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PECARN algorithms for minor head trauma: Risk stratification estimates from a prospective PREDICT cohort study
Silvia Bressan1,2, Nitaa Eapen2,3, Natalie Phillips4,5
1Department of Women's and Children's Health, University of Padova, Padova, Italy.
Insights
The Pediatric Emergency Care Applied Research Network (PECARN) head trauma rules accurately predicted clinically important traumatic brain injury (ciTBI) rates in an external cohort. These findings support refining clinical judgment for neuroimaging in children with head injuries.
Area of Science:
- Pediatric Emergency Medicine
- Trauma Surgery
- Pediatric Neurology
Background:
- The Pediatric Emergency Care Applied Research Network (PECARN) developed clinical decision rules for managing pediatric head trauma.
- These rules stratify children by risk of clinically important traumatic brain injury (ciTBI).
Purpose of the Study:
- To validate PECARN head trauma algorithm risk group ciTBI rates in an independent patient cohort.
- To assess ciTBI risk associated with specific high- or intermediate-risk predictor combinations.
Main Methods:
- Secondary analysis of a large, multicenter prospective dataset from Australia and New Zealand.
- Included patients with Glasgow Coma Scale scores of 14 or 15.
- Calculated ciTBI rates and 95% confidence intervals (CIs) for PECARN risk categories and predictor combinations.
Main Results:
- ciTBI frequencies were 8.5% (high-risk) and 0.2% (intermediate-risk) in children <2 years; 5.7% and 0.7% in older children.
- "Signs of palpable skull fracture" (11.4%) and "signs of basilar skull fracture" (11.1%) were high-risk predictors in younger and older children, respectively.
- In older children, "all four predictors" (25.0%) and "severe mechanism/severe headache" (7.7%) indicated higher intermediate-risk ciTBI.
Conclusions:
- PECARN algorithm risk estimates for ciTBI were consistent with the original study.
- Risk estimates for high- and intermediate-risk predictors can refine clinical judgment and neuroimaging decisions in pediatric head trauma.
Background:
The Pediatric Emergency Care Applied Research Network (PECARN) head trauma clinical decision rules informed the development of algorithms that risk stratify the management of children based on their risk of clinically important traumatic brain injury (ciTBI). We aimed to determine the rate of ciTBI for each PECARN algorithm risk group in an external cohort of patients and that of ciTBI associated with different combinations of high- or intermediate-risk predictors.
Methods:
This study was a secondary analysis of a large multicenter prospective data set, including patients with Glasgow Coma Scale scores of 14 or 15 conducted in Australia and New Zealand. We calculated ciTBI rates with 95% confidence intervals (CIs) for each PECARN risk category and combinations of related predictor variables.
Results:
Of the 15,163 included children, 4,011 (25.5%) were aged <2 years. The frequency of ciTBI was 8.5% (95% CI = 6.0%-11.6%), 0.2% (95% CI = 0.0%-0.6%), and 0.0% (95% CI = 0.0%-0.2%) in the high-, intermediate-, and very-low-risk groups, respectively, for children <2 years and 5.7% (95% CI = 4.4%-7.2%), 0.7% (95% CI = 0.5%-1.0%), and 0.0% (95% CI = 0.0%-0.1%) in older children. The isolated high-risk predictor with the highest risk of ciTBI was "signs of palpable skull fracture" for younger children (11.4%, 95% CI = 5.3%-20.5%) and "signs of basilar skull fracture" in children ≥2 years (11.1%, 95% CI = 3.7%-24.1%). For older children in the intermediate-risk category, the presence of all four predictors had the highest risk of ciTBI (25.0%, 95% CI = 0.6%-80.6%) followed by the combination of "severe mechanism of injury" and "severe headache" (7.7%, 95% CI = 0.2%-36.0%). The very few children <2 years at intermediate risk with ciTBI precluded further analysis.
Conclusions:
The risk estimates of ciTBI for each of the PECARN algorithms risk group were consistent with the original PECARN study. The risk estimates of ciTBI within the high- and intermediate-risk predictors will help further refine clinical judgment and decision making on neuroimaging.
