TuberculosisSPOT-TBNCT06017843
Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding
A study testing Camps site selection for active case finding for TB using MATCH-AI in people with Tuberculosis, run by Centre for Global Public Health Pakistan at 1 site in Pakistan.
Trial summary
The aim of this pragmatic, stepped wedge cluster-randomized trial is to measure the comparative yield (number of incident TB cases diagnosed during active case-finding camps) using a site selection approach based on predictions generated via an artificial intelligence software called MATCH-AI (intervention group)… Read more about this study
- Who is it for?
- Ages 15 years and olderHealthy volunteers can take part
- Conditions the study is for
- Tuberculosis
- What is being tested?
- Camps site selection for active case finding for TB using MATCH-AI
- Type of treatment
- Vaccines
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 180,000 participants
- Where
- 1 sitePakistan
- Study dates
- 1 Sep 2023to 30 Jun 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 15 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- All individuals >15 years of age presenting to camp sites
- Individuals with previous history of TB disease
Who cannot join
- Children and adolescents <15 years of age
- Pregnant women
Trial locations and contacts
1 site in 1 country.
Pakistan1 site, 1 recruiting
- Recruiting
Mercy Corps Pakistan
Islamabad
Nainan Nawaz, nanawaz@mercycorps.org
Abdullah Latif
About this study
Official title: Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding in Pakistan: a Pragmatic Stepped Wedge Cluster Randomized Trial
The aim of this pragmatic, stepped wedge cluster-randomized trial is to measure the comparative yield (number of incident TB cases diagnosed during active case-finding camps) using a site selection approach based on predictions generated via an artificial intelligence software called MATCH-AI (intervention group) versus the conventional approach of camp site selection using field-staff knowledge and experience (control group). The trial will help inform whether a targeted approach towards screening for TB using artificial-intelligence can improve yields of TB cases detected through community-based active case-finding.
Read the detailed description
Despite significant progress over the past decades, an estimated 10.6 million individuals fell ill with tuberculosis (TB) in 2021 and the disease caused 1.6 million deaths globally. Pakistan is ranked as the 5th highest TB burden country in the world and TB causes 42,000 deaths annually in the country. A key challenge in the Pakistan's response to TB is ensuring diagnosis and treatment of all individuals with TB. In 2020, out of the 573,000 cases, a total of 276,736 (48%) were notified. Bridging this case-detection gap is a critical objective for the National TB Program (NTP). Active case-finding (ACF), is a potential strategy to increase case-detection by systematic screening of communities for TB. Recent evidence, indicates that ACF can also reduce population-level TB incidence and prevalence through early detection. While ACF interventions have demonstrated effectiveness in community-trials and are now being conducted at scale in Pakistan, concerns remain regarding their yields and cost-effectiveness in programmatic settings.
The primary aim of this study is to investigate whether a targeted approach towards community-based screening using MATCH-AI, an artificial intelligence software that models sub-district TB prevalence, can improve the yield of ACF interventions in Pakistan. In the intervention arm, field-team will conduct community-based ACF activities (called chest camps) primarily in locations predicted by MATCH-AI to have a higher prevalence of TB. In the control arm, field-teams will continue to utilize existing approaches towards camp site-selection. The trial will be conducted in 65 districts of Pakistan in collaboration with implementation partners of the NTP.
- Conditions
- Tuberculosis
- Being tested
- Camps site selection for active case finding for TB using MATCH-AI
- Keywords
- Active case finding, Artificial Intelligence, Hotspots
- Medical subject headings
- Tuberculosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Camps site selection for active case finding for TB using MATCH-AIOther
The primary intervention in this study is the roll-out of MATCH-AI, an artificial intelligence software that models sub-district TB prevalence, to guide site selection of ACF camps. The MATCH-AI tool uses a Bayesian modelling approach to predict TB prevalence to a resolution of 10,000 population that are mapped as polygons. The model integrates data from a range of sources including historical TB facility notification data, previous ACF data as well as contextual factors such as demographics, income, population density, health indicators such as vaccination coverage and climate related variables to predict localized TB prevalence. In the intervention arm, camps will be conducted primarily in locations guided by MATCH-AI.
