RESUMEN
A reliable and fast instrument for prehospital detection of large vessel occlusion (LVO) stroke would be a game-changer in stroke care, because it would enable direct transportation of LVO stroke patients to the nearest comprehensive stroke center for endovascular treatment. This strategy would substantially improve treatment times and thus clinical outcomes of patients. Here, we outline our view on the requirements of an effective prehospital LVO detection method, namely: high diagnostic accuracy; fast application and interpretation; user-friendliness; compactness; and low costs. We argue that existing methods for prehospital LVO detection, including clinical scales, mobile stroke units and transcranial Doppler, do not fulfill all criteria, hindering broad implementation of these methods. Instead, electroencephalography may be suitable for prehospital LVO detection since in-hospital studies have shown that quantification of hypoxia-induced changes in the electroencephalography signal have good diagnostic accuracy for LVO stroke. Although performing electroencephalography measurements in the prehospital setting comes with challenges, solutions for fast and simple application of this method are available. Currently, the feasibility and diagnostic accuracy of electroencephalography in the prehospital setting are being investigated in clinical trials.
Asunto(s)
Trastornos Cerebrovasculares/fisiopatología , Electroencefalografía/métodos , Servicios Médicos de Urgencia/métodos , Accidente Cerebrovascular Isquémico/fisiopatología , Triaje/métodos , Trastornos Cerebrovasculares/diagnóstico por imagen , Trastornos Cerebrovasculares/terapia , Humanos , Accidente Cerebrovascular Isquémico/diagnóstico por imagen , Accidente Cerebrovascular Isquémico/terapia , Telemedicina/métodos , Ultrasonografía Doppler Transcraneal/métodosRESUMEN
BACKGROUND: Endovascular thrombectomy is standard treatment for patients with anterior circulation large vessel occlusion stroke (LVO-a). Prehospital identification of these patients would enable direct routing to an endovascular thrombectomy-capable hospital and consequently reduce time-to-endovascular thrombectomy. Electroencephalography (EEG) has previously proven to be promising for LVO-a stroke detection. Fast and reliable electrode application, however, can remain a challenge. A potential alternative is subhairline EEG. We evaluated the diagnostic accuracy of subhairline EEG for LVO-a stroke detection. METHODS AND RESULTS: We included adult patients with a suspected stroke or known LVO-a stroke and symptom onset time <24 hours. A single 3-minute EEG recording was performed at the emergency department, before endovascular thrombectomy, using 9 self-adhesive electrodes placed on the forehead and behind the ears. We evaluated the diagnostic accuracies of EEG features quantifying frequency band power and brain symmetry (pairwise derived Brain Symmetry Index) for LVO-a stroke detection using receiver operating characteristic analysis. EEG data were of sufficient quality for analysis in 51/52 (98%) included patients. Of these patients, 16 (31%) had an LVO-a stroke, 16 (31%) a non-LVO-a ischemic stroke, 5 (10%) a transient ischemic attack, and 14 (27%) a stroke mimic. Median symptom-onset-to-EEG-time was 266 (interquartile range 130-709) minutes. The highest diagnostic accuracy for LVO-a stroke detection was reached by the pairwise derived Brain Symmetry Index in the theta frequency band (area under the receiver operating characteristic curve 0.90; sensitivity 86%; specificity 83%). CONCLUSIONS: Subhairline EEG could detect LVO-a stroke with high diagnostic accuracy and had high data reliability. These data suggest that subhairline EEG is potentially suitable as a prehospital stroke triage instrument.
