Can Interferