TB - TuberculosisSCREEN-TBNCT07517471
Start4All SCREEN-TB PROTOCOL
A study testing Near point of care (NPOC) NAATs, Low-complexity nucleic acid amplification tests (LC-NAATs) and LC-NAAT using pooled testing in people with TB - Tuberculosis, run by Liverpool School of Tropical Medicine.
Trial summary
Tuberculosis (TB) remains the leading cause of death from a single infectious agent globally, with millions of people still undiagnosed or diagnosed late. Conventional case-finding strategies rely heavily on symptom screening using the WHO Four-Symptom Screen ((W4SS; comprising any one of current cough, fever, night… Read more about this study
- Who is it for?
- Ages 10 years and olderHealthy volunteers can take part
- Conditions the study is for
- TB - Tuberculosis
- What is being tested?
- Near point of care (NPOC) NAATs, Low-complexity nucleic acid amplification tests (LC-NAATs) and LC-NAAT using pooled testing
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 37,000 participants
- Where
- No sites listed yet
- Study dates
- Apr 2026to May 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 10 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Age
- Adults aged 15 years and above or
- Adolescents aged 10-14 years
- Facility setting
- o Participating healthcare facilities (e.g., primary health centres, district hospitals), including both rural and urban facilities.
- Screening eligibility
Show all 9Show fewer
- o All individuals presenting to the facility, regardless of symptoms or ability to produce sputum, will be eligible for inclusion.
- Consent
- Written informed consent (and assent for adolescents (10-14 years) and adults (≥15 - 17 years) if included) must be obtained according to local ethics and regulatory requirements.
Who cannot join
- Age
- o Below 10 years at enrolment
- Screening eligibility
- o Do not screen positive on any tools.
- Consent and follow-up
- o Unable or unwilling to provide written informed consent (and assent where applicable) or unwilling to agree to follow-up visits.
Show all 13Show fewer
- Current TB treatment
- o Receiving anti-TB treatment at the time of enrolment, defined as having taken ≥3 doses of TB treatment.
- Recent TB preventive therapy
- o Receipt of TB preventive therapy within the last 6 months prior to enrolment.
- Clinical danger signs
- o Presence of severe illness at screening, including but not limited to: Respiratory rate >30/min Fever >39°C Pulse rate >120/min Inability to walk unaided
- Duplicate enrolment o Previous enrolment in SCREEN-TB.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
Vibol Lem, PhD contact
About this study
Official title: Start4All - Start Taking Action for TB Diagnosis SCREEN-TB
Tuberculosis (TB) remains the leading cause of death from a single infectious agent globally, with millions of people still undiagnosed or diagnosed late. Conventional case-finding strategies rely heavily on symptom screening using the WHO Four-Symptom Screen ((W4SS; comprising any one of current cough, fever, night sweats, or weight loss) and sputum testing, but these approaches miss a substantial proportion of individuals with active TB disease, particularly those who are asymptomatic or unable to produce sputum. Missed and delayed diagnoses drive ongoing transmission and undermine global TB elimination goals.
Recent evidence has shown that diagnostic tools which are more accessible, even if somewhat less sensitive, can still substantially improve TB case detection by reducing diagnostic loss associated with access barriers. This suggests that near point-of-care (NPOC) tests might be highly cost-effective in many settings, because the gains from earlier diagnosis, reduced delays, and broader reach could outweigh losses from slightly lower accuracy.
The purpose of this study is to evaluate new, symptom-agnostic screening and diagnostic approaches that can be implemented at lower-level health facilities in high TB-burden, low and middle-income (LMIC) countries for adults ≥15 years and 10-14 years old young adolescents
Read the detailed description
The study will generate evidence on the performance, cost-effectiveness, feasibility, acceptability, and scalability of symptom-agnostic algorithms initiated by of computer-aided detection chest radiography (CAD CXR-AI) and near point-of-care (NPOC) molecular assays applied to tongue and sputum swabs. These tools have the potential to identify TB earlier, including among asymptomatic individuals, and to reduce dependence on sputum-based diagnostics alone.
The research questions being addressed are of direct global relevance. There is currently limited real-world evidence on: how CAD CXR-AI and NPOC tongue swab and sputum swab assays compare as initial screening tools; how they can be integrated with WHO-recommended low-complexity nucleic acid amplification tests (LC-NAATs), in efficient algorithms; and whether these approaches can be delivered effectively in primary care and outpatient settings in high TB burden LMIC. Data generated through this study will directly inform WHO guideline development and national TB programme decisions, especially concerning the detection of asymptomatic TB and the role of non-sputum samples.
- Conditions
- TB - Tuberculosis
- Being tested
- Near point of care (NPOC) NAATs, Low-complexity nucleic acid amplification tests (LC-NAATs), LC-NAAT using pooled testing, Portable Chest X-ray Image Acquisition, Computer-Aided Detection (CAD) Chest X-ray (CXR-AI)
- Keywords
- TB, Diagnostics
- Medical subject headings
- Latent Tuberculosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Near point of care (NPOC) NAATsDiagnostic test
- Near point of care instrument that can test tongue swabs and sputum swabs.
- Rapid molecular detection system for detecting infectious diseases included TB, able to provide accurate test results that are comparable to top laboratory PCR tests, while it is easier to use and move around and only takes 15 to 35 minutes to conclude the result.