RESUMEN
BACKGROUND AND OBJECTIVES: Endovascular thrombectomy (EVT) is standard treatment for anterior large vessel occlusion stroke (LVO-a stroke). Prehospital diagnosis of LVO-a stroke would reduce time to EVT by allowing direct transportation to an EVT-capable hospital. We aim to evaluate the diagnostic accuracy of dry electrode EEG for the detection of LVO-a stroke in the prehospital setting. METHODS: ELECTRA-STROKE was an investigator-initiated, prospective, multicenter, diagnostic study, performed in the prehospital setting. Adult patients were eligible if they had suspected stroke (as assessed by the attending ambulance nurse) and symptom onset <24 hours. A single dry electrode EEG recording (8 electrodes) was performed by ambulance personnel. Primary endpoint was the diagnostic accuracy of the theta/alpha frequency ratio for LVO-a stroke (intracranial ICA, A1, M1, or proximal M2 occlusion) detection among patients with EEG data of sufficient quality, expressed as the area under the receiver operating characteristic curve (AUC). Secondary endpoints were diagnostic accuracies of other EEG features quantifying frequency band power and the pairwise derived Brain Symmetry Index. Neuroimaging was assessed by a neuroradiologist blinded to EEG results. RESULTS: Between August 2020 and September 2022, 311 patients were included. The median EEG duration time was 151 (interquartile range [IQR] 151-152) seconds. For 212/311 (68%) patients, EEG data were of sufficient quality for analysis. The median age was 74 (IQR 66-81) years, 90/212 (42%) were women, and the median baseline NIH Stroke Scale was 1 (IQR 0-4). Six (3%) patients had an LVO-a stroke, 109/212 (51%) had a non-LVO-a ischemic stroke, 32/212 (15%) had a transient ischemic attack, 8/212 (4%) had a hemorrhagic stroke, and 57/212 (27%) had a stroke mimic. AUC of the theta/alpha ratio was 0.80 (95% CI 0.58-1.00). Of the secondary endpoints, the pairwise derived Brain Symmetry Index in the delta frequency band had the highest diagnostic accuracy (AUC 0.91 [95% CI 0.73-1.00], sensitivity 80% [95% CI 38%-96%], specificity 93% [95% CI 88%-96%], positive likelihood ratio 11.0 [95% CI 5.5-21.7]). DISCUSSION: The data from this study suggest that dry electrode EEG has the potential to detect LVO-a stroke among patients with suspected stroke in the prehospital setting. Toward future implementation of EEG in prehospital stroke care, EEG data quality needs to be improved. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov identifier: NCT03699397. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that prehospital dry electrode scalp EEG accurately detects LVO-a stroke among patients with suspected acute stroke.
Asunto(s)
Arteriopatías Oclusivas , Isquemia Encefálica , Servicios Médicos de Urgencia , Accidente Cerebrovascular Isquémico , Accidente Cerebrovascular , Adulto , Humanos , Femenino , Anciano , Masculino , Servicios Médicos de Urgencia/métodos , Estudios Prospectivos , Accidente Cerebrovascular/diagnóstico por imagen , Accidente Cerebrovascular/terapia , Isquemia Encefálica/diagnóstico por imagen , Isquemia Encefálica/terapiaRESUMEN
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).
Asunto(s)
Isquemia Encefálica , Servicios Médicos de Urgencia , Accidente Cerebrovascular Isquémico , Accidente Cerebrovascular , Isquemia Encefálica/diagnóstico , Electroencefalografía , Servicios Médicos de Urgencia/métodos , Servicio de Urgencia en Hospital , Humanos , Accidente Cerebrovascular/diagnósticoRESUMEN
Background: Endovascular thrombectomy (EVT) is the standard treatment for large vessel occlusion stroke of the anterior circulation (LVO-a stroke). Approximately half of EVT-eligible patients are initially presented to hospitals that do not offer EVT. Subsequent inter-hospital transfer delays treatment, which negatively affects patients' prognosis. Prehospital identification of patients with LVO-a stroke would allow direct transportation of these patients to an EVT-capable center. Electroencephalography (EEG) may be suitable for this purpose because of its sensitivity to cerebral ischemia. The hypothesis of ELECTRA-STROKE is that dry electrode EEG is feasible for prehospital detection of LVO-a stroke. Methods: ELECTRA-STROKE is an investigator-initiated, diagnostic study. EEG recordings will be performed in patients with a suspected stroke in the ambulance. The primary endpoint is the diagnostic accuracy of the theta/alpha ratio for the diagnosis of LVO-a stroke, expressed by the area under the receiver operating characteristic (ROC) curve. EEG recordings will be performed in 386 patients. Discussion: If EEG can be used to identify LVO-a stroke patients with sufficiently high diagnostic accuracy, it may enable direct routing of these patients to an EVT-capable center, thereby reducing time-to-treatment and improving patient outcomes. Clinical trial registration: ClinicalTrials.gov, identifier: NCT03699397.