Hemorrhagic Stroke, IntracerebralSMART-EEGNCT07886957
AI-based Algorithm Development for Improved Diagnostics in the Intensive Care and Acute Stroke Setting Using Electroencephalography
An observational study of Hemorrhagic Stroke, Intracerebral and Subarachnoid Haemorrhage (SAH), run by Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA) at 1 site in Netherlands.
Trial summary
The goal of this observational study is to develop and validate artificial intelligence (AI) algorithms that use subhairline EEG (a simplified EEG patch placed on the forehead and behind the ears) to predict neurological outcome and to detect intracranial hemorrhage. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hemorrhagic Stroke, Intracerebral, Subarachnoid Haemorrhage (SAH), Postanoxic Coma
- How many people
- 825 participants
- Where
- 1 siteNetherlands
- Study dates
- 26 Mar 2026to 26 Mar 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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
Study population: Adults (18 years or older) in three cohorts at Amsterdam UMC: patients in postanoxic coma after cardiac arrest admitted to the intensive care unit, patients with suspected or confirmed stroke presenting to the emergency department, and patients with suspected or confirmed subarachnoid hemorrhage presenting to the emergency department.
Who may be able to join
- All participants:
- Age 18 years or older
- Postanoxic coma (ICU):
- Postanoxic coma after cardiac arrest, admitted to the intensive care unit
- No objection to participation within 4 weeks of receiving an opt-out information letter
- Suspected stroke (emergency department):
Show all 13Show fewer
- Suspected stroke as assessed by the ambulance personnel presenting the patient to the emergency department, or confirmed stroke.
- Symptom onset, or last seen well if onset was not witnessed, less than 24 hours ago.
- Written (deferred) informed consent by the patient or legal representative
- Suspected subarachnoid hemorrhage (emergency department):
- Suspected subarachnoid hemorrhage as judged by the attending ambulance paramedic, or confirmed subarachnoid hemorrhage.
- Symptom onset, or last seen well if onset was not witnessed, less than 48 hours ago.
- Written (deferred) informed consent by the patient or legal representative.
Who cannot join
- All participants:
- (Suspected) skin damage or infection at the placement area of the subhairline EEG patch
- Postanoxic coma (ICU):
- Other types of severe brain injury, either pre-existing or coinciding with cardiac arrest. These include stroke, traumatic brain injury and progressive neurodegenerative disease.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Netherlands1 site, 1 recruiting
- Recruiting
Amsterdam University Medical Center
Amsterdam, North Holland, 1105 AZ
Kars Tjepkema, 0031644761222, k.r.tjepkema@amsterdamumc.nl
About this study
Official title: AI-based Algorithm Development for Improved Diagnostics in the Intensive Care and Acute Stroke Setting Using Electroencephalography (SMART-EEG)
The goal of this observational study is to develop and validate artificial intelligence (AI) algorithms that use subhairline EEG (a simplified EEG patch placed on the forehead and behind the ears) to predict neurological outcome and to detect intracranial hemorrhage. The study consists of two use cases: (UC1) adults (18 years or older) in postanoxic coma after cardiac arrest who are admitted to the intensive care unit (ICU), and UC2) adults with suspected stroke or suspected subarachnoid hemorrhage (SAH) who present to the emergency department. The primary objectives are:
- UC1: to develop and validate an AI-algorithm for outcome prediction in postanoxic coma at 24h after CA based on subhairline EEG.
- UC2: to develop and validate an AI-algorithm for detection of ICH among patients with (suspected) stroke based on subhairline EEG and clinical symptoms.
Read more
Participants will:
- UC1: receive a continuous subhairline EEG recording, in addition to the standard scalp EEG used in routine care. The recording starts as soon as possible after ICU admission and continues for up to 72 hours after cardiac arrest.
- UC2: receive a single subhairline EEG recording of about 2 minutes as soon as possible after arrival. Clinical data is collected from their medical records. This includes patient characteristics, imaging, treatment and outcome at discharge. There are no follow-up visits.
