<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/NCT03227848</url>
  </required_header>
  <id_info>
    <org_study_id>OTT03</org_study_id>
    <nct_id>NCT03227848</nct_id>
  </id_info>
  <brief_title>eMurmur ID - Clinical Performance Evaluation</brief_title>
  <official_title>eMurmur ID - Clinical Performance Evaluation</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>CSD Labs GmbH</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>CSD Labs GmbH</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>Yes</is_fda_regulated_device>
    <is_unapproved_device>Yes</is_unapproved_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The differentiation between innocent and pathologic murmurs through traditional auscultation&#xD;
      can often be challenging, which in the end makes the diagnosis strongly dependent on the&#xD;
      clinitians experience and clinical expertise. With the development of technology it is now&#xD;
      possible to help diagnose heart murmurs using computer aided auscultation systems (CAA).&#xD;
      eMurmur ID is an investigational CAA system (not FDA cleared) and the investigators&#xD;
      hypothesize that it can distinguish between AHA class I (pathologic murmurs) and AHA class&#xD;
      III heart sounds (innocent murmurs and/or no murmurs) with a sensitivity and specificity not&#xD;
      worse compared to a similar FDA cleared CAA system on market.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">January 4, 2017</start_date>
  <completion_date type="Actual">April 30, 2018</completion_date>
  <primary_completion_date type="Actual">April 24, 2018</primary_completion_date>
  <study_type>Observational [Patient Registry]</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>
  <target_duration>1 Day</target_duration>
  <primary_outcome>
    <measure>eMurmur ID sensitivity and specificity</measure>
    <time_frame>1 day</time_frame>
    <description>The primary endpoints of the study are sensitivity and specificity. The clinical reference gold standard diagnosis is defined as expert physicians' diagnosis confirmed by independently interpreted echocardiogram diagnosis.&#xD;
True positive (TP), true negative (TN), false positive (FP) and false negative (FN) will be determined via comparison of the heart murmur classification results with the clinical gold standard (echocardiogram) diagnosis.</description>
  </primary_outcome>
  <enrollment type="Actual">120</enrollment>
  <condition>Heart Murmurs</condition>
  <condition>Pathologic Murmurs</condition>
  <condition>Innocent Murmurs</condition>
  <condition>Congenital Heart Defect</condition>
  <condition>Systolic Murmurs</condition>
  <condition>Diastolic Murmurs</condition>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Automated Heart Murmur Detection AI</intervention_name>
    <description>Automated AI algorithm-based analysis of digital heart sound recordings to detect and classify heart murmurs. Heart sound recordings were fully blinded before undergoing one-time automated analysis. AI algorithm results for each recording include: AHA classification (Class I (pathologic heart murmur) versus class III (innocent heart murmur or no heart murmur), murmur timing, murmur grade, heart rate and S1/S2 identification.</description>
    <other_name>eMurmur ID</other_name>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Study participants will be chosen based on the heart murmur types required to meet a&#xD;
        specified target patient population. Pre-selection of AHA class I and AHA class III&#xD;
        patients will be done to achieve a reasonably similar distribution of murmur types compared&#xD;
        to a US patient population. All major pathological and innocent murmur types will be&#xD;
        included and their occurrence depending on age will be considered. Number of participants&#xD;
        drawn: 120 participants across all ages will be included, 75% pediatric and 25% adult.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  All age groups of patients will be included from 1day old&#xD;
&#xD;
          -  Patients who are being followed for known congenital heart disease and are returning&#xD;
             for follow up&#xD;
&#xD;
          -  Patients referred for a suspected heart murmur&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Mismatch between the expert physician's diagnosis (auscultation based) and the&#xD;
             diagnosis resulting from echocardiography (independently read by a cardiologist&#xD;
             blinded to the auscultation results). Note: both, the expert physician and&#xD;
             echocardiography results must independently reach the same diagnosis, which is then&#xD;
