TuberculosisNCT05220163
Evaluating the Impact of Computer-assisted X-ray Diagnosis and Other Triage Tools to Optimise Xpert Orientated Community-based Active Case Finding for TB and COVID-19
A study testing CAD and Xpert in people with Tuberculosis and COVID-19, run by University of Cape Town at 3 sites in 3 countries.
Trial summary
Tuberculosis (TB) is now the commonest cause of death in many African countries. Globally, ~35% (almost 1 in 3) of TB cases are 'missed' (remain undiagnosed or undetected). In sub-Saharan Africa, 40-50% of the TB case burden remains undiagnosed within the community. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Tuberculosis, COVID-19, HIV Infections
- What is being tested?
- CAD and Xpert
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 26,200 participants
- Where
- 3 sitesin 3 countries
- Study dates
- 23 Feb 2022to 30 Dec 2026
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Participants willing to complete community-based symptom screening, finger-prick and venepuncture blood sampling, urine testing, and/or undergo TB and/or COVID-19 diagnostic testing.
- Provision of informed consent.
- Participant 18 years or above.
- HIV-positive or negative participants will be included.
Who cannot join
- Inability to provide informed consent (e.g., mentally impaired).
- Participants who have completed TB treatment in the last two months, or who have self-presented to their local TB clinic and are currently being worked up for suspected TB.
- Participants already diagnosed with active TB on treatment.
- Participants unable to commit to at least a two-month follow-up.
- Female participants who are pregnant or who refuse a urine pregnancy test.
- Participants in the community who cannot access healthcare due to severe ill health or lack of access to the local clinic.
Trial locations and contacts
3 sites in 3 countries.
3 sites in 3 countries
About this study
Tuberculosis (TB) is now the commonest cause of death in many African countries. Globally, ~35% (almost 1 in 3) of TB cases are 'missed' (remain undiagnosed or undetected). In sub-Saharan Africa, 40-50% of the TB case burden remains undiagnosed within the community. These 'missed' TB cases (at primary care level) serve as a reservoir, which severely undermines TB control. With rapid advances in the development of TB screening tests, the investigators aim to determine the pragmatic utility of computer-assisted x-ray diagnosis (CAD). Recent data suggest that CAD performs on par with experienced radiologists to identify potential TB cases, hereby reducing the frequency at which Xpert tests are requested and helps to focus limited resources on the relevant cases. In addition, the investigators aim to test nascent screening technologies for TB diagnosis such as evaluating urine-based TB screening biosignatures. The COVID-19 pandemic has ravaged African peri-urban communities where TB is also common. With the pressing need to improve screening and diagnosis of COVID-19, the investigators plan to explore the potential for urine- and blood-based COVID-19 screening assays. Symptoms of COVID-19 and TB overlap, and limited affordability, as well as the stigma associated with both diseases, severely limits testing. Data are now urgently needed about the feasibility of co-screening and testing for TB and COVID-19. The utility of such an approach, if any, has not been studied in African communities.
Read the detailed description
Tuberculosis (TB) is now the commonest cause of death in many African countries. Several factors drive this; however, transmission is the mechanism by which these risk factors translate into active TB. Globally, ~35% (almost 1 in 3) of TB cases are 'missed' (remain undiagnosed or undetected). In sub-Saharan Africa, 40-50% of the TB case burden remains undiagnosed within the community and ~30% of such cases are microscopically smear-positive. These 'missed' TB cases (at primary care level) serve as a reservoir, which severely undermines TB control. Thus, primary care and community-based case finding should be a critical component for TB control.
Detecting cases in the community, however, has been restricted by the lack of sensitive and user-friendly Point-of-Care (POC) diagnostic tools. To address this unmet need, in 2013 the investigators planned a programme of activities (sequential interlinked studies) with the overarching aim of optimising a model for Xpert-related community-based active case finding (ACF) for TB (XACT). By 2017, through the EDCTP-funded XACT-I study, the investigators solved the impasse of rapid POC diagnosis by showing that molecular Xpert-based community-based screening was effective in identifying missing TB cases in the peri-urban 'slums' of Cape Town and Harare using a mini-truck with a generator. However, such an approach was neither broadly affordable nor scalable. The investigators therefore derived a scalable model using portable battery-operated Xpert Edge installed within a low-cost (< US$) 15 000 Nissan panel van manned by two health care workers (thus making the ACF model affordable and scalable). This completed study, XACT-II, screened over 5 000 participants in the community. The model worked well and was more effective than smear microscopy. Based on these successes, and to translate the XACT concept into policy, the Wellcome Trust and UK MRC has funded the XACT-III study. Currently commenced, XACT-III was initiated as a multi-country demonstration project in four sub-Saharan African countries.
