<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/NCT04407585</url>
  </required_header>
  <id_info>
    <org_study_id>Covid-19 Validation Study</org_study_id>
    <nct_id>NCT04407585</nct_id>
  </id_info>
  <brief_title>Testing the Accuracy of a Digital Test to Diagnose Covid-19</brief_title>
  <official_title>Validation of Machine Learning (ML) Models as Diagnostic Tools to Predict Infection With SARS-CoV-2</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>King's College London</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Zoe Global Limited</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Department of Health, United Kingdom</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>King's College London</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 Covid-19 viral pandemic has caused significant global losses and disruption to all&#xD;
      aspects of society. One of the major difficulties in controlling the spread of this&#xD;
      coronavirus has been the delayed and mild (or lack of) presentation of symptoms in infected&#xD;
      individuals, and the insufficient Covid-19 testing capacity in the UK. This warrants the&#xD;
      development of alternative diagnostic tools that reliably assess Covid-19 infection in the&#xD;
      early stages of infection, while also being low- cost, low-burden, and easily administered to&#xD;
      a wide proportion of the population.&#xD;
&#xD;
      This study aims to validate machine learning models as a diagnostic tool that predicts&#xD;
      infection with SARS-CoV-2 based on app-reported symptoms and phenotypic data, against the&#xD;
      'gold-standard' swab PCR-test. This study will take place within the Covid Symptom Study app,&#xD;
      the free symptom tracking mobile application launched in March 2020.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The Covid-19 viral pandemic has caused significant global losses and disruption to all&#xD;
      aspects of society (including health, education, and business and economic security). One of&#xD;
      the major difficulties in controlling the spread of this coronavirus has been the delayed and&#xD;
      mild (or lack of) presentation of symptoms in infected individuals. Moreover, there is&#xD;
      insufficient Covid-19 testing capacity in the UK, and only moderate accuracy of such tests at&#xD;
      confirming coronavirus infection. Together, these obstacles have led to countless unknown&#xD;
      coronavirus cases going unobserved and fuelling the viral spread in the population, by&#xD;
      compromising the stringency of self- isolation measures undertaken by infected individuals&#xD;
      who may have otherwise curbed or prevented their transmission of the virus. The profound and&#xD;
      widespread cost of the continuing Covid-19 progression, coinciding with the lack of testing&#xD;
      capacity, warrants the development of alternative diagnostic tools that reliably assess&#xD;
      Covid-19 infection in the early stages of infection, while also being low- cost, low-burden,&#xD;
      and easily administered to a wide proportion of the population.&#xD;
&#xD;
      The free symptom-monitoring app 'Covid Symptom Study' was launched in mid-March by health&#xD;
      technology start-up Zoe Global Ltd, and is currently being used in the UK, US and Sweden,&#xD;
      with more than 2.7 million users in the UK alone who use the app to self-report their&#xD;
      Covid-19 symptoms. Upon registering to use the app, users are asked to report demographic and&#xD;
      phenotypic data such as age, sex, BMI, ethnicity, contact with infected individuals (through&#xD;
      a healthcare professional capacity), smoking behaviour, existing health conditions, among&#xD;
      other information. From then on, users are asked to report, on a daily basis, their&#xD;
      presentation of symptoms attributable to Covid-19 (or lack thereof) through the use of&#xD;
      app-administered questionnaires, thus enabling real-time tracking of disease progression&#xD;
      across the UK. The app also allows users to report their Covid-19 test results, thus enabling&#xD;
      the development of prediction algorithms based solely on self-reported user data to predict&#xD;
      the presence of infection in untested users.&#xD;
&#xD;
      On behalf of Zoe Global Ltd, the UK Department of Health and Social Care with support from&#xD;
      the UK's Chief Scientific Advisor has committed to test up to 10,000 app-users per week for&#xD;
      infection with SARS-CoV-2 across England and Northern Ireland, for the purpose of rapidly&#xD;
      improving the accuracy of symptom-based predictions. Similar testing allowance may follow in&#xD;
      Scotland and Wales.&#xD;
&#xD;
      Symptomatic app-users will be asked to get tested for SARS-CoV-2 infection, using the popular&#xD;
      swab and qRT-PCR technique, and asked to report their test results in the app, while&#xD;
      continuing to log their symptoms.&#xD;
&#xD;
      This validation study, conducted at King's College London, aims to validate the sensitivity&#xD;
      and specificity of machine learning models as a diagnostic tool that predicts infection with&#xD;
      SARS-CoV-2 based on app-reported symptoms and phenotypic data, against the 'gold-standard'&#xD;
      swab PCR-test, by utilising the Covid Symptom Study app as a research platform.&#xD;
&#xD;
      It is hypothesised that by training the symptom-based models using swab test results and&#xD;
      through multiple model iterations following continuous data input from reporting and tested&#xD;
      app users, predictions of infection will be made with considerable accuracy, thus enabling&#xD;
      the Covid Symptom Study app to be used as a diagnostic tool that alleviates the strain of&#xD;
      testing capacity in the UK while being easily accessible and posing low user burden.&#xD;
&#xD;
      Study Design:&#xD;
&#xD;
      Due to the rapidly developing and uncertain duration and intensity of the Covid-19 pandemic,&#xD;
      the present study design is prospective and one that enables regular iteration on prediction&#xD;
      models and continuous accumulation of validation data. The study consists of a series of&#xD;
      phases, each lasting 14 days. Before the start of each phase (day 0), a set of machine&#xD;
      learning models will be frozen and submitted for validation on data collected during this and&#xD;
      subsequent phases.&#xD;
&#xD;
      Machine learning algorithms improve with increasing data. Therefore, validation phases will&#xD;
      continue as long as tests are available and app users consent to joining the study. Due to&#xD;
      the uncertainty around the progression of UK infection rates, the validation study will be&#xD;
      continue whilst it is of value to public health.&#xD;
&#xD;
      A detailed statistical analysis plan is described in the document attached to this record. A&#xD;
      record of all machine learning models used for validation will be regularly updated on GitHub&#xD;
      (https://github.com/zoe/covid-validation-study).&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">June 1, 2020</start_date>
  <completion_date type="Anticipated">May 10, 2023</completion_date>
  <primary_completion_date type="Anticipated">May 10, 2023</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>SARS-CoV-2 infection</measure>
    <time_frame>3 days</time_frame>
    <description>Likelihood of infection with Covid-19, based on app-reported symptoms</description>
