Subarachnoid Hemorrhage, AneurysmalAISAHNCT04415736
Artificial Intelligence in Subarachnoid Hemorrhage
An observational study of Subarachnoid Hemorrhage, Aneurysmal, run by Göteborg University at 1 site in Sweden.
Trial summary
The overall aim of this study is to, with the help of computer/data scientist and machine learning processes, analyse collected heart rate variability data in order to evaluate whether specific patterns could be found in patients developing delayed cerebral ischemia after subarachnoid hemorrhage. Read more about this study
- Who is it for?
- Ages 16 to 100 years
- Conditions the study is for
- Subarachnoid Hemorrhage, Aneurysmal
- How many people
- 64 participants
- Where
- 1 siteSweden
- Study dates
- 1 Oct 2015to May 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
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 16 to 100 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Adult patients with aneurysmal subarachnoid hemorrhage, admitted to the Intensive Care Unit at Sahlgrenska University Hospital.
Who may be able to join
- Adult patients
- aneurysmal subarachnoid hemorrhage
- admitted to Neurointensive care unit at Sahlgrenska University Hospital, Gothenburg, Sweden
Who cannot join
- cardiac arrythmias
- use of pacemaker
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Using Machine Learning With Heart Rate Variability Signals to Detect Delayed Cerebral Ischemia After Subarachnoid Hemorrhage
The overall aim of this study is to, with the help of computer/data scientist and machine learning processes, analyse collected heart rate variability data in order to evaluate whether specific patterns could be found in patients developing delayed cerebral ischemia after subarachnoid hemorrhage.
Read the detailed description
Patients with aneurysmal subarachnoid haemorrhage (aSAH), develop delayed cerebral ischemia (DCI) in about 30% of the cases. DCI is associated with increased mortality, persistent neurological deficit as well as impaired quality of life. It would benefit both patients and society to decrease these neurological injuries. One clinical problem is that the diagnosis of cerebral ischemia in SAH patients often is delayed due to limitations in monitoring abilities. When detected, the neurological damage often turns out to be irreversible.
Several studies have used univariate and multivariate logistic regression analysis to identify risk factors for the development of delayed cerebral ischemia (DCI) in patients with subarachnoid haemorrhage. However, these studies are based on data collected about the patients (e.g. age, gender), and the precision of these statistical models has generally been found to be low. Recently, machine learning algorithms for the prediction of DCI using a combination of clinical and image data have also been evaluated .
However, prediction of DCI does not prevent DCI, to prevent DCI a monitoring system needs to be developed that can warn physicians of imminent risk of cerebral ischemia, making it possible to intervene and prevent cerebral ischemia.
Variations in the autonomous nervous system, such as changes in the balance between the sympathetic and the parasympathetic nervous systems, can be detected by using heart rate variability (HRV) monitoring. HRV has been reported as a predictor of poor outcome after traumatic brain injury and stroke, including subarachnoid haemorrhage. However, HRV monitoring for detection of incipient cerebral ischemia has not been thoroughly evaluated. In a study of patients with aSAH, we collected HRV continuously in up to 10 days after admission, but just a small part of the HRV data was analysed off-line. Fifteen of 55 patients developed DCI during the acute phase, and the off-line analyse of HRV showed that the low/high-frequency ratio increased more in patients that developed DCI (Ref). This led us to try to analyse all of the collected HRV with the help of machine learning processes, and a collaboration with computer/data scientists was initiated.
The overall aim of this study is to, with the help of computer/data scientist and machine learning processes, analyse collected HRV data in order to evaluate whether specific patterns could be found in patients developing DCI during the acute phase after subarachnoid hemorrhage.
- Conditions
- Subarachnoid Hemorrhage, Aneurysmal
- Being tested
- No intervention, observational study
- Medical subject headings
- Subarachnoid Hemorrhage
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No intervention, observational studyOther
| Study group | Role | What they receive |
|---|---|---|
| Delayed cerebral ischemia | Patients with subarachnoid hemorrhage that develop delayed cerebral ischemia Receives: Other: No intervention, observational study | |
| Non delayed cerebral ischemia | Patients with subarachnoid hemorrhage that do not develop subarachnoid hemorrhage Receives: Other: No intervention, observational study |
What is being measured
The primary outcome is the main question the study is designed to answer.
HRV data analysed by artificial intelligence for detection of DCI
Variability of the time of R-R intervals of each heartbeat is measured.
