<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/NCT04445012</url>
  </required_header>
  <id_info>
    <org_study_id>P02087</org_study_id>
    <nct_id>NCT04445012</nct_id>
  </id_info>
  <brief_title>Cardiovascular Acoustics and an Intelligent Stethoscope</brief_title>
  <acronym>CAIS</acronym>
  <official_title>Cardiovascular Acoustics and an Intelligent Stethoscope. An Observational Study to Collect Heart Sound Recordings From Patients to Understand the Acoustic Characteristics of Murmurs and Develop an Artificially Intelligent Stethoscope.</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Papworth Hospital NHS Foundation Trust</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Papworth Hospital NHS Foundation Trust</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>
      The aim of the project is to develop an artificial intelligence software capable of analysing&#xD;
      heart sounds to provide early diagnosis of a variety heart diseases at an early stage. Since&#xD;
      the invention of the stethoscope by Laennec in 1816, the basic design has not changed&#xD;
      significantly. Our software could be coupled with existing electronic stethoscopes to create&#xD;
      an 'intelligent' stethoscope that could be used by healthcare assistants or practice nurses&#xD;
      to screen for sound producing heart diseases. It could also be used at home by patients who&#xD;
      would otherwise go undiagnosed.&#xD;
&#xD;
      The study investigators at Cambridge University Engineering Department (CUED) have developed&#xD;
      a proof-of-concept AI algorithm to detect heart murmurs. However, in order to accurately&#xD;
      detect the specific pathology and severity underlying the murmur, more heart sound recordings&#xD;
      (matched with the ground truth from the patient's echocardiogram) are required. Patients&#xD;
      presenting to one of the partner hospitals requiring an echocardiogram as part of their&#xD;
      routine care will be invited to consent to this study. Participation will entail recording of&#xD;
      a patient's heart sounds using an electronic stethoscope as well as collection of routine&#xD;
      clinical data and a routine clinical echocardiogram at a single routine out patient visit.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      This project will develop an AI algorithm which can be imported into a stethoscope to make it&#xD;
      capable of automatically diagnosing any valve disease present and its severity. This will&#xD;
      help GPs produce more accurate diagnoses, reduce costs by having fewer unnecessary referrals&#xD;
      for echocardiogram, and produce more accurate diagnoses in countries where echocardiograms&#xD;
      are not readily available due to their cost. Using a small sample of data as well as some&#xD;
      which has been labelled by clinician auscultation, the team has created an award-winning AI&#xD;
      algorithm capable of accurate detection of heart murmurs. However, in order to improve the&#xD;
      accuracy and capability of this system more heart sound recordings from a range of diseases&#xD;
      (matched with echocardiogram diagnosis) are required. The key to the success of this study&#xD;
      will be to produce an AI algorithm that is more accurate than different grades of doctors at&#xD;
      detecting the specific abnormality and severity underlying a heart murmur. This methodology&#xD;
      will also provide a comprehensive study on acoustic characteristics of different heart&#xD;
      sounds. So far all the acoustic characteristics of heart sounds taught to medical students&#xD;
      are based on subjective opinion. This study will be able to objectively analyse these&#xD;
      acoustic characteristics.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">October 24, 2019</start_date>
  <completion_date type="Anticipated">January 31, 2023</completion_date>
  <primary_completion_date type="Anticipated">March 30, 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>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>To assess specificity of an algorithm for detecting clinically significant valve disease and congenital heart disease relative to the performance of General Practitioners</measure>
    <time_frame>Day 1</time_frame>
    <description>We will obtain 4, 15 second heart sound recordings from patients (at the Aortic, Pulmonary, Mitral, and Tricuspid sites) using a Littmann 3200 electronic stethoscope.</description>
  </primary_outcome>
  <number_of_groups>10</number_of_groups>
  <enrollment type="Anticipated">1150</enrollment>
  <condition>Heart Valve Diseases</condition>
  <arm_group>
    <arm_group_label>Aortic Valve Stenosis (Adults)</arm_group_label>
    <description>30 patients with mild, 30 with moderate, and 30 with severe mitral regurgitation, using the BSE gradings [Wharton 2014].</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Mitral Valve Regurgitation (Adults)</arm_group_label>
    <description>30 patients with mild, 30 with moderate, and 30 with severe mitral regurgitation, using the BSE gradings [Wharton 2014].</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Aortic Regurgitation (Adults)</arm_group_label>
    <description>30 patients with mild, 30 with moderate, and 30 with severe aortic regurgitation, using the BSE gradings [Wharton 2014].</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Mitral Stenosis (Adults)</arm_group_label>
    <description>30 patients with mild, 30 with moderate, and 30 with severe mitral stenosis, using the BSE gradings [Wharton 2014].</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Mixed Valve Disease (Adults)</arm_group_label>