Low-complexity nucleic acid amplification tests (LC-NAATs)Diagnostic test
Semi-quantitative, nested real-time polymerase chain reaction (PCR) diagnostic test for the detection of Mycobacterium tuberculosis (MTB) complex DNA in unprocessed sputum samples[18]. It can also detect rifampicin-resistance associated mutations in MTB. Results are automatically displayed on the screen of the system in less than 80 minutes
LC-NAAT using pooled testingDiagnostic test
Pooled testing involves combining equal volumes from multiple individuals' samples and testing them together using a single test[. Pools will be created using remaining samples from 2-4 participants who have screened positive and were able to produce a sputum, guided by CAD CXR-AI thresholds[20]. To the possible extend, pools will be suggested by CAD band score: CAD <0.3 pooled together and 0.3 ≤ CAD < 0.8 pooled together.
Portable Chest X-ray Image AcquisitionDiagnostic test
Portable X-ray systems are designed to bring diagnostic imaging to environments where conventional radiography is impractical. They are lightweight, compact, and battery-powered, making them suitable for use in remote or resource-limited settings, or for reaching people with limited mobility. Depending on the model, they can produce between 100 and 400 images on a full charge, allowing extended use without access to electricity.
Computer-Aided Detection (CAD) Chest X-ray (CXR-AI)Diagnostic test
Computer-aided detection (CAD) software for chest X-rays is designed to support rapid, automated screening for tuberculosis and other thoracic abnormalities. Software for the study has not been selected yet. It will be a WHO-approved CAD software with final selection through tender processes and in compliance with national regulatory approvals. Operating on mobile or computer platforms, these tools can analyse chest X-rays in less than a minute, distinguishing normal from abnormal scans and highlighting findings in the lungs, pleura, mediastinum, bones, diaphragm, and heart. In addition to detecting disease, some systems can assist clinicians with tasks such as verifying device placement and measuring distances from anatomical landmarks.
Screen TB&HIV sub-study diagnostic testOther
The SCREEN TB\&HIV substudy is implemented only in Cameroon, Nigeria and Kenya. HIV testing will therefore not be conducted in Bangladesh or Viet Nam, as HIV testing is not part of routine care pathways at the participating facilities and the study does not introduce additional HIV testing. In addition, Bangladesh and Viet Nam have substantially lower HIV prevalence, making implementation of the HIV substudy operationally unnecessary and not aligned with clinical need
Screen TB&HIV sub-study diagnostic testOther
The CD4 cell count is performed in venous blood in HIV positive patients to assess progression of HIV disease, including risk for developing opportunistic infections. The normal range of CD4 count is from 500 to 1500 cells/mm3 of blood, and it progressively decreases over time in persons who are not receiving or not responding well to ART. Someone with a CD4 count below 200 is described as having advanced HIV disease.
Screen TB&HIV sub-study diagnostic testOther
LAM is a glycolipid of the cell wall of Mycobacterium tuberculosis. LAM is excreted in urine, where it can be detected using rapid lateral flow tests. In inpatient settings, WHO strongly recommends using LAM to assist in the diagnosis of active TB in HIV-positive adults, adolescents and children with signs and symptoms of TB (pulmonary and/or extrapulmonary), or with advanced HIV disease (1) or who are seriously ill (2) or else irrespective of signs and symptoms of TB and with a CD4 cell count of less than 200 cells/mm3[23]. In outpatient settings, WHO suggests using LF-LAM to assist in the diagnosis of active TB in HIV-positive adults, adolescents and children: with signs and symptoms of TB (pulmonary and/or extrapulmonary) or seriously ill; or else irrespective of signs and symptoms of TB and with a CD4 cell count of less than 100 cells/mm3[23]. LAM tests evaluated in SCREEN TB\&HIV are:
| Study group | Role | What they receive |
|---|---|---|
| Diagnostic | Other | Diagnostic Receives: Diagnostic Test: Near point of care (NPOC) NAATs; Diagnostic Test: Low-complexity nucleic acid amplification tests (LC-NAATs); Diagnostic Test: LC-NAAT using pooled testing; Diagnostic Test: Portable Chest X-ray Image Acquisition; Diagnostic Test: Computer-Aided Detection (CAD) Chest X-ray (CXR-AI); Other: Screen TB&HIV sub-study diagnostic test |
What is being measured
The primary outcome is the main question the study is designed to answer.
Primary Objective 1: To evaluate the diagnostic yield and comparative accuracy of diagnostic algorithms initiated by CAD CXR-AI and/or NPOC tongue swab and sputum swab screening as initial screening tools in a facility-based case finding strategy
Primary Endpoint 1.1: Diagnostic Yield of TB by algorithm, disaggregated by symptom status
- WHO: All participants enrolled in the study (≥10 years)
- WHAT: Number and proportion (out of attempted TB testing) of participants diagnosed with TB (microbiologically confirmed and clinically diagnosed) per diagnostic algorithm pathway
- WHEN: Primary = at completion of diagnostic work-up (Day 1-3); Secondary = confirmed via NTP registry data at treatment initiation
- WHERE: Study healthcare facilities and referral laboratories in Bangladesh, Cameroon, Kenya, Nigeria, Viet Nam
- WHY: To determine incremental case detection across different screening approaches
- HOW MEASURED: oNumerator = number of TB cases identified; oDenominator = total participants screened by each algorithm (including those with invalid and inconclusive results); oStratification = symptomatic vs asymptomatic (per WHO 4-symptom screen); oCase definitions = WHO TB definitions (microbiologically confirmed, clinicaly diagnose
Measured: Completed within 6 month of data collection
Primary Objective 1: To evaluate the diagnostic yield and comparative accuracy of diagnostic algorithms initiated by CAD CXR-AI and/or NPOC tongue swab and sputum swab screening as initial screening tools in a facility-based case finding strategy
Primary Endpoint 1.2: Comparative diagnostic accuracy (sensitivity, specificity, PPV, NPV) of (i) CAD CXR-AI as an initial screening tool, (ii) NPOC tongue swab and sputum swab testing as initial screening tools.
