Treatment for radiographically active, sputum culture-negative pulmonary tuberculosis: A systematic review and meta-analysis.

BACKGROUND: People with radiographic evidence for pulmonary tuberculosis (TB), but negative sputum cultures, have increased risk of developing culture-positive TB. Recent expansion of X-ray screening is leading to increased identification of this group. We set out to synthesise the evidence for treatment to prevent progression to culture-positive disease. METHODS: We conducted a systematic review and meta-analysis. We searched for prospective trials evaluating the efficacy of TB regimens against placebo, observation, or alternative regimens, for the treatment of adults and children with radiographic evidence of TB but culture-negative respiratory samples. Databases were searched up to 18 Oct 2022. Study quality was assessed using ROB 2·0 and ROBINS-I. The primary outcome was progression to culture-positive TB. Meta-analysis with a random effects model was conducted to estimate pooled efficacy. This study was registered with PROSPERO (CRD42021248486). FINDINGS: We included 13 trials (32,568 individuals) conducted between 1955 and 2018. Radiographic and bacteriological criteria for inclusion varied. 19·1% to 57·9% of participants with active x-ray changes and no treatment progressed to culture-positive disease. Progression was reduced with any treatment (6 studies, risk ratio [RR] 0·27, 95%CI 0·13-0·56), although multi-drug TB treatment (RR 0·11, 95%CI 0·05-0·23) was significantly more effective than isoniazid treatment (RR 0·63, 95%CI 0·35-1·13) (p = 0·0002). INTERPRETATION: Multi-drug regimens were associated with significantly reduced risk of progression to TB disease for individuals with radiographically apparent, but culture-negative TB. However, most studies were old, conducted prior to the HIV epidemic and with outdated regimens. New clinical trials are required to identify the optimal treatment approach.

Diagnostic accuracy of three computer-aided detection systems for detecting pulmonary tuberculosis on chest radiography when used for screening: Analysis of an international, multicenter migrants screening study.

The aim of this study was to independently evaluate the diagnostic accuracy of three artificial intelligence (AI)-based computer aided detection (CAD) systems for detecting pulmonary tuberculosis (TB) on global migrants screening chest x-ray (CXR) cases when compared against both microbiological and radiological reference standards (MRS and RadRS, respectively). Retrospective clinical data and CXR images were collected from the International Organization for Migration (IOM) pre-migration health assessment TB screening global database for US-bound migrants. A total of 2,812 participants were included in the dataset used for analysis against RadRS, of which 1,769 (62.9%) had accompanying microbiological test results and were included against MRS. All CXRs were interpreted by three CAD systems (CAD4TB v6, Lunit INSIGHT v4.9.0, and qXR v2) in offline setting, and re-interpreted by two expert radiologists in a blinded fashion. The performance was evaluated using receiver operating characteristics curve (ROC), estimates of sensitivity and specificity at different CAD thresholds against both microbiological and radiological reference standards (MRS and RadRS, respectively), and was compared with that of the expert radiologists. The area under the curve against MRS was highest for Lunit (0.85; 95% CI 0.83-0.87), followed by qXR (0.75; 95% CI 0.72-0.77) and then CAD4TB (0.71; 95% CI 0.68-0.73). At a set specificity of 70%, Lunit had the highest sensitivity (81.4%; 95% CI 77.9-84.6); at a set sensitivity of 90%, specificity was also highest for Lunit (54.5%; 95% CI 51.7-57.3). The CAD systems performed comparable to the sensitivity (98.3%), and except CAD4TB, to specificity (13.7%) of the expert radiologists. Similar trends were observed when using RadRS. Area under the curve against RadRS was highest for CAD4TB (0.87; 95% CI 0.86-0.89) and Lunit (0.87; 95% CI 0.85-0.88) followed by qXR (0.81; 95% CI 0.80-0.83). At a set specificity of 70%, CAD4TB had highest sensitivity (84.1%; 95% CI 82.3-85.8) followed by Lunit (80.9%; 95% CI 78.9-82.7); and at a set sensitivity of 90%, specificity was also highest for CAD4TB (54.6%; 95% CI 51.3-57.8). In conclusion, the study demonstrated that the three CAD systems had broadly similar diagnostic accuracy with regard to TB screening and comparable accuracy to an expert radiologist against MRS. Compared with different reference standards, Lunit performed better than both qXR and CAD4TB against MRS, and CAD4TB and Lunit better than qXR against RadRS. Moreover, the performance of the CADs can be impacted by characteristics of subgroup of population. The main limitation was that our study relied on retrospective data and MRS was not routinely done in individuals with a low suspicion of TB and a normal CXR. Our findings suggest that CAD systems could be a useful tool for TB screening programs in remote, high TB prevalent places where access to expert radiologists may be limited. However, further large-scale prospective studies are needed to address outstanding questions around the operational performance and technical requirements of the CAD systems.