    <description>30 patients with mild, 30 with moderate, and 30 with severe mixed valve disease. Overall classification based on the most severe disease using the BSE gradings [Wharton 2014].</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Ventricular Septal Defects (Paediatric Patients)</arm_group_label>
    <description>36 paediatric patients with mild, 37 with moderate, and 36 with severe ventricular septal defects, using gradings from [Samaan 1970].</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Aortic Stenosis (Paediatric Patients)</arm_group_label>
    <description>36 paediatric patients with mild, 37 with moderate, and 36 with severe aortic stenosis</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Pulmonary Stenosis (Paediatric Patients)</arm_group_label>
    <description>36 paediatric patients with mild, 37 with moderate, and 36 with severe pulmonary stenosis.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Patent Ductus Arteriosus (Paediatric Patients)</arm_group_label>
    <description>36 paediatric patients with mild, 37 with moderate, and 36 with severe patent ductus arteriosus, graded using ductal size [Arlettaz 2017].</description>
  </arm_group>
  <arm_group>
    <arm_group_label>No Disease (Paediatric Patients)</arm_group_label>
    <description>264 paediatric patients with no heart disease. Note that we are only taking recordings from those who have been referred for an echocardiogram with a suspected heart condition but are subsequently found to have no heart disease.</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        Patients attending cardiology clinics at the investigation sites who will be having an&#xD;
        echocardiogram&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Participant willing and able to give informed consent for participation in study&#xD;
&#xD;
          -  Participant to undergo an echocardiogram as part of their routine assessment&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Informed consent is not given&#xD;
&#xD;
          -  New York Heart Association (NYHA) functional class = 4&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>14 Days</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Bushra Rana, MB BS</last_name>
    <role>Principal Investigator</role>
    <affiliation>Imperial College Healthcare NHS Trust</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Nicky Watson, MSc</last_name>
    <phone>01223639684</phone>
    <email>nicky.watson2@nhs.net</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Victoria Hughes, PhD</last_name>
    <phone>01223 639678</phone>
    <email>victoria.hughes1@nhs.net</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>University Hospitals Birmingham NHS Foundation Trust</name>
      <address>
        <city>Birmingham</city>
        <zip>B15 2TH</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Rick Steeds, MA, MBBS, MD</last_name>
      <email>Rick.Steeds@uhb.nhs.uk</email>
    </contact>
  </location>
  <location>
    <facility>
      <name>Royal Papworth Hospital NHS Foundation Trust</name>
      <address>
        <city>Cambridge</city>
        <zip>CB2 0AY</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Len Shapiro, MB BChir MD</last_name>
      <email>Len.shapiro@papworth.nhs.uk</email>
    </contact>
  </location>
  <location>
    <facility>
      <name>Guy's and St Thomas' NHS Foundation Trust</name>
      <address>
        <city>London</city>
        <zip>SE1 9RT</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Shakeel Qureshi, MBCHB, MRCP</last_name>
      <email>Shakeel.Qureshi@gstt.nhs.uk</email>
    </contact>
  </location>
  <location>
    <facility>
      <name>King's College Hospital NHS Foundation Trust</name>
      <address>
        <city>London</city>
        <zip>SE5 9RS</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Mark Monaghan, MSc, PhD</last_name>
      <email>mark.monaghan@nhs.net</email>
    </contact>
  </location>
  <location>
    <facility>
      <name>Imperial College Healthcare NHS Trust</name>
      <address>
        <city>London</city>
        <zip>W2 1NY</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Bushra Rana, MB BS</last_name>
      <email>Bushra.Rana@nhs.net</email>
    </contact>
  </location>
  <location_countries>
    <country>United Kingdom</country>
  </location_countries>
  <verification_date>September 2021</verification_date>
  <study_first_submitted>June 21, 2020</study_first_submitted>
  <study_first_submitted_qc>June 21, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 24, 2020</study_first_posted>
  <last_update_submitted>September 14, 2021</last_update_submitted>
  <last_update_submitted_qc>September 14, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">September 16, 2021</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 Valve Diseases</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/NCT04445012
org study id: P02087
nct id: NCT04445012
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The aim of the project is to develop an artificial intelligence software capable of analysing
      heart sounds to provide early diagnosis of a variety heart diseases at an early stage. Since
      the invention of the stethoscope by Laennec in 1816, the basic design has not changed
      significantly. Our software could be coupled with existing electronic stethoscopes to create
      an 'intelligent' stethoscope that could be used by healthcare assistants or practice nurses
      to screen for sound producing heart diseases. It could also be used at home by patients who
      would otherwise go undiagnosed.

