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/NCT06009718</url>
    </required_header>
    <id_info>
      <org_study_id>RJH-PEG</org_study_id>
      <nct_id>NCT06009718</nct_id>
    </id_info>
    <brief_title>Artificial Intelligence (AI) Analysis of Synchronized Phonocardiography (PCG) and Electrocardiogram(ECG)</brief_title>
    <official_title>A Deep-learning-based Multi-modal Phonocardiogram(PCG) and Electrocardiogram(ECG) Processing Framework for Screening Depressed Left Ventricular Ejection Fraction (dLVEF) Using a Wearable Cardiac Patch</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Ruijin Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Ruijin 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>
        The diagnosis of depressed left ventricular ejection fraction (dLVEF) (EF&lt;50%) depends on&#xD;
        golden standard ultrasound cardiography (UCG). A wearable synchronized phonocardiography&#xD;
        (PCG) and electrocardiogram (ECG) device can assist in the diagnosis of dLVEF, which can both&#xD;
        expedite access to life-saving therapies and reduce the need for costly testing.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        The synchronized PCG and ECG is wirelessly paired with the WenXin Mobile application,&#xD;
        allowing for simultaneous recording and visualization of PCG and ECG. These features uniquely&#xD;
        enable this device to accumulate large sets of acoustic data on patients both with and&#xD;
        without heart failure(HF).&#xD;
  &#xD;
        This study is a Case-control study. In this study, the investigators seek to develop an&#xD;
        artificial intelligence (AI) analysis system to identify dLVEF (EF&lt;50%) by PCG and ECG. All&#xD;
        adults (aged ≥18 years) planned for UCG were eligible to participate (inpatients and&#xD;
        outpatients). Specifically, the investigators will attempt to develop machine learning&#xD;
        algorithms to learn synchronized PCG and ECG of patients with dLVEF. Then we use these&#xD;
        algorithms to identify dLVEF subjects. The investigators anticipate to demonstrate the&#xD;
        wearable cardiac patch with synchronized PCG and ECG can reliably and accurately diagnose&#xD;
        dLVEF in the primary care setting.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">June 1, 2020</start_date>
    <completion_date type="Anticipated">June 1, 2028</completion_date>
    <primary_completion_date type="Anticipated">June 1, 2027</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Control</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Determination of Heart Failure Disease</measure>
      <time_frame>one time assessment at baseline (approx. 5 minutes)</time_frame>
      <description>Heart Failure Disease was determined by EMAT （millisecond, ms）calculate from synchronized PCG and ECG signals using an artificial intelligence (AI) guided model.</description>
    </primary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">3000</enrollment>
    <condition>Heart Failure</condition>
    <arm_group>
      <arm_group_label>Model training group</arm_group_label>
      <description>Compare the results of PCG and ECG with UCG, and conduct model training analysis</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Model validation group</arm_group_label>
      <description>Compare the results of PCG and ECG with UCG, and conduct model validation analysis</description>
    </arm_group>
    <eligibility>
      <study_pop>
        <textblock>
          This is one-center cohort study. Patients undergo echocardiogram at Ruijin Hospital.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Attendance at RuiJin hospital for UCG&#xD;
  &#xD;
            -  Signed dated informed consent&#xD;
  &#xD;
            -  Commit to follow the research procedures and cooperate in the implementation of the&#xD;
               whole process research&#xD;
  &#xD;
            -  UCG has been completed&#xD;
  &#xD;
            -  Age ≥ 18&#xD;
  &#xD;
            -  At least 8 consecutive cycles of sinus rhythm can be recorded&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Patients with pacemakers&#xD;
  &#xD;
            -  Complete left bundle branch block or block or QRS wave widening&gt;120ms&#xD;
  &#xD;
            -  Left chest skin damaged or allergic to patch&#xD;
  &#xD;
            -  Refusal to participate&#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_official>
      <last_name>Ruiyan Zhang, MD, PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Ruijin Hospital, Shanghai Jiaotong School of Medicine</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Wenli Zhang, MD</last_name>
