Emergency Department Triage of Traumatic Head Injury Using a Brain Electrical Activity Biomarker: A Multisite Prospective Observational Validation Trial
Daniel Hanley, Leslie S Prichep, Jeffrey Bazarian, J Stephen Huff, Rosanne Naunheim, John Garrett, Elizabeth B Jones, David W Wright, John O'Neill, Neeraj Badjatia, Dheeraj Gandhi, Kenneth C Curley, Richard Chiacchierini, Brian O'Neil, Dallas C Hack, Daniel Hanley, Leslie S Prichep, Jeffrey Bazarian, J Stephen Huff, Rosanne Naunheim, John Garrett, Elizabeth B Jones, David W Wright, John O'Neill, Neeraj Badjatia, Dheeraj Gandhi, Kenneth C Curley, Richard Chiacchierini, Brian O'Neil, Dallas C Hack
Abstract
Objectives: A brain electrical activity biomarker for identifying traumatic brain injury (TBI) in emergency department (ED) patients presenting with high Glasgow Coma Scale (GCS) after sustaining a head injury has shown promise for objective, rapid triage. The main objective of this study was to prospectively evaluate the efficacy of an automated classification algorithm to determine the likelihood of being computed tomography (CT) positive, in high-functioning TBI patients in the acute state.
Methods: Adult patients admitted to the ED for evaluation within 72 hours of sustaining a closed head injury with GCS 12 to 15 were candidates for study. A total of 720 patients (18-85 years) meeting inclusion/exclusion criteria were enrolled in this observational, prospective validation trial, at 11 U.S. EDs. GCS was 15 in 97%, with the first and third quartiles being 15 (interquartile range = 0) in the study population at the time of the evaluation. Standard clinical evaluations were conducted and 5 to 10 minutes of electroencephalogram (EEG) was acquired from frontal and frontal-temporal scalp locations. Using an a priori derived EEG-based classification algorithm developed on an independent population and applied to this validation population prospectively, the likelihood of each subject being CT+ was determined, and performance metrics were computed relative to adjudicated CT findings.
Results: Sensitivity of the binary classifier (likely CT+ or CT-) was 92.3% (95% confidence interval [CI] = 87.8%-95.5%) for detection of any intracranial injury visible on CT (CT+), with specificity of 51.6% (95% CI = 48.1%-55.1%) and negative predictive value (NPV) of 96.0% (95% CI = 93.2%-97.9%). Using ternary classification (likely CT+, equivocal, likely CT-) demonstrated enhanced sensitivity to traumatic hematomas (≥1 mL of blood), 98.6% (95% CI = 92.6%-100.0%), and NPV of 98.2% (95% CI = 95.5%-99.5%).
Conclusion: Using an EEG-based biomarker high accuracy of predicting the likelihood of being CT+ was obtained, with high NPV and sensitivity to any traumatic bleeding and to hematomas. Specificity was significantly higher than standard CT decision rules. The short time to acquire results and the ease of use in the ED environment suggests that EEG-based classifier algorithms have potential to impact triage and clinical management of head-injured patients.
Trial registration: ClinicalTrials.gov NCT02367300.
© 2017 by the Society for Academic Emergency Medicine.
Source: PubMed