More recently, there have been rapid advances in the development of triage testing for TB, which refers to screening tests that are generally applied in a community-based setting (either at individual community or primary care clinic level). These tests have very high sensitivity (>95%) but modest specificity (>70%) as defined by TB-specific target product profiles. A forerunner TB-orientated triage test is computer-assisted x-ray diagnosis (CAD). This entails using artificial intelligence-enabled software to read a digital x-ray and produce a probability of TB within seconds. Recent data suggest that CAD performs on par with experienced radiologists to identify potential TB cases, hereby reducing the frequency at which Xpert tests are requested and helps to focus limited resources on the relevant cases. Although these data appear promising, the feasibility of this strategy in a pragmatic field setting has not been extensively tested. There are several other unanswered questions. Is the strategy of CAD combined with Xpert cost-effective and can it reduce Xpert usage without missing an unacceptable number of TB cases? The investigators will therefore determine the utility of CAD as a triage tool to further optimise the XACT model.
The COVID-19 pandemic, due to SARS-CoV-2, has ravaged African peri-urban communities where TB is also common. Symptoms of COVID-19 and TB overlap, and limited affordability, as well as the stigma associated with both diseases, severely limits testing. Data are now urgently needed about the feasibility of co-screening and testing for TB and COVID-19. The utility of such an approach, if any, has not been studied in African communities. As Xpert POC TB testing and x-rays for CAD will be performed in the proposed study, it affords a unique and easy opportunity to seamlessly screen for both diseases when appropriate.
Other nascent screening technologies are rapidly emerging for TB and COVID-19, including urine- and blood-based triage tests. XACT-19 provides a unique opportunity to collect the relevant samples and test new technologies in a pragmatic community-based setting.
In summary, the XACT-19 study results will have substantial implications for public health policy and practice and will likely define a new standard for community-based ACF for TB, and potentially COVID-19 in tandem.
- Conditions
- Tuberculosis, COVID-19, HIV Infections
- Being tested
- CAD, Xpert
- Keywords
- Tuberculosis, Active case finding, Computer assisted diagnosis, Screening, COVID-19
- Medical subject headings
- Tuberculosis, COVID-19, HIV Infections
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CADDiagnostic test
Also called CAD4TB and/or other AI/CAD software
It is an artificial intelligence (AI) system for detection of TB on CXR images. The system input is a frontal CXR, and the outputs are 1) a heatmap indicating suspicious regions on the image; and 2) a score (0-100) which implies the likelihood that the x-ray image shows TB.
XpertDiagnostic test
Also called GeneXpert System
A novel diagnostic for active case finding (GeneXpert MTB/RIF) for TB on sputum collected and performed at POC in a mobile van.
| Study group | Role | What they receive |
|---|---|---|
| CAD + POC Xpert | Experimental | CAD followed by Xpert in CAD-positive participants (performed at POC) employing a low-cost panel van that is staffed by three health care workers. CAD-negative participants will be followed up, while CAD-positive participants will be offered POC Xpert. Xpert-positive participants will be referred for TB treatment initiation, while Xpert-negative (but CAD-positive) participants will undergo a clinical review. Thus, the active case finding (ACF) interventional package is one of CAD + POC Xpert (only in CAD positive participants). Receives: Diagnostic Test: CAD; Diagnostic Test: Xpert |
| POC Xpert only | Active comparator | Participants who are Xpert-positive will be referred for TB treatment initiation while Xpert-negative participants will be followed up. Thus, the active case finding (ACF) standard of care package is POC Xpert. Receives: Diagnostic Test: Xpert |
What is being measured
The primary outcome is the main question the study is designed to answer.
Time to detection of microbiologically proven TB
The microbiological reference standard for TB will be culture and/or Xpert positivity. Thus, the overall time to detection (using a proportional hazards model) and the proportion of TB cases detected at a specific time-point (e.g., 14-, 30- and 60-days) with and without culture (Xpert alone) will be reported.
