Risk Stratification in Children With Concussion

This project will measure concussion symptoms, biological markers, and academic and social factors across the first year postconcussion to develop a model that enables early identification of and symptom management for children at higher risk for persistent postconcussive symptoms. Findings will provide novel insights into the longer-term effects of concussion on children's physical, psychological, and social well-being and support the development of personalized healthcare and school-based plans to reduce disparities in children's ability to return-to-learn and -play and improve postconcussion quality of life.

Concussions occur at an alarming rate among U.S. schoolchildren, with one in five children experiencing a concussion by age 16. The number of children visiting emergency departments for concussions annually has increased by 50% over the past decade, with an estimated cost to the healthcare system of $1 billion/year. Compared to adults, children experience longer and more severe postconcussive symptoms (PCS). Severity and duration of PCS, however, vary considerably among children, complicating clinical care and return to learn and play. Persistent PCS including physical, emotional, and cognitive symptoms, result in increased school absenteeism, social isolation, and psychological distress. Early PCS diagnosis and access to evidence-based return-to-health and -school interventions are strongly linked to positive health and academic outcomes. Yet models to identify children at high risk for persistent PCS are lacking. PCS have been linked to inflammatory processes occurring within the injured brain. Preliminary evidence suggests that fatigue, another symptom likely contributing to poor outcomes, is also a biological byproduct of pediatric concussions. Importantly, even though 73% of children report continuous fatigue after concussion, this symptom is rarely studied along with other PCS. Prior research has focused on the relationship between inflammatory biomarkers and PCS severity but has not examined this relationship longitudinally. Acute symptom severity alone, however, is a poor prognostic of clinical outcomes in concussed children. Symptom severity immediately postinjury does not explain why at least 25% of children still experience PCS after 1 year or why even children who may appear asymptomatic still report academic and social challenges months after concussion. To identify which children are at high risk for persistent PCS and poor health, academic, and social outcomes, research tracking PCS trajectories and describing school-based impacts across the entire first year postinjury is critically needed. This proposal will 1) define novel PCS trajectory typologies in a racially/ethnically diverse population of 500 children with concussion (11-17 years, near equal distribution by sex), 2) identify associations between these typologies and patterns of inflammatory biomarkers, 3) develop a risk stratification model to identify children at risk for persistent PCS; and 4) gain unique insights and describe PCS impact, including fatigue, on longer-term academic and social outcomes. We will be the first to use NIH's symptom science model and patient-reported outcomes to explore the patterns of fatigue and other physical, cognitive, psychological, emotional and academic responses to concussion in children over a full year. Our model will enable clinicians and educators to identify children most at risk for poor long-term health, social, and academic outcomes after concussion. This work is critical to meeting our long-term goal of developing personalized concussion symptom management strategies to improve outcomes and reduce disparities in the health and quality of life of children.

Eligibility Criteria:
Inclusion Criteria:

* Diagnosed with concussion that occurred within the past 7 days
* Glasgow Coma Scale (GCS) score between 13-15
* English speaking

Exclusion Criteria:

* Diagnosed with moderate or severe traumatic brain injury
* Polytrauma
* Nontraumatic brain injury
* Pregnancy