      <role>Study Director</role>
      <affiliation>Ruijin Hospital, Shanghai Jiaotong School of Medicine</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Bei Song, MD</last_name>
      <role>Study Chair</role>
      <affiliation>Ruijin Hospital, Shanghai Jiaotong School of Medicine</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Wenli Zhang, MD</last_name>
      <phone>+86 21 13917615339</phone>
      <email>zwl11929@rjh.com.cn</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Bei Song, MD</last_name>
      <phone>+86 21 15821960139</phone>
      <email>belasong@163.com</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Ruijin Hospital, Shanghai Jiaotong School of Medicine</name>
        <address>
          <city>Shanghai</city>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Jian LI</last_name>
        <phone>0086 021 64370045</phone>
        <email>ruijincrc@126.com</email>
      </contact>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>July 6, 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>Ruijin Hospital</investigator_affiliation>
      <investigator_full_name>RUIYAN ZHANG</investigator_full_name>
      <investigator_title>Director of Cardiology Department, Chief Physician</investigator_title>
    </responsible_party>
    <keyword>Phonocardiography (PCG)</keyword>
    <keyword>Electrocardiogram (ECG)</keyword>
    <keyword>Heart Failure (HF)</keyword>
    <keyword>depressed left ventricular ejection fraction (dLVEF)</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Heart Failure</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/NCT06009718
  org study id: RJH-PEG
  nct id: NCT06009718
  lead sponsor: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  The diagnosis of depressed left ventricular ejection fraction (dLVEF) (EF<50%) depends on
        golden standard ultrasound cardiography (UCG). A wearable synchronized phonocardiography
        (PCG) and electrocardiogram (ECG) device can assist in the diagnosis of dLVEF, which can both
        expedite access to life-saving therapies and reduce the need for costly testing.
  The synchronized PCG and ECG is wirelessly paired with the WenXin Mobile application,
        allowing for simultaneous recording and visualization of PCG and ECG. These features uniquely
        enable this device to accumulate large sets of acoustic data on patients both with and
        without heart failure(HF).
  
        This study is a Case-control study. In this study, the investigators seek to develop an
        artificial intelligence (AI) analysis system to identify dLVEF (EF<50%) by PCG and ECG. All
        adults (aged ≥18 years) planned for UCG were eligible to participate (inpatients and
        outpatients). Specifically, the investigators will attempt to develop machine learning
        algorithms to learn synchronized PCG and ECG of patients with dLVEF. Then we use these
        algorithms to identify dLVEF subjects. The investigators anticipate to demonstrate the
        wearable cardiac patch with synchronized PCG and ECG can reliably and accurately diagnose
        dLVEF in the primary care setting.
  observational model: Case-Control
  time perspective: Prospective
  measure: Determination of Heart Failure Disease
  time frame: one time assessment at baseline (approx. 5 minutes)
  description: Heart Failure Disease was determined by EMAT （millisecond, ms）calculate from synchronized PCG and ECG signals using an artificial intelligence (AI) guided model.
  arm group label: Model training group
  description: Compare the results of PCG and ECG with UCG, and conduct model training analysis
  arm group label: Model validation group
  description: Compare the results of PCG and ECG with UCG, and conduct model validation analysis
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 100 Years
  healthy volunteers: No
  last name: Ruiyan Zhang, MD, PhD
  role: Principal Investigator
  affiliation: Ruijin Hospital, Shanghai Jiaotong School of Medicine
  last name: Wenli Zhang, MD
  role: Study Director
  affiliation: Ruijin Hospital, Shanghai Jiaotong School of Medicine
  last name: Bei Song, MD
  role: Study Chair
  affiliation: Ruijin Hospital, Shanghai Jiaotong School of Medicine
  last name: Wenli Zhang, MD
  phone: +86 21 13917615339
  email: zwl11929@rjh.com.cn
  last name: Bei Song, MD
  phone: +86 21 15821960139
  email: belasong@163.com
  facility: 
  status: Recruiting
  contact: 
  country: China
  responsible party type: Principal Investigator
  investigator affiliation: Ruijin Hospital
  investigator full name: RUIYAN ZHANG
  investigator title: Director of Cardiology Department, Chief Physician
  mesh term: Heart Failure