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Endovascular thrombectomy (EVT) is the standard treatment for large vessel occlusion (LVO) strokes, but it can only be performed in specialized hospitals. Since ambulance personnel cannot determine if a patient is eligible for EVT, 54% of LVO stroke patients are initially taken to non-EVT-capable hospitals, resulting in an average delay of 1 hour in time-to-EVT in the Netherlands. To reduce this delay, it is crucial for ambulance personnel to identify potential LVO stroke patients and directly transport them to EVT-capable hospitals. Dry electrode electroencephalography (EEG) has shown high diagnostic accuracy for detecting LVO strokes, but in 32% of patients, the EEG signal quality was too poor to analyze.
To address this issue, TrianecT developed StrokePointer, a portable EEG-based triage device designed to collect and analyze EEG data in patients with suspected acute stroke. The objective of this study is to validate the effectiveness and safety of StrokePointer in detecting LVO stroke among patients with a suspected stroke in the pre-hospital setting.
RATIONALE Endovascular thrombectomy (EVT) is the standard treatment for large vessel occlusion (LVO) stroke. However, EVT can only be performed in specialized hospitals and its effect on functional outcome rapidly decreases with passing time (time = brain). Since ambulance personnel cannot determine whether a patient has a stroke that is eligible for EVT, 54% of patients with an LVO stroke are primarily presented at a non-EVT capable hospital. These patients then require interhospital transfer, resulting in average delay in time-to-EVT of 1 hour in the Netherlands. Therefore, providing ambulance personnel with tools to identify patients with a possible LVO stroke in the ambulance, allowing direct transport to an EVT capable hospital, is much needed. Dry electrode electroencephalography (EEG) has shown to have a high diagnostic accuracy for LVO stroke detection among patients with a suspected stroke (area under the receiving operating curve [AUC]: 0.91). However, in 32% of patients EEG signal quality was too poor to analyse. A new portable EEG-based triage device (StrokePointer) has been developed by TrianecT with the aim to collect and analyse EEG data in patients suspected of acute stroke. In this study, we intend to validate the safety and effectiveness of the device.
HYPOTHESIS:
OBJECTIVE Primary objective is to validate the data quality and diagnostic accuracy of StrokePointer to detect LVO stroke among patients with a suspected stroke in the pre-hospital setting.
STUDY DESIGN CROSSROADS-EEG is an investigator-initiated, prospective, multi-centre cohort study.
STUDY POPULATION Adult patients with a suspected stroke, onset of symptoms (or last seen well) <24 hours in the pre-hospital setting.
INTERVENTION A single measurement with a dry electrode headset EEG (approximately 2 minutes recording duration) will be performed in each patient. Clinical and radiological data will be collected. EEG data will be acquired with the improved TrianecT EEG device, StrokePointer.
MAIN STUDY END POINTS
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Dry electrode StrokePointer EEG | Experimental | All patients that are included in the study will undergo a dry electrode electroencephalography (EEG). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Dry electrode EEG | Diagnostic Test | A single dry electrode electroencephalography (EEG) will be performed in each patient that is included in this study. EEG data will be acquired with Strokepointer (TrianecT B.V., Utrecht, The Netherlands). StrokePointer is a portable EEG acquisition and analysis device. The device consists of three parts: (1) StrokePointer headset with dry electrodes to measure EEG data, (2) Portable StrokePointer suitcase that contains a mobile computing device (Android phone), CE-marked (IIa) EEG amplifier and storage compartments and (3) Software to acquire, analyse and upload EEG data. |
| Measure | Description | Time Frame |
|---|---|---|
| Data quality of StrokePointer | Proportion of patients with a technically successful EEG dataset: at least 20 seconds of usable EEG (at least 3 electrodes with good skin-electrode quality on either side, no movement artifacts, no muscle artifacts) within a measurement time of 3 minutes. | EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well. |
| Diagnostic accuracy of StrokePointer for LVO stroke | Diagnostic accuracy of StrokePointer for LVO stroke among patients with a suspected stroke, as measured with Area under the curve (AUC) as well as sensitivity and specificity | EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well. |
| Measure | Description | Time Frame |
|---|---|---|
| Predictive value of StrokePointer in identifying LVO stroke | Positive and negative predictive value of StrokePointer in identifying LVO stroke | EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well. |
| User friendliness |
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Inclusion Criteria:
Exclusion Criteria:
- Injuries or infections of the scalp in the area of the electrode headset placement.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Amsterdam University Medical Centers, location AMC | Recruiting | Amsterdam | North Holland | 1105AZ | Netherlands |
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User-friendliness rating of StrokePointer by ambulance personnel and researchers: (1) after the training 80% of the users should be able to start StrokePointer and start measuring EEG-data within 120s. (2) Interviewed users score (a) usability of StrokePointer hardware on average as "makkelijk" (easy) or better, (b) StrokePointer software on average as "duidelijk" (clear) or better. |
| EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well. |
| Incidence of serious adverse device-related events (Safety of StrokePointer) | Provided that the device is being used in line with the intended use, there are no occurrences of serious adverse device-related events in the study. | EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well. |
| Incidence of skin reactions (Safety of StrokePointer) | Number of patients with an (allergic) skin reaction observed at the site of the electrode. | EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well. |
| Diagnostic accuracy for identifying LVO stroke subgroups | Diagnostic accuracy for identifying LVO stroke within the following subgroups: sex (men vs. women) and age (above vs. below 60), as measured with sensitivity and specificity. | EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well. |
| Discriminative power of StrokePointer | Discriminative power of StrokePointer for ischemic stroke vs stroke mimic as measured with Area under the curve (AUC) as well as sensitivity and specificity. | EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well. |
| ID | Term |
|---|---|
| D000083242 | Ischemic Stroke |
| D020521 | Stroke |
| D002318 | Cardiovascular Diseases |
| D014652 | Vascular Diseases |
| D002493 | Central Nervous System Diseases |
| D001927 | Brain Diseases |
| D009422 | Nervous System Diseases |
| D002561 | Cerebrovascular Disorders |
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