Ischemic StrokeAI-STROKENCT05437237
Algorithm Development Through AI for the Triage of Stroke Patients in the Ambulance With EEG
A study testing Dry electrode EEG in people with Ischemic Stroke, run by Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA) at 1 site in Netherlands.
Trial summary
Endovascular thrombectomy (EVT) enormously improves the prognosis of patients with large vessel occlusion (LVO) stroke, but its effect is highly time-dependent. Direct presentation of patients with an LVO stroke to an EVT-capable hospital reduces onset-to-treatment time by 40-115 minutes and thereby improves clinical… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Ischemic Stroke
- What is being tested?
- Dry electrode EEG
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,192 participants
- Where
- 1 siteNetherlands
- Study dates
- 19 Jun 2022to Jun 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Suspected AIS, as assessed by the attending ambulance paramedic, or a known LVO stroke;
- Onset of symptoms or last seen well < 24 hours before EEG acquisition;
- Age of 18 years or older;
- Written informed consent by patient or legal representative (deferred).
Who cannot join
- Skin defect or active infection of the scalp in the area of the electrode cap placement;
- (Suspected) COVID-19 infection.
Trial locations and contacts
1 site in 1 country.
Netherlands1 site, 1 recruiting
- Recruiting
Amsterdam University Medical Centers, location AMC
Amsterdam, North Holland, 1105AZ
Maritta N van Stigt, MSc, 0031 20 566 8417, m.n.vanstigt@amsterdamumc.nl
About this study
Official title: Algorithm Development Through Artificial Intelligence for the Triage of Stroke Patients in the Ambulance With Electroencephalography
Endovascular thrombectomy (EVT) enormously improves the prognosis of patients with large vessel occlusion (LVO) stroke, but its effect is highly time-dependent. Direct presentation of patients with an LVO stroke to an EVT-capable hospital reduces onset-to-treatment time by 40-115 minutes and thereby improves clinical outcome. Electroencephalography (EEG) may be a suitable prehospital stroke triage instrument for identifying LVO stroke, as differences have been found between EEG recordings of patients with an LVO stroke and those of suspected acute ischemic stroke patients with a smaller or no vessel occlusion. The investigators expect EEG can be performed in less than five minutes in the prehospital setting using a dry electrode EEG cap. An automatic LVO-detection algorithm will be the key to reliable, simple and fast interpretation of EEG recordings by ambulance paramedics. 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 acute ischemic stroke in the prehospital setting, based on ambulant EEG data.
Read the detailed description
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.
- Conditions
- Ischemic Stroke
- Being tested
- Dry electrode EEG
- Medical subject headings
- Ischemic Stroke
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Dry electrode EEGDiagnostic test
A single dry electrode electroencephalography (EEG) will be performed in each patient that is included in this study. For this purpose the Waveguard touch dry electrode EEG cap and compatible eego mini amplifier (ANT Neuro B.V., Hengelo, Netherlands) are used to record and amplify the EEG signal, respectively. Both products are CE marked as medical devices in the European Union and will be used within the intended use as described in the user manuals.
| Study group | Role | What they receive |
|---|---|---|
| Dry electrode cap EEG | Experimental | All patients that are included in the study will undergo a dry electrode electroencephalography (EEG). Receives: Diagnostic Test: Dry electrode EEG |
What is being measured
The primary outcome is the main question the study is designed to answer.