In a final pilot phase, up to 10 patients per setting will be measured with the AI algorithms built into the EEG recording device, to evaluate feasibility and user-friendliness. In all phases, EEG and algorithm results are not used for clinical decisions.
Read the detailed description
RATIONALE
Electroencephalography (EEG) is a noninvasive tool for detecting and monitoring neuronal activity. It plays a crucial role in predicting neurological outcome in postanoxic coma after cardiac arrest (CA). It is also promising for prehospital detection of intracranial hemorrhage (ICrH), including intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH).
Conventional scalp EEG has two practical limitations. Its electrodes must be carefully placed on the scalp by specialized personnel. Its interpretation requires expertise, is time-consuming and is prone to interrater variability.
In critical care and emergency settings, time is critical and specialist availability is often limited. In acute stroke care, rapid detection of ICrH is essential to start early appropriate treatment. In postanoxic coma after CA, early EEG monitoring after ICU admission is recommended to accurately predict neurological outcome.
Recent pilot studies suggest that subhairline EEG is a reliable and easy alternative to scalp EEG for these time-critical settings. Subhairline EEG is a simplified configuration consisting of a single patch applied on the forehead and behind the ears. In addition, artificial intelligence (AI) algorithms for automated interpretation of subhairline EEG could provide rapid and reliable decision support in both settings.
HYPOTHESIS
The investigators hypothesize that AI algorithms based on subhairline EEG can accurately predict neurological outcome in patients with postanoxic coma after CA. The investigators also hypothesize that such algorithms can accurately detect ICH in patients with suspected stroke.
OBJECTIVE
- Use case 1 (UC1): To develop and validate an AI algorithm based on subhairline EEG for outcome prediction in postanoxic coma at 24 hours after CA.
- Use case 2 (UC2): To develop and validate an AI algorithm based on subhairline EEG and clinical symptoms for detection of ICH in patients with (suspected) stroke.
STUDY DESIGN
This is an investigator-initiated, monocenter, observational study at Amsterdam UMC. It consists of three steps.
Step 1: Algorithm development. Several EEG-based AI algorithms will be developed and pre-trained using existing scalp EEG data and clinical data (UC1: >900 patients, UC2: >1000 patients).
Step 2: Prospective data collection. Subhairline EEG data and clinical data will be collected with the TrianecT subhairline EEG recording device. Sample sizes are based on 10-20 events per predictor.
- UC1: 180-360 patients
- UC2.1 (suspected stroke): 300-600 patients
- UC2.2 (suspected SAH): 100 patients
Step 3: Algorithm fine-tuning, validation and feasibility. The pre-trained algorithms will be fine-tuned and validated on the collected subhairline EEG data using transfer learning. The algorithms will then be integrated into the TrianecT software (SMART-EEG). Practical and clinical feasibility will be evaluated in up to 10 patients per use case. This evaluation is not intended for CE certification.
STUDY POPULATION
All participants are 18 years or older.
- UC1: Patients in postanoxic coma after CA admitted to the ICU.
- UC2.1: Patients with suspected stroke, as assessed by ambulance personnel, or with confirmed stroke.
- UC2.2: Patients with suspected SAH, as judged by the attending paramedic, or with confirmed SAH.
INTERVENTION
All participants undergo a subhairline EEG recording with a single patch applied on the forehead and behind the ears.
- UC1: continuous subhairline EEG recording, in addition to the standard scalp EEG used in routine care. The recording starts as soon as possible after ICU admission and continues up to 72 hours after CA.
- UC2: a single subhairline EEG recording of approximately 2 minutes, performed as soon as possible after presentation at the hospital.
MAIN END POINTS
Primary end points:
- UC1: the prognostic performance of subhairline EEG-based AI algorithms for prediction of neurological outcome at 24 hours after CA in postanoxic coma patients in the ICU. This is expressed as the area under the ROC curve (AUC), sensitivity, specificity and predictive values, with 95% confidence intervals. The optimal cut-off for poor outcome is defined as the highest sensitivity at a false positive rate of 0%.