  </primary_outcome>
  <primary_outcome>
    <measure>SARS-CoV-2 infection</measure>
    <time_frame>1 day</time_frame>
    <description>Active infection with Covid-19 as assessed by PCR swab test</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">1000000</enrollment>
  <condition>Covid-19</condition>
  <arm_group>
    <arm_group_label>Covid-19 Symptom Study app-user</arm_group_label>
    <description>UK-based Covid-19 Symptom Study primary app-user completing self-reports in the app</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Covid-19 swab PCR test</intervention_name>
    <description>Participants satisfying machine learning test criteria will be asked to take a swab test for Covid-19.</description>
    <arm_group_label>Covid-19 Symptom Study app-user</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        The study population includes individuals are UK-based primary users of the Covid Symptom&#xD;
        Study app, who provide informed consent to participate.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Study Inclusion Criteria - app users will be eligible to join the study if they:&#xD;
&#xD;
          -  Are based in the UK (are using the UK version of the Covid-19 Symptom Study app, and&#xD;
             have listed a UK postcode)&#xD;
&#xD;
          -  Are the primary app user (are reporting directly for themselves)&#xD;
&#xD;
          -  Are at least 18 years of age&#xD;
&#xD;
          -  Have not tested positive for a Covid-19 test before (but may have been tested)&#xD;
&#xD;
        Study Exclusion Criteria - participants are ineligible for the study if they:&#xD;
&#xD;
          -  Do not meet inclusion criteria&#xD;
&#xD;
          -  Do not provide informed consent to participate&#xD;
&#xD;
        Participants will be subject to further screening to identify them as eligible for swab&#xD;
        testing during the course of the study.&#xD;
&#xD;
        Swab inclusion criteria - participants will be eligible for swab testing if they:&#xD;
&#xD;
          -  Have reported in the app at least once in the previous 3 days (days -2 to 0), and at&#xD;
             least two times in the previous 9 days (days -8 to 0). All reports must be healthy&#xD;
             (i.e. not experiencing any symptoms).&#xD;
&#xD;
          -  On the previous day (day 1), have reported that they are experiencing at least one&#xD;
             symptom described in the app. Symptoms in the app are updated when deemed appropriate&#xD;
             by study investigators using evidence based reports in the scientific and medical&#xD;
             field.&#xD;
&#xD;
          -  Have answered the phenotype fields required for the prediction model with&#xD;
             physiologically plausible values.&#xD;
&#xD;
        Swab exclusion criteria - participants are ineligible for swab testing if they:&#xD;
&#xD;
          -  Are asymptomatic&#xD;
&#xD;
          -  Do not satisfy the inclusion criteria for testing.&#xD;
&#xD;
        Insufficient testing capacity:&#xD;
&#xD;
        If insufficient testing capacity is available for the study population as described, then&#xD;
        recruitment will be prioritised according to:&#xD;
&#xD;
          -  Firstly, most recent final healthy report before reporting symptoms&#xD;
&#xD;
          -  Secondly, highest number of healthy reports during the previous 9 days before&#xD;
             reporting symptoms&#xD;
&#xD;
          -  Thirdly, randomised selection to stratify between participants of equal priority&#xD;
             according to the first two rules above.&#xD;
&#xD;
        Excess testing capacity:&#xD;
&#xD;
        If excess testing capacity is available beyond the study population as described, then&#xD;
        inclusion criteria will be expanded in order to adequately sample across under-represented&#xD;
        population groups.&#xD;
&#xD;
        Specifically, on day 7 of each validation phase, investigators will assess:&#xD;
&#xD;
          -  What excess testing capacity is available, if any&#xD;
&#xD;
          -  Which subgroups are under-represented compared to their proportion in the UK&#xD;
             population (as best as can be established given that some participants may not have&#xD;
             completed some phenotype fields):&#xD;
&#xD;
             (i) Age decade (ii) Sex (iii) Ethnicity (iv) BMI category&#xD;
&#xD;
        For underrepresented groups, investigators may additionally recruit participants with only&#xD;
        one report during the previous 3 days (days -2 to 0) and no other report during the&#xD;
        previous 9 days (days -8 to 0).&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <overall_contact>
    <last_name>Inbar Linenberg, MSc</last_name>
    <phone>+447791871699</phone>
    <email>inbar.linenberg@kcl.ac.uk</email>
  </overall_contact>
  <location>
    <facility>
      <name>King's College London</name>
      <address>
        <city>London</city>
        <zip>SE1 9NH</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Inbar Linenberg, MSc</last_name>
      <phone>+447791871699</phone>
      <email>inbar.linenberg@kcl.ac.uk</email>
    </contact>
    <investigator>
      <last_name>Tim Spector</last_name>
      <role>Principal Investigator</role>
    </investigator>
    <investigator>
      <last_name>Sarah Berry</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Claire Steves</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Sebastien Ourselin</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Peter Sasieni</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Andrew Chan</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>United Kingdom</country>
  </location_countries>
  <verification_date>March 2022</verification_date>
  <study_first_submitted>May 27, 2020</study_first_submitted>
  <study_first_submitted_qc>May 27, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">May 29, 2020</study_first_posted>
  <last_update_submitted>March 30, 2022</last_update_submitted>
  <last_update_submitted_qc>March 30, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">March 31, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Covid-19</keyword>
  <keyword>Machine learning</keyword>
  <keyword>Covid-19 diagnostic</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <provided_document_section>
    <provided_document>
      <document_type>Statistical Analysis Plan</document_type>
      <document_has_protocol>No</document_has_protocol>
      <document_has_icf>No</document_has_icf>
      <document_has_sap>Yes</document_has_sap>
      <document_date>May 26, 2020</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/85/NCT04407585/SAP_000.pdf</document_url>
    </provided_document>
  </provided_document_section>
  <!-- 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/NCT04407585
org study id: Covid-19 Validation Study
nct id: NCT04407585
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The Covid-19 viral pandemic has caused significant global losses and disruption to all
      aspects of society. One of the major difficulties in controlling the spread of this
      coronavirus has been the delayed and mild (or lack of) presentation of symptoms in infected
      individuals, and the insufficient Covid-19 testing capacity in the UK. This warrants the
      development of alternative diagnostic tools that reliably assess Covid-19 infection in the
      early stages of infection, while also being low- cost, low-burden, and easily administered to
      a wide proportion of the population.

