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/NCT06011590</url>
    </required_header>
    <id_info>
      <org_study_id>1244/2022</org_study_id>
      <nct_id>NCT06011590</nct_id>
    </id_info>
    <brief_title>QRS Microfragmentation in ECG as Predictor of Mortality and Morbidity - a Retrospective Analysis</brief_title>
    <official_title>QRS Microfragmentation in ECG as Predictor of Mortality and Morbidity - a Retrospective Analysis</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Medical University Innsbruck</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Medical University Innsbruck</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>
        Over the past decades, several ECG-based parameters have been identified as independent&#xD;
        predictors of worsened prognosis in affected patients. In addition to visual assessment of&#xD;
        morphology, methods of computer-based machine ECG analysis have gained importance in recent&#xD;
        years. These methods allow the detection of systemic abnormalities in ECGs that are not&#xD;
        visible to the naked eye. An example of this is provided by the so-called &quot;QRS&#xD;
        microfragmentations&quot;.&#xD;
  &#xD;
        The aim of this evaluation is to retrospectively collect all established as well as new&#xD;
        quantitative and qualitative ECG parameters (such as QRS microfragmentation) in a large&#xD;
        patient collective. Subsequently, after characterization of the patients, an independent&#xD;
        multivariate risk prediction model should be developed based on computer-based ECG analysis&#xD;
        using maschnine learning algorithms.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Completed</overall_status>
    <start_date type="Actual">January 1, 2000</start_date>
    <completion_date type="Actual">July 31, 2022</completion_date>
    <primary_completion_date type="Actual">July 31, 2022</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>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>All cause mortality</measure>
      <time_frame>between 01.01.2000 and 31.07.2022</time_frame>
      <description>Death of any cause</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Hospitalization</measure>
      <time_frame>between 01.01.2000 and 31.07.2022</time_frame>
      <description>Unplanned Hospitalization</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Lenght of hospital stay</measure>
      <time_frame>between 01.01.2000 and 31.07.2022</time_frame>
      <description>Lenght of hospital stay</description>
    </secondary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Actual">150000</enrollment>
    <condition>Risk Stratification</condition>
    <arm_group>
      <arm_group_label>Study Cohort</arm_group_label>
      <description>All patients treated at the Department of Internal Medicine at the Medical University of Innsbruck between 01.01.2000 and 31.07.2022, in whom a 10 second 12-lead ECG is available from clinical routine.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>No intervention.</intervention_name>
      <description>No intervention.</description>
      <arm_group_label>Study Cohort</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Patients who presented to the Department of Internal Medicine at the Medical University in&#xD;
          Innsbruck between 01.01.2000 and 31.07.2022 and received an ECG recording as part of&#xD;
          routine clinical practice.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients who presented to the Department of Internal Medicine at the Medical&#xD;
               University in Innsbruck between 01.01.2000 and 31.07.2022 and received an ECG&#xD;
               recording as part of routine clinical practice.&#xD;
  &#xD;
            -  Age ≥ 18 years&#xD;
  &#xD;
            -  Residence in Austria&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Insuffizient ECG Recording quality&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 21, 2023</study_first_submitted>
    <study_first_submitted_qc>August 21, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 25, 2023</study_first_posted>
    <last_update_submitted>August 21, 2023</last_update_submitted>
    <last_update_submitted_qc>August 21, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 25, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
      <ipd_description>IPD will be shared upon reasonable request and after concluding all legal and ethic formalities.</ipd_description>
    </patient_data>
    <!-- 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/NCT06011590
  org study id: 1244/2022
  nct id: NCT06011590
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  Over the past decades, several ECG-based parameters have been identified as independent
        predictors of worsened prognosis in affected patients. In addition to visual assessment of
        morphology, methods of computer-based machine ECG analysis have gained importance in recent
        years. These methods allow the detection of systemic abnormalities in ECGs that are not
        visible to the naked eye. An example of this is provided by the so-called "QRS
        microfragmentations".
  
        The aim of this evaluation is to retrospectively collect all established as well as new
        quantitative and qualitative ECG parameters (such as QRS microfragmentation) in a large
        patient collective. Subsequently, after characterization of the patients, an independent
        multivariate risk prediction model should be developed based on computer-based ECG analysis
        using maschnine learning algorithms.
  observational model: Cohort
  time perspective: Retrospective
  measure: All cause mortality
  time frame: between 01.01.2000 and 31.07.2022
  description: Death of any cause
  measure: Hospitalization
  time frame: between 01.01.2000 and 31.07.2022
  description: Unplanned Hospitalization
  measure: Lenght of hospital stay
  time frame: between 01.01.2000 and 31.07.2022
  description: Lenght of hospital stay
  arm group label: Study Cohort
  description: All patients treated at the Department of Internal Medicine at the Medical University of Innsbruck between 01.01.2000 and 31.07.2022, in whom a 10 second 12-lead ECG is available from clinical routine.
  intervention type: Other
  intervention name: No intervention.
  description: No intervention.
  arm group label: Study Cohort
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  responsible party type: Sponsor
  sharing ipd: Undecided
  ipd description: IPD will be shared upon reasonable request and after concluding all legal and ethic formalities.