<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/NCT04400435</url>
  </required_header>
  <id_info>
    <org_study_id>2020.4</org_study_id>
    <nct_id>NCT04400435</nct_id>
  </id_info>
  <brief_title>Detection of Heart Conditions With Single Lead ECG Using Artificial Intelligence</brief_title>
  <acronym>DAVID1</acronym>
  <official_title>Detection of Reduced Left Ventricular Ejection Fraction and Atrial Arrhythmias With Single Lead ECG Using Artificial Intelligence</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Eko Devices, Inc.</agency>
      <agency_class>Industry</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Eko Devices, Inc.</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The purpose of this research is to prospectively test and validate the single-lead Low EF&#xD;
      algorithm in outpatients in order to test the performance of a single-lead ECG based&#xD;
      algorithm to identify people with decreased left ventricular EF.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Heart failure with reduced left ventricular ejection fraction (EF) is a relatively common&#xD;
      cardiac pathology with major clinical implications. People with reduced left ventricular EF&#xD;
      are at increased risk for sudden death, ventricular and atrial arrhythmias, and acute&#xD;
      hemodynamic decompensation due to heart failure. There are proven medical interventions that&#xD;
      prevent sudden cardiac death and complications in people with decreased left ventricular EF.&#xD;
      Unfortunately, some people with decreased left ventricular EF are asymptomatic, or have&#xD;
      non-specific symptoms like dyspnea, and would not receive those interventions in a timely&#xD;
      manner. Currently, there are no effective ways to screen for asymptomatic decreased left&#xD;
      ventricular EF in the population, because detection of low EF requires the use of&#xD;
      echocardiography. There is a significant need to identify novel technologies that can help to&#xD;
      detect people with decreased left ventricular EF in a simple, effective, and reliable manner.&#xD;
&#xD;
      Eko Devices features a cloud-based platform of point-of-care cardiac screening devices and&#xD;
      machine learning algorithms that enables more effective detection and management of&#xD;
      cardiovascular disease. In this study, we will use the Eko DUO device to collect single-lead&#xD;
      ECG data.&#xD;
&#xD;
      The Eko DUO is an FDA-cleared and CE-marked electronic stethoscope that allows audio&#xD;
      recording of heart sound to produce a phonocardiogram (PCG) as well as recording a&#xD;
      single-lead electrocardiogram (ECG). The DUO features 60x audio amplification, ambient noise&#xD;
      reduction, a 4000Hz sample rate, and 4 audio filters. The ECG component is made up of 2&#xD;
      stainless steel electrodes, 0.01Hz high-pass filter, selectable 50/60Hz mains filter, and a&#xD;
      500Hz sample rate. The de-identified auscultatory DUO recordings transmit wirelessly via&#xD;
      Bluetooth to the secure, HIPAA-compliant Eko application on a smartphone or tablet, which&#xD;
      allows the user to playback heart sound recordings, annotate notes on recorded audio, and&#xD;
      save recordings. This data is synced in real-time to a secure, HIPAA-compliant, cloud-based&#xD;
      Amazon Web Services (AWS) database server managed by Eko Devices.&#xD;
&#xD;
      It has been previously demonstrated that artificial intelligence processing information from&#xD;
      a 12-lead ECG can help to identify people with decreased left ventricular EF1. Using paired&#xD;
      12-lead ECG and echocardiogram data, including the left ventricular ejection fraction, from&#xD;
      44,959 patients at the Mayo Clinic, a convolutional neural was trained to identify patients&#xD;
      with low ejection fraction. When tested on an independent set of 52,870 patients, the model&#xD;
      showed an Area Under the Curve (&quot;AUC&quot;) of 0.93 and an accuracy of 86%. We have also developed&#xD;
      a single-lead version of the same algorithm, which will be more easily accessible in a&#xD;
      clinical setting since it can be used with a single-lead ECG device like the Eko DUO device.&#xD;
      We propose to validate performance of this new model using the current study.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">July 15, 2020</start_date>
  <completion_date type="Actual">April 29, 2022</completion_date>
  <primary_completion_date type="Actual">April 29, 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>Cross-Sectional</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Single-lead ECG based algorithm development</measure>
    <time_frame>Within two minutes of device use</time_frame>
    <description>Evaluate performance of single-lead ECG based algorithm to identify individuals with reduced ejection fraction.</description>
  </primary_outcome>
  <enrollment type="Actual">1258</enrollment>
  <condition>Left Ventricular Dysfunction</condition>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Use of Eko DUO electronic stethoscope</intervention_name>
    <description>Auscultation of heart sounds using electronic stethoscope</description>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Patients undergoing a transthoracic echocardiogram will be enrolled. Patients who are over&#xD;
        the age of 60 or who are at risk for heart failure (hypertension, murmur, etc.) will be&#xD;
        targeted for enrollment. We will also target patients with a referral diagnosis of dyspnea&#xD;
        on exertion, orthopnea, lower extremity edema, and possible heart failure.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  English-speaking adults who are 18 years and older&#xD;
&#xD;
          -  Able and willing to provide informed consent&#xD;
&#xD;
          -  Complete a clinical echocardiogram within 7 days before or after study procedures&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Unwilling or unable to provide informed consent&#xD;
&#xD;
          -  Patients who are hospitalized&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <location>
    <facility>
      <name>Los Alamitos Cardiovascular</name>
      <address>
        <city>Los Alamitos</city>
        <state>California</state>
        <zip>90720</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>MedStar Cardiovascular Network</name>
      <address>
        <city>Washington</city>
        <state>District of Columbia</state>
        <zip>20010</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Albert Einstein Medical Center</name>
      <address>
        <city>Philadelphia</city>
        <state>Pennsylvania</state>
        <zip>19141</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>December 2022</verification_date>
  <study_first_submitted>May 19, 2020</study_first_submitted>
  <study_first_submitted_qc>May 21, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">May 22, 2020</study_first_posted>
  <last_update_submitted>December 7, 2022</last_update_submitted>
  <last_update_submitted_qc>December 7, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">December 9, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Ejection Fraction</keyword>
  <keyword>machine learning</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Ventricular Dysfunction</mesh_term>
    <mesh_term>Ventricular Dysfunction, Left</mesh_term>
  </condition_browse>
  <!-- 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/NCT04400435
org study id: 2020.4
nct id: NCT04400435
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
The purpose of this research is to prospectively test and validate the single-lead Low EF
      algorithm in outpatients in order to test the performance of a single-lead ECG based
      algorithm to identify people with decreased left ventricular EF.
Heart failure with reduced left ventricular ejection fraction (EF) is a relatively common
      cardiac pathology with major clinical implications. People with reduced left ventricular EF
      are at increased risk for sudden death, ventricular and atrial arrhythmias, and acute
      hemodynamic decompensation due to heart failure. There are proven medical interventions that
      prevent sudden cardiac death and complications in people with decreased left ventricular EF.
      Unfortunately, some people with decreased left ventricular EF are asymptomatic, or have
      non-specific symptoms like dyspnea, and would not receive those interventions in a timely
      manner. Currently, there are no effective ways to screen for asymptomatic decreased left
      ventricular EF in the population, because detection of low EF requires the use of
      echocardiography. There is a significant need to identify novel technologies that can help to
      detect people with decreased left ventricular EF in a simple, effective, and reliable manner.