Measured: Through study completion, up to 48 months
Secondary outcomes (15)
Feasibility of CAD + POC Xpert performed by minimally trained healthcare workers
Measured: Through study completion, up to 48 months
Number of infectious TB cases detected (defined by cough aerosol sampling system [CASS] and/or smear and/or cavitatory disease positive)
Measured: Through study completion, up to 48 months
Time-specific proportion of participants initiated on TB treatment up to 60 days post-sample donation in each arm (7-, 14-, 30- and 60-days)
Measured: Through study completion, up to 48 months
Time to TB treatment initiation (both the median time to treatment in each group and time to event [treatment] analyses will be conducted)
Measured: Through study completion, up to 48 months
Yield of culture positive TB in household contacts of index participants
Measured: Through study completion, up to 48 months
NPV and false negative rate (TB cases missed per 1 000 persons screened) of CAD and other screening tests for TB
Measured: Through study completion, up to 48 months
Reduction in number of sputum induction procedures and/or Xpert tests performed
Measured: Through study completion, up to 48 months
Global and country-specific cost-effectiveness analysis for each strategy
Measured: Through study completion, up to 48 months
Transmission and disease burden impact using modelling based on exposure scores, imaging, and CASS
Measured: Through study completion, up to 48 months
Rates or prevalence of microbiological versus probable (clinical TB)
Measured: Through study completion, up to 48 months
Proportion of culture-positive TB cases completing three- and six-months of TB treatment in each study arm
Measured: Through study completion, up to 48 months
Middleware/dashboard design requirements and deployment models for each strategy
Measured: Through study completion, up to 48 months
Feasibility and yield of POC Xpert (Xpress cartridge) for COVID-19 detection
Measured: Through study completion, up to 48 months
Feasibility and performance of CAD4COVID for PCR-positive COVID-19 detection
Measured: Through study completion, up to 48 months
Feasibility of a novel mass screening strategy for COVID-19 that uses pooling of specimen from a group of COVID-19 suspects
Measured: Through study completion, up to 48 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Feb 2022
- Study start23 Feb 2022
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for 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
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 26,200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Cape Town
- Sponsor type
- University, hospital or other
- Collaborators
- European and Developing Countries Clinical Trials Partnership (EDCTP), Zambart, Biomedical Research and Training Institute, Ospedale San Raffaele, Radboud University Medical Center, Foundation for Innovative New Diagnostics, Switzerland, University of Stellenbosch
- Responsible party
- Principal investigator, Keertan Dheda, University of Cape Town
- Investigators
- Keertan Dheda, PhD, Principal investigator, University of Cape Town
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05220163
- Sponsor's protocol ID
- XACT-19
- Status last verified
- May 2026
- First submitted
- 1 Feb 2022
- Last updated
- 8 May 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating the Impact of Computer-assisted X-ray Diagnosis and Other Triage Tools to Optimise Xpert Orientated Community-based Active Case Finding for TB and COVID-19
- ClinicalTrials.gov ID
- NCT05220163
- Acronym / sponsor ID
- XACT-19
- Conditions
- Tuberculosis, COVID-19, HIV Infections
- Keywords
- Tuberculosis, Active case finding, Computer assisted diagnosis, Screening, COVID-19
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 26,200 participants (estimated)
- Lead sponsor
- University of Cape TownUniversity, hospital or other
- Collaborators
- European and Developing Countries Clinical Trials Partnership (EDCTP), Zambart, Biomedical Research and Training Institute, Ospedale San Raffaele, Radboud University Medical Center, Foundation for Innovative New Diagnostics, Switzerland, University of Stellenbosch
- Study start
- 23 Feb 2022 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 2 Feb 2022 (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) is now the commonest cause of death in many African countries. Globally, ~35% (almost 1 in 3) of TB cases are 'missed' (remain undiagnosed or undetected). In sub-Saharan Africa, 40-50% of the TB case burden remains undiagnosed within the community. These 'missed' TB cases (at primary care level) serve as a reservoir, which severely undermines TB control. With rapid advances in the development of TB screening tests, the investigators aim to determine the pragmatic utility of computer-assisted x-ray diagnosis (CAD). Recent data suggest that CAD performs on par with experienced radiologists to identify potential TB cases, hereby reducing the frequency at which Xpert tests are requested and helps to focus limited resources on the relevant cases. In addition, the investigators aim to test nascent screening technologies for TB diagnosis such as evaluating urine-based TB screening biosignatures. The COVID-19 pandemic has ravaged African peri-urban communities where TB is also common. With the pressing need to improve screening and diagnosis of COVID-19, the investigators plan to explore the potential for urine- and blood-based COVID-19 screening assays. Symptoms of COVID-19 and TB overlap, and limited affordability, as well as the stigma associated with both diseases, severely limits testing. Data are now urgently needed about the feasibility of co-screening and testing for TB and COVID-19. The utility of such an approach, if any, has not been studied in African communities.