- WHO: All participants with interpretable test results
- WHAT: Sensitivity, specificity, PPV, NPV of CAD CXR-AI and NPOC tongue swab and sputum swab as initial screening tools
- WHEN: Calculated after completion of reference standard testing for all participants
- WHERE: Central data analysis
- WHY: To compare performance characteristics of screening tools
- HOW MEASURED:
- Reference standard = LC-NAAT;
- Using standard diagnostic accuracy definitions and reporting all estimates together with 95% confidence intervals;
- Receiver operating characteristic (ROC) curves for CAD CXR-AI thresholds
- Pre-specified handling of indeterminate/uninterpretable results
Measured: Completed within 6 month of data collection
Secondary outcomes (8)
Secondary Objective 1: To assess timeliness (time to treatment initiation) and proportion initiated on treatment, of CAD CXR-AI and/or NPOC tongue swab and sputum swab -based initiated algorithms
Secondary Endpoint 1.1: Time from entering healthcare facility to being initiated on TB treatment according to National TB Programme (NTP) register record
- WHO: Participants diagnosed with TB
- WHAT: Time from entering healthcare facility to initiation of TB treatment, using NTP register records
- WHEN: From date of facility attendance (and, where available, reported symptom onset) to date of treatment start
- WHERE: Facility records and NTP registers
- WHY: To evaluate timeliness of linkage to care under different algorithms
- HOW MEASURED: Extract dates of facility attendance (or specimen collection), diagnosis confirmation, and treatment start from registers; calculate time to treatment initiation in days; analyse using descriptive and time-to-event methods; assessment limited to treatment initiation (no post-treatment follow-up).
Measured: Completed within 6 month of data collection
Secondary Objective 1: To assess timeliness (time to treatment initiation) and proportion initiated on treatment, of CAD CXR-AI and/or NPOC tongue swab and sputum swab -based initiated algorithms
Secondary Endpoint 1.2: Proportion of individuals screening positive on the diagnostic algorithm who then initiate TB treatment.
- WHO: All participants screening positive on diagnostic algorithms
- WHAT: Proportion who initiate TB treatment
- WHEN: At time of registry-confirmed treatment initiation
- WHERE: NTP registers
- WHY: To determine effectiveness of linkage to care
- HOW MEASURED: Numerator = number starting treatment; Denominator = number screening positive
Measured: Completed within 6 month of data collection
Secondary Objective 2: To evaluate the cost-effectiveness of CAD CXR-AI and NPOC tongue swab and sputum swab as initial screening tools in facility-based case finding
Secondary Endpoint 2.1: Modelled incremental cost per person diagnosed with TB from a societal perspective (disaggregated by provider and beneficiary), comparing multiple screening and diagnostic algorithms where:
CAD CXR-AI and NPOC are incorporated as initial screening tests Varying CAD thresholds for determining whether pooled or individual LC-NAAT testing is subsequently used are treated as independent and separate initial CAD CXR-AI screening tests
- WHO: All participants enrolled
- WHAT: Direct evaluation of costs and cost-effectiveness of CAD vs NPOC tongue swab and sputum swab as:
- stand-alone initial screening tools
- part of diagnostic algorithms
- WHEN: During study and at study end
- WHERE: Facility costing and central analysis
- WHY: To inform programmatic adoption
- HOW MEASURED: Measured cost per TB case diagnosed, modelled incremental cost-effectiveness ratios
Measured: Completed within 6 month of data collection
Secondary Objective 2: To evaluate the cost-effectiveness of CAD CXR-AI and NPOC tongue swab and sputum swab as initial screening tools in facility-based case finding
Secondary Endpoint 2.2: Modelled incremental cost per person diagnosed with TB who initiates treatment from a societal perspective (disaggregated by provider and beneficiary), comparing multiple screening and diagnostic algorithms where:
CAD CXR-AI and NPOC are incorporated as initial screening tests Varying CAD thresholds for determining whether pooled or individual LC-NAAT testing is subsequently used are treated as independent and separate initial CAD CXR-AI screening tests
- WHO: All participants enrolled
- WHAT: Direct evaluation of costs and cost-effectiveness of CAD vs NPOC tongue swab and sputum swab as:
- stand-alone initial screening tools
- part of diagnostic algorithms
- WHEN: During study and at study end
- WHERE: Facility costing and central analysis
- WHY: To inform programmatic adoption
- HOW MEASURED: Modelled cost per person who initiates treatment, modelled incremental cost-effectiveness ratios
Measured: Completed within 6 month of data collection
Secondary Objective 3: To evaluate feasibility, acceptability, and scalability of CAD CXR-AI and NPOC tongue swab and sputum swab in routine facility workflows
Secondary Endpoint 3.1: Feasibility and acceptability of CAD CXR-AI and NPOC tongue swab and sputum swab from diverse perspectives including people seeking care, and health system, and policy makers
- WHO: People seeking care, healthcare providers, policymakers
- WHAT: Feasibility, acceptability, and scalability of CAD CXR-AI and NPOC tongue swab and sputum swab
- WHEN: During and after implementation
- WHERE: Study facilities and through qualitative sub-studies
- WHY: To evaluate real-world integration and sustainability
- HOW MEASURED: Participant and provider interviews, FGDs; structured observation; time-motion analysis; metrics such as proportion able to provide samples and proportion of valid results
Measured: Completed within 6 month of data collection
Secondary Objective 4: To evaluate the diagnostic performance, efficiency, and feasibility of CAD-guided pooling compared with individual testing
Secondary Endpoint 4.1: Sensitivity and specificity of CAD-guided pooling relative to individual testing
- WHO: All enrolled participants providing tongue or sputum swabs eligible for both individual and pooled testing.