- UC2: the diagnostic accuracy of subhairline EEG-based AI algorithms for detection of ICH in (suspected) stroke patients in the emergency department. This is expressed as the AUC, and as sensitivity, specificity, PPV and NPV at the optimal cut-off, each with 95% confidence intervals.
Secondary end points:
- Prognostic performance of AI algorithms based on subhairline EEG for outcome prediction at each hour between 6 and 24 hours after CA.
- Diagnostic accuracy of AI algorithms based on subhairline EEG and clinical symptoms for detection of SAH.
- Feasibility of SMART-EEG use in the ICU setting.
- Feasibility of SMART-EEG use in the acute stroke setting, including user-friendliness as rated by users.
- Medical subject headings
- Hemorrhagic Stroke, Subarachnoid Hemorrhage
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Postanoxic coma (UC1) | adults in postanoxic coma after cardiac arrest admitted to the ICU | |
| Suspected stroke (UC2.1) | adults with suspected or confirmed stroke presenting to the emergency department | |
| Suspected SAH (UC2.2) | adults with suspected or confirmed subarachnoid hemorrhage presenting to the emergency department |
What is being measured
The primary outcome is the main question the study is designed to answer.
Prognostic performance of a subhairline EEG-based AI algorithm for neurological outcome prediction in postanoxic coma
Performance of the AI algorithm, based on subhairline EEG recorded at 24 hours after cardiac arrest, in predicting poor neurological outcome. Poor outcome is defined as CPC 3-5 at hospital discharge. Performance is expressed as the area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values, each with 95% confidence intervals. The optimal cut-off for poor outcome is defined as the highest sensitivity at a false positive rate of 0%.
Measured: EEG at 24 hours after cardiac arrest; neurological outcome at hospital discharge (up to 3 months after CA)
Diagnostic accuracy of a subhairline EEG-based AI algorithm for detection of intracerebral hemorrhage
Diagnostic accuracy of the AI algorithm, based on subhairline EEG and clinical symptoms, in detecting intracerebral hemorrhage (ICH) in patients with suspected stroke. The reference standard is the final diagnosis based on neuroimaging. Accuracy is expressed as the area under the ROC curve (AUC), and as sensitivity, specificity, positive and negative predictive values at the optimal cut-off, each with 95% confidence intervals.
Measured: EEG at presentation (within 24 hours of symptom onset); final diagnosis at hospital discharge (up to 3 months after presentation)
Secondary outcomes (4)
Prognostic performance of subhairline EEG-based AI algorithms for neurological outcome prediction at hourly intervals between 6 and 24 hours after cardiac arrest
Performance of AI algorithms, based on subhairline EEG recorded at each hour between 6 and 24 hours after cardiac arrest, in predicting poor neurological outcome. Poor outcome is defined as CPC 3-5 at hospital discharge. Performance is expressed as AUC, sensitivity, specificity, positive and negative predictive values, each with 95% confidence intervals. The cut-off is defined as the highest sensitivity at a false positive rate of 0%.
Measured: EEG at each hour from 6 to 24 hours after cardiac arrest; neurological outcome at hospital discharge (up to 3 months after CA)
Diagnostic accuracy of a subhairline EEG-based AI algorithm for detection of subarachnoid hemorrhage
Diagnostic accuracy of the AI algorithm, based on subhairline EEG and clinical symptoms, in detecting subarachnoid hemorrhage (SAH) in patients with suspected SAH. Accuracy is expressed as AUC, and as sensitivity, specificity, positive and negative predictive values at the optimal cut-off, each with 95% confidence intervals.
Measured: EEG at presentation (within 48 hours of symptom onset); final diagnosis at hospital discharge (up to 3 months after presentation)
Feasibility of SMART-EEG in the intensive care unit
Practical and clinical feasibility of the subhairline EEG device with integrated AI algorithms (SMART-EEG) in up to 10 postanoxic coma patients in the ICU. Feasibility includes user-friendliness as rated by users . Algorithm results are not used for clinical decisions.
