Automated Smartphone-based Cough Audio Classification for Rapid Tuberculosis Triage Testing (Cough Audio triaGE for TB; CAGE-TB)

Status: Recruiting
Location: See all (2) locations...
Intervention Type: Diagnostic Test
Study Type: Observational

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.

Participation Requirements
Sex: All
Minimum Age: 12
Healthy Volunteers: No

• 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

Other Locations
South Africa
Stellenbosch University
Cape Town
Makerere University
Not yet recruiting
Contact Information
Grant Theron, PhD
+27 21 9389693
Daphne Naidoo, Hons
+27 60 5037703
Time Frame
Start Date: April 19, 2022
Estimated Completion Date: September 30, 2024
Target number of participants: 1751
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.
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.
Related Therapeutic Areas
Collaborators: Makerere University, University of Göttingen, Amsterdam Institute for Global Health and Development
Leads: University of Stellenbosch

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