xml: <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/NCT06009315</url>
    </required_header>
    <id_info>
      <org_study_id>LF20230819</org_study_id>
      <nct_id>NCT06009315</nct_id>
    </id_info>
    <brief_title>Prognostic Prediction Model of Patients With AcUte Stroke undeRgoing EndOvascular TheRApy (AURORA)</brief_title>
    <acronym>AURORA</acronym>
    <official_title>Development and Validation for Prognostic Prediction Model of Patients With AcUte Stroke undeRgoing EndOvascular TheRApy (AURORA)</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Beijing Tiantan Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Beijing Tiantan Hospital</source>
    <oversight_info>
      <has_dmc>Yes</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>
        Stroke is the leading cause of disability-adjusted life years (DALYs) in China, imposing a&#xD;
        heavy burden on society and families. Endovascular therapy (EVT) has opened the 2.0 era of&#xD;
        acute ischemic stroke (AIS) treatment, but still up to 1/3 of patients have poor neurological&#xD;
        prognosis. The results of several studies at home and abroad and by our team indicate that&#xD;
        anesthesia method and perioperative management are one of the key factors affecting the&#xD;
        neurological prognosis of EVT treatment in AIS patients. Based on machine learning big data&#xD;
        analysis methods, a prognostic model for EVT treatment of AIS patients can be established to&#xD;
        guide individualized treatment decisions. Current prediction models only include patients'&#xD;
        baseline variables, and lack the inclusion of intraoperative (anesthesia management and&#xD;
        interventional process) and postoperative (intensive monitoring treatment) variables, which&#xD;
        limits the clinical application of prediction tools. We will establish a large prospective&#xD;
        cohort database including preoperative, intraoperative, and postoperative variables,&#xD;
        integrate heterogeneous information from multiple sources based on artificial intelligence&#xD;
        machine learning algorithms, and build prognostic prediction models with better clinical&#xD;
        applicability and calibration, with the aim of optimizing perioperative management of&#xD;
        endovascular therapy, guiding individualized clinical decision-making, and improving&#xD;
        patients' clinical prognosis.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 10, 2023</start_date>
    <completion_date type="Anticipated">December 31, 2026</completion_date>
    <primary_completion_date type="Anticipated">December 31, 2026</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>Functional independence at 90 days</measure>
      <time_frame>90±7days after treatment</time_frame>
      <description>modified Rankin Scale of 0-2</description>
    </primary_outcome>
    <enrollment type="Anticipated">949</enrollment>
    <condition>Acute Ischemic Stroke</condition>
    <eligibility>
      <study_pop>
        <textblock>
          Patients with acute ischemic stroke caused by large undergoing endovascular therapy&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Age ≥18 years;&#xD;
  &#xD;
            -  NIHSS score ≥4;&#xD;
  &#xD;
            -  Image-confirmed (CTA/MRA/DSA) intracranial large artery occlusion;&#xD;
  &#xD;
            -  ASPECT (anterior circulation) or PC-ASPECT (posterior circulation) score ≥3;&#xD;
  &#xD;
            -  Endovascular treatment including arterial thrombolysis, mechanical thrombolysis, and&#xD;
               angioplasty (onset to puncture time is recommended to be less than 8 hours for&#xD;
               anterior circulation and less than 12 hours for posterior circulation; those exceeding&#xD;
               the time window will be determined by the neurointerventionalist through imaging&#xD;
               assessment);&#xD;
  &#xD;
            -  Signed informed consent by the patient or legal representative&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Pre-stroke mRS score &gt;2;&#xD;
  &#xD;
            -  Intracranial bleeding disorders: cerebral hemorrhage, subarachnoid hemorrhage, etc.;&#xD;
  &#xD;
            -  presence of coagulation disorders, history of systemic bleeding, history of&#xD;
               thrombocytopenia or neutropenia;&#xD;
  &#xD;
            -  Renal insufficiency with elevated blood creatinine (greater than 2 times the upper&#xD;
               limit of normal);&#xD;
  &#xD;
            -  Presence of severe cardiopulmonary disease that, in the opinion of the investigator,&#xD;
               makes participation in this study unsuitable; patients with a life expectancy of less&#xD;
               than 3 months or otherwise unable to complete this study;&#xD;
  &#xD;
            -  Contraindication to DSA examination, severe contrast allergy or absolute&#xD;
               contraindication to iodine contrast; women of childbearing age who have a negative&#xD;
               pregnancy test but refuse to use effective contraception, are pregnant or breast&#xD;
               feeding;&#xD;
  &#xD;
            -  Those who are unable to complete the study due to psychiatric disorders, cognitive or&#xD;
               mood disorders;&#xD;
  &#xD;
            -  Other patients who, in the opinion of the investigator, are not suitable for&#xD;
               enrollment in the study (specify reason).&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>100 Years</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Ruquan Han</last_name>
      <phone>8610-59976660</phone>
      <email>ruquan.han@gmail.com</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Fa Liang</last_name>
      <phone>8610-59976658</phone>
      <email>liangfa1001@163.com</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Beijing Tiantan Hospital, Capital Medical University</name>
        <address>
          <city>Beijing</city>
          <state>Beijing</state>
          <zip>100070</zip>
          <country>China</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 19, 2023</study_first_submitted>
    <study_first_submitted_qc>August 19, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 24, 2023</study_first_posted>
    <last_update_submitted>August 19, 2023</last_update_submitted>
    <last_update_submitted_qc>August 19, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 24, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Beijing Tiantan Hospital</investigator_affiliation>
      <investigator_full_name>Ruquan Han</investigator_full_name>
      <investigator_title>Professor &amp; Director</investigator_title>
    </responsible_party>
    <keyword>Acute ischemic stroke,</keyword>
    <keyword>Anesthesia</keyword>
    <keyword>Predictive Model</keyword>
    <keyword>Endovascular therapy</keyword>
    <keyword>Deep learning</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Stroke</mesh_term>
      <mesh_term>Ischemic Stroke</mesh_term>
    </condition_browse>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT06009315
  org study id: LF20230819
  nct id: NCT06009315
  lead sponsor: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  Stroke is the leading cause of disability-adjusted life years (DALYs) in China, imposing a
        heavy burden on society and families. Endovascular therapy (EVT) has opened the 2.0 era of
        acute ischemic stroke (AIS) treatment, but still up to 1/3 of patients have poor neurological
        prognosis. The results of several studies at home and abroad and by our team indicate that
        anesthesia method and perioperative management are one of the key factors affecting the
        neurological prognosis of EVT treatment in AIS patients. Based on machine learning big data
        analysis methods, a prognostic model for EVT treatment of AIS patients can be established to
        guide individualized treatment decisions. Current prediction models only include patients'
        baseline variables, and lack the inclusion of intraoperative (anesthesia management and
        interventional process) and postoperative (intensive monitoring treatment) variables, which
        limits the clinical application of prediction tools. We will establish a large prospective
        cohort database including preoperative, intraoperative, and postoperative variables,
        integrate heterogeneous information from multiple sources based on artificial intelligence
        machine learning algorithms, and build prognostic prediction models with better clinical
        applicability and calibration, with the aim of optimizing perioperative management of
        endovascular therapy, guiding individualized clinical decision-making, and improving
        patients' clinical prognosis.
  observational model: Cohort
  time perspective: Prospective
  measure: Functional independence at 90 days
  time frame: 90±7days after treatment
  description: modified Rankin Scale of 0-2
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 100 Years
  healthy volunteers: No
  last name: Ruquan Han
  phone: 8610-59976660
  email: ruquan.han@gmail.com
  last name: Fa Liang
  phone: 8610-59976658
  email: liangfa1001@163.com
  facility: 
  country: China
  responsible party type: Principal Investigator
  investigator affiliation: Beijing Tiantan Hospital
  investigator full name: Ruquan Han
  investigator title: Professor & Director
  mesh term: Stroke
  mesh term: Ischemic Stroke