<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04415736</url>
  </required_header>
  <id_info>
    <org_study_id>AISAH</org_study_id>
    <nct_id>NCT04415736</nct_id>
  </id_info>
  <brief_title>Artificial Intelligence in Subarachnoid Hemorrhage</brief_title>
  <acronym>AISAH</acronym>
  <official_title>Using Machine Learning With Heart Rate Variability Signals to Detect Delayed Cerebral Ischemia After Subarachnoid Hemorrhage</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Göteborg University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Göteborg University</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The overall aim of this study is to, with the help of computer/data scientist and machine&#xD;
      learning processes, analyse collected heart rate variability data in order to evaluate&#xD;
      whether specific patterns could be found in patients developing delayed cerebral ischemia&#xD;
      after subarachnoid hemorrhage.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Patients with aneurysmal subarachnoid haemorrhage (aSAH), develop delayed cerebral ischemia&#xD;
      (DCI) in about 30% of the cases. DCI is associated with increased mortality, persistent&#xD;
      neurological deficit as well as impaired quality of life. It would benefit both patients and&#xD;
      society to decrease these neurological injuries. One clinical problem is that the diagnosis&#xD;
      of cerebral ischemia in SAH patients often is delayed due to limitations in monitoring&#xD;
      abilities. When detected, the neurological damage often turns out to be irreversible.&#xD;
&#xD;
      Several studies have used univariate and multivariate logistic regression analysis to&#xD;
      identify risk factors for the development of delayed cerebral ischemia (DCI) in patients with&#xD;
      subarachnoid haemorrhage. However, these studies are based on data collected about the&#xD;
      patients (e.g. age, gender), and the precision of these statistical models has generally been&#xD;
      found to be low. Recently, machine learning algorithms for the prediction of DCI using a&#xD;
      combination of clinical and image data have also been evaluated .&#xD;
&#xD;
      However, prediction of DCI does not prevent DCI, to prevent DCI a monitoring system needs to&#xD;
      be developed that can warn physicians of imminent risk of cerebral ischemia, making it&#xD;
      possible to intervene and prevent cerebral ischemia.&#xD;
&#xD;
      Variations in the autonomous nervous system, such as changes in the balance between the&#xD;
      sympathetic and the parasympathetic nervous systems, can be detected by using heart rate&#xD;
      variability (HRV) monitoring. HRV has been reported as a predictor of poor outcome after&#xD;
      traumatic brain injury and stroke, including subarachnoid haemorrhage. However, HRV&#xD;
      monitoring for detection of incipient cerebral ischemia has not been thoroughly evaluated. In&#xD;
      a study of patients with aSAH, we collected HRV continuously in up to 10 days after&#xD;
      admission, but just a small part of the HRV data was analysed off-line. Fifteen of 55&#xD;
      patients developed DCI during the acute phase, and the off-line analyse of HRV showed that&#xD;
      the low/high-frequency ratio increased more in patients that developed DCI (Ref). This led us&#xD;
      to try to analyse all of the collected HRV with the help of machine learning processes, and a&#xD;
      collaboration with computer/data scientists was initiated.&#xD;
&#xD;
      The overall aim of this study is to, with the help of computer/data scientist and machine&#xD;
      learning processes, analyse collected HRV data in order to evaluate whether specific patterns&#xD;
      could be found in patients developing DCI during the acute phase after subarachnoid&#xD;
      hemorrhage.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">October 1, 2015</start_date>
  <completion_date type="Actual">May 2020</completion_date>
  <primary_completion_date type="Actual">October 2016</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>HRV data analysed by artificial intelligence for detection of DCI</measure>
    <time_frame>2020</time_frame>
    <description>Variability of the time of R-R intervals of each heartbeat is measured.</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">64</enrollment>
  <condition>Subarachnoid Hemorrhage, Aneurysmal</condition>
  <arm_group>
    <arm_group_label>Delayed cerebral ischemia</arm_group_label>
    <description>Patients with subarachnoid hemorrhage that develop delayed cerebral ischemia</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Non delayed cerebral ischemia</arm_group_label>
    <description>Patients with subarachnoid hemorrhage that do not develop subarachnoid hemorrhage</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>No intervention, observational study</intervention_name>
    <arm_group_label>Delayed cerebral ischemia</arm_group_label>
    <arm_group_label>Non delayed cerebral ischemia</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Adult patients with aneurysmal subarachnoid hemorrhage, admitted to the Intensive Care Unit&#xD;
        at Sahlgrenska University Hospital.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Adult patients&#xD;
&#xD;
          -  aneurysmal subarachnoid hemorrhage&#xD;
&#xD;
          -  admitted to Neurointensive care unit at Sahlgrenska University Hospital, Gothenburg,&#xD;
             Sweden&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  cardiac arrythmias&#xD;
&#xD;
          -  use of pacemaker&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>16 Years</minimum_age>
    <maximum_age>100 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Miroslaw Staron, Prof</last_name>
    <role>Study Chair</role>
    <affiliation>Göteborg University</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Sahlgrenska University Hospital</name>
      <address>
        <city>Gothenburg</city>
        <zip>41345</zip>
        <country>Sweden</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Sweden</country>
  </location_countries>
  <verification_date>September 2020</verification_date>
  <study_first_submitted>May 26, 2020</study_first_submitted>
  <study_first_submitted_qc>May 31, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 4, 2020</study_first_posted>
  <last_update_submitted>September 1, 2020</last_update_submitted>
  <last_update_submitted_qc>September 1, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">September 3, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Subarachnoid Hemorrhage</mesh_term>
    <mesh_term>Hemorrhage</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04415736
org study id: AISAH
nct id: NCT04415736
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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.
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.
observational model: Cohort
time perspective: Prospective
measure: HRV data analysed by artificial intelligence for detection of DCI
time frame: 2020
description: Variability of the time of R-R intervals of each heartbeat is measured.
arm group label: Delayed cerebral ischemia
description: Patients with subarachnoid hemorrhage that develop delayed cerebral ischemia
arm group label: Non delayed cerebral ischemia
description: Patients with subarachnoid hemorrhage that do not develop subarachnoid hemorrhage
intervention type: Other
intervention name: No intervention, observational study
arm group label: Delayed cerebral ischemia
arm group label: Non delayed cerebral ischemia
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 16 Years
maximum age: 100 Years
healthy volunteers: No
last name: Miroslaw Staron, Prof
role: Study Chair
affiliation: Göteborg University
facility: 
country: Sweden
responsible party type: Sponsor
mesh term: Subarachnoid Hemorrhage
mesh term: Hemorrhage
sharing ipd: No