RATIONALE
Large vessel occlusion (LVO) stroke causes around 30% of acute ischemic strokes (AIS) and is
associated with severe deficits and poor neurological outcomes. Endovascular thrombectomy
(EVT) enormously improves the prognosis of patients with LVO stroke, but its effect is highly
time-dependent. Because of its complexity and required resources, EVT can be performed in
selected hospitals only. In the Netherlands, approximately half of the EVT-eligible patients
are initially admitted to a hospital incapable of performing EVT, and - once it has been
ascertained that the patient requires EVT - the patient needs to be transported a second time
by ambulance to an EVT-capable hospital. Interhospital transfer leads to a treatment delay of
40-115 minutes, which decreases the absolute chance of a good outcome of the patient by
5-15%. To solve this issue, a prehospital stroke triage instrument is needed, which reliably
identifies LVO stroke in the ambulance, so that these patients can be brought directly to an
EVT-capable hospital. Electroencephalography (EEG) may be suitable for this purpose, since it
shows almost instantaneous changes in response to cerebral blood flow reduction. Moreover,
significant differences between EEGs of patients with an LVO stroke and those of suspected
AIS patients with a smaller or no vessel occlusion have been found. A dry electrode EEG cap
enables ambulance paramedics to perform an EEG in the prehospital setting, with significant
reduced preparation time compared to conventional wet electrode EEG. An automatic
LVO-detection algorithm will be the key to reliable, simple and fast interpretation of the
EEG by paramedics, enabling direct admission of suspected AIS patients to the right hospital.
HYPOTHESIS
An EEG-based algorithm, developed with artificial intelligence (AI), will have sufficiently
high diagnostic accuracy to be used by ambulance paramedics for prehospital LVO detection.
OBJECTIVE
The primary objective of this study is to develop one or more novel AI-based algorithms (the
AI-STROKE algorithms) with optimal diagnostic accuracy for identification of LVO stroke in
patients with a suspected AIS in the prehospital setting, based on ambulant EEG data.
STUDY DESIGN
AI-STROKE is an investigator-initiated, multicenter, diagnostic test accuracy study.
STUDY POPULATION
Part A: Adult patients with a (suspected) AIS, in the prehospital setting. Part B: Adult
patients with a (suspected) AIS, in the in-hospital setting.
INTERVENTION
A single EEG measurement with a dry electrode cap (approximately 2 minutes recording
duration) will be performed in each patient. In addition, clinical and radiological data will
be collected. EEG data will be acquired with a CE approved portable dry electrode EEG device.
MAIN END POINTS
Primary end point: Based on the EEG data, and using the final diagnosis based on CT
angiography data established by an adjudication committee as the gold standard, one or more
novel AI-based EEG algorithms (the AI-STROKE algorithms) will be developed with maximal
diagnostic accuracy (i.e. area under the receiver operating characteristic curve; AUC) to
identify patients with an LVO stroke of the anterior circulation in a population of patients
with suspected AIS.
Secondary end points:
AUC, sensitivity, specificity, positive predictive value (PPV) and negative predictive
value (NPV) of the AI-STROKE algorithms based on ambulant EEG for diagnosis of LVO of
the anterior circulation in suspected AIS patients in the prehospital setting;
AUC, sensitivity, specificity, PPV and NPV of existing EEG algorithms based on ambulant
EEG for diagnosis of LVO stroke of the anterior circulation in suspected AIS patients in
the prehospital setting;
AUC, sensitivity, specificity, PPV and NPV of existing and newly developed EEG
algorithms based on ambulant EEG for detection of LVO stroke of the posterior
circulation, intracerebral hemorrhage, transient ischemic attack, and stroke mimics;
Technical and logistical feasibility (e.g. in terms of EEG channel reliability) of
paramedics performing ambulant EEG in patients with a suspected AIS in the prehospital
setting.