergency departments.
Main Methods:
- Prospective enrollment of consecutive children presenting with HI to an Australian pediatric Emergency Department.
- Collection of published inclusion/exclusion criteria and predictor variables for CATCH, CHALICE, and PECARN CDRs.
- Determination of the applicability frequency of each CDR based on collected patient data.
Main Results:
- Out of 949 analyzed patients, no single CDR was applicable to all children with HI.
- The CHALICE CDR was applicable to the highest proportion of patients (97%).
- The CATCH CDR was applicable to the fewest (26%), while PECARN showed applicability to 76% of patients under 2 years and 74% of those aged 2 to 18 years.
Conclusions:
- Each CDR demonstrates varying applicability across different pediatric head injury populations.
- Direct comparison of CDRs is challenging due to differing applicability.
- Further validation, performance accuracy, usability, and cost-effectiveness analyses are recommended before implementing any specific CDR.
Background:
Clinical decision rules (CDRs) for paediatric head injury (HI) exist to identify children at risk of traumatic brain injury. Those of the highest quality are the Canadian assessment of tomography for childhood head injury (CATCH), Children's head injury algorithm for the prediction of important clinical events (CHALICE) and Pediatric Emergency Care Applied Research Network (PECARN) CDRs. They target different cohorts of children with HI and have not been compared in the same setting. We set out to quantify the proportion of children with HI to which each CDR was applicable.
Methods:
Consecutive children presenting to an Australian paediatric Emergency Department with HIs were enrolled. Published inclusion/exclusion criteria and predictor variables from the CDRs were collected prospectively. Using these we determined the frequency with which each CDR was applicable.
Results:
1012 patients (69.9%) were enrolled with 949 available for analysis. Mean age was 6.8 years (21% <2 years). 95% had initial Glasgow Coma Scale 15. CT rate was 12.8% and neurosurgery rate was 0.7%. No CDR was applicable to all patients. CHALICE was applicable to the most (97%, 95% CI 96% to 98%) and CATCH to the fewest (26%, 95% CI 24% to 29%). PECARN was applicable to 76% (95% CI 70% to 82%) aged <2 years, and 74% (95% CI 71% to 77%) aged 2-<18 years.
Conclusions:
Each CDR is applicable to a different proportion of children with HI. This makes a direct comparison of the CDRs difficult. Prior to selection of any for implementation they should undergo validation outside the derivation setting coupled with an analysis of their performance accuracy, usability and cost effectiveness.
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