Economic analysis of different throughput scenarios and implementation strategies of computer-aided detection software as a screening and triage test for pulmonary TB.

BACKGROUND: Artificial Intelligence (AI) systems have demonstrated potential in detecting tuberculosis (TB) associated abnormalities from chest X-ray (CXR) images. Thus, they might provide a solution to radiologist shortages in high TB burden countries. However, the cost of implementing computer-aided detection (CAD) software has thus far been understudied. In this study, we performed a costing analysis of CAD software when used as a screening or triage test for pulmonary TB, estimated the incremental cost compared to a radiologist reading of different throughput scenarios, and predicted the cost for the national scale-up plan in Pakistan. METHODS: For the study, we focused on CAD software reviewed by the World Health Organization (CAD4TB, Lunit INSIGHT CXR, qXR) or listed in the Global Drug Facility diagnostics catalogue (CAD4TB, InferRead). Costing information was obtained from the CAD software developers. CAD4TB and InferRead use a perpetual license pricing model, while Lunit and qXR are priced per license for restricted number of scans. A major implementer in Pakistan provided costing information for human resource and software training. The per-screen cost was estimated for each CAD software and for radiologist for 1) active case finding, and 2) facility based CXR testing scenarios with throughputs ranging from 50,000-100,000 scans. Moreover, we estimated the scale-up cost for CAD or radiologist CXR reading in Pakistan based on the National Strategic Plan, considering that to reach 80% diagnostic coverage, 50% of TB patients would need to be found through facility-based triage and 30% through active case finding (ACF). RESULTS: The per-screen cost for CAD4TB (0.25 USD- 2.33 USD) and InferRead (0.19 USD- 2.78 USD) was lower than that of a radiologist (0.70 USD- 0.93 USD) for high throughput scenarios studied. In comparison, the per-screen cost for Lunit (0.94 USD- 1.69 USD) and qXR (0.95 USD-1.9 USD) were only comparable with that of the radiologists in the highest throughput scenario in ACF. To achieve 80 percent diagnostic coverage at scale in Pakistan, the projected additional cost of deploying CAD software to complement the current infrastructure over a four-year period were estimated at 2.65-19.23 million USD, whereas Human readers, would cost an additional 23.97 million USD. CONCLUSIONS: Our findings suggest that using CAD software could enable large-scale screening programs in high TB-burden countries and be less costly than radiologist. To achieve minimum cost, the target number of screens in a specific screening strategy should be carefully considered when selecting CAD software, along with the offered pricing structure and other aspects such as performance and operational features. Integrating CAD software in implementation strategies for case finding could be an economical way to attain the intended programmatic goals.

Cost of three models of care for drug-resistant tuberculosis patients in Nigeria.

