Clinical trial NCT06871969: Clinical Evaluation of EEG Device for the Triage of Stroke Patients in the Ambulance
Official title: Clinical Evaluation of Pre-hospital Stroke Triage Devices - Electroencephalography
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Ischemic Stroke
Stroke
Cardiovascular Diseases
Vascular Diseases
Central Nervous System Diseases
Brain Diseases
Nervous System Diseases
Cerebrovascular Disease
Lead sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Sponsor class: OTHER
First posted: 2025-03-12
Enrollment: 275
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief summary: 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.

Detailed description: 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:

1. StrokePointer device can measure EEG data of sufficient quality in \>85% of patients, and has a good diagnostic accuracy (AUC\>0.8) for LVO stroke detection in the pre-hospital setting.
2. Usability of StrokePointer device is rated as "good" on average by ambulance personnel.
3. StrokePointer is safe to use in an acute care setting.

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

* 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.
* Diagnostic accuracy of StrokePointer for LVO stroke among patients with a suspected stroke, as measured with AUC as well as sensitivity and specificity.

Eligibility criteria: Inclusion Criteria:

* Suspected acute stroke as per judgement of the ambulance personnel.
* Age 18 years or older.
* Onset of symptoms (or last seen well) \<24 hours.
* Written informed consent by patient or legal representative (deferred).

Exclusion Criteria:

- Injuries or infections of the scalp in the area of the electrode headset placement.

Primary outcomes:
measure: Data quality of StrokePointer
timeFrame: EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well.
measure: Diagnostic accuracy of StrokePointer for LVO stroke
timeFrame: EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well.
Secondary outcomes:
measure: Predictive value of StrokePointer in identifying LVO stroke
timeFrame: EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well.
measure: User friendliness
timeFrame: EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well.
measure: Incidence of serious adverse device-related events (Safety of StrokePointer)
timeFrame: EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well.
measure: Incidence of skin reactions (Safety of StrokePointer)
timeFrame: EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well.
measure: Diagnostic accuracy for identifying LVO stroke subgroups
timeFrame: EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well.
measure: Discriminative power of StrokePointer
timeFrame: EEG-data for analysis will be recorded within 24 hours after onset of symptoms or last seen well.
Other outcomes:
Locations:
city: Amsterdam
country: Netherlands
facility: Amsterdam University Medical Centers, location AMC
state: Noord-Holland
Study officials:
References: