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Cited by 48Traumatic Brain Injury ResearchTraumatic Brain Injury and Neurovascular DisturbancesTrauma and Emergency Care Studies

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Emergency Department Triage of Traumatic Head Injury Using a Brain Electrical Activity Biomarker: A Multisite Prospective Observational Validation Trial

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Hanley, Daniel, Prichep, Leslie S., Bazarian, Jeffrey, Huff, J. Stephen, Naunheim, Rosanne, Garrett, John, Jones, Elizabeth B., Wright, David W., O'Neill, John, Badjatia, Neeraj, Gandhi, Dheeraj, Curley, Kenneth C., Chiacchierini, Richard, O'Neil, Brian, Hack, Dallas C.·2017·Academic Emergency Medicine
DOI 10.1111/acem.13175
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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.

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  • One product in the FDA database fit the multimodality criterion by incorporating EEG, neurocognitive test scores, and clinical symptoms to assess the need for CT imaging after potential structural brain injury, but no deep neural networks were involved, placing it out of scope for the review.

    “Although one product was found to fit the multimodality criterion by incorporating EEG, neurocognitive test scores, and clinical symptoms to assess the need for CT imaging after potential structural brain injury (Hanley et al., 2017), no deep neural networks were involved, which would place this out of scope for the current review.”

    From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Hanley et al., 2017

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Research works printed but not linked: 2

  1. Bell JM, 2015, Report to Congress on Traumatic Brain Injury in the United States: Epidemiology and Rehabilitation
  2. 2013, ACEP Announces Lists of Tests as Part of Choosing Wisely Campaigns

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Metadata

Authors
Hanley, D., Prichep, L. S., Bazarian, J., Huff, J. S., Naunheim, R., Garrett, J., Jones, E. B., Wright, D. W., O'Neill, J., Badjatia, N., Gandhi, D., Curley, K. C., Chiacchierini, R., O'Neil, B., & Hack, D. C.
Affiliations
  • Johns Hopkins University (US)
  • BrainScope (United States) (US)
  • University of Rochester Medicine (US)
  • University of Virginia Health System (US)
  • Washington University Barnes Jewish Medical Center St. Louis MO
  • Baylor University Medical Center (US)
  • Memorial Hermann (US)
  • Grady Memorial Hospital (US)
  • Allegheny General Hospital (US)
  • R. Adams Cowley Shock Trauma Center Baltimore MD
  • University of Maryland, Baltimore (US)
  • Uniformed Services University of the Health Sciences (US)
  • R. P. Chiacchierini Consulting LLC Gaithersburg MD
  • Detroit Receiving Hospital Detroit MI
  • Brain Health Harpers Ferry WV
Corresponding author
Prichep, Leslie S.
ORCID
Venue
Academic Emergency Medicine
Publisher
Wiley
Published
2017-03-31
Volume
24
Issue
5
Pages
617-627
ISSN
  • 1069-6563 (ISSN-L)
  • 1553-2712
DOI
10.1111/acem.13175
OpenAlex
W2586946749
Cited by
48
References
28
Citations by year
2026: 5 · 2025: 5 · 2024: 1 · 2023: 5 · 2022: 5 · 2021: 6 · 2020: 5 · 2019: 7 · 2018: 3 · 2017: 6
FWCI
4.50
Type
journal article
Language
en
Open access
Licence
Locations
  • Academic Emergency Medicine (publishedVersion)
  • PubMed (publishedVersion)
Funders
U.S. Army (W81XWH‐14‐C‐1405)
Topics
  • Health Sciences › Medicine › Clinical Neurology › Traumatic Brain Injury Research
  • Health Sciences › Medicine › Clinical Neurology › Traumatic Brain Injury and Neurovascular Disturbances
  • Health Sciences › Medicine › Emergency Medicine › Trauma and Emergency Care Studies
Keywords
traumatic brain injury, emergency department triage, EEG biomarkers, brain electrical activity, intracranial hemorrhage, traumatic hematoma, Glasgow Coma Scale, negative predictive value, closed head injury
MeSH terms
Brain Injuries, Traumatic, Adolescent, Adult, Aged, Aged, 80 and over, Algorithms, Electroencephalography, Emergency Service, Hospital, Female, Humans, Male, Middle Aged, Prospective Studies, Sensitivity and Specificity, Tomography, X-Ray Computed, Triage, Biomarkers, Glasgow Coma Scale, Head Injuries, Closed, Young Adult

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