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Clinical Trials/NCT06069973
NCT06069973
Recruiting
Not Applicable

Machine Learning and Biomarkers for Early Detection of Delayed Cerebral Ischemia

Sahlgrenska University Hospital2 sites in 1 country1,500 target enrollmentJanuary 1, 2024

Overview

Phase
Not Applicable
Intervention
No intervention, observational study
Conditions
Ischemic Stroke
Sponsor
Sahlgrenska University Hospital
Enrollment
1500
Locations
2
Primary Endpoint
Autoregulation
Status
Recruiting
Last Updated
3 months ago

Overview

Brief Summary

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

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.

Registry
clinicaltrials.gov
Start Date
January 1, 2024
End Date
December 31, 2033
Last Updated
3 months ago
Study Type
Observational
Sex
All

Investigators

Sponsor
Sahlgrenska University Hospital
Responsible Party
Principal Investigator
Principal Investigator

Linda Block

Dr

Sahlgrenska University Hospital

Eligibility Criteria

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

Arms & Interventions

Non-delayed cerebral ischemia

No signs of cerebral ischemia clinically or by computed tomography.

Intervention: No intervention, observational study

Delayed cerebral ischemia

Definition by Vergouwen et al. Verified by computed tomography

Intervention: No intervention, observational study

Outcomes

Primary Outcomes

Autoregulation

Time Frame: 2023-2033

xyz

Early warning system

Time Frame: 2023-2033

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.

Study Sites (2)

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