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Blunt Cerebrovascular Injury: Pediatric Risk Factors

Please contact Dr. Irma Ugalde to become a site participant.

Background: Blunt Cerebrovascular Injury (BCVI) rate in pediatric trauma patients ranges from 0.3% to 1.3% of all blunt trauma patients. Stroke can occur from occluded vessels with inadequate collaterals, thrombus propagation or clot embolization. If left untreated BCVI in children can lead to stroke 26% 38% and subsequent long-term sequela. The Eastern Association recommends the Modified Denver Criteria for screening pediatric patients but when validated externally, can miss up to 33% of cases of pediatric BCVI. Other strategies have been developed including the Utah and McGovern scores with 59% and 81% sensitivity, respectively. Others have found other discrete risk factors not included in the two prior rules which require validation. A large multi-center study is needed to generate sufficient numbers of patients with BCVI to parse out risk factors associated with BCVI.

Methods: We hypothesize that a core set of clinical features and coinciding injuries exists which can be used to differentiate BCVI in children with blunt trauma with those without BCVI. Our primary outcome will be the presence of BCVI, grade I-V and the secondary outcome is stroked at presentation or delayed. Our first aim is to identify a set of risk factors associated with BCVI among children with blunt trauma. Secondly, we aim to determine if there are changes in risk factors between two sub-groups in pediatrics: < 13, 14-17 years old. Our third aim is to determine treatments used for BCVI per grade of lesion. Using 0.1% as an alternative incidence and 1-sided sample size calculation with a null BCVI incidence rate of 0.5%, power of 80% and significant level of 0.05, the sample size required to detect 0.4% difference is 387 which is why we are recruiting multiple sites. In this multi-center retrospective cohort study, we will include patients receiving a CTA of the neck over a 3 to 5-year period (depending on the number of sites included.) Individual chart review will include data abstraction of candidate risk factor variables: patient demographics, clinical findings, and other imaging results. We will construct a multivariable logistic regression model with identified risk factors. We will evaluate performance of the model by comparing area under curve (AUC) of receiver operating characteristic (ROC) curve with a second model constructed using stepwise backwards elimination. We will estimate adjusted odds ratio and 95% confidence interval for all identified risk factors as well as calculate sensitivity, specificity, positive likelihood ratio and negative likelihood ratio for each identified risk factors present among patients in our study sample. To determine risk factors within different sub age groups, we will construct a mixed logistic regression model with BCVI as the outcome with main predictors including age groups, identified risk factors and their interactions adjusting for other patient characteristics. We will analyze the association of treatments received per BCVI grade through descriptive statistics.

Significance: Results will help identify children at risk for BCVI to provide timely diagnosis and treatments to prevent stroke and long-term deficits. It will set the stage for prospective studies to validate a decision rule and treatment protocols.