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

Tuberculosis Clinical Trial

Official title:

Rapid Research in Diagnostics Development for Tuberculosis Network (R2D2 Kids) and Assessing Diagnostics At Point-of-care for Tuberculosis in Children (ADAPT for Kids)

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05989802
• Other study ID #: R01HL169449
• Secondary ID: U01AI1520877200A
• Status: Recruiting
• Phase: N/A
• Start date: January 26, 2024
• Est. completion date: September 1, 2026

Study information

• Verified date: January 2024
• Source: University of California, San Francisco
• Contact: Devan Jaganath, MD
• Phone: 415-514-4692
• Email: devan.jaganath[@]ucsf.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 2100
• Est. completion date: September 1, 2026
• Est. primary completion date: September 1, 2026
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A to 65 Years

Eligibility

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

Related Conditions & MeSH terms

• Diagnostics
• Global Health
• Tuberculosis

Intervention

Diagnostic Test:
• 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.
• 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.
• 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.

Other:
• 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.

Locations

Country	Name	City	State
Mozambique	Instituto Nacional de Saúde	Maputo
South Africa	Dora Nginza Hospital	Cape Town
Uganda	Mulago National Referral Hospital	Kampala

Sponsors (8)

Lead Sponsor	Collaborator
University of California, San Francisco	Elizabeth Glaser Pediatric AIDS Foundation, Instituto Nacional de Saúde, Mozambique, Johns Hopkins University, National Institute of Allergy and Infectious Diseases (NIAID), National Referral Hospital, University of California, Irvine, University of Cape Town

Countries where clinical trial is conducted

Mozambique,  South Africa,  Uganda, 

References & Publications (19)

Andama A, Whitman GR, Crowder R, Reza TF, Jaganath D, Mulondo J, Nalugwa TK, Semitala FC, Worodria W, Cook C, Wood RC, Weigel KM, Olson AM, Lohmiller Shaw J, Kato-Maeda M, Denkinger CM, Nahid P, Cangelosi GA, Cattamanchi A. Accuracy of Tongue Swab Testing Using Xpert MTB-RIF Ultra for Tuberculosis Diagnosis. J Clin Microbiol. 2022 Jul 20;60(7):e0042122. doi: 10.1128/jcm.00421-22. Epub 2022 Jun 27. — View Citation

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Graham SM, Cuevas LE, Jean-Philippe P, Browning R, Casenghi M, Detjen AK, Gnanashanmugam D, Hesseling AC, Kampmann B, Mandalakas A, Marais BJ, Schito M, Spiegel HM, Starke JR, Worrell C, Zar HJ. Clinical Case Definitions for Classification of Intrathoracic Tuberculosis in Children: An Update. Clin Infect Dis. 2015 Oct 15;61Suppl 3(Suppl 3):S179-87. doi: 10.1093/cid/civ581. — View Citation

Harris M, Qi A, Jeagal L, Torabi N, Menzies D, Korobitsyn A, Pai M, Nathavitharana RR, Ahmad Khan F. A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis. PLoS One. 2019 Sep 3;14(9):e0221339. doi: 10.1371/journal.pone.0221339. eCollection 2019. — View Citation

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Kevat A, Kalirajah A, Roseby R. Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes. Respir Res. 2020 Sep 29;21(1):253. doi: 10.1186/s12931-020-01523-9. — View Citation

Lewinsohn DM, Leonard MK, LoBue PA, Cohn DL, Daley CL, Desmond E, Keane J, Lewinsohn DA, Loeffler AM, Mazurek GH, O'Brien RJ, Pai M, Richeldi L, Salfinger M, Shinnick TM, Sterling TR, Warshauer DM, Woods GL. Official American Thoracic Society/Infectious Diseases Society of America/Centers for Disease Control and Prevention Clinical Practice Guidelines: Diagnosis of Tuberculosis in Adults and Children. Clin Infect Dis. 2017 Jan 15;64(2):111-115. doi: 10.1093/cid/ciw778. — View Citation

Nkereuwem E, Togun T, Gomez MP, Szekely R, Mace A, Jobe D, Schumacher SG, Kampmann B, Denkinger CM; Reach4KidsAfrica (R4KA) Consortium. Comparing accuracy of lipoarabinomannan urine tests for diagnosis of pulmonary tuberculosis in children from four African countries: a cross-sectional study. Lancet Infect Dis. 2021 Mar;21(3):376-384. doi: 10.1016/S1473-3099(20)30598-3. Epub 2020 Dec 11. — View Citation

Perez-Velez CM, Marais BJ. Tuberculosis in children. N Engl J Med. 2012 Jul 26;367(4):348-61. doi: 10.1056/NEJMra1008049. No abstract available. — View Citation

UCSF Human Research Protection Program. The Level of Review and Minimal Risk. Available at: https://irb.ucsf.edu/levels-review#Category-2-Blood-sampling-limited-amounts. Last accessed 4 Jan 2023.

WHO consolidated guidelines on tuberculosis: Module 5: Management of tuberculosis in children and adolescents [Internet]. Geneva: World Health Organization; 2022. Available from http://www.ncbi.nlm.nih.gov/books/NBK579387/ — View Citation

WHO. High-priority target product profiles for new tuberculosis diagnostics: Report of a consensus meeting. Geneva, Switzerland. Available at: http://www.who.int/tb/publications/tpp_report/en/, 2014.

World Health Organization. Global Tuberculosis Report 2021. Geneva: World Health Organization, 2021.

World Health Organization. High-priority target product profiles for new tuberculosis diagnostics: report of a consensus meeting. Available at: http://apps.who.int/iris/bitstream/handle/10665/135617/WHO_HTM_TB_2014.18_eng.pdf?sequence=12014.

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World Health Organization. Roadmap towards ending TB in children and adolescents. Geneva: World Health Organization, 2018. Available at: https://www.who.int/tb/publications/2018/tb-childhoodroadmap/en/. Last accessed 1 June 2022.

Yellapu GD, Rudraraju G, Sripada NR, Mamidgi B, Jalukuru C, Firmal P, Yechuri V, Varanasi S, Peddireddi VS, Bhimarasetty DM, Kanisetti S, Joshi N, Mohapatra P, Pamarthi K. Development and clinical validation of Swaasa AI platform for screening and prioritization of pulmonary TB. Sci Rep. 2023 Mar 23;13(1):4740. doi: 10.1038/s41598-023-31772-9. Erratum In: Sci Rep. 2023 Jun 26;13(1):10353. — View Citation

Zawedde-Muyanja S, Reuter A, Tovar MA, Hussain H, Loando Mboyo A, Detjen AK, Yuen CM. Provision of Decentralized TB Care Services: A Detect-Treat-Prevent Strategy for Children and Adolescents Affected by TB. Pathogens. 2021 Dec 1;10(12):1568. doi: 10.3390/pathogens10121568. — View Citation

* Note: There are 19 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Proportion 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)	2 years
Primary	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)	2 years