- WHAT: Diagnostic accuracy (sensitivity, specificity, PPV, NPV) of CAD-guided pooling compared with individual LC-NAAT results as reference.
- WHEN: completion of diagnostic work-up; secondary confirmation against NTP registry data at treatment initiation.
- WHERE: Study facilities and laboratories in Bangladesh, Cameroon, Kenya, Nigeria, Viet Nam.
- WHY: To determine whether CAD-guided pooling maintains diagnostic accuracy while reducing testing volumes.
- HOW MEASURED:
- Numerator (sensitivity) = number of TB-positive pools correctly identified.
- Denominator (specificity) = total number of TB-negative individuals per reference testing.
- Stratification = by CAD score band and specimen type (tongue swab, sputum swab).
Measured: Completed within 6 month of data collection
Secondary Objective 4: To evaluate the diagnostic performance, efficiency, and feasibility of CAD-guided pooling compared with individual testing
Secondary Endpoint 4.2: Proportion of tests saved and associated change in turnaround time
- WHO: All pooled samples from enrolled participants eligible for CAD-guided pooling.
- WHAT: Number and proportion of tests saved by CAD-guided pooling compared with individual and turnaround time for reporting results.
- WHEN: Measured in real-time during study implementation; time recorded from specimen collection to availability of test result.
- WHERE: Study facilities and laboratories in Bangladesh, Cameroon, Kenya, Nigeria, Viet Nam.
- WHY: To assess efficiency gains from pooling strategies and their impact on timeliness of results.
- HOW MEASURED:
- Numerator (tests saved) = number of individual cartridges not required due to pooling.
- Denominator (tests saved) = total number of cartridges that would have been required for individual testing alone.
- Turnaround time = median hours from specimen collection to result availability; stratified by pooled vs individual pathways.
Measured: Completed within 6 month of data collection
Secondary Objective 4: To evaluate the diagnostic performance, efficiency, and feasibility of CAD-guided pooling compared with individual testing
Secondary Endpoint 4.3: Feasibility and acceptability of CAD-guided pooling from perspectives of laboratory staff, providers, and policymakers
- WHO: Laboratory staff, healthcare and policymakers engaged in or overseeing CAD-guided pooling activities.
- WHAT: Perceptions of feasibility (integration into, training , workload, error rates) and acceptability (appropriateness, confidence, value)
- WHEN: During implementation and at study end via interviews, discussions, and surveys.
- WHERE: Healthcare facilities, laboratories, and NTB programme offices in-country.
- WHY: To identify barriers and facilitators to implementation of CAD-guided pooling informing potential scale-up.
- HOW MEASURED:
- Qualitative data: thematically analysed from key informant interviews and focus groups.
- Quantitative data: survey scores on feasibility and acceptability domains (e.g. workload, clarity of purpose, perceived reliability).
- Stratification = by stakeholder group (lab staff, providers, policymakers).
Measured: Completed within 6 months of data collection
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Apr 2026
- Study startApr 2026 (estimated)
- Primary completionJan 2027 (estimated)
- Study completionMay 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (30)Papers the sponsor has linked to this study.
- Other trials for TB - TuberculosisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 37,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Liverpool School of Tropical Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Tom Wingfield, PhD FRCP DTMH DipHIV, Study director, Liverpool School of Tropical MedicineVibol Lem, PhD, Study director, Liverpool School of Tropical Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07517471
- Sponsor's protocol ID
- 125
- Acronym
- SCREEN-TB
- Status last verified
- Nov 2025
- First submitted
- 4 Feb 2026
- Last updated
- 8 Apr 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: At the end of the study, after the primary results have been published, the individual participant data (IPD) and associated documentation (e.g. protocol, statistical analysis plan, annotated blank CRF) will be prepared in order to be shared with external researchers. IPD will only be shared with external researchers if the participants have consented to this onward disclosure, IPD has been fully anonymised prior to sharing.
Publications
David A, Singh L, Peloakgosi-Shikwambani K, Nsingwane Z, Molepo V, Cangelosi G, da Silva P, Stevens W, Scott L. Diagnostic accuracy of self-collected tongue swabs for Mycobacterium tuberculosis complex detection in individuals being assessed for tuberculosis in South Africa using the Xpert MTB/RIF Ultra assay. Clin Microbiol Infect. 2025 Jun;31(6):1040-1045. doi: 10.1016/j.cmi.2025.01.029. Epub 2025 Jan 28.
PubMed 39884504Ricks S, Denkinger CM, Schumacher SG, Hallett TB, Arinaminpathy N. The potential impact of urine-LAM diagnostics on tuberculosis incidence and mortality: A modelling analysis. PLoS Med. 2020 Dec 11;17(12):e1003466. doi: 10.1371/journal.pmed.1003466. eCollection 2020 Dec.
PubMed 33306694Bjerrum S, Schiller I, Dendukuri N, Kohli M, Nathavitharana RR, Zwerling AA, Denkinger CM, Steingart KR, Shah M. Lateral flow urine lipoarabinomannan assay for detecting active tuberculosis in people living with HIV. Cochrane Database Syst Rev. 2019 Oct 21;10(10):CD011420. doi: 10.1002/14651858.CD011420.pub3.
PubMed 31633805Cohen JF, Korevaar DA, Altman DG, Bruns DE, Gatsonis CA, Hooft L, Irwig L, Levine D, Reitsma JB, de Vet HC, Bossuyt PM. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016 Nov 14;6(11):e012799. doi: 10.1136/bmjopen-2016-012799.
