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RATIONALE Endovascular thrombectomy (EVT) is standard treatment for acute ischemic stroke (AIS) if there is a large vessel occlusion in the anterior circulation (LVO-a). Because of its complexity, EVT is performed in selected hospitals only. Currently, approximately half of EVT eligible patients are initially admitted to hospitals that do not provide this therapy. This delays initiation of treatment by approximately an hour, which decreases the chance of a good clinical outcome. Direct presentation of all patients with a suspected AIS in EVT capable hospitals is not feasible, since only approximately 7% of these patients are eligible for EVT. Therefore, an advanced triage method that reliably identifies patients with an LVO-a in the ambulance is necessary. Electroencephalography (EEG) may be suitable for this purpose, as preliminary studies suggest that slow EEG activity in the delta frequency range correlates with lesion location on cerebral imaging. Use of dry electrode EEG caps will enable relatively unexperienced paramedics to perform a reliable measurement without the EEG preparation time associated with 'wet' EEGs. Combined with algorithms for automated signal analysis, the investigators expect the time of EEG recording and analysis to eventually be below five minutes, which would make stroke triage in the ambulance by EEG logistically feasible.
HYPOTHESIS The investigators hypothesize that dry electrode cap EEG can be used in patients with a suspected AIS to identify patients with an LVO-a in the ambulance.
OBJECTIVE To develop and validate an algorithm based on dry electrode cap EEG data that accurately determines the likelihood of an LVO-a in patients with a suspected AIS in the ambulance.
This diagnostic study consists of four phases:
Phase 1: Optimization of measurement time and software settings of the dry electrode cap EEG in a non-emergency setting in patients in whom a regular EEG is/will be performed for standard medical care.
Phase 2: Optimization of measurement time and software settings of the dry electrode cap EEG in patients close to our target population in a non-emergency setting.
Phase 3: Validation of several existing algorithms and development of one or more new algorithms; selection of algorithm with best diagnostic accuracy for validation in phase 4.
Phase 4: Validation of the algorithm selected in phase 3 in patients with a suspected AIS in the ambulance, as well as assessment of technical and logistical feasibility of performing EEG with dry electrode caps in patients with a suspected AIS in the ambulance. This phase is the only multicenter phase of this study.
STUDY POPULATION Phase 1: Patients in the outpatient clinic of the Clinical Neurophysiology department of the AMC, in whom a regular EEG has been/will be performed for standard medical care.
Phase 2: Patients with an AIS admitted to the Neurology ward of the coordinating hospital with an LVO-a (after reperfusion therapy).
Phase 3: Patients with a suspected AIS in the emergency room (ER) of the coordinating hospital (before reperfusion therapy).
Phase 4: Patients with a suspected AIS in the ambulance.
INTERVENTION Performing a dry electrode cap EEG (in phase 1 in the outpatient clinic, in phase 2 during hospital admission, in phase 3 in the ER and in phase 4 in the ambulance).
MAIN END POINTS
- Primary end point: specificity of dry electrode cap EEG for diagnosis of LVO-a in suspected AIS patients in the ambulance;
- Secondary end points:
- Developing an algorithm with optimal diagnostic accuracy for LVO-a detection with ambulant EEG;
- Sensitivity, positive predictive value (PPV) and negative predictive value (NPV) of dry electrode cap EEG for diagnosis of LVO-a in suspected AIS patients in the ambulance;
- Technical and logistical feasibility of performing dry electrode cap EEGs on patients with a suspected AIS in the ambulance;
- Diagnostic accuracy of dry electrode cap EEG for diagnosis of LVO-a in the ambulance in an 'enriched' population (a population with a higher incidence of LVO-a compared to the primary target population because it includes, alongside patients with a suspected AIS, patients with a known LVO-a).
Dry electrode EEG
Not yet recruiting
Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Published on BioPortfolio: 2018-10-15T12:13:15-0400
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