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Testing the Accuracy of a Digital Test to Diagnose Covid-19

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04407585
Recruitment Status : Recruiting
First Posted : May 29, 2020
Last Update Posted : March 31, 2022
Sponsor:
Collaborators:
Zoe Global Limited
Department of Health, United Kingdom
Information provided by (Responsible Party):
King's College London

Brief Summary:

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.


Condition or disease Intervention/treatment
Covid-19 Diagnostic Test: Covid-19 swab PCR test

Show Show detailed description

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Study Type : Observational
Estimated Enrollment : 1000000 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Validation of Machine Learning (ML) Models as Diagnostic Tools to Predict Infection With SARS-CoV-2
Actual Study Start Date : June 1, 2020
Estimated Primary Completion Date : May 10, 2023
Estimated Study Completion Date : May 10, 2023

Resource links provided by the National Library of Medicine


Group/Cohort Intervention/treatment
Covid-19 Symptom Study app-user
UK-based Covid-19 Symptom Study primary app-user completing self-reports in the app
Diagnostic Test: Covid-19 swab PCR test
Participants satisfying machine learning test criteria will be asked to take a swab test for Covid-19.




Primary Outcome Measures :
  1. SARS-CoV-2 infection [ Time Frame: 3 days ]
    Likelihood of infection with Covid-19, based on app-reported symptoms

  2. SARS-CoV-2 infection [ Time Frame: 1 day ]
    Active infection with Covid-19 as assessed by PCR swab test



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Sampling Method:   Non-Probability Sample
Study Population
The study population includes individuals are UK-based primary users of the Covid Symptom Study app, who provide informed consent to participate.
Criteria

Study Inclusion Criteria - app users will be eligible to join the study if they:

  • Are based in the UK (are using the UK version of the Covid-19 Symptom Study app, and have listed a UK postcode)
  • Are the primary app user (are reporting directly for themselves)
  • Are at least 18 years of age
  • Have not tested positive for a Covid-19 test before (but may have been tested)

Study Exclusion Criteria - participants are ineligible for the study if they:

  • Do not meet inclusion criteria
  • Do not provide informed consent to participate

Participants will be subject to further screening to identify them as eligible for swab testing during the course of the study.

Swab inclusion criteria - participants will be eligible for swab testing if they:

  • Have reported in the app at least once in the previous 3 days (days -2 to 0), and at least two times in the previous 9 days (days -8 to 0). All reports must be healthy (i.e. not experiencing any symptoms).
  • On the previous day (day 1), have reported that they are experiencing at least one symptom described in the app. Symptoms in the app are updated when deemed appropriate by study investigators using evidence based reports in the scientific and medical field.
  • Have answered the phenotype fields required for the prediction model with physiologically plausible values.

Swab exclusion criteria - participants are ineligible for swab testing if they:

  • Are asymptomatic
  • Do not satisfy the inclusion criteria for testing.

Insufficient testing capacity:

If insufficient testing capacity is available for the study population as described, then recruitment will be prioritised according to:

  • Firstly, most recent final healthy report before reporting symptoms
  • Secondly, highest number of healthy reports during the previous 9 days before reporting symptoms
  • Thirdly, randomised selection to stratify between participants of equal priority according to the first two rules above.

Excess testing capacity:

If excess testing capacity is available beyond the study population as described, then inclusion criteria will be expanded in order to adequately sample across under-represented population groups.

Specifically, on day 7 of each validation phase, investigators will assess:

  • What excess testing capacity is available, if any
  • Which subgroups are under-represented compared to their proportion in the UK population (as best as can be established given that some participants may not have completed some phenotype fields):

    (i) Age decade (ii) Sex (iii) Ethnicity (iv) BMI category

For underrepresented groups, investigators may additionally recruit participants with only one report during the previous 3 days (days -2 to 0) and no other report during the previous 9 days (days -8 to 0).


Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04407585


Contacts
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Contact: Inbar Linenberg, MSc +447791871699 inbar.linenberg@kcl.ac.uk

Locations
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United Kingdom
King's College London Recruiting
London, United Kingdom, SE1 9NH
Contact: Inbar Linenberg, MSc    +447791871699    inbar.linenberg@kcl.ac.uk   
Principal Investigator: Tim Spector         
Sub-Investigator: Sarah Berry         
Sub-Investigator: Claire Steves         
Sub-Investigator: Sebastien Ourselin         
Sub-Investigator: Peter Sasieni         
Sub-Investigator: Andrew Chan         
Sponsors and Collaborators
King's College London
Zoe Global Limited
Department of Health, United Kingdom
  Study Documents (Full-Text)

Documents provided by King's College London:
Statistical Analysis Plan  [PDF] May 26, 2020

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Responsible Party: King's College London
ClinicalTrials.gov Identifier: NCT04407585    
Other Study ID Numbers: Covid-19 Validation Study
First Posted: May 29, 2020    Key Record Dates
Last Update Posted: March 31, 2022
Last Verified: March 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by King's College London:
Covid-19
Machine learning
Covid-19 diagnostic
Additional relevant MeSH terms:
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COVID-19
Respiratory Tract Infections
Infections
Pneumonia, Viral
Pneumonia
Virus Diseases
Coronavirus Infections
Coronaviridae Infections
Nidovirales Infections
RNA Virus Infections
Lung Diseases
Respiratory Tract Diseases