      This study aims to validate machine learning models as a diagnostic tool that predicts
      infection with SARS-CoV-2 based on app-reported symptoms and phenotypic data, against the
      'gold-standard' swab PCR-test. This study will take place within the Covid Symptom Study app,
      the free symptom tracking mobile application launched in March 2020.
The Covid-19 viral pandemic has caused significant global losses and disruption to all
      aspects of society (including health, education, and business and economic security). One of
      the major difficulties in controlling the spread of this coronavirus has been the delayed and
      mild (or lack of) presentation of symptoms in infected individuals. Moreover, there is
      insufficient Covid-19 testing capacity in the UK, and only moderate accuracy of such tests at
      confirming coronavirus infection. Together, these obstacles have led to countless unknown
      coronavirus cases going unobserved and fuelling the viral spread in the population, by
      compromising the stringency of self- isolation measures undertaken by infected individuals
      who may have otherwise curbed or prevented their transmission of the virus. The profound and
      widespread cost of the continuing Covid-19 progression, coinciding with the lack of testing
      capacity, warrants the development of alternative diagnostic tools that reliably assess
      Covid-19 infection in the early stages of infection, while also being low- cost, low-burden,
      and easily administered to a wide proportion of the population.

      The free symptom-monitoring app 'Covid Symptom Study' was launched in mid-March by health
      technology start-up Zoe Global Ltd, and is currently being used in the UK, US and Sweden,
      with more than 2.7 million users in the UK alone who use the app to self-report their
      Covid-19 symptoms. Upon registering to use the app, users are asked to report demographic and
      phenotypic data such as age, sex, BMI, ethnicity, contact with infected individuals (through
      a healthcare professional capacity), smoking behaviour, existing health conditions, among
      other information. From then on, users are asked to report, on a daily basis, their
      presentation of symptoms attributable to Covid-19 (or lack thereof) through the use of
      app-administered questionnaires, thus enabling real-time tracking of disease progression
      across the UK. The app also allows users to report their Covid-19 test results, thus enabling
      the development of prediction algorithms based solely on self-reported user data to predict
      the presence of infection in untested users.