Measured: During the recording, from ICU admission up to 72 hours after cardiac arrest
Feasibility of SMART-EEG in the in-hospital acute stroke setting
Practical and clinical feasibility of SMART-EEG in up to 10 patients with suspected stroke in the emergency department. Feasibility includes user-friendliness as rated by users. Algorithm results are not used for clinical decisions.
Measured: At the time of the EEG recording, at presentation (Day 1)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Oct 2026
- Study start26 Mar 2026
- Primary completion26 Mar 2028 (estimated)
- Study completion26 Mar 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Hemorrhagic Stroke, IntracerebralStudies 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 825 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, Kars Tjepkema, Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07886957
- Sponsor's protocol ID
- 2025.0562
- Acronym
- SMART-EEG
- Status last verified
- Oct 2026
- First submitted
- 14 Oct 2026
- Last updated
- 21 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be made publicly available, due to privacy regulations (GDPR) and the consent procedures used in this study. Pseudonymized data may be shared with collaborating parties, including the device manufacturer, only under a data sharing agreement and only for participants who gave consent or did not object to such sharing.
Study at a glance
From the registry record, last updated by the sponsor on 21 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-based Algorithm Development for Improved Diagnostics in the Intensive Care and Acute Stroke Setting Using Electroencephalography (SMART-EEG)
- ClinicalTrials.gov ID
- NCT07886957
- Acronym / sponsor ID
- SMART-EEG / 2025.0562
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 825 participants (estimated)
- Lead sponsor
- Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)University, hospital or other
- Study start
- 26 Mar 2026 (actual)
- Primary completion
- 26 Mar 2028 (estimated)
- Study completion
- 26 Mar 2028 (estimated)
- First posted
- 21 Oct 2026 (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.
The goal of this observational study is to develop and validate artificial intelligence (AI) algorithms that use subhairline EEG (a simplified EEG patch placed on the forehead and behind the ears) to predict neurological outcome and to detect intracranial hemorrhage. The study consists of two use cases: (UC1) adults (18 years or older) in postanoxic coma after cardiac arrest who are admitted to the intensive care unit (ICU), and UC2) adults with suspected stroke or suspected subarachnoid hemorrhage (SAH) who present to the emergency department. The primary objectives are:
- UC1: to develop and validate an AI-algorithm for outcome prediction in postanoxic coma at 24h after CA based on subhairline EEG.
- UC2: to develop and validate an AI-algorithm for detection of ICH among patients with (suspected) stroke based on subhairline EEG and clinical symptoms.
Participants will:
- UC1: receive a continuous subhairline EEG recording, in addition to the standard scalp EEG used in routine care. The recording starts as soon as possible after ICU admission and continues for up to 72 hours after cardiac arrest.
- UC2: receive a single subhairline EEG recording of about 2 minutes as soon as possible after arrival. Clinical data is collected from their medical records. This includes patient characteristics, imaging, treatment and outcome at discharge. There are no follow-up visits.
In a final pilot phase, up to 10 patients per setting will be measured with the AI algorithms built into the EEG recording device, to evaluate feasibility and user-friendliness. In all phases, EEG and algorithm results are not used for clinical decisions.
Detailed description
RATIONALE
Electroencephalography (EEG) is a noninvasive tool for detecting and monitoring neuronal activity. It plays a crucial role in predicting neurological outcome in postanoxic coma after cardiac arrest (CA). It is also promising for prehospital detection of intracranial hemorrhage (ICrH), including intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH).
Conventional scalp EEG has two practical limitations. Its electrodes must be carefully placed on the scalp by specialized personnel. Its interpretation requires expertise, is time-consuming and is prone to interrater variability.
In critical care and emergency settings, time is critical and specialist availability is often limited. In acute stroke care, rapid detection of ICrH is essential to start early appropriate treatment. In postanoxic coma after CA, early EEG monitoring after ICU admission is recommended to accurately predict neurological outcome.
