TB - Tuberculosis
Conditions
Keywords
TB, Diagnostics
Brief 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 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.
Interventions
DIAGNOSTIC_TESTNear 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_TESTLow-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_TESTLC-NAAT using pooled testing
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.
DIAGNOSTIC_TESTPortable Chest X-ray Image Acquisition
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.
DIAGNOSTIC_TESTComputer-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.
OTHERScreen TB&HIV sub-study diagnostic test
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
Sponsors
Liverpool School of Tropical Medicine
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
10 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Age * Adults aged 15 years and above or * Adolescents aged 10-14 years 2. Facility setting o Participating healthcare facilities (e.g., primary health centres, district hospitals), including both rural and urban facilities. 3. Screening eligibility o All individuals presenting to the facility, regardless of symptoms or ability to produce sputum, will be eligible for inclusion. 4. 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.
Exclusion criteria
1. Age o Below 10 years at enrolment 2. Screening eligibility o Do not screen positive on any tools. 3. 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. 4. Current TB treatment o Receiving anti-TB treatment at the time of enrolment, defined as having taken ≥3 doses of TB treatment. 5. Recent TB preventive therapy o Receipt of TB preventive therapy within the last 6 months prior to enrolment. 6. 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 7. Duplicate enrolment o Previous enrolment in SCREEN-TB.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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 | Completed within 6 month of data collection | 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). |
| 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 | Completed within 6 month of data collection | 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: 1. stand-alone initial screening tools 2. 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 |
| Secondary Objective 3: To evaluate feasibility, acceptability, and scalability of CAD CXR-AI and NPOC tongue swab and sputum swab in routine facility workflows | Completed within 6 month of data collection | 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 |
| Secondary Objective 4: To evaluate the diagnostic performance, efficiency, and feasibility of CAD-guided pooling compared with individual testing | Completed within 6 month of data collection | 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). |
Contacts
CONTACTLucy Read, BA
CONTACTVibol Lem, PhD
STUDY_DIRECTORTom Wingfield, PhD FRCP DTMH DipHIV
Liverpool School of Tropical Medicine
STUDY_DIRECTORVibol Lem, PhD
Liverpool School of Tropical Medicine
Outcome results
None listed