PubMed 28137831Sekhon M, Cartwright M, Francis JJ. Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework. BMC Health Serv Res. 2017 Jan 26;17(1):88. doi: 10.1186/s12913-017-2031-8.
PubMed 28126032French SD, Green SE, O'Connor DA, McKenzie JE, Francis JJ, Michie S, Buchbinder R, Schattner P, Spike N, Grimshaw JM. Developing theory-informed behaviour change interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework. Implement Sci. 2012 Apr 24;7:38. doi: 10.1186/1748-5908-7-38.
PubMed 22531013Barker PM, Reid A, Schall MW. A framework for scaling up health interventions: lessons from large-scale improvement initiatives in Africa. Implement Sci. 2016 Jan 29;11:12. doi: 10.1186/s13012-016-0374-x.
PubMed 26821910Klaic M, Kapp S, Hudson P, Chapman W, Denehy L, Story D, Francis JJ. Implementability of healthcare interventions: an overview of reviews and development of a conceptual framework. Implement Sci. 2022 Jan 27;17(1):10. doi: 10.1186/s13012-021-01171-7.
PubMed 35086538Husereau D, Drummond M, Augustovski F, de Bekker-Grob E, Briggs AH, Carswell C, Caulley L, Chaiyakunapruk N, Greenberg D, Loder E, Mauskopf J, Mullins CD, Petrou S, Pwu RF, Staniszewska S; CHEERS 2022 ISPOR Good Research Practices Task Force. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. BMC Med. 2022 Jan 12;20(1):23. doi: 10.1186/s12916-021-02204-0.
PubMed 35022047Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009 Apr;42(2):377-81. doi: 10.1016/j.jbi.2008.08.010. Epub 2008 Sep 30.
PubMed 18929686Hamasur B, Okunola AO, Sserubiri J, Nwamba WV, Abdulgader SM, Ignatowich L, Rasool O, Nabatanzi R, Lorente SP, Giraldos D, Joloba M, Warren RM, Ssengooba W, Theron G. A new rapid lipoarabinomannan urine assay for tuberculosis: a two-centre diagnostic accuracy evaluation in outpatients with and without HIV. Res Sq [Preprint]. 2025 Feb 18:rs.3.rs-5386988. doi: 10.21203/rs.3.rs-5386988/v2.
PubMed 40034451Gils T, Hella J, Jacobs BKM, Sossen B, Mukoka M, Muyoyeta M, Nakabugo E, Van Nguyen H, Ubolyam S, Mace A, Vermeulen M, Nyangu S, Sanjase N, Sasamalo M, Dinh HT, Ngo TA, Manosuthi W, Jirajariyavej S, Denkinger CM, Nguyen NV, Avihingsanon A, Nakiyingi L, Szekely R, Kerkhoff AD, MacPherson P, Meintjes G, Reither K, Ruhwald M. A Prospective Evaluation of the Diagnostic Accuracy of the Point-of-Care VISITECT CD4 Advanced Disease Test in 7 Countries. J Infect Dis. 2025 Feb 4;231(1):e82-e90. doi: 10.1093/infdis/jiae374.
PubMed 39046150Codlin AJ, Vo LNQ, Garg T, Banu S, Ahmed S, John S, Abdulkarim S, Muyoyeta M, Sanjase N, Wingfield T, Iem V, Squire B, Creswell J. Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries. BMC Glob Public Health. 2024;2(1):52. doi: 10.1186/s44263-024-00081-2. Epub 2024 Aug 1.
PubMed 39100507Iem V, Byrne RL, Garg T, Santos V, Yassin MA, Kathure I, Donkeng Donfack VF, Vuchas C, Bilder CR, Creswell J, Squire SB, Wingfield T. Pooled testing for TB: revisiting a cost-saving innovation. Lancet Respir Med. 2025 Nov;13(11):958-961. doi: 10.1016/S2213-2600(25)00328-5. Epub 2025 Sep 25. No abstract available.
PubMed 41016409Kohli M, Inbaraj LR, Salomon A, Scandrett K, Korobitsyn A, Ismail N, Srinivasalu VA, Daniel J, Steingart KR, Takwoingi Y. Low-complexity automated nucleic acid amplification tests for extrapulmonary tuberculosis and rifampicin resistance in adults and adolescents. Cochrane Database Syst Rev. 2025 Aug 4;8(8):CD012768. doi: 10.1002/14651858.CD012768.pub4.
PubMed 40757508
Study at a glance
From the registry record, last updated by the sponsor on 8 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Start4All - Start Taking Action for TB Diagnosis SCREEN-TB
- ClinicalTrials.gov ID
- NCT07517471
- Acronym / sponsor ID
- SCREEN-TB / 125
- Conditions
- TB - Tuberculosis
- Keywords
- TB, Diagnostics
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 37,000 participants (estimated)
- Lead sponsor
- Liverpool School of Tropical MedicineUniversity, hospital or other
- Study start
- Apr 2026 (estimated)
- Primary completion
- Jan 2027 (estimated)
- Study completion
- May 2027 (estimated)
- First posted
- 8 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Tuberculosis (TB) remains the leading cause of death from a single infectious agent globally, with millions of people still undiagnosed or diagnosed late. Conventional case-finding strategies rely heavily on symptom screening using the WHO Four-Symptom Screen ((W4SS; comprising any one of current cough, fever, night sweats, or weight loss) and sputum testing, but these approaches miss a substantial proportion of individuals with active TB disease, particularly those who are asymptomatic or unable to produce sputum. Missed and delayed diagnoses drive ongoing transmission and undermine global TB elimination goals.