| Study group | Role | What they receive |
|---|---|---|
| Intervention | Experimental | Camps site selection for active case finding for TB using MATCH-AI Receives: Other: Camps site selection for active case finding for TB using MATCH-AI |
| Control | No intervention | Camps site selection for active case finding for TB using existing approaches. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Camp positivity yield
Counts of bacteriologically confirmed TB (B+) cases diagnosed in each camp
Measured: 12 months
Secondary outcomes (3)
Camp positivity rate
Bacteriologically confirmed TB (B+) cases per population screened
Measured: 12 months
Camp All-Forms yield
Counts of All-Forms TB (AF-TB) cases diagnosed in each camp
Measured: 12 months
Camp All-Forms TB rate
All-Forms TB (AF-TB) cases per population screened
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Aug 2023
- Study start1 Sep 2023
- Primary completion31 Oct 2024 (estimated)
- Study completion30 Jun 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- 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
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- The MATCH-AI evaluation is designed as a stepped-wedge cluster randomized trial. In this study, clusters (mobile X-ray van teams) will successively switch over in groups of 3 to the intervention in a randomly assigned order until all clusters are eventually exposed to the intervention. In the intervention arm, TB active case finding camps will be conducted primarily in locations guided by MATCH-AI a modelling software that predicts TB hotspots. In the control sites, field-teams will continue to utilize existing approaches such as local knowledge, historical data etc towards camp site-selection.
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 180,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Centre for Global Public Health Pakistan
- Sponsor type
- University, hospital or other
- Collaborators
- Mercy Corps Pakistan
- Responsible party
- Sponsor
- Investigators
- Faran Emmanuel, Principal investigator, Centre for Global Public Health Pakistan
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06017843
- Sponsor's protocol ID
- CGPH-TB2023-24
- Acronym
- SPOT-TB
- Status last verified
- Jul 2024
- First submitted
- 21 Aug 2023
- Last updated
- 25 Jul 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: At the conclusion of the trial, aggregate, summary data used for the final analysis will be stored on a public repository for archiving.
Publications
Zaidi SMA, Mahfooz A, Latif A, Nawaz N, Fatima R, Rehman FU, Reza TE, Emmanuel F. Geographical targeting of active case finding for tuberculosis in Pakistan using hotspots identified by artificial intelligence software (SPOT-TB): study protocol for a pragmatic stepped wedge cluster randomised control trial. BMJ Open Respir Res. 2024 Jul 11;11(1):e002079. doi: 10.1136/bmjresp-2023-002079.
PubMed 38991950
Study at a glance
From the registry record, last updated by the sponsor on 25 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding in Pakistan: a Pragmatic Stepped Wedge Cluster Randomized Trial
- ClinicalTrials.gov ID
- NCT06017843
- Acronym / sponsor ID
- SPOT-TB / CGPH-TB2023-24
- Conditions
- Tuberculosis
- Keywords
- Active case finding, Artificial Intelligence, Hotspots
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 180,000 participants (estimated)
- Lead sponsor
- Centre for Global Public Health PakistanUniversity, hospital or other
- Collaborators
- Mercy Corps Pakistan
- Study start
- 1 Sep 2023 (actual)
- Primary completion
- 31 Oct 2024 (estimated)
- Study completion
- 30 Jun 2025 (estimated)
- First posted
- 30 Aug 2023 (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.
The aim of this pragmatic, stepped wedge cluster-randomized trial is to measure the comparative yield (number of incident TB cases diagnosed during active case-finding camps) using a site selection approach based on predictions generated via an artificial intelligence software called MATCH-AI (intervention group) versus the conventional approach of camp site selection using field-staff knowledge and experience (control group). The trial will help inform whether a targeted approach towards screening for TB using artificial-intelligence can improve yields of TB cases detected through community-based active case-finding.
Detailed description
Despite significant progress over the past decades, an estimated 10.6 million individuals fell ill with tuberculosis (TB) in 2021 and the disease caused 1.6 million deaths globally. Pakistan is ranked as the 5th highest TB burden country in the world and TB causes 42,000 deaths annually in the country. A key challenge in the Pakistan's response to TB is ensuring diagnosis and treatment of all individuals with TB. In 2020, out of the 573,000 cases, a total of 276,736 (48%) were notified. Bridging this case-detection gap is a critical objective for the National TB Program (NTP). Active case-finding (ACF), is a potential strategy to increase case-detection by systematic screening of communities for TB. Recent evidence, indicates that ACF can also reduce population-level TB incidence and prevalence through early detection. While ACF interventions have demonstrated effectiveness in community-trials and are now being conducted at scale in Pakistan, concerns remain regarding their yields and cost-effectiveness in programmatic settings.