      On behalf of Zoe Global Ltd, the UK Department of Health and Social Care with support from
      the UK's Chief Scientific Advisor has committed to test up to 10,000 app-users per week for
      infection with SARS-CoV-2 across England and Northern Ireland, for the purpose of rapidly
      improving the accuracy of symptom-based predictions. Similar testing allowance may follow in
      Scotland and Wales.

      Symptomatic app-users will be asked to get tested for SARS-CoV-2 infection, using the popular
      swab and qRT-PCR technique, and asked to report their test results in the app, while
      continuing to log their symptoms.

      This validation study, conducted at King's College London, aims to validate the sensitivity
      and specificity of machine learning models as a diagnostic tool that predicts infection with
      SARS-CoV-2 based on app-reported symptoms and phenotypic data, against the 'gold-standard'
      swab PCR-test, by utilising the Covid Symptom Study app as a research platform.

      It is hypothesised that by training the symptom-based models using swab test results and
      through multiple model iterations following continuous data input from reporting and tested
      app users, predictions of infection will be made with considerable accuracy, thus enabling
      the Covid Symptom Study app to be used as a diagnostic tool that alleviates the strain of
      testing capacity in the UK while being easily accessible and posing low user burden.

      Study Design:

      Due to the rapidly developing and uncertain duration and intensity of the Covid-19 pandemic,
      the present study design is prospective and one that enables regular iteration on prediction
      models and continuous accumulation of validation data. The study consists of a series of
      phases, each lasting 14 days. Before the start of each phase (day 0), a set of machine
      learning models will be frozen and submitted for validation on data collected during this and
      subsequent phases.

      Machine learning algorithms improve with increasing data. Therefore, validation phases will
      continue as long as tests are available and app users consent to joining the study. Due to
      the uncertainty around the progression of UK infection rates, the validation study will be
      continue whilst it is of value to public health.

      A detailed statistical analysis plan is described in the document attached to this record. A
      record of all machine learning models used for validation will be regularly updated on GitHub
      (https://github.com/zoe/covid-validation-study).
observational model: Cohort
time perspective: Prospective
measure: SARS-CoV-2 infection
time frame: 3 days
description: Likelihood of infection with Covid-19, based on app-reported symptoms
measure: SARS-CoV-2 infection
time frame: 1 day
description: Active infection with Covid-19 as assessed by PCR swab test
arm group label: Covid-19 Symptom Study app-user
description: UK-based Covid-19 Symptom Study primary app-user completing self-reports in the app
intervention type: Diagnostic Test
intervention name: Covid-19 swab PCR test
description: Participants satisfying machine learning test criteria will be asked to take a swab test for Covid-19.
arm group label: Covid-19 Symptom Study app-user
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
last name: Inbar Linenberg, MSc
phone: +447791871699
email: inbar.linenberg@kcl.ac.uk
facility: 
status: Recruiting
contact: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
country: United Kingdom
responsible party type: Sponsor
mesh term: COVID-19
sharing ipd: No
provided document: 