BACKGROUND: Nigeria accounts for a significant proportion of the global drug-resistant tuberculosis (DR-TB) burden, a large proportion of which goes untreated. Different models for managing DR-TB treatment with varying levels of hospitalization are in use across Nigeria, however costing evidence is required to guide the scale up of DR-TB care. We aimed to estimate and compare the costs of different DR-TB treatment and care models in Nigeria. METHODS: We estimated the costs associated with three models of DR-TB treatment and care: Model (A) patients are hospitalized throughout the 8-month intensive phase, Model (B) patients are partially hospitalized during the intensive phase and Model (C) is entirely ambulatory. Costs of treatment, in-patient and outpatient care and diagnostic and monitoring tests were collected using a standardized data collection sheet from six sites through an ingredient's approach and cost models were based on the Nigerian National Tuberculosis, Leprosy and Buruli Ulcer Guideline - Sixth Edition (2014) and Guideline for programmatic and clinical management of drug-resistant tuberculosis in Nigeria (2015). RESULTS: Assuming adherence to the Nigerian DR-TB guidelines, the per patient cost of Model A was $18,528 USD, Model B $15,159 USD and Model C $9425 USD. Major drivers of cost included hospitalization (Models A and B) and costs of out-patient consultations and supervision (Model C). CONCLUSION: Utilizing a decentralized ambulatory model, is a more economically viable approach for the expansion of DR-TB care in Nigeria, given that patient beds for DR-TB treatment and care are limited and costs of hospitalized treatment are considerably more expensive than ambulatory models. Scale-up of less expensive ambulatory care models should be carefully considered in particular, when treatment efficacy is demonstrated to be similar across the different models to allow for patients not requiring hospitalization to be cared for in the least expensive way.

An evaluation framework for new tests that predict progression from tuberculosis infection to clinical disease.

Novel accurate tests are needed that identify individuals infected with Mycobacterium tuberculosis who have incipient disease and are likely to develop clinical tuberculosis (TB) in the near future to allow for targeted preventive treatment beyond the current risk groups. Recently, a target product profile was developed that outlines the minimal and optimal characteristics for such an incipient TB test. We describe an evaluation framework for generating evidence to inform the development of policy guidance for the use of such a new test by the World Health Organization. Two research objectives are addressed. 1) The predictive ability of an incipient TB test should be assessed in clinical evaluation studies that include the intended target population and follow-up of sufficient duration to observe whether individuals do or do not progress to clinical TB disease. 2) Studies are needed to evaluate the test under routine programmatic conditions and measure its impact on patient- or health-system-important outcomes. For both research objectives, study designs, methods and analysis are described, with the intent to inform the clinical development plans of test manufacturers, researchers and funders.

Mycobacterial growth inhibition is associated with trained innate immunity.

The lack of defined correlates of protection hampers development of vaccines against tuberculosis (TB). In vitro mycobacterial outgrowth assays are thought to better capture the complexity of the human host/Mycobacterium tuberculosis (Mtb) interaction. Here, we used a mycobacterial growth inhibition assay (MGIA) based on peripheral blood mononuclear cells to investigate the capacity to control outgrowth of bacille Calmette-Guérin (BCG). Interestingly, strong control of BCG outgrowth was observed almost exclusively in individuals with recent exposure to Mtb, but not in (long-term) latent TB infection, and only modestly in BCG vaccinees. Mechanistically, control of mycobacterial outgrowth strongly correlated with the presence of a CD14dim monocyte population, but also required the presence of T cells. The nonclassical monocytes produced CXCL10, and CXCR3 receptor blockade inhibited the capacity to control BCG outgrowth. Expression of CXCR3 splice variants was altered in recently Mtb-exposed individuals. Cytokines previously associated with trained immunity were detected in MGIA supernatants, and CXCL9, CXCL10, and CXCL11 represent new markers of trained immunity. These data indicate that CXCR3 ligands are associated with trained immunity and are critical factors in controlling mycobacterial outgrowth. In conclusion, control of mycobacterial outgrowth early after exposure to Mtb is the result of trained immunity mediated by a CXCL10-producing nonclassical CD14dim monocyte subset.

Serial testing for latent tuberculosis using QuantiFERON-TB Gold In-Tube: A Markov model.

