RT Journal Article SR Electronic T1 Applicability of the CATCH, CHALICE and PECARN paediatric head injury clinical decision rules: pilot data from a single Australian centre JF Emergency Medicine Journal JO Emerg Med J FD BMJ Publishing Group Ltd and the British Association for Accident & Emergency Medicine SP 790 OP 794 DO 10.1136/emermed-2012-201887 VO 30 IS 10 A1 Lyttle, Mark D A1 Cheek, John A A1 Blackburn, Carol A1 Oakley, Ed A1 Ward, Brenton A1 Fry, Amanda A1 Jachno, Kim A1 Babl, Franz E YR 2013 UL http://emj.bmj.com/content/30/10/790.abstract AB 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.