Detailed description
Tuberculosis (TB) is now the commonest cause of death in many African countries. Several factors drive this; however, transmission is the mechanism by which these risk factors translate into active TB. Globally, ~35% (almost 1 in 3) of TB cases are 'missed' (remain undiagnosed or undetected). In sub-Saharan Africa, 40-50% of the TB case burden remains undiagnosed within the community and ~30% of such cases are microscopically smear-positive. These 'missed' TB cases (at primary care level) serve as a reservoir, which severely undermines TB control. Thus, primary care and community-based case finding should be a critical component for TB control.
Detecting cases in the community, however, has been restricted by the lack of sensitive and user-friendly Point-of-Care (POC) diagnostic tools. To address this unmet need, in 2013 the investigators planned a programme of activities (sequential interlinked studies) with the overarching aim of optimising a model for Xpert-related community-based active case finding (ACF) for TB (XACT). By 2017, through the EDCTP-funded XACT-I study, the investigators solved the impasse of rapid POC diagnosis by showing that molecular Xpert-based community-based screening was effective in identifying missing TB cases in the peri-urban 'slums' of Cape Town and Harare using a mini-truck with a generator. However, such an approach was neither broadly affordable nor scalable. The investigators therefore derived a scalable model using portable battery-operated Xpert Edge installed within a low-cost (< US$) 15 000 Nissan panel van manned by two health care workers (thus making the ACF model affordable and scalable). This completed study, XACT-II, screened over 5 000 participants in the community. The model worked well and was more effective than smear microscopy. Based on these successes, and to translate the XACT concept into policy, the Wellcome Trust and UK MRC has funded the XACT-III study. Currently commenced, XACT-III was initiated as a multi-country demonstration project in four sub-Saharan African countries.
More recently, there have been rapid advances in the development of triage testing for TB, which refers to screening tests that are generally applied in a community-based setting (either at individual community or primary care clinic level). These tests have very high sensitivity (>95%) but modest specificity (>70%) as defined by TB-specific target product profiles. A forerunner TB-orientated triage test is computer-assisted x-ray diagnosis (CAD). This entails using artificial intelligence-enabled software to read a digital x-ray and produce a probability of TB within seconds. Recent data suggest that CAD performs on par with experienced radiologists to identify potential TB cases, hereby reducing the frequency at which Xpert tests are requested and helps to focus limited resources on the relevant cases. Although these data appear promising, the feasibility of this strategy in a pragmatic field setting has not been extensively tested. There are several other unanswered questions. Is the strategy of CAD combined with Xpert cost-effective and can it reduce Xpert usage without missing an unacceptable number of TB cases? The investigators will therefore determine the utility of CAD as a triage tool to further optimise the XACT model.
The COVID-19 pandemic, due to SARS-CoV-2, has ravaged African peri-urban communities where TB is also common. Symptoms of COVID-19 and TB overlap, and limited affordability, as well as the stigma associated with both diseases, severely limits testing. Data are now urgently needed about the feasibility of co-screening and testing for TB and COVID-19. The utility of such an approach, if any, has not been studied in African communities. As Xpert POC TB testing and x-rays for CAD will be performed in the proposed study, it affords a unique and easy opportunity to seamlessly screen for both diseases when appropriate.
Other nascent screening technologies are rapidly emerging for TB and COVID-19, including urine- and blood-based triage tests. XACT-19 provides a unique opportunity to collect the relevant samples and test new technologies in a pragmatic community-based setting.
In summary, the XACT-19 study results will have substantial implications for public health policy and practice and will likely define a new standard for community-based ACF for TB, and potentially COVID-19 in tandem.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| CAD + POC Xpert | Experimental | Diagnostic Test: CAD, Diagnostic Test: XpertCAD followed by Xpert in CAD-positive participants (performed at POC) employing a low-cost panel van that is staffed by three health care workers. CAD-negative participants will be followed up, while CAD-positive participants will be offered POC Xpert. Xpert-positive participants will be referred for TB treatment initiation, while Xpert-negative (but CAD-positive) participants will undergo a clinical review. Thus, the active case finding (ACF) interventional package is one of CAD + POC Xpert (only in CAD positive participants). |
| POC Xpert only | Active comparator | Diagnostic Test: XpertParticipants who are Xpert-positive will be referred for TB treatment initiation while Xpert-negative participants will be followed up. Thus, the active case finding (ACF) standard of care package is POC Xpert. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Participants willing to complete community-based symptom screening, finger-prick and venepuncture blood sampling, urine testing, and/or undergo TB and/or COVID-19 diagnostic testing.