      The study investigators at Cambridge University Engineering Department (CUED) have developed
      a proof-of-concept AI algorithm to detect heart murmurs. However, in order to accurately
      detect the specific pathology and severity underlying the murmur, more heart sound recordings
      (matched with the ground truth from the patient's echocardiogram) are required. Patients
      presenting to one of the partner hospitals requiring an echocardiogram as part of their
      routine care will be invited to consent to this study. Participation will entail recording of
      a patient's heart sounds using an electronic stethoscope as well as collection of routine
      clinical data and a routine clinical echocardiogram at a single routine out patient visit.
This project will develop an AI algorithm which can be imported into a stethoscope to make it
      capable of automatically diagnosing any valve disease present and its severity. This will
      help GPs produce more accurate diagnoses, reduce costs by having fewer unnecessary referrals
      for echocardiogram, and produce more accurate diagnoses in countries where echocardiograms
      are not readily available due to their cost. Using a small sample of data as well as some
      which has been labelled by clinician auscultation, the team has created an award-winning AI
      algorithm capable of accurate detection of heart murmurs. However, in order to improve the
      accuracy and capability of this system more heart sound recordings from a range of diseases
      (matched with echocardiogram diagnosis) are required. The key to the success of this study
      will be to produce an AI algorithm that is more accurate than different grades of doctors at
      detecting the specific abnormality and severity underlying a heart murmur. This methodology
      will also provide a comprehensive study on acoustic characteristics of different heart
      sounds. So far all the acoustic characteristics of heart sounds taught to medical students
      are based on subjective opinion. This study will be able to objectively analyse these
      acoustic characteristics.
observational model: Cohort
time perspective: Prospective
measure: To assess specificity of an algorithm for detecting clinically significant valve disease and congenital heart disease relative to the performance of General Practitioners
time frame: Day 1
description: We will obtain 4, 15 second heart sound recordings from patients (at the Aortic, Pulmonary, Mitral, and Tricuspid sites) using a Littmann 3200 electronic stethoscope.
arm group label: Aortic Valve Stenosis (Adults)
description: 30 patients with mild, 30 with moderate, and 30 with severe mitral regurgitation, using the BSE gradings [Wharton 2014].
arm group label: Mitral Valve Regurgitation (Adults)
description: 30 patients with mild, 30 with moderate, and 30 with severe mitral regurgitation, using the BSE gradings [Wharton 2014].
arm group label: Aortic Regurgitation (Adults)
description: 30 patients with mild, 30 with moderate, and 30 with severe aortic regurgitation, using the BSE gradings [Wharton 2014].
arm group label: Mitral Stenosis (Adults)
description: 30 patients with mild, 30 with moderate, and 30 with severe mitral stenosis, using the BSE gradings [Wharton 2014].
arm group label: Mixed Valve Disease (Adults)
description: 30 patients with mild, 30 with moderate, and 30 with severe mixed valve disease. Overall classification based on the most severe disease using the BSE gradings [Wharton 2014].
arm group label: Ventricular Septal Defects (Paediatric Patients)
description: 36 paediatric patients with mild, 37 with moderate, and 36 with severe ventricular septal defects, using gradings from [Samaan 1970].
arm group label: Aortic Stenosis (Paediatric Patients)
description: 36 paediatric patients with mild, 37 with moderate, and 36 with severe aortic stenosis
arm group label: Pulmonary Stenosis (Paediatric Patients)
description: 36 paediatric patients with mild, 37 with moderate, and 36 with severe pulmonary stenosis.
arm group label: Patent Ductus Arteriosus (Paediatric Patients)
description: 36 paediatric patients with mild, 37 with moderate, and 36 with severe patent ductus arteriosus, graded using ductal size [Arlettaz 2017].
arm group label: No Disease (Paediatric Patients)
description: 264 paediatric patients with no heart disease. Note that we are only taking recordings from those who have been referred for an echocardiogram with a suspected heart condition but are subsequently found to have no heart disease.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 14 Days
maximum age: N/A
healthy volunteers: No
last name: Bushra Rana, MB BS
role: Principal Investigator
affiliation: Imperial College Healthcare NHS Trust
last name: Nicky Watson, MSc
phone: 01223639684
email: nicky.watson2@nhs.net
last name: Victoria Hughes, PhD
phone: 01223 639678
email: victoria.hughes1@nhs.net
facility: 
status: Recruiting
contact: 
facility: 
status: Recruiting
contact: 
facility: 
status: Recruiting
contact: 
facility: 
status: Recruiting
contact: 
facility: 
status: Recruiting
contact: 
country: United Kingdom
responsible party type: Sponsor
mesh term: Heart Valve Diseases
sharing ipd: No