      Eko Devices features a cloud-based platform of point-of-care cardiac screening devices and
      machine learning algorithms that enables more effective detection and management of
      cardiovascular disease. In this study, we will use the Eko DUO device to collect single-lead
      ECG data.

      The Eko DUO is an FDA-cleared and CE-marked electronic stethoscope that allows audio
      recording of heart sound to produce a phonocardiogram (PCG) as well as recording a
      single-lead electrocardiogram (ECG). The DUO features 60x audio amplification, ambient noise
      reduction, a 4000Hz sample rate, and 4 audio filters. The ECG component is made up of 2
      stainless steel electrodes, 0.01Hz high-pass filter, selectable 50/60Hz mains filter, and a
      500Hz sample rate. The de-identified auscultatory DUO recordings transmit wirelessly via
      Bluetooth to the secure, HIPAA-compliant Eko application on a smartphone or tablet, which
      allows the user to playback heart sound recordings, annotate notes on recorded audio, and
      save recordings. This data is synced in real-time to a secure, HIPAA-compliant, cloud-based
      Amazon Web Services (AWS) database server managed by Eko Devices.

      It has been previously demonstrated that artificial intelligence processing information from
      a 12-lead ECG can help to identify people with decreased left ventricular EF1. Using paired
      12-lead ECG and echocardiogram data, including the left ventricular ejection fraction, from
      44,959 patients at the Mayo Clinic, a convolutional neural was trained to identify patients
      with low ejection fraction. When tested on an independent set of 52,870 patients, the model
      showed an Area Under the Curve ("AUC") of 0.93 and an accuracy of 86%. We have also developed
      a single-lead version of the same algorithm, which will be more easily accessible in a
      clinical setting since it can be used with a single-lead ECG device like the Eko DUO device.
      We propose to validate performance of this new model using the current study.
observational model: Cohort
time perspective: Cross-Sectional
measure: Single-lead ECG based algorithm development
time frame: Within two minutes of device use
description: Evaluate performance of single-lead ECG based algorithm to identify individuals with reduced ejection fraction.
intervention type: Device
intervention name: Use of Eko DUO electronic stethoscope
description: Auscultation of heart sounds using electronic stethoscope
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
facility: 
facility: 
facility: 
country: United States
responsible party type: Sponsor
mesh term: Ventricular Dysfunction
mesh term: Ventricular Dysfunction, Left