Using Machine Learning and Biomarkers for Early Detection of Delayed Cerebral Ischemia

NCT ID: NCT06069973

Last Updated: 2026-01-12

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

1500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-01-01

Study Completion Date

2033-12-31

Brief Summary

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The overall goal of this project is to determine if machine learning and analysis of neurospecific biomarkers can enable early detection of upcoming or ongoing cerebral ischaemia in patients suffering from subarachnoid haemorrhage with altered consciousness due to cerebral injury or sedation. Analyses of heart rate variability, electroencephalgraphy,nearinfrared spectroscopy, cerebral autoregulation, and brain injury specific biomarkers in blood and cerebrospinal fluid will be performed.

Detailed Description

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A new and promising approach to detect ongoing cerebral ischemia might be the detection of neurospecific biomarkers in blood. A biomarker for cerebral ischaemia, similar to troponin T and troponin I for detecting cardiac ischaemia, would be precious; however, such a biomarker for cerebral ischaemia is currently lacking. (9) There are several interesting neurospecific biomarkers for this purpose, such as Glial fibrillary acidic protein (GFAP), neuron-specific enolase (NSE), total tau, S-100, and neurofilament light chains (NFL). At this point, we do not have enough knowledge about levels of neurospecific biomarkers in blood and cerebrospinal fluid during delayed cerebral ischemia after subarachnoid hemorrhage. The sampling of neurospecific biomarkers have a dual purpose, the first is to investigate if we can detect ongoing cerebral ischemia with these biomarkers, and the second purpose is to compare levels of biomarkers to outcome in mortality and morbidity determined by the Glasgow Coma Scale Extended at 1-year, 3-years and 5-years after admission.

Machine learning algorithms for predicting outcomes after delayed cerebral ischemia using a combination of clinical and imaging data have emerged. Nevertheless, prediction of delayed cerebral ischemia does not prevent it; to prevent delayed cerebral ischemia, an easily applied, cheap and reliable monitoring system that can warn physicians of the imminent risk of cerebral ischemia needs to be developed, making it possible to intervene.

The overall goal of this project is to develop methods that enable the detection of upcoming or ongoing cerebral ischaemia in patients with subarachnoid haemorrhage

Our primary aims are:

* To develop a machine learning-based model that can identify patterns in signals obtained from HRV, NIRS, and EEG monitoring, which are consistent with upcoming cerebral ischemia and provide a warning about this to attending physicians.
* To define the specificity and time relation of neurospecific biomarkers in blood and cerebrospinal fluid in patients with subarachnoid haemorrhage with and without delayed cerebral ischemia to evaluate if any of these biomarkers can be used as an indicator for ongoing cerebral ischemia.
* To assess the prognostic value of changes in physiological and neurospecific biomarkers changes during the acute phase after subarachnoid hemorrhage on long-term outcome.

Conditions

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Ischemic Stroke Hemorrhagic Stroke

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Delayed cerebral ischemia

Definition by Vergouwen et al. Verified by computed tomography

No intervention, observational study

Intervention Type OTHER

No intervention

Non-delayed cerebral ischemia

No signs of cerebral ischemia clinically or by computed tomography.

No intervention, observational study

Intervention Type OTHER

No intervention

Interventions

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No intervention, observational study

No intervention

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

Patients over the age of 18 with aneurysmal subarachnoid hemorrhage admitted to intensive care units at Sahlgrenska University hospital.

Exclusion Criteria

* Unable to consent,
* Cardiac arrythmia,
* Previous brain damage
Minimum Eligible Age

18 Years

Maximum Eligible Age

110 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Sahlgrenska University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Linda Block

Dr

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Sahlgrenska University Hospital

Gothenburg, , Sweden

Site Status RECRUITING

Countries

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Sweden

Facility Contacts

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Block, PI, Associate professor

Role: primary

+46313428173

Other Identifiers

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CIDAI-BAS-SAH

Identifier Type: -

Identifier Source: org_study_id

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