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This will be a multi-country study, recruiting both adults and children with TB and Pneumonia. The sites selected for the study will ensure geographical representation, ethnic diversity, and variability in disease burden and comorbidities, which are key factors for building a robust and generalisable artificial intelligence (AI) algorithm for point-of-care ultrasound (POCUS). This study aims to collect data to build a comprehensive database of patient data that will include clinical picture including symptomatology, digital auscultation and cough sound recordings, paired radiological images, microbiological tests, and other blood tests required to establish a diagnosis, as well as the socio-demographic information of the participants. Data will be collected from similar retrospective and ongoing projects, as well as through targeted prospective data collection, as planned in this proposal.
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Respiratory diseases, including tuberculosis (TB) and community acquired pneumonia, are major global health problems that affect both adults and children. The 2024 Global TB report from the WHO has documented TB as the number one killer among all infectious pathogens globally in 2023, contributing to approximately 1.25 million deaths worldwide. Although Chest X-rays (CXR) and WHO-recommended molecular tests (mWRD) are available, access to these diagnostics in remote and rural settings remains limited. Moreover, diagnosis is more challenging in children owing to the paucibacillary nature of TB, difficulty in obtaining adequate samples for testing, and non-specific symptoms, especially in the under 5 age group. Community acquired pneumonia (lower respiratory infections) cause 2.5 million deaths each year, including 610,000 in children under 5 years of age. These illnesses predominantly affect populations in low- and middle-income countries (LMICs).
Point Of Care Ultrasound (POCUS) -based thoracic ultrasound (TUS) has been increasingly used to detect lung abnormalities in respiratory infections, and its use has been expanded to screen and detect pneumonia and other diseases. Evidence also suggests that TUS can differentiate between viral and bacterial pneumonias, thus triaging the need for antibiotics.
Artificial Intelligence (AI) based Computer Aided Detection (CAD) has emerged as a tool for standardized interpretation of CXRs and has been extensively studied and deployed globally for detecting radiological signs of TB in the absence of human readers, following the WHO recommendation in 2021.
The multi-site, multi-country prospective arm of the study will be recruiting approximately 15,000 to 18,000 subjects including children and adults with presumptive TB or pneumonia, targeting 1,500 children and 2,000 adults with confirmed diagnosis. |
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Experimental Group, Microbiological Clinical and Radiological Data Collection from Adults and Children , Not applicable, 18 Months of recruitment; 3 months of follow-up, The study aims to recruit approximately 15,000 to 18,000 participants to achieve the total number of 3500 cases of tuberculosis (n=1000 adults, n=500 children) or pneumonia (n=1000 adults, n=1000 children) across over 20 study recruitment sites in South Africa, India, and Nigeria. Cases are defined as subjects who present with symptoms of a lower respiratory illness, presumably TB or community-acquired pneumonia, and undergo microbiological, serological, and radiological tests subsequently to establish the final diagnosis of TB or pneumonia through a composite reference standard encompassing microbiological, clinical, or clinico-radiological diagnosis to initiate appropriate treatment. Asymptomatic subjects who are contacts of active TB disease and children or adults living with HIV, who are under evaluation for active TB disease or for TB Preventive Therapy (TPT) eligibility with or without symptoms, will also be recruited to represent about 10-15% of the prospective cohort.
The clinical data, TUS, digital auscultation, cough sound recordings, and CXR image acquisition will be performed at that point, along with additional tests at the clinician’s discretion by the trained Research Assistant (RA).
Mandatory additional data variables, including blood biomarkers, and the pneumonia panel will only be performed if the diagnosis of TB or pneumonia has been confirmed and for a predefined proportion of controls. Thus, there will be a larger database of TUS+ CXR from all presumptive participants., 18000, |
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Principal Investigator, Adegoke, Falade, Prof., afalade33@hotmail.com, , 00000000000, Ibadan University, Box 4078, Oyo, , Nigeria, Professor |
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Principal Investigator, Adegoke, Falade, Prof., afalade33@hotmail.com, , 00000000000, Ibadan University, Box 4078, Oyo, , Nigeria, Professor |
Principal Investigator, Adegoke, Falade, Prof., afalade33@hotmail.com, , +2349084625231, Oxygen for Life Initiative, Department of Paediatrics, University College Hospital, Ibadan, , Nigeria, Professor |
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Principal Investigator, Ali, Esmail, Dr., ali.esmail@uct.ac.za, , +27832711444, NY3, Gugulethu, Cape Town, , South Africa, Site Investigator |
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Principal Investigator, DJ , Christopher, Dr., djchris@cmcvellore.ac.in, , +919443306573, Department of Pulmonary Medicine, CMC Vellore Ranipet Campus, Kilminnal Village, Ranipet, Tamil Nadu, 632517, India, Site Pulmonologist |
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Principal Investigator, DJ , Christopher, Dr., djchris@cmcvellore.ac.in, , +919443306573, Department of Pulmonary Medicine, CMC Vellore Ranipet Campus, Kilminnal Village, Ranipet, Tamil Nadu, 632517, India, Site Pulmonologist |
Scientific Enquiries, DJ , Christopher, Dr., djchris@cmcvellore.ac.in, , +919443306573, Department of Pulmonary Medicine, CMC Vellore Ranipet Campus, Kilminnal Village, Ranipet, Tamil Nadu, 632517, India, Site Pulmonologist |
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Principal Investigator, Philip Verghese, Valsan , Dr., valsan@cmcvellore.ac.in, , +917708303319, Child Health Unit III, CMCH Vellore, Town campus, Tamil Nadu, 632004, India, Paediatrics Site Investigator |