Recent pilot studies suggest that subhairline EEG is a reliable and easy alternative to scalp EEG for these time-critical settings. Subhairline EEG is a simplified configuration consisting of a single patch applied on the forehead and behind the ears. In addition, artificial intelligence (AI) algorithms for automated interpretation of subhairline EEG could provide rapid and reliable decision support in both settings.
HYPOTHESIS
The investigators hypothesize that AI algorithms based on subhairline EEG can accurately predict neurological outcome in patients with postanoxic coma after CA. The investigators also hypothesize that such algorithms can accurately detect ICH in patients with suspected stroke.
OBJECTIVE
- Use case 1 (UC1): To develop and validate an AI algorithm based on subhairline EEG for outcome prediction in postanoxic coma at 24 hours after CA.
- Use case 2 (UC2): To develop and validate an AI algorithm based on subhairline EEG and clinical symptoms for detection of ICH in patients with (suspected) stroke.
STUDY DESIGN
This is an investigator-initiated, monocenter, observational study at Amsterdam UMC. It consists of three steps.
Step 1: Algorithm development. Several EEG-based AI algorithms will be developed and pre-trained using existing scalp EEG data and clinical data (UC1: >900 patients, UC2: >1000 patients).
Step 2: Prospective data collection. Subhairline EEG data and clinical data will be collected with the TrianecT subhairline EEG recording device. Sample sizes are based on 10-20 events per predictor.
- UC1: 180-360 patients
- UC2.1 (suspected stroke): 300-600 patients
- UC2.2 (suspected SAH): 100 patients
Step 3: Algorithm fine-tuning, validation and feasibility. The pre-trained algorithms will be fine-tuned and validated on the collected subhairline EEG data using transfer learning. The algorithms will then be integrated into the TrianecT software (SMART-EEG). Practical and clinical feasibility will be evaluated in up to 10 patients per use case. This evaluation is not intended for CE certification.
STUDY POPULATION
All participants are 18 years or older.
- UC1: Patients in postanoxic coma after CA admitted to the ICU.
- UC2.1: Patients with suspected stroke, as assessed by ambulance personnel, or with confirmed stroke.
- UC2.2: Patients with suspected SAH, as judged by the attending paramedic, or with confirmed SAH.
INTERVENTION
All participants undergo a subhairline EEG recording with a single patch applied on the forehead and behind the ears.
- UC1: continuous subhairline EEG recording, in addition to the standard scalp EEG used in routine care. The recording starts as soon as possible after ICU admission and continues up to 72 hours after CA.
- UC2: a single subhairline EEG recording of approximately 2 minutes, performed as soon as possible after presentation at the hospital.
MAIN END POINTS
Primary end points:
- UC1: the prognostic performance of subhairline EEG-based AI algorithms for prediction of neurological outcome at 24 hours after CA in postanoxic coma patients in the ICU. This is expressed as the area under the ROC curve (AUC), sensitivity, specificity and predictive values, with 95% confidence intervals. The optimal cut-off for poor outcome is defined as the highest sensitivity at a false positive rate of 0%.
- UC2: the diagnostic accuracy of subhairline EEG-based AI algorithms for detection of ICH in (suspected) stroke patients in the emergency department. This is expressed as the AUC, and as sensitivity, specificity, PPV and NPV at the optimal cut-off, each with 95% confidence intervals.
Secondary end points:
- Prognostic performance of AI algorithms based on subhairline EEG for outcome prediction at each hour between 6 and 24 hours after CA.
- Diagnostic accuracy of AI algorithms based on subhairline EEG and clinical symptoms for detection of SAH.
- Feasibility of SMART-EEG use in the ICU setting.
