Rapid Research in Diagnostics Development for TB Network (R2D2 Kids) and Assessing Diagnostics At POC for TB in Children (ADAPT for Kids)

NCT ID: NCT05989802

Last Updated: 2025-03-13

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 2100 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-01-26
• Study Completion Date: 2026-09-01

Brief Summary

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.

Detailed Description

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.

Conditions

Tuberculosis; Diagnostics; Global Health

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Evaluation of various novel TB triage and diagnostic tests

The investigators will conduct evaluation of novel TB triage and diagnostic tests in a cohort of children with presumed TB. The investigators aim to enroll 250 participants per year at each of three enrollment sites for evaluation of various novel TB triage and diagnostic tests and 30 health workers to assess test usability. Novel test evaluations will be conducted on a rolling basis as new tests ready for field evaluation are identified.

Group Type: EXPERIMENTAL

Oral swab molecular testing

Intervention Type: DIAGNOSTIC_TEST

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.

Automated Cough Sound Analysis

Intervention Type: DIAGNOSTIC_TEST

Cough sounds can be collected through a mobile phone and tablet, and then analyzed with machine learning algorithms to predict TB.

Automated Lung Sound Analysis

Intervention Type: DIAGNOSTIC_TEST

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.

Chest X Ray Computer Aided Detection

Intervention Type: OTHER

Several artificial intelligence algorithms have been developed to predict TB, though this has not yet been validated in children.

Interventions

Oral swab molecular testing

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.

Intervention Type: DIAGNOSTIC_TEST

Automated Cough Sound Analysis

Cough sounds can be collected through a mobile phone and tablet, and then analyzed with machine learning algorithms to predict TB.

Intervention Type: DIAGNOSTIC_TEST

Automated Lung Sound Analysis

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.

Intervention Type: DIAGNOSTIC_TEST

Chest X Ray Computer Aided Detection

Several artificial intelligence algorithms have been developed to predict TB, though this has not yet been validated in children.

Intervention Type: OTHER

Other Intervention Names

Artificial Intelligence algorithm

Eligibility Criteria

Inclusion Criteria

Participant eligibility criteria: Participants will include children (age <15 years) who present to care with: A. 2 or more of the following: * Unexplained cough for any duration * TB contact or tuberculin skin test or interferon gamma release assay positive * Abnormal chest X-ray (any abnormality) OR B. Any one of criteria A AND any one of the following: * Unexplained weight loss OR unexplained failure to thrive OR Severe Acute Malnutrition * Unexplained fever ≥2 weeks * Unexplained lethargy or reduced playfulness ≥2 weeks The study will exclude participants who: 1. Completed preventive or active TB treatment within the past 12 months (to increase TB prevalence and reduce false-positive results, respectively); 2. Have taken any medication with anti-mycobacterial activity for any reason for greater than 3 days at the time of enrollment (to reduce false-negatives); 3. Are unable to return for follow-up visits; or 4. Whose parents/guardians are unwilling to provide informed consent or who are unwilling to provide assent if applicable (age determined by local IRB) Assessment of the usability of novel TB tests: The study will also include health workers at each clinical site who are 1) aged ≥18 years and 2) involved in routine TB testing (collecting specimens for or performing TB tests).

Exclusion Criteria

Personnel who are unwilling to provide informed consent will be excluded.

• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Instituto Nacional de Saúde, Mozambique

OTHER_GOV

Sponsor Role: collaborator

National Referral Hospital

OTHER_GOV

Sponsor Role: collaborator

University of Cape Town

OTHER

Sponsor Role: collaborator

Johns Hopkins University

OTHER

Sponsor Role: collaborator

Elizabeth Glaser Pediatric AIDS Foundation

OTHER

Sponsor Role: collaborator

National Institute of Allergy and Infectious Diseases (NIAID)

NIH

Sponsor Role: collaborator

University of California, Irvine

OTHER

Sponsor Role: collaborator

University of California, San Francisco

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Adithya Cattamanchi, MD, MAS

Role: PRINCIPAL_INVESTIGATOR

University of California, Irvine

Nilesh Bhatt

Role: PRINCIPAL_INVESTIGATOR

Elizabeth Glazer Pediatric AIDS Foundation

Locations

Instituto Nacional de Saúde

Maputo, , Mozambique

Site Status: RECRUITING

Dora Nginza Hospital

Cape Town, , South Africa

Site Status: NOT_YET_RECRUITING

Mulago National Referral Hospital

Kampala, , Uganda

Site Status: NOT_YET_RECRUITING

Countries

Mozambique; South Africa; Uganda

Central Contacts

Devan Jaganath, MD

Role: CONTACT

devan.jaganath@ucsf.edu

415-514-4692

Brittney Sweetser, MPH

Role: CONTACT

bsweetse@hs.uci.edu

Facility Contacts

Celso Khosa

Role: primary

celso.khosa@ins.gov.mz

Heather Zar

Role: primary

heather.zar@uct.ac.za

Eric Wobudeya

Role: primary

ewobudeya@mujhu.org

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Other Identifiers

U01AI152087

Identifier Type: NIH

Identifier Source: secondary_id

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7200AA22RFA00004

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

R01HL169449

Identifier Type: NIH

Identifier Source: org_study_id

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