Cough Audio Classification as a TB Triage Test

NCT05317247 | Recruiting DMC

• 1 other identifier: M21/10/022
• Study Type: observational
• Target: 1,751
• Locations: 2 countries
• Sites: 2

Brief Summary

TB is the single biggest infectious cause of death (1.5 million died in 2018), killing more HIV-positive people than any other disease, and is arguably the most important poverty-related disease in the world. TB's estimated incidence in Africa has been declining over recent years but progress is slow and plateauing. To avert stagnation, truly innovative and ambitious technologies are needed, especially those that improve case finding and time-to-diagnosis as, in mathematical models based on the TB care cascade framework, interventions that accomplish this will have the most impact on disrupting population-level transmission, including when deployed at facilities where patients are readily accessible. Critically, these interventions (triage tests) must promote access to confirmatory testing (e.g., Xpert MTB/RIF Ultra) by enabling patients to be referred rapidly and efficiently during the same visit. The investigators will optimise and evaluate a technology that, aside from the investigators early case-controlled study to show feasibility, is hitherto not meaningfully investigated for TB. This gap is alarming given, on one hand, the enormity of the TB epidemic and the need for a triage test and, on the other hand, promising proofs-of-concept that demonstrate high diagnostic accuracy of cough audio classifier for respiratory diseases such as pneumonia, asthma. pertussis, croup, and COPD. In some cases, these classification systems are CE-marked, awaiting FDA-approval, and subject to late-stage clinical trials. This demonstrates the promise of the underlying technological principle. CAGE-TB's innovation is further enhanced by: applying advanced machine learning methods that the team have specifically developed for TB patient cough audio analysis, use of mixed methods research - drawing from health economics, implementation science, and medical anthropology - to inform product design and assess barriers and facilitators to implementation, and uniquely for a TB diagnostic test, its potential deployment as a pure mHealth (smartphone-based) innovation that mitigates many barriers that typically jeopardise TPP criteria fulfilment.

Conditions

Tuberculosis

Keywords

Triage; Cough audio classification; Smartphone application

Outcome Measures

Primary Outcomes (3)

• Develop and validate algorithms that can distinguish between TB and non-TB coughs

  Cough audio data will be collected and used to define the cough audio signal specific for TB. The optimised TB audio signature will then have its sensitivity and specificity measured in new patients to evaluate the performance of the algorithms.

  24 months

• Finalised smartphone-based mHealth application

  The best-performing algorithm will be incorporated into a smartphone app, which will be designed with human-centered approach, that can be used as a point-of-care triage test for TB.

  30 months

• Avert unnecessary Ultra tests

  The investigators will calculate potential cost savings that the application will be able to facilitate to avoid unnecessary tests.

  24 months

Study Arms (2)

Discovery Cohort

An anticipated number of 473 participants will be recruited in Cape Town, South Africa. Data (cough audio) will be collected and used to train a machine learning algorithm. The cough audio signal specific for TB will be refined. During the discovery phase, the ground truth obtained through biological testing of sputum specimens will be used to inform the machine learning.

Diagnostic Test: Cough sounds

Validation Cohort

In the validation phase, the cough audio signature will have its sensitivity and specificity measured in new patients in Cape Town, South Africa (n=511) and Kampala, Uganda (n=767). The data will be used to evaluate the performance of the algorithm.

Diagnostic Test: Cough sounds

Interventions

Cough sounds DIAGNOSTIC_TEST

The investigators will discover a cough audio signature and then validate it.

Discovery Cohort; Validation Cohort

Eligibility Criteria

• Age: 12 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with a cough of at least two weeks duration self-reporting to primary care clinics in Cape Town and Kampala, in areas with a high prevalence of TB.

You may qualify if:

• participant must be at least 12 years old
• participant must have a prolonged cough (for at least two weeks)
• participant must provide informed consent
• participant shall have a known HIV status or be willing to undergo standard of care HIV testing and counseling

You may not qualify if:

• individuals who refuse informed consent
• individuals who have received treatment for TB in the 60 days prior to enrolment
• individuals who are unable to provide a sputum specimen for microbiological testing
• individuals who have haemoptysis or a bloody cough with any forced coughs for audio recordings

Sponsors & Collaborators

• University of Stellenbosch: lead
• Amsterdam Institute for Global Health and Development: collaborator
• University of Göttingen: collaborator
• Makerere University: collaborator

Study Sites (2)

Stellenbosch University

Cape Town, Western Cape, 7505, South Africa

RECRUITING

Makerere University

Kampala, Kampala, 7062, Uganda

RECRUITING

Biospecimen

Retention: SAMPLES WITHOUT DNA

Sputum will be collected to test for TB. Other samples will be collected to test for TB markers in blood and urine. No human DNA will be collected

MeSH Terms

Conditions

Tuberculosis

Condition Hierarchy (Ancestors)

Mycobacterium Infections; Actinomycetales Infections; Gram-Positive Bacterial Infections; Bacterial Infections; Bacterial Infections and Mycoses; Infections

Study Officials

• Grant Theron, PhD

  University of Stellenbosch

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Grant Theron, PhD

CONTACT

+27 21 9389693; gtheron@sun.ac.za

Daphne Naidoo, Hons

CONTACT

+27 60 5037703; daphnenaidoo@sun.ac.za

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: March 30, 2022
• First Posted: April 7, 2022
• Study Start: April 19, 2022
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): December 30, 2027
• Last Updated: May 6, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ICF
• Time Frame: Data will be shared one year after study completion.
• Access Criteria: Applicants will need to submit an application to the Trial Steering Committee for data access. The Trial Steering Committee will review the application. The applicant will also need to sign a DTA with Stellenbosch University.

Locations

UG (1); ZA (1)