- Feasibility of SMART-EEG use in the acute stroke setting, including user-friendliness as rated by users.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Postanoxic coma (UC1) | – | –adults in postanoxic coma after cardiac arrest admitted to the ICU |
| Suspected stroke (UC2.1) | – | –adults with suspected or confirmed stroke presenting to the emergency department |
| Suspected SAH (UC2.2) | – | –adults with suspected or confirmed subarachnoid hemorrhage presenting to the emergency department |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adults (18 years or older) in three cohorts at Amsterdam UMC: patients in postanoxic coma after cardiac arrest admitted to the intensive care unit, patients with suspected or confirmed stroke presenting to the emergency department, and patients with suspected or confirmed subarachnoid hemorrhage presenting to the emergency department.
Key inclusion criteria (13 listed)
- Age 18 years or older
- Postanoxic coma after cardiac arrest, admitted to the intensive care unit
- No objection to participation within 4 weeks of receiving an opt-out information letter
- Suspected stroke as assessed by the ambulance personnel presenting the patient to the emergency department, or confirmed stroke.
- Symptom onset, or last seen well if onset was not witnessed, less than 24 hours ago.
Key exclusion criteria (4 listed)
- (Suspected) skin damage or infection at the placement area of the subhairline EEG patch
- Other types of severe brain injury, either pre-existing or coinciding with cardiac arrest. These include stroke, traumatic brain injury and progressive neurodegenerative disease.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Prognostic performance of a subhairline EEG-based AI algorithm for neurological outcome prediction in postanoxic coma
Time frame: EEG at 24 hours after cardiac arrest; neurological outcome at hospital discharge (up to 3 months after CA)
Performance of the AI algorithm, based on subhairline EEG recorded at 24 hours after cardiac arrest, in predicting poor neurological outcome. Poor outcome is defined as CPC 3-5 at hospital discharge. Performance is expressed as the area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values, each with 95% confidence intervals. The optimal cut-off for poor outcome is defined as the highest sensitivity at a false positive rate of 0%.
Diagnostic accuracy of a subhairline EEG-based AI algorithm for detection of intracerebral hemorrhage
Time frame: EEG at presentation (within 24 hours of symptom onset); final diagnosis at hospital discharge (up to 3 months after presentation)
Diagnostic accuracy of the AI algorithm, based on subhairline EEG and clinical symptoms, in detecting intracerebral hemorrhage (ICH) in patients with suspected stroke. The reference standard is the final diagnosis based on neuroimaging. Accuracy is expressed as the area under the ROC curve (AUC), and as sensitivity, specificity, positive and negative predictive values at the optimal cut-off, each with 95% confidence intervals.
4 secondary outcomes
Prognostic performance of subhairline EEG-based AI algorithms for neurological outcome prediction at hourly intervals between 6 and 24 hours after cardiac arrest
Time frame: EEG at each hour from 6 to 24 hours after cardiac arrest; neurological outcome at hospital discharge (up to 3 months after CA)
Diagnostic accuracy of a subhairline EEG-based AI algorithm for detection of subarachnoid hemorrhage
Time frame: EEG at presentation (within 48 hours of symptom onset); final diagnosis at hospital discharge (up to 3 months after presentation)
Feasibility of SMART-EEG in the intensive care unit
Time frame: During the recording, from ICU admission up to 72 hours after cardiac arrest
Feasibility of SMART-EEG in the in-hospital acute stroke setting
Time frame: At the time of the EEG recording, at presentation (Day 1)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Oct 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be made publicly available, due to privacy regulations (GDPR) and the consent procedures used in this study. Pseudonymized data may be shared with collaborating parties, including the device manufacturer, only under a data sharing agreement and only for participants who gave consent or did not object to such sharing.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hemorrhagic Stroke, Intracerebral and Subarachnoid Haemorrhage (SAH), 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 13 inclusion and 4 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: looking for participants now, as of the sponsor's last update on 21 Oct 2026. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Kars Tjepkema (0031644761222, k.r.tjepkema@amsterdamumc.nl), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prognostic performance of a subhairline EEG-based AI algorithm for neurological outcome prediction in postanoxic coma (time frame: EEG at 24 hours after cardiac arrest; neurological outcome at hospital discharge (up to 3 months after CA)). The study lists 2 primary outcomes in all. 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 21 Oct 2026.