Measured: 2020
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jun 2020
- Study start1 Oct 2015
- Primary completionOct 2016
- Study completionMay 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Subarachnoid Hemorrhage, AneurysmalStudies 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
- 64 participants (actual)
Sponsor and investigators
- Lead sponsor
- Göteborg University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Miroslaw Staron, Prof, Study chair, Göteborg University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04415736
- Sponsor's protocol ID
- AISAH
- Acronym
- AISAH
- Status last verified
- Sep 2020
- First submitted
- 26 May 2020
- Last updated
- 3 Sep 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Sep 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Machine Learning With Heart Rate Variability Signals to Detect Delayed Cerebral Ischemia After Subarachnoid Hemorrhage
- ClinicalTrials.gov ID
- NCT04415736
- Acronym / sponsor ID
- AISAH / AISAH
- Conditions
- Subarachnoid Hemorrhage, Aneurysmal
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 64 participants (actual)
- Lead sponsor
- Göteborg UniversityUniversity, hospital or other
- Study start
- 1 Oct 2015 (actual)
- Primary completion
- Oct 2016 (actual)
- Study completion
- May 2020 (actual)
- First posted
- 4 Jun 2020 (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 overall aim of this study is to, with the help of computer/data scientist and machine learning processes, analyse collected heart rate variability data in order to evaluate whether specific patterns could be found in patients developing delayed cerebral ischemia after subarachnoid hemorrhage.
Detailed description
Patients with aneurysmal subarachnoid haemorrhage (aSAH), develop delayed cerebral ischemia (DCI) in about 30% of the cases. DCI is associated with increased mortality, persistent neurological deficit as well as impaired quality of life. It would benefit both patients and society to decrease these neurological injuries. One clinical problem is that the diagnosis of cerebral ischemia in SAH patients often is delayed due to limitations in monitoring abilities. When detected, the neurological damage often turns out to be irreversible.
Several studies have used univariate and multivariate logistic regression analysis to identify risk factors for the development of delayed cerebral ischemia (DCI) in patients with subarachnoid haemorrhage. However, these studies are based on data collected about the patients (e.g. age, gender), and the precision of these statistical models has generally been found to be low. Recently, machine learning algorithms for the prediction of DCI using a combination of clinical and image data have also been evaluated .
However, prediction of DCI does not prevent DCI, to prevent DCI a monitoring system needs to be developed that can warn physicians of imminent risk of cerebral ischemia, making it possible to intervene and prevent cerebral ischemia.
Variations in the autonomous nervous system, such as changes in the balance between the sympathetic and the parasympathetic nervous systems, can be detected by using heart rate variability (HRV) monitoring. HRV has been reported as a predictor of poor outcome after traumatic brain injury and stroke, including subarachnoid haemorrhage. However, HRV monitoring for detection of incipient cerebral ischemia has not been thoroughly evaluated. In a study of patients with aSAH, we collected HRV continuously in up to 10 days after admission, but just a small part of the HRV data was analysed off-line. Fifteen of 55 patients developed DCI during the acute phase, and the off-line analyse of HRV showed that the low/high-frequency ratio increased more in patients that developed DCI (Ref). This led us to try to analyse all of the collected HRV with the help of machine learning processes, and a collaboration with computer/data scientists was initiated.
The overall aim of this study is to, with the help of computer/data scientist and machine learning processes, analyse collected HRV data in order to evaluate whether specific patterns could be found in patients developing DCI during the acute phase after subarachnoid hemorrhage.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Delayed cerebral ischemia | – | Other: No intervention, observational studyPatients with subarachnoid hemorrhage that develop delayed cerebral ischemia |
| Non delayed cerebral ischemia | – | Other: No intervention, observational studyPatients with subarachnoid hemorrhage that do not develop subarachnoid hemorrhage |
Who may qualify
- Ages
- 16 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Adult patients with aneurysmal subarachnoid hemorrhage, admitted to the Intensive Care Unit at Sahlgrenska University Hospital.
Key inclusion criteria (3 listed)
- Adult patients
- aneurysmal subarachnoid hemorrhage
- admitted to Neurointensive care unit at Sahlgrenska University Hospital, Gothenburg, Sweden
Key exclusion criteria (2 listed)
- cardiac arrythmias
- use of pacemaker
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No intervention, observational study | Other | Delayed cerebral ischemia, Non delayed cerebral ischemia |
Primary outcome
HRV data analysed by artificial intelligence for detection of DCI
Time frame: 2020
Variability of the time of R-R intervals of each heartbeat is measured.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Sep 2020.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
Miroslaw Staron, Prof study chair
Göteborg University
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 Subarachnoid Hemorrhage, Aneurysmal, run by Göteborg University at 1 site in Sweden.
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 3 inclusion and 2 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: finished, as of the sponsor's last update on 3 Sep 2020. 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 Sweden. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
HRV data analysed by artificial intelligence for detection of DCI (time frame: 2020). 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 3 Sep 2020.