More than 400,000 children present annually to emergency departments (EDs) in the U.S. with chief complaints of headaches. A small but meaningful proportion (0.5-1%) of these children will have abnormalities in the brain requiring emergent identification, such as tumors, bleeding, or strokes. However, a much larger proportion undergo neuroimaging of the brain in the ED, with up to one-third of children with headaches being unnecessarily exposed to the risks associated with neuroimaging. The most prominent of these risks is a lethal malignancy due to radiation from computed tomography (CT) scanning. The primary reason for the overuse of neuroimaging in the ED is the lack of clarity regarding which clinical characteristics, or ?red flag? findings, accurately identify children with headaches who are at risk of having emergent brain abnormalities. Current red flag findings were discovered from studies that were limited in their methods and/or had small numbers of patients; in fact, frequently used red flag findings (e.g. headache waking the patient from sleep) are common and non-specific, with certain findings occurring in as many as 30-40% of children with headaches. The long- term goal of our research is to widely implement a decision support tool that will help clinicians make balanced and informed decisions based on precise estimates of the risk of emergent brain abnormalities in children with headaches. The goal of the current study is to generate the definitive evidence that will allow clinicians to identify the risks of emergent brain abnormalities in otherwise healthy children presenting to EDs with headaches. The primary aim of our current study is to derive and internally validate a decision tool that stratifies the risk for children presenting to EDs with headaches. This model will use clinically sensible and reliable factors to identify children at near-zero risk of emergent brain abnormalities with near perfect accuracy. We will accomplish this aim by conducting a prospective multicenter research study in which we enroll 28,000 children 2 to 17-years-old with headaches presenting to one of 18 EDs in the Pediatric Emergency Care Applied Research Network (PECARN). We will prospectively collect a comprehensive list of history and physical examination findings for these patients and use sophisticated statistical modeling analyses to derive a model to stratify risk.
We aim to derive a model which is highly accurate for identifying patients at near-zero risk of emergent brain abnormalities. The availability of a decision tool which identifies children with near-zero risk and higher risk of emergent brain abnormalities based on specific headache characteristics will fundamentally improve how children with acute headaches are managed. This information will help optimize the use of emergent neuroimaging, including the safe reduction of unnecessary neuroimaging in children.
Headaches in children are common, with a small proportion associated with brain abnormalities (e.g. brain tumors) that require emergent identification. However, a large proportion of children with headaches unnecessarily receive neuroimaging in emergency departments, exposing them to risks, such as an increased lifetime risk of cancer from the radiation in computed tomography scans. Our research will create the first decision-making algorithm that will allow physicians to determine the precise risk of emergent brain abnormalities in children with headaches based on clinical findings, allowing physicians to accurately identify those who require emergent neuroimaging and those who do not.