The primary aim of this study is to investigate whether a targeted approach towards community-based screening using MATCH-AI, an artificial intelligence software that models sub-district TB prevalence, can improve the yield of ACF interventions in Pakistan. In the intervention arm, field-team will conduct community-based ACF activities (called chest camps) primarily in locations predicted by MATCH-AI to have a higher prevalence of TB. In the control arm, field-teams will continue to utilize existing approaches towards camp site-selection. The trial will be conducted in 65 districts of Pakistan in collaboration with implementation partners of the NTP.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- The MATCH-AI evaluation is designed as a stepped-wedge cluster randomized trial. In this study, clusters (mobile X-ray van teams) will successively switch over in groups of 3 to the intervention in a randomly assigned order until all clusters are eventually exposed to the intervention. In the intervention arm, TB active case finding camps will be conducted primarily in locations guided by MATCH-AI a modelling software that predicts TB hotspots. In the control sites, field-teams will continue to utilize existing approaches such as local knowledge, historical data etc towards camp site-selection.
| Arm | Type | Receives |
|---|---|---|
| Intervention | Experimental | Other: Camps site selection for active case finding for TB using MATCH-AICamps site selection for active case finding for TB using MATCH-AI |
| Control | No intervention | –Camps site selection for active case finding for TB using existing approaches. |
Who may qualify
- Ages
- 15 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (2 listed)
- All individuals >15 years of age presenting to camp sites
- Individuals with previous history of TB disease
Key exclusion criteria (2 listed)
- Children and adolescents <15 years of age
- Pregnant women
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 |
|---|---|---|
| Camps site selection for active case finding for TB using MATCH-AIThe primary intervention in this study is the roll-out of MATCH-AI, an artificial intelligence software that models sub-district TB prevalence, to guide site selection of ACF camps. The MATCH-AI tool uses a Bayesian modelling approach to predict TB prevalence to a resolution of 10,000 population that are mapped as polygons. The model integrates data from a range of sources including historical TB facility notification data, previous ACF data as well as contextual factors such as demographics, income, population density, health indicators such as vaccination coverage and climate related variables to predict localized TB prevalence. In the intervention arm, camps will be conducted primarily in locations guided by MATCH-AI. | Other | Intervention |
Primary outcome
Camp positivity yield
Time frame: 12 months
Counts of bacteriologically confirmed TB (B+) cases diagnosed in each camp
3 secondary outcomes
Camp positivity rate
Time frame: 12 months
Camp All-Forms yield
Time frame: 12 months
Camp All-Forms TB rate
Time frame: 12 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Jul 2024.
- Derived from this study: PubMed 38991950Zaidi SMA, Mahfooz A, Latif A, Nawaz N, Fatima R, Rehman FU, Reza TE, Emmanuel F. Geographical targeting of active case finding for tuberculosis in Pakistan using hotspots identified by artificial intelligence software (SPOT-TB): study protocol for a pragmatic stepped wedge cluster randomised control trial. BMJ Open Respir Res. 2024 Jul 11;11(1):e002079. doi: 10.1136/bmjresp-2023-002079.
- Individual participant dataSharing planned: No. At the conclusion of the trial, aggregate, summary data used for the final analysis will be stored on a public repository for archiving.
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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.
Faran Emmanuel principal investigator
Centre for Global Public Health Pakistan
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Camps site selection for active case finding for TB using MATCH-AI in people with Tuberculosis, run by Centre for Global Public Health Pakistan at 1 site in Pakistan.
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 2 inclusion and 2 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: status not confirmed, as of the sponsor's last update on 25 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Pakistan. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Amna Mahfooz, MS(PH) (+923438101441, amna.mahfooz@cgph.org.pk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Camp positivity yield (time frame: 12 months). 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 NCT06017843, which also lists 1 linked publication.
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 25 Jul 2024.