Cough Audio Classification as a TB Triage Test

Tuberculosis Clinical Trial

— CAGE-TB  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05317247
• Other study ID #: M21/10/022
• Status: Recruiting
• Start date: April 19, 2022
• Est. completion date: September 30, 2024

Study information

• Verified date: November 2023
• Source: University of Stellenbosch
• Contact: Grant Theron, PhD
• Phone: +27 21 9389693
• Email: gtheron[@]sun.ac.za
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Description:

CAGE-TB is a diagnostic evaluation study that assesses a TB cough audio signature's potential to be used in a smartphone application to detect potential TB from a cough sound to screen (triage) TB. The purpose of CAGE-TB is to promote the adoption of mobile health (mHealth) based cough audio triage testing for active pulmonary TB in health facilities located in high burden settings. The study is funded by the EDCTP2 programme supported by the European Union and involves four international partners. The study participants, older than 12 years, include participants who have a cough for a duration exceeding two weeks that present to healthcare clinics where the investigators have clinical recruitment infrastructure and permissions to conduct TB research. In this two-phase observational, cross-sectional study, each participant will be seen once only, at diagnosis, and no intervention is planned. In the first phase, the investigators will collect data from a discovery cohort, which will be used to train a machine learning algorithm. During the second phase, data will be collected from a validation cohort, comprising a larger number of participants from two geographically distinct study sites, which will be used to evaluate the performance of the algorithm. The aims of this study are to: (1) generate and separately validate a cough audio classifier that meets WHO triage test TPP sensitivity and specificity criteria. This aim lays the foundation for CAGE-TB by generating a classifier and a common public resource (cough sounds database) for potential later use in other studies. (2) Produce data on potential cost savings of cough audio app for triage by collecting primary data to demonstrate potential cost savings estimated using state-of-the-art methods to satisfy a key TPP criterion (

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1751
• Est. completion date: September 30, 2024
• Est. primary completion date: June 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 12 Years and older

Eligibility

Inclusion Criteria:

• 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

Exclusion Criteria:

• 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

Related Conditions & MeSH terms

• Tuberculosis

Intervention

Diagnostic Test:
• Cough sounds
The investigators will discover a cough audio signature and then validate it.

Locations

Country	Name	City	State
South Africa	Stellenbosch University	Cape Town	Western Cape
Uganda	Makerere University	Kampala

Sponsors (4)

Lead Sponsor	Collaborator
University of Stellenbosch	Amsterdam Institute for Global Health and Development, Makerere University, University of Göttingen

Countries where clinical trial is conducted

South Africa,  Uganda, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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
Primary	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