Every year there are an estimated 230,000 childhood deaths from TB. There is an urgent need for novel tests for TB diagnosis in children under 15 years. The Rapid Research in Diagnostics Development for TB Network (R2D2 Kids) and the Assessing Diagnostics at Point-of-care for Tuberculosis in children (ADAPT for Kids) studies seek to reduce the burden of TB worldwide by evaluating faster, simpler, and less expensive TB triage and diagnostic tests for use in children.
The Rapid Research in Diagnostics Development for TB Network (R2D2 Kids) and the Assessing Diagnostics at Point-of-care for Tuberculosis in children (ADAPT for Kids) studies will rigorously assess promising, point-of-care (POC) TB diagnostic tests in clinical studies conducted among children at settings of intended use. There is an urgent need for novel tests for TB diagnosis in children under 15 years because of the challenge of obtaining sputum samples from children and the low sputum bacillary burden among children with TB even when a sample is obtained. This creates delays in diagnosis and treatment initiation, and is a major contributor to the 230,000 childhood deaths from TB each year. Therefore, a non-sputum biomarker-based test has been ranked among the highest priority target product profiles for new TB diagnostics. If inexpensive and simple to perform, such a diagnostic tool could have significant impact by facilitating rapid diagnosis and TB treatment in children. The studies will evaluate the sensitivity and specificity of novel diagnostic tests in children in reference to NIH consensus definitions for childhood TB. In addition, the usability and acceptability of the novel TB diagnostic tests will be assessed through direct observations and surveys of routine health workers.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
2,100
Swab-based testing provides a non-invasive approach to collect respiratory specimens for TB testing. Data in adults suggests that swab-based testing could be valuable when sputum collection is not feasible or available.
Cough sounds can be collected through a mobile phone and tablet, and then analyzed with machine learning algorithms to predict TB.
Lung sounds can be collected with a non-invasive digital stethoscope, and then saved on a tablet or phone and analyzed by machine learning algorithms to predict TB.
Several artificial intelligence algorithms have been developed to predict TB, though this has not yet been validated in children.
Instituto Nacional de Saúde
Maputo, Mozambique
RECRUITINGDora Nginza Hospital
Cape Town, South Africa
NOT_YET_RECRUITINGMulago National Referral Hospital
Kampala, Uganda
NOT_YET_RECRUITINGProportion with positive index test result among participants with tuberculosis (TB)
Sensitivity - Number with positive index test result/(Number with positive or negative index t test result) among participants with TB. TB will be defined based on a microbiological reference standard (sputum mycobacterial culture results)
Time frame: 2 years
Proportion with negative index test result among participants without tuberculosis (TB)
Specificity - Number with negative index test results/(Number with positive or negative index t test result) among participants without TB. TB will be defined based on a microbiological reference standard (sputum mycobacterial culture results)
Time frame: 2 years
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