- Provision of informed consent.
- Participant 18 years or above.
- HIV-positive or negative participants will be included.
Key exclusion criteria (6 listed)
- Inability to provide informed consent (e.g., mentally impaired).
- Participants who have completed TB treatment in the last two months, or who have self-presented to their local TB clinic and are currently being worked up for suspected TB.
- Participants already diagnosed with active TB on treatment.
- Participants unable to commit to at least a two-month follow-up.
- Female participants who are pregnant or who refuse a urine pregnancy test.
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 |
|---|---|---|
| CADAlso: CAD4TB and/or other AI/CAD softwareIt is an artificial intelligence (AI) system for detection of TB on CXR images. The system input is a frontal CXR, and the outputs are 1) a heatmap indicating suspicious regions on the image; and 2) a score (0-100) which implies the likelihood that the x-ray image shows TB. | Diagnostic test | CAD + POC Xpert |
| XpertAlso: GeneXpert SystemA novel diagnostic for active case finding (GeneXpert MTB/RIF) for TB on sputum collected and performed at POC in a mobile van. | Diagnostic test | CAD + POC Xpert, POC Xpert only |
Primary outcome
Time to detection of microbiologically proven TB
Time frame: Through study completion, up to 48 months
The microbiological reference standard for TB will be culture and/or Xpert positivity. Thus, the overall time to detection (using a proportional hazards model) and the proportion of TB cases detected at a specific time-point (e.g., 14-, 30- and 60-days) with and without culture (Xpert alone) will be reported.
15 secondary outcomes
Feasibility of CAD + POC Xpert performed by minimally trained healthcare workers
Time frame: Through study completion, up to 48 months
Number of infectious TB cases detected (defined by cough aerosol sampling system [CASS] and/or smear and/or cavitatory disease positive)
Time frame: Through study completion, up to 48 months
Time-specific proportion of participants initiated on TB treatment up to 60 days post-sample donation in each arm (7-, 14-, 30- and 60-days)
Time frame: Through study completion, up to 48 months
Time to TB treatment initiation (both the median time to treatment in each group and time to event [treatment] analyses will be conducted)
Time frame: Through study completion, up to 48 months
Yield of culture positive TB in household contacts of index participants
Time frame: Through study completion, up to 48 months
NPV and false negative rate (TB cases missed per 1 000 persons screened) of CAD and other screening tests for TB
Time frame: Through study completion, up to 48 months
Reduction in number of sputum induction procedures and/or Xpert tests performed
Time frame: Through study completion, up to 48 months
Global and country-specific cost-effectiveness analysis for each strategy
Time frame: Through study completion, up to 48 months
Transmission and disease burden impact using modelling based on exposure scores, imaging, and CASS
Time frame: Through study completion, up to 48 months
Rates or prevalence of microbiological versus probable (clinical TB)
Time frame: Through study completion, up to 48 months
Proportion of culture-positive TB cases completing three- and six-months of TB treatment in each study arm
Time frame: Through study completion, up to 48 months
Middleware/dashboard design requirements and deployment models for each strategy
Time frame: Through study completion, up to 48 months
Feasibility and yield of POC Xpert (Xpress cartridge) for COVID-19 detection
Time frame: Through study completion, up to 48 months
Feasibility and performance of CAD4COVID for PCR-positive COVID-19 detection
Time frame: Through study completion, up to 48 months
Feasibility of a novel mass screening strategy for COVID-19 that uses pooling of specimen from a group of COVID-19 suspects
Time frame: Through study completion, up to 48 months
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 May 2026.
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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. This study is not recruiting at the moment.
Keertan Dheda, PhD principal investigator
University of Cape Town
3 sites in 3 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing CAD and Xpert in people with Tuberculosis and COVID-19, run by University of Cape Town at 3 sites in 3 countries.
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 4 inclusion and 6 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: under way, not looking for participants, as of the sponsor's last update on 8 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in South Africa, Zambia, Zimbabwe. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Time to detection of microbiologically proven TB (time frame: Through study completion, up to 48 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 May 2026.