One or more novel AI-based EEG algorithms based on dry electrode EEG-data with optimal diagnostic accuracy for LVO-a
One or more novel artificial intelligence (AI) based electroencephalography (EEG) algorithms (the AI-STROKE algorithms) with maximal diagnostic accuracy to identify patients with an large vessel occlusion of the anterior circulation (LVO-a) in a population of patients with suspected acute ischemic stroke. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Secondary outcomes (17)
AUC of the AI-STROKE algorithms for diagnosis of LVO-a
Area under the receiver operating characteristic curve (AUC) of the AI-STROKE algorithms based on ambulant electroencephalography (EEG) for diagnosis of large vessel occlusion of the anterior circulation (LVO-a) in suspected acute ischemic stroke patients in the prehospital setting. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Sensitivity of the AI-STROKE algorithms for diagnosis of LVO-a
Sensitivity of the AI-STROKE algorithms based on ambulant electroencephalography (EEG) for diagnosis of large vessel occlusion of the anterior circulation (LVO-a) in suspected acute ischemic stroke patients in the prehospital setting. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Specificity of the AI-STROKE algorithms for diagnosis of LVO-a
Specificity of the AI-STROKE algorithms based on ambulant electroencephalography (EEG) for diagnosis of large vessel occlusion of the anterior circulation (LVO-a) in suspected acute ischemic stroke patients in the prehospital setting. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
PPV of the AI-STROKE algorithms for diagnosis of LVO-a
Positive predictive value (PPV) of the AI-STROKE algorithms based on ambulant electroencephalography (EEG) for diagnosis of large vessel occlusion of the anterior circulation (LVO-a) in suspected acute ischemic stroke patients in the prehospital setting. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
NPV of the AI-STROKE algorithms for diagnosis of LVO-a
Negative predictive value (NPV) of the AI-STROKE algorithms based on ambulant electroencephalography (EEG) for diagnosis of large vessel occlusion of the anterior circulation (LVO-a) in suspected acute ischemic stroke patients in the prehospital setting. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
AUC of existing EEG algorithms for diagnosis of LVO-a
Area under the receiver operating characteristic curve (AUC) of existing electroencephalography (EEG) algorithms based on ambulant EEG for diagnosis of large vessel occlusion of the anterior circulation (LVO-a) in suspected acute ischemic stroke patients in the prehospital setting. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Sensitivity of existing EEG algorithms for diagnosis of LVO-a
Sensitivity of existing electroencephalography (EEG) algorithms based on ambulant EEG for diagnosis of large vessel occlusion of the anterior circulation (LVO-a) in suspected acute ischemic stroke patients in the prehospital setting. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Specificity of existing EEG algorithms for diagnosis of LVO-a
Specificity of existing electroencephalography (EEG) algorithms based on ambulant EEG for diagnosis of large vessel occlusion of the anterior circulation (LVO-a) in suspected acute ischemic stroke patients in the prehospital setting. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
PPV of existing EEG algorithms for diagnosis of LVO-a
Positive predictive value (PPV) of existing electroencephalography (EEG) algorithms based on ambulant EEG for diagnosis of large vessel occlusion of the anterior circulation (LVO-a) in suspected acute ischemic stroke patients in the prehospital setting. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
NPV of existing EEG algorithms for diagnosis of LVO-a
Negative predictive value (NPV) of existing electroencephalography (EEG) algorithms based on ambulant EEG for diagnosis of large vessel occlusion of the anterior circulation (LVO-a) in suspected acute ischemic stroke patients in the prehospital setting. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
AUC of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics
Area under the receiver operating characteristic curve (AUC) of existing and newly developed electroencephalography (EEG) algorithms based on ambulant EEG for detection of an large vessel occlusion of the posterior circulation (LVO-p), intracerebral hemorrhage, transient ischemic attack, and stroke mimics.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Sensitivity of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics
Sensitivity of existing and newly developed electroencephalography (EEG) algorithms based on ambulant EEG for detection of an large vessel occlusion of the posterior circulation (LVO-p), intracerebral hemorrhage, transient ischemic attack, and stroke mimics.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Specificity of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics
Specificity of existing and newly developed electroencephalography (EEG) algorithms based on ambulant EEG for detection of an large vessel occlusion of the posterior circulation (LVO-p), intracerebral hemorrhage, transient ischemic attack, and stroke mimics.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
PPV of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics
Positive predictive value (PPV) of existing and newly developed electroencephalography (EEG) algorithms based on ambulant EEG for detection of an large vessel occlusion of the posterior circulation (LVO-p), intracerebral hemorrhage, transient ischemic attack, and stroke mimics.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
NPV of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics
Negative predictive value (NPV) of existing and newly developed electroencephalography (EEG) algorithms based on ambulant EEG for detection of an large vessel occlusion of the posterior circulation (LVO-p), intracerebral hemorrhage, transient ischemic attack, and stroke mimics.
Measured: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Technical feasibility of performing ambulant EEGs in the prehospital setting
Assessing whether it is technically possible for paramedics to perform ambulant electroencephalography (EEG) in patients with a suspected AIS in the prehospital setting.
Measured: Feedback on technical issues by the paramedic that performs the EEG and by the EEG-expert, will be collected directly at arrival in the emergency department (within 24 hours after the patient is included in the study)
Logistical feasibility of performing ambulant EEGs in the prehospital setting
Assessing whether it is logistically possible for paramedics to perform ambulant electroencephalography (EEG) in patients with a suspected AIS in the prehospital setting.