             accepted as the gold standard reference diagnosis to which both devices are compared&#xD;
             to. This is necessary because not every pathology visible on an echocardiogram causes&#xD;
             an audible murmur, and not every murmur heard by a medical expert might correlate to&#xD;
             pathology.&#xD;
&#xD;
          -  Patient whose behaviour does not allow for a standard auscultation by the physician&#xD;
             (e.g. a screaming fit).&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>1 Day</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Lillian Lai, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Children's Hopsital of Eastern Ontario, Canada</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Children's Hospital of Eastern Ontario</name>
      <address>
        <city>Ottawa</city>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Canada</country>
  </location_countries>
  <link>
    <url>https://www.ncbi.nlm.nih.gov/pubmed/26990211</url>
    <description>Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study.</description>
  </link>
  <reference>
    <citation>Lai LS, Redington AN, Reinisch AJ, Unterberger MJ, Schriefl AJ. Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study. Congenit Heart Dis. 2016 Sep;11(5):386-395. doi: 10.1111/chd.12328. Epub 2016 Mar 15.</citation>
    <PMID>26990211</PMID>
  </reference>
  <verification_date>July 2018</verification_date>
  <study_first_submitted>July 21, 2017</study_first_submitted>
  <study_first_submitted_qc>July 21, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">July 24, 2017</study_first_posted>
  <last_update_submitted>July 16, 2018</last_update_submitted>
  <last_update_submitted_qc>July 16, 2018</last_update_submitted_qc>
  <last_update_posted type="Actual">July 18, 2018</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>Heart Defects, Congenital</mesh_term>
    <mesh_term>Systolic Murmurs</mesh_term>
    <mesh_term>Heart Murmurs</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/NCT03227848
org study id: OTT03
nct id: NCT03227848
lead sponsor: 
is fda regulated drug: No
is fda regulated device: Yes
is unapproved device: Yes
The differentiation between innocent and pathologic murmurs through traditional auscultation
      can often be challenging, which in the end makes the diagnosis strongly dependent on the
      clinitians experience and clinical expertise. With the development of technology it is now
      possible to help diagnose heart murmurs using computer aided auscultation systems (CAA).
      eMurmur ID is an investigational CAA system (not FDA cleared) and the investigators
      hypothesize that it can distinguish between AHA class I (pathologic murmurs) and AHA class
      III heart sounds (innocent murmurs and/or no murmurs) with a sensitivity and specificity not
      worse compared to a similar FDA cleared CAA system on market.
observational model: Cohort
time perspective: Cross-Sectional
measure: eMurmur ID sensitivity and specificity
time frame: 1 day
description: The primary endpoints of the study are sensitivity and specificity. The clinical reference gold standard diagnosis is defined as expert physicians' diagnosis confirmed by independently interpreted echocardiogram diagnosis.
True positive (TP), true negative (TN), false positive (FP) and false negative (FN) will be determined via comparison of the heart murmur classification results with the clinical gold standard (echocardiogram) diagnosis.
intervention type: Device
intervention name: Automated Heart Murmur Detection AI
description: Automated AI algorithm-based analysis of digital heart sound recordings to detect and classify heart murmurs. Heart sound recordings were fully blinded before undergoing one-time automated analysis. AI algorithm results for each recording include: AHA classification (Class I (pathologic heart murmur) versus class III (innocent heart murmur or no heart murmur), murmur timing, murmur grade, heart rate and S1/S2 identification.
other name: eMurmur ID
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 1 Day
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Lillian Lai, MD
role: Principal Investigator
affiliation: Children's Hopsital of Eastern Ontario, Canada
facility: 
country: Canada
url: https://www.ncbi.nlm.nih.gov/pubmed/26990211
description: Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study.
citation: Lai LS, Redington AN, Reinisch AJ, Unterberger MJ, Schriefl AJ. Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study. Congenit Heart Dis. 2016 Sep;11(5):386-395. doi: 10.1111/chd.12328. Epub 2016 Mar 15.
PMID: 26990211
responsible party type: Sponsor
mesh term: Heart Defects, Congenital
mesh term: Systolic Murmurs
mesh term: Heart Murmurs
sharing ipd: No