Recent evidence has shown that diagnostic tools which are more accessible, even if somewhat less sensitive, can still substantially improve TB case detection by reducing diagnostic loss associated with access barriers. This suggests that near point-of-care (NPOC) tests might be highly cost-effective in many settings, because the gains from earlier diagnosis, reduced delays, and broader reach could outweigh losses from slightly lower accuracy.
The purpose of this study is to evaluate new, symptom-agnostic screening and diagnostic approaches that can be implemented at lower-level health facilities in high TB-burden, low and middle-income (LMIC) countries for adults ≥15 years and 10-14 years old young adolescents
Detailed description
The study will generate evidence on the performance, cost-effectiveness, feasibility, acceptability, and scalability of symptom-agnostic algorithms initiated by of computer-aided detection chest radiography (CAD CXR-AI) and near point-of-care (NPOC) molecular assays applied to tongue and sputum swabs. These tools have the potential to identify TB earlier, including among asymptomatic individuals, and to reduce dependence on sputum-based diagnostics alone.
The research questions being addressed are of direct global relevance. There is currently limited real-world evidence on: how CAD CXR-AI and NPOC tongue swab and sputum swab assays compare as initial screening tools; how they can be integrated with WHO-recommended low-complexity nucleic acid amplification tests (LC-NAATs), in efficient algorithms; and whether these approaches can be delivered effectively in primary care and outpatient settings in high TB burden LMIC. Data generated through this study will directly inform WHO guideline development and national TB programme decisions, especially concerning the detection of asymptomatic TB and the role of non-sputum samples.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Diagnostic | Other | Diagnostic Test: Near point of care (NPOC) NAATs, Diagnostic Test: Low-complexity nucleic acid amplification tests (LC-NAATs), Diagnostic Test: LC-NAAT using pooled testing, Diagnostic Test: Portable Chest X-ray Image Acquisition, Diagnostic Test: Computer-Aided Detection (CAD) Chest X-ray (CXR-AI), Other: Screen TB&HIV sub-study diagnostic testDiagnostic |
Who may qualify
- Ages
- 10 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (9 listed)
- Age
- Facility setting
- o Participating healthcare facilities (e.g., primary health centres, district hospitals), including both rural and urban facilities.
- Screening eligibility
- o All individuals presenting to the facility, regardless of symptoms or ability to produce sputum, will be eligible for inclusion.
Key exclusion criteria (13 listed)
- Age
- o Below 10 years at enrolment
- Screening eligibility
- o Do not screen positive on any tools.
- Consent and follow-up
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Near point of care (NPOC) NAATs* Near point of care instrument that can test tongue swabs and sputum swabs. * Rapid molecular detection system for detecting infectious diseases included TB, able to provide accurate test results that are comparable to top laboratory PCR tests, while it is easier to use and move around and only takes 15 to 35 minutes to conclude the result. | Diagnostic test | Diagnostic |
| Low-complexity nucleic acid amplification tests (LC-NAATs)Semi-quantitative, nested real-time polymerase chain reaction (PCR) diagnostic test for the detection of Mycobacterium tuberculosis (MTB) complex DNA in unprocessed sputum samples[18]. It can also detect rifampicin-resistance associated mutations in MTB. Results are automatically displayed on the screen of the system in less than 80 minutes | Diagnostic test | Diagnostic |
| LC-NAAT using pooled testingPooled testing involves combining equal volumes from multiple individuals' samples and testing them together using a single test[. Pools will be created using remaining samples from 2-4 participants who have screened positive and were able to produce a sputum, guided by CAD CXR-AI thresholds[20]. To the possible extend, pools will be suggested by CAD band score: CAD <0.3 pooled together and 0.3 ≤ CAD < 0.8 pooled together. | Diagnostic test | Diagnostic |
| Portable Chest X-ray Image AcquisitionPortable X-ray systems are designed to bring diagnostic imaging to environments where conventional radiography is impractical. They are lightweight, compact, and battery-powered, making them suitable for use in remote or resource-limited settings, or for reaching people with limited mobility. Depending on the model, they can produce between 100 and 400 images on a full charge, allowing extended use without access to electricity. | Diagnostic test | Diagnostic |
| Computer-Aided Detection (CAD) Chest X-ray (CXR-AI)Computer-aided detection (CAD) software for chest X-rays is designed to support rapid, automated screening for tuberculosis and other thoracic abnormalities. Software for the study has not been selected yet. It will be a WHO-approved CAD software with final selection through tender processes and in compliance with national regulatory approvals. Operating on mobile or computer platforms, these tools can analyse chest X-rays in less than a minute, distinguishing normal from abnormal scans and highlighting findings in the lungs, pleura, mediastinum, bones, diaphragm, and heart. In addition to detecting disease, some systems can assist clinicians with tasks such as verifying device placement and measuring distances from anatomical landmarks. | Diagnostic test | Diagnostic |
| Screen TB&HIV sub-study diagnostic testThe SCREEN TB\&HIV substudy is implemented only in Cameroon, Nigeria and Kenya. HIV testing will therefore not be conducted in Bangladesh or Viet Nam, as HIV testing is not part of routine care pathways at the participating facilities and the study does not introduce additional HIV testing. In addition, Bangladesh and Viet Nam have substantially lower HIV prevalence, making implementation of the HIV substudy operationally unnecessary and not aligned with clinical need | Other | Diagnostic |