Measured: Feedback on logistical issues by the paramedic that performs the EEG, will be collected directly at arrival in the emergency department (within 24 hours after the patient is included in the study)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jun 2022
- Study start19 Jun 2022
- Primary completionJun 2026 (estimated)
- Study completionJun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ischemic StrokeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 1,192 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jonathan Coutinho, Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
- Investigators
- Jonathan M Coutinho, MD, PhD, Principal investigator, Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05437237
- Sponsor's protocol ID
- NL75429.018.20
- Acronym
- AI-STROKE
- Status last verified
- Jun 2022
- First submitted
- 7 Jun 2022
- Last updated
- 29 Jun 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Jun 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Algorithm Development Through Artificial Intelligence for the Triage of Stroke Patients in the Ambulance With Electroencephalography
- ClinicalTrials.gov ID
- NCT05437237
- Acronym / sponsor ID
- AI-STROKE / NL75429.018.20
- Conditions
- Ischemic Stroke
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 1,192 participants (estimated)
- Lead sponsor
- Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)University, hospital or other
- Study start
- 19 Jun 2022 (actual)
- Primary completion
- Jun 2026 (estimated)
- Study completion
- Jun 2026 (estimated)
- First posted
- 29 Jun 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Endovascular thrombectomy (EVT) enormously improves the prognosis of patients with large vessel occlusion (LVO) stroke, but its effect is highly time-dependent. Direct presentation of patients with an LVO stroke to an EVT-capable hospital reduces onset-to-treatment time by 40-115 minutes and thereby improves clinical outcome. Electroencephalography (EEG) may be a suitable prehospital stroke triage instrument for identifying LVO stroke, as differences have been found between EEG recordings of patients with an LVO stroke and those of suspected acute ischemic stroke patients with a smaller or no vessel occlusion. The investigators expect EEG can be performed in less than five minutes in the prehospital setting using a dry electrode EEG cap. An automatic LVO-detection algorithm will be the key to reliable, simple and fast interpretation of EEG recordings by ambulance paramedics. 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 acute ischemic stroke in the prehospital setting, based on ambulant EEG data.
Detailed description
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Dry electrode cap EEG | Experimental | Diagnostic Test: Dry electrode EEGAll patients that are included in the study will undergo a dry electrode electroencephalography (EEG). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Suspected AIS, as assessed by the attending ambulance paramedic, or a known LVO stroke;
- Onset of symptoms or last seen well < 24 hours before EEG acquisition;
- Age of 18 years or older;
- Written informed consent by patient or legal representative (deferred).
Key exclusion criteria (2 listed)
- Skin defect or active infection of the scalp in the area of the electrode cap placement;
- (Suspected) COVID-19 infection.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Dry electrode EEGA single dry electrode electroencephalography (EEG) will be performed in each patient that is included in this study. For this purpose the Waveguard touch dry electrode EEG cap and compatible eego mini amplifier (ANT Neuro B.V., Hengelo, Netherlands) are used to record and amplify the EEG signal, respectively. Both products are CE marked as medical devices in the European Union and will be used within the intended use as described in the user manuals. | Diagnostic test | Dry electrode cap EEG |
Primary outcome
One or more novel AI-based EEG algorithms based on dry electrode EEG-data with optimal diagnostic accuracy for LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
One or more novel artificial intelligence (AI) based electroencephalography (EEG) algorithms (the AI-STROKE algorithms) with maximal diagnostic accuracy to identify patients with an large vessel occlusion of the anterior circulation (LVO-a) in a population of patients with suspected acute ischemic stroke. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.
17 secondary outcomes
AUC of the AI-STROKE algorithms for diagnosis of LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Sensitivity of the AI-STROKE algorithms for diagnosis of LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Specificity of the AI-STROKE algorithms for diagnosis of LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
PPV of the AI-STROKE algorithms for diagnosis of LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
NPV of the AI-STROKE algorithms for diagnosis of LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
AUC of existing EEG algorithms for diagnosis of LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Sensitivity of existing EEG algorithms for diagnosis of LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Specificity of existing EEG algorithms for diagnosis of LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
PPV of existing EEG algorithms for diagnosis of LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
NPV of existing EEG algorithms for diagnosis of LVO-a
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
AUC of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Sensitivity of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Specificity of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
PPV of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
NPV of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics
Time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
Technical feasibility of performing ambulant EEGs in the prehospital setting
Time frame: Feedback on technical issues by the paramedic that performs the EEG and by the EEG-expert, will be collected directly at arrival in the emergency department (within 24 hours after the patient is included in the study)
Logistical feasibility of performing ambulant EEGs in the prehospital setting
Time frame: Feedback on logistical issues by the paramedic that performs the EEG, will be collected directly at arrival in the emergency department (within 24 hours after the patient is included in the study)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jun 2022.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jonathan M Coutinho, MD, PhD principal investigator
Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Dry electrode EEG in people with Ischemic Stroke, run by Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA) at 1 site in Netherlands.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 29 Jun 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Maritta N van Stigt, MSc (0031 20 566 8417, m.n.vanstigt@amterdamumc.nl), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
One or more novel AI-based EEG algorithms based on dry electrode EEG-data with optimal diagnostic accuracy for LVO-a (time frame: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jun 2022.