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

February 8, 2024 updated by: Linda Block, Sahlgrenska University Hospital, Sweden

Machine Learning and Biomarkers for Early Detection of Delayed Cerebral Ischemia

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Estimated)

1500

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Adult people with aneurysmal subarachnoid hemorrhage

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Delayed cerebral ischemia
Definition by Vergouwen et al. Verified by computed tomography
Other: No intervention, observational study
No intervention
Non-delayed cerebral ischemia
No signs of cerebral ischemia clinically or by computed tomography.
Other: No intervention, observational study
No intervention

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
2023-2033
Autoregulation
Time Frame: 2023-2033
xyz
2023-2033

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sahlgrenska University Hospital, Sweden

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

September 1, 2024

Primary Completion (Estimated)

December 31, 2033

Study Completion (Estimated)

December 31, 2033

Study Registration Dates

First Submitted

September 26, 2023

First Submitted That Met QC Criteria

October 3, 2023

First Posted (Actual)

October 6, 2023

Study Record Updates

Last Update Posted (Actual)

February 9, 2024

Last Update Submitted That Met QC Criteria

February 8, 2024

Last Verified

February 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CIDAI-BAS-SAH

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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