| Screen TB&HIV sub-study diagnostic testThe CD4 cell count is performed in venous blood in HIV positive patients to assess progression of HIV disease, including risk for developing opportunistic infections. The normal range of CD4 count is from 500 to 1500 cells/mm3 of blood, and it progressively decreases over time in persons who are not receiving or not responding well to ART. Someone with a CD4 count below 200 is described as having advanced HIV disease. | Other | Diagnostic |
| Screen TB&HIV sub-study diagnostic testLAM is a glycolipid of the cell wall of Mycobacterium tuberculosis. LAM is excreted in urine, where it can be detected using rapid lateral flow tests. In inpatient settings, WHO strongly recommends using LAM to assist in the diagnosis of active TB in HIV-positive adults, adolescents and children with signs and symptoms of TB (pulmonary and/or extrapulmonary), or with advanced HIV disease (1) or who are seriously ill (2) or else irrespective of signs and symptoms of TB and with a CD4 cell count of less than 200 cells/mm3[23]. In outpatient settings, WHO suggests using LF-LAM to assist in the diagnosis of active TB in HIV-positive adults, adolescents and children: with signs and symptoms of TB (pulmonary and/or extrapulmonary) or seriously ill; or else irrespective of signs and symptoms of TB and with a CD4 cell count of less than 100 cells/mm3[23]. LAM tests evaluated in SCREEN TB\&HIV are: | Other | Diagnostic |
Primary outcome
Primary Objective 1: To evaluate the diagnostic yield and comparative accuracy of diagnostic algorithms initiated by CAD CXR-AI and/or NPOC tongue swab and sputum swab screening as initial screening tools in a facility-based case finding strategy
Time frame: Completed within 6 month of data collection
Primary Endpoint 1.1: Diagnostic Yield of TB by algorithm, disaggregated by symptom status * WHO: All participants enrolled in the study (≥10 years) * WHAT: Number and proportion (out of attempted TB testing) of participants diagnosed with TB (microbiologically confirmed and clinically diagnosed) per diagnostic algorithm pathway * WHEN: Primary = at completion of diagnostic work-up (Day 1-3); Secondary = confirmed via NTP registry data at treatment initiation * WHERE: Study healthcare facilities and referral laboratories in Bangladesh, Cameroon, Kenya, Nigeria, Viet Nam * WHY: To determine incremental case detection across different screening approaches * HOW MEASURED: oNumerator = number of TB cases identified; oDenominator = total participants screened by each algorithm (including those with invalid and inconclusive results); oStratification = symptomatic vs asymptomatic (per WHO 4-symptom screen); oCase definitions = WHO TB definitions (microbiologically confirmed, clinicaly diagnose
Primary Objective 1: To evaluate the diagnostic yield and comparative accuracy of diagnostic algorithms initiated by CAD CXR-AI and/or NPOC tongue swab and sputum swab screening as initial screening tools in a facility-based case finding strategy
Time frame: Completed within 6 month of data collection
Primary Endpoint 1.2: Comparative diagnostic accuracy (sensitivity, specificity, PPV, NPV) of (i) CAD CXR-AI as an initial screening tool, (ii) NPOC tongue swab and sputum swab testing as initial screening tools. * WHO: All participants with interpretable test results * WHAT: Sensitivity, specificity, PPV, NPV of CAD CXR-AI and NPOC tongue swab and sputum swab as initial screening tools * WHEN: Calculated after completion of reference standard testing for all participants * WHERE: Central data analysis * WHY: To compare performance characteristics of screening tools * HOW MEASURED: * Reference standard = LC-NAAT; * Using standard diagnostic accuracy definitions and reporting all estimates together with 95% confidence intervals; * Receiver operating characteristic (ROC) curves for CAD CXR-AI thresholds * Pre-specified handling of indeterminate/uninterpretable results
8 secondary outcomes
Secondary Objective 1: To assess timeliness (time to treatment initiation) and proportion initiated on treatment, of CAD CXR-AI and/or NPOC tongue swab and sputum swab -based initiated algorithms
Time frame: Completed within 6 month of data collection
Secondary Objective 1: To assess timeliness (time to treatment initiation) and proportion initiated on treatment, of CAD CXR-AI and/or NPOC tongue swab and sputum swab -based initiated algorithms
Time frame: Completed within 6 month of data collection
Secondary Objective 2: To evaluate the cost-effectiveness of CAD CXR-AI and NPOC tongue swab and sputum swab as initial screening tools in facility-based case finding
Time frame: Completed within 6 month of data collection
Secondary Objective 2: To evaluate the cost-effectiveness of CAD CXR-AI and NPOC tongue swab and sputum swab as initial screening tools in facility-based case finding
Time frame: Completed within 6 month of data collection
Secondary Objective 3: To evaluate feasibility, acceptability, and scalability of CAD CXR-AI and NPOC tongue swab and sputum swab in routine facility workflows
Time frame: Completed within 6 month of data collection
Secondary Objective 4: To evaluate the diagnostic performance, efficiency, and feasibility of CAD-guided pooling compared with individual testing
Time frame: Completed within 6 month of data collection
Secondary Objective 4: To evaluate the diagnostic performance, efficiency, and feasibility of CAD-guided pooling compared with individual testing
Time frame: Completed within 6 month of data collection
Secondary Objective 4: To evaluate the diagnostic performance, efficiency, and feasibility of CAD-guided pooling compared with individual testing
Time frame: Completed within 6 months of data collection
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Apr 2026.
- Background: PubMed 39884504David A, Singh L, Peloakgosi-Shikwambani K, Nsingwane Z, Molepo V, Cangelosi G, da Silva P, Stevens W, Scott L. Diagnostic accuracy of self-collected tongue swabs for Mycobacterium tuberculosis complex detection in individuals being assessed for tuberculosis in South Africa using the Xpert MTB/RIF Ultra assay. Clin Microbiol Infect. 2025 Jun;31(6):1040-1045. doi: 10.1016/j.cmi.2025.01.029. Epub 2025 Jan 28.
