TY - JOUR
T1 - Detection of large vessel occlusion stroke with electroencephalography in the emergency room: first results of the ELECTRA-STROKE study
AU - van Meenen, Laura C. C.
AU - van Stigt, Maritta N.
AU - Marquering, Henk A.
AU - Majoie, Charles B. L. M.
AU - Roos, Yvo B. W. E. M.
AU - Koelman, Johannes H. T. M.
AU - Potters, Wouter V.
AU - Coutinho, Jonathan M.
N1 - Funding Information: ELECTRA-STROKE is funded by the Dutch Heart Foundation, Health ~ Holland and by an unrestricted research grant from Medtronic. The study was designed and conducted by independent academic investigators without assistance from the funding parties. Manuscript preparation and data analysis were also done by the investigators; the funding parties did not have access to the data, nor to the manuscript prior to publication. Funding Information: Charles Majoie reports grants from CVON/Dutch Heart Foundation, European Commission, TWIN Foundation, Stryker, and Dutch Health Evaluation Program, all outside the submitted work (paid to institution), and is shareholder of Nico.lab, a company that focuses on the use of artificial intelligence for medical image analysis. Yvo Roos is a minor shareholder of Nico.lab. Henk Marquering is co-founder and shareholder of Nico.lab. Jonathan Coutinho received related research support from the Dutch Heart Foundation and Medtronic and unrelated research support from Bayer and Boehringer. All fees were paid to his employer. The other authors report no conflicts. Publisher Copyright: © 2021, The Author(s).
PY - 2021
Y1 - 2021
N2 - Background: Prehospital detection of large vessel occlusion stroke of the anterior circulation (LVO-a) would enable direct transportation of these patients to an endovascular thrombectomy (EVT) capable hospital. The ongoing ELECTRA-STROKE study investigates the diagnostic accuracy of dry electrode electroencephalography (EEG) for LVO-a stroke in the prehospital setting. To determine which EEG features are most useful for this purpose and assess EEG data quality, EEG recordings are also performed in the emergency room (ER). Here, we report data of the first 100 patients included in the ER. Methods: Patients presented to the ER with a suspected stroke or known LVO-a stroke underwent a single EEG prior to EVT. Diagnostic accuracy for LVO-a stroke of frequency band power, brain symmetry and phase synchronization measures were evaluated by calculating receiver operating characteristic curves. Optimal cut-offs were determined as the highest sensitivity at a specificity of ≥ 80%. Results: EEG data were of sufficient quality for analysis in 65/100 included patients. Of these, 35/65 (54%) had an acute ischemic stroke, of whom 9/65 (14%) had an LVO-a stroke. Median onset-to-EEG-time was 266 min (IQR 121–655) and median EEG-recording-time was 3 min (IQR 3–5). The EEG feature with the highest diagnostic accuracy for LVO-a stroke was theta–alpha ratio (AUC 0.83; sensitivity 75%; specificity 81%). Combined, weighted phase lag index and relative theta power best identified LVO-a stroke (sensitivity 100%; specificity 84%). Conclusion: Dry electrode EEG is a promising tool for LVO-a stroke detection, but data quality needs to be improved and validation in the prehospital setting is necessary. (TRN: NCT03699397, registered October 9 2018).
AB - Background: Prehospital detection of large vessel occlusion stroke of the anterior circulation (LVO-a) would enable direct transportation of these patients to an endovascular thrombectomy (EVT) capable hospital. The ongoing ELECTRA-STROKE study investigates the diagnostic accuracy of dry electrode electroencephalography (EEG) for LVO-a stroke in the prehospital setting. To determine which EEG features are most useful for this purpose and assess EEG data quality, EEG recordings are also performed in the emergency room (ER). Here, we report data of the first 100 patients included in the ER. Methods: Patients presented to the ER with a suspected stroke or known LVO-a stroke underwent a single EEG prior to EVT. Diagnostic accuracy for LVO-a stroke of frequency band power, brain symmetry and phase synchronization measures were evaluated by calculating receiver operating characteristic curves. Optimal cut-offs were determined as the highest sensitivity at a specificity of ≥ 80%. Results: EEG data were of sufficient quality for analysis in 65/100 included patients. Of these, 35/65 (54%) had an acute ischemic stroke, of whom 9/65 (14%) had an LVO-a stroke. Median onset-to-EEG-time was 266 min (IQR 121–655) and median EEG-recording-time was 3 min (IQR 3–5). The EEG feature with the highest diagnostic accuracy for LVO-a stroke was theta–alpha ratio (AUC 0.83; sensitivity 75%; specificity 81%). Combined, weighted phase lag index and relative theta power best identified LVO-a stroke (sensitivity 100%; specificity 84%). Conclusion: Dry electrode EEG is a promising tool for LVO-a stroke detection, but data quality needs to be improved and validation in the prehospital setting is necessary. (TRN: NCT03699397, registered October 9 2018).
KW - Acute ischemic stroke
KW - Diagnostic method
KW - EEG
KW - Large vessel occlusion
UR - http://www.scopus.com/inward/record.url?scp=85114101986&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/s00415-021-10781-6
DO - https://doi.org/10.1007/s00415-021-10781-6
M3 - Article
C2 - 34476587
SN - 0340-5354
JO - Journal of neurology
JF - Journal of neurology
ER -