Healthcare workers (HCWs) in low-incidence settings are often serially tested for latent TB infection (LTBI) with the QuantiFERON-TB Gold In-Tube (QFT) assay, which exhibits frequent conversions and reversions. The clinical impact of such variability on serial testing remains unknown. We used a microsimulation Markov model that accounts for major sources of variability to project diagnostic outcomes in a simulated North American HCW cohort. Serial testing using a single QFT with the recommended conversion cutoff (IFN-g > 0.35 IU/mL) resulted in 24.6% (95% uncertainty range, UR: 23.8-25.5) of the entire population testing false-positive over ten years. Raising the cutoff to >1.0 IU/mL or confirming initial positive results with a (presumed independent) second test reduced this false-positive percentage to 2.3% (95%UR: 2.0-2.6%) or 4.1% (95%UR: 3.7-4.5%), but also reduced the proportion of true incident infections detected within the first year of infection from 76.5% (95%UR: 66.3-84.6%) to 54.8% (95%UR: 44.6-64.5%) or 61.5% (95%UR: 51.6-70.9%), respectively. Serial QFT testing of HCWs in North America may result in tremendous over-diagnosis and over-treatment of LTBI, with nearly thirty false-positives for every true infection diagnosed. Using higher cutoffs for conversion or confirmatory tests (for initial positives) can mitigate these effects, but will also diagnose fewer true infections.

Defining the needs for next generation assays for tuberculosis.

To accelerate the fight against tuberculosis, major diagnostic challenges need to be addressed urgently. Post-2015 targets are unlikely to be met without the use of novel diagnostics that are more accurate and can be used closer to where patients first seek care in affordable diagnostic algorithms. This article describes the efforts by the stakeholder community that led to the identification of the high-priority diagnostic needs in tuberculosis. Subsequently target product profiles for the high-priority diagnostic needs were developed and reviewed in a World Health Organization (WHO)-led consensus meeting. The high-priority diagnostic needs included (1) a sputum-based replacement test for smear-microscopy; (2) a non-sputum-based biomarker test for all forms of tuberculosis, ideally suitable for use at levels below microscopy centers; (3) a simple, low cost triage test for use by first-contact care providers as a rule-out test, ideally suitable for use by community health workers; and (4) a rapid drug susceptibility test for use at the microscopy center level. The developed target product profiles, along with complimentary work presented in this supplement, will help to facilitate the interaction between the tuberculosis community and the diagnostics industry with the goal to lead the way toward the post-2015 global tuberculosis targets.

Potential market for novel tuberculosis diagnostics: worth the investment?

BACKGROUND: The potential available market (PAM) for new diagnostics for tuberculosis that meet the specifications of the high-priority target product profiles (TPPs) is currently unknown. METHODS: We estimated the PAM in 2020 in 4 high-burden countries (South Africa, Brazil, China, and India) for tests that meet the specifications outlined in the TPPs. The yearly PAM was estimated for the most likely application of each TPP. RESULTS: In 2020 the PAM for all 4 countries together was estimated to be (1) 12M tests/year with a value of 48M-71M USD for a sputum smear-replacement test; (2) 16M tests/year with a value of 65M-97M USD for a biomarker test; (3) 18M tests/year with a value of 18M-35M USD for a triage test; (4) 12M tests/year with a value of 59M-2238M USD for a tuberculosis detection plus drug susceptibility test (DST) all-in-one or 1.5M tests/year for a DST that follows a positive tuberculosis detection test with a corresponding value of 75M-121M for both tuberculosis detection and DST. CONCLUSIONS: Although there is a considerable potential market for novel tuberculosis diagnostics that fit the specification of the TPPs in the 4 high-burden countries, the actual market for an individual product remains uncertain.

Costs of novel tuberculosis diagnostics--will countries be able to afford it?

