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, we 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
We hypothesize that EEG accurately identifies the presence of an LVO-a stroke in patients
with a suspected AIS when applied in the ambulance.
OBJECTIVE
To determine the diagnostic accuracy of dry-electrode EEG for diagnosis of LVO-a stroke
when performed by ambulance personnel in patients with a suspected AIS.
STUDY DESIGN
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. Sample size: maximum of 20 patients.
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. Sample size:
maximum of 20 patients.
Phase 3: Validation of several existing algorithms and development of one or more new
algorithms for LVO-a detection, as well as optimization of logistics and software
settings of the dry electrode EEG cap in patients close to our target population in an
in-hospital emergency setting. Sample size: maximum of 300 patients.
Phase 4: Validation of several existing algorithms and algorithms developed in phase 3
for LVO-a detection 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. Sample size: maximum of 386
patients.
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 endovascular treatment).
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: the diagnostic accuracy of dry electrode cap EEG to discriminate LVO-a
stroke from all other strokes and stroke mimics in the prehospital setting (study phase
4) expressed as the area under the receiver operating characteristics (ROC) curve of the
theta/alpha ratio.
Secondary end points:
Sensitivity, specificity, PPV and NPV of the theta/alpha ratio, and test
characteristics of other existing EEG data based algorithms for LVO-a detection
(e.g. Weighted Phase Lag Index, delta/alpha ratio);
Logistical and technical feasibility of paramedics performing dry electrode cap EEG
in the ambulance in suspected AIS patients;
Developing one or more novel EEG data based algorithms with an optimal diagnostic
accuracy for LVO-a detection in suspected AIS patients with ambulant dry electrode
cap EEG.