- Background: PubMed 33306694Ricks S, Denkinger CM, Schumacher SG, Hallett TB, Arinaminpathy N. The potential impact of urine-LAM diagnostics on tuberculosis incidence and mortality: A modelling analysis. PLoS Med. 2020 Dec 11;17(12):e1003466. doi: 10.1371/journal.pmed.1003466. eCollection 2020 Dec.
- Background: PubMed 31633805Bjerrum S, Schiller I, Dendukuri N, Kohli M, Nathavitharana RR, Zwerling AA, Denkinger CM, Steingart KR, Shah M. Lateral flow urine lipoarabinomannan assay for detecting active tuberculosis in people living with HIV. Cochrane Database Syst Rev. 2019 Oct 21;10(10):CD011420. doi: 10.1002/14651858.CD011420.pub3.
- Background: PubMed 28137831Cohen JF, Korevaar DA, Altman DG, Bruns DE, Gatsonis CA, Hooft L, Irwig L, Levine D, Reitsma JB, de Vet HC, Bossuyt PM. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016 Nov 14;6(11):e012799. doi: 10.1136/bmjopen-2016-012799.
- Background: PubMed 28126032Sekhon M, Cartwright M, Francis JJ. Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework. BMC Health Serv Res. 2017 Jan 26;17(1):88. doi: 10.1186/s12913-017-2031-8.
- Background: PubMed 22531013French SD, Green SE, O'Connor DA, McKenzie JE, Francis JJ, Michie S, Buchbinder R, Schattner P, Spike N, Grimshaw JM. Developing theory-informed behaviour change interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework. Implement Sci. 2012 Apr 24;7:38. doi: 10.1186/1748-5908-7-38.
- Background: PubMed 26821910Barker PM, Reid A, Schall MW. A framework for scaling up health interventions: lessons from large-scale improvement initiatives in Africa. Implement Sci. 2016 Jan 29;11:12. doi: 10.1186/s13012-016-0374-x.
- Background: PubMed 35086538Klaic M, Kapp S, Hudson P, Chapman W, Denehy L, Story D, Francis JJ. Implementability of healthcare interventions: an overview of reviews and development of a conceptual framework. Implement Sci. 2022 Jan 27;17(1):10. doi: 10.1186/s13012-021-01171-7.
- Background: PubMed 35022047Husereau D, Drummond M, Augustovski F, de Bekker-Grob E, Briggs AH, Carswell C, Caulley L, Chaiyakunapruk N, Greenberg D, Loder E, Mauskopf J, Mullins CD, Petrou S, Pwu RF, Staniszewska S; CHEERS 2022 ISPOR Good Research Practices Task Force. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. BMC Med. 2022 Jan 12;20(1):23. doi: 10.1186/s12916-021-02204-0.
- Background: PubMed 18929686Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009 Apr;42(2):377-81. doi: 10.1016/j.jbi.2008.08.010. Epub 2008 Sep 30.
- Background: PubMed 40034451Hamasur B, Okunola AO, Sserubiri J, Nwamba WV, Abdulgader SM, Ignatowich L, Rasool O, Nabatanzi R, Lorente SP, Giraldos D, Joloba M, Warren RM, Ssengooba W, Theron G. A new rapid lipoarabinomannan urine assay for tuberculosis: a two-centre diagnostic accuracy evaluation in outpatients with and without HIV. Res Sq [Preprint]. 2025 Feb 18:rs.3.rs-5386988. doi: 10.21203/rs.3.rs-5386988/v2.
- Background: PubMed 39046150Gils T, Hella J, Jacobs BKM, Sossen B, Mukoka M, Muyoyeta M, Nakabugo E, Van Nguyen H, Ubolyam S, Mace A, Vermeulen M, Nyangu S, Sanjase N, Sasamalo M, Dinh HT, Ngo TA, Manosuthi W, Jirajariyavej S, Denkinger CM, Nguyen NV, Avihingsanon A, Nakiyingi L, Szekely R, Kerkhoff AD, MacPherson P, Meintjes G, Reither K, Ruhwald M. A Prospective Evaluation of the Diagnostic Accuracy of the Point-of-Care VISITECT CD4 Advanced Disease Test in 7 Countries. J Infect Dis. 2025 Feb 4;231(1):e82-e90. doi: 10.1093/infdis/jiae374.
- Individual participant dataSharing planned: Yes. At the end of the study, after the primary results have been published, the individual participant data (IPD) and associated documentation (e.g. protocol, statistical analysis plan, annotated blank CRF) will be prepared in order to be shared with external researchers. IPD will only be shared with external researchers if the participants have consented to this onward disclosure, IPD has been fully anonymised prior to sharing.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Vibol Lem, PhD contact
Tom Wingfield, PhD FRCP DTMH DipHIV study director
Liverpool School of Tropical Medicine
Vibol Lem, PhD study director
Liverpool School of Tropical Medicine
Questions about this trial
What is this clinical trial studying?
A study testing Near point of care (NPOC) NAATs, Low-complexity nucleic acid amplification tests (LC-NAATs) and LC-NAAT using pooled testing in people with TB - Tuberculosis, run by Liverpool School of Tropical Medicine.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 13 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 8 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Lucy Read, BA (+44 (0)151 705 3715, start4all@lstmed.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Primary Objective 1: To evaluate the diagnostic yield and comparative accuracy of diagnostic algorithms initiated by CAD CXR-AI and/or NPOC tongue swab and sputum swab screening as initial screening tools in a facility-based case finding strategy (time frame: Completed within 6 month of data collection). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07517471, which also lists 30 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Apr 2026.