BACKGROUND: Four priority target product profiles for the development of diagnostic tests for tuberculosis were identified: 1) Rapid sputum-based (RSP), 2) non-sputum Biomarker-based (BMT), 3) triage test followed by confirmatory test (TT), and 4) drug-susceptibility testing (DST). METHODS: We assessed the cost of the new tests in suitable strategies and of the conventional diagnosis of tuberculosis as per World Health Organization guidelines, in 36 high tuberculosis and MDR burden countries. Costs were then compared to the available funding for tuberculosis at country level. RESULTS: Costs of diagnosing tuberculosis using RSP ranged US$93-187 million/year; if RSP unit cost is of US$2-4 it would be lower/similar cost than conventional strategy with sputum smear microscopy (US$ 119 million/year). Using BMT (with unit cost of US$2-4) would cost US$70-121 million/year and be lower/comparable cost than conventional diagnostics. Using TT with TPP characteristics (unit cost of US$1-2) followed by Xpert would reduce diagnostic costs up to US$36 million/year. Costs of using different novel DST strategies for the diagnosis of drug resistance would be higher compared with conventional diagnosis. CONCLUSIONS: Introducing a TT or a biomarker test with optimal characteristics would be affordable from a cost and affordability perspective at the current available funding for tuberculosis. Additional domestic or donor funding would be needed in most countries to achieve affordability for other new diagnostic tests.

Reproducibility of interferon gamma (IFN-γ) release Assays. A systematic review.

RATIONALE: Interferon gamma (IFN-γ) release assays for latent tuberculosis infection result in a larger-than-expected number of conversions and reversions in occupational screening programs, and reproducibility of test results is a concern. OBJECTIVES: Knowledge of the relative contribution and extent of the individual sources of variability (immunological, preanalytical, or analytical) could help optimize testing protocols. METHODS: We performed a systematic review of studies published by October 2013 on all potential sources of variability of commercial IFN-γ release assays (QuantiFERON-TB Gold In-Tube and T-SPOT.TB). The included studies assessed test variability under identical conditions and under different conditions (the latter both overall and stratified by individual sources of variability). Linear mixed effects models were used to estimate within-subject SD. MEASUREMENTS AND MAIN RESULTS: We identified a total of 26 articles, including 7 studies analyzing variability under the same conditions, 10 studies analyzing variability with repeat testing over time under different conditions, and 19 studies reporting individual sources of variability. Most data were on QuantiFERON (only three studies on T-SPOT.TB). A considerable number of conversions and reversions were seen around the manufacturer-recommended cut-point. The estimated range of variability of IFN-γ response in QuantiFERON under identical conditions was ±0.47 IU/ml (coefficient of variation, 13%) and ±0.26 IU/ml (30%) for individuals with an initial IFN-γ response in the borderline range (0.25-0.80 IU/ml). The estimated range of variability in noncontrolled settings was substantially larger (±1.4 IU/ml; 60%). Blood volume inoculated into QuantiFERON tubes and preanalytic delay were identified as key sources of variability. CONCLUSIONS: This systematic review shows substantial variability with repeat IFN-γ release assays testing even under identical conditions, suggesting that reversions and conversions around the existing cut-point should be interpreted with caution.

Gamma interferon release assays for detection of Mycobacterium tuberculosis infection.

Identification and treatment of latent tuberculosis infection (LTBI) can substantially reduce the risk of developing active disease. However, there is no diagnostic gold standard for LTBI. Two tests are available for identification of LTBI: the tuberculin skin test (TST) and the gamma interferon (IFN-γ) release assay (IGRA). Evidence suggests that both TST and IGRA are acceptable but imperfect tests. They represent indirect markers of Mycobacterium tuberculosis exposure and indicate a cellular immune response to M. tuberculosis. Neither test can accurately differentiate between LTBI and active TB, distinguish reactivation from reinfection, or resolve the various stages within the spectrum of M. tuberculosis infection. Both TST and IGRA have reduced sensitivity in immunocompromised patients and have low predictive value for progression to active TB. To maximize the positive predictive value of existing tests, LTBI screening should be reserved for those who are at sufficiently high risk of progressing to disease. Such high-risk individuals may be identifiable by using multivariable risk prediction models that incorporate test results with risk factors and using serial testing to resolve underlying phenotypes. In the longer term, basic research is necessary to identify highly predictive biomarkers.