Cough Audio Classification as a TB Triage Test

MI-összefoglaló

A vizsgálat saját részvételi feltételeiből. Gép olvasta ki az oldalon álló regiszterszövegből, ezért tévedhet. Hogy ki vehet részt, azt a lenti regiszterszöveg és a vizsgálat csapata dönti el.

A vizsgálat olyan, legalább 12 éves embereket keres, akik legalább két hete köhögnek. Egy okostelefonos alkalmazást próbálnak ki, amely a köhögés hangjából segít a tuberkulózis (TBC) kiszűrésében.

Életkor

12 éves vagy idősebb

Nem

Mindkét nem részt vehet.

A betegség stádiuma

Legalább két hete tartó, elhúzódó köhögés.

Korábbi kezelés

A csatlakozás előtti 60 napban nem kaphatott TBC-kezelést.

Biomarker vagy leletérték

Ismert HIV-státusz, vagy hajlandóság a szokásos HIV-vizsgálatra és tanácsadásra.

A részvétel fő kizáró okai

• Nem adja beleegyezését
• A csatlakozás előtti 60 napban TBC-kezelést kapott
• Nem tud köpetmintát adni a laborvizsgálathoz
• Vért köhög, vagy a hangfelvételhez végzett erőltetett köhögésnél véres a köhögése

A regiszter feltételei, szó szerint

Mit akar kideríteni a vizsgálat

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.

A vizsgálat 2 csoportja

• 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.

  Kezelés: 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.

  Kezelés: Cough sounds