Trial · NCT07886957
AI-based Algorithm Development for Improved Diagnostics in the Intensive Care and Acute Stroke Setting Using Electroencephalography
NCT07886957, Recruiting, Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
3 sourcestrials-2026-11-12-0.5.0
Cite this record
ConductScience Library. AI-based Algorithm Development for Improved Diagnostics in the Intensive Care and Acute Stroke Setting Using Electroencephalography. Trials, record NCT07886957. Release trials-2026-11-12-0.5.0, 12 Nov 2026. https://conductscience.org/library/trials/NCT07886957
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Trial
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
- Title
- AI-based Algorithm Development for Improved Diagnostics in the Intensive Care and Acute Stroke Setting Using Electroencephalography
- NCT number
- NCT07886957
- Status
- Recruiting
- Enrollment
- 825 participants, estimated
- Lead sponsor
- Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
- Conditions
- Hemorrhagic Stroke, Intracerebral, Subarachnoid Haemorrhage (SAH), Postanoxic Coma
- First posted
- 2026-10-21
- Last updated
- 2026-10-21
Source ClinicalTrials.gov
Registered outcomes6
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description |
|---|---|---|---|
| Primary | Prognostic performance of a subhairline EEG-based AI algorithm for neurological outcome prediction in postanoxic coma | 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%. |
| Primary | Diagnostic accuracy of a subhairline EEG-based AI algorithm for detection of intracerebral hemorrhage | 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. |
| Secondary | Prognostic performance of subhairline EEG-based AI algorithms for neurological outcome prediction at hourly intervals between 6 and 24 hours after cardiac arrest | EEG at each hour from 6 to 24 hours after cardiac arrest; neurological outcome at hospital discharge (up to 3 months after CA) | 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%. |
| Secondary | Diagnostic accuracy of a subhairline EEG-based AI algorithm for detection of subarachnoid hemorrhage | EEG at presentation (within 48 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 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. |
| Secondary | Feasibility of SMART-EEG in the intensive care unit | During the recording, from ICU admission up to 72 hours after cardiac arrest | 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. |
| Secondary | Feasibility of SMART-EEG in the in-hospital acute stroke setting | At the time of the EEG recording, at presentation (Day 1) | 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. |
Source ClinicalTrials.gov
Outcomes0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
None in this release.
Results by group0
Each measurement the trial posted, group by group, in the words and numbers it posted.
None in this release.
Participant flow0
How many started, completed or left each group, period by period.
None in this release.
Adverse events0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
None in this release.
Adverse event totals0
The totals the trial posted for each group.
None in this release.
Papers0
The papers the trial lists on ClinicalTrials.gov.
None in this release.
Grants0
The grants NIH RePORTER links to the trial, newest funded first: NIH's, and some of AHRQ's, CDC's, FDA's and VA's. Each shows its title, principal investigators, organization and funder in its newest year.
None in this release.
Trial comparisons0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
None in this release.
Tests0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
None in this release.
Data dates
The day each source was copied for this release.
Sources 3
- ClinicalTrials.gov's posted results, through CTTI's AACTFreely available. Acknowledgement requested
- ClinicalTrials.govNo license stated
- NIH RePORTER's clinical studies and project filesPublic domain
Each source's terms
- ClinicalTrials.gov's posted results, through CTTI's AACTClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299
- ClinicalTrials.govnone stated
- NIH RePORTER's clinical studies and project filesPublic domain (NIH RePORTER)
ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
For software
Read this trial from software.
The record and each of its lists are JSON, twenty rows a page. No key.
GET https://conductscience.org/api/trials/v1/NCT07886957
GET https://conductscience.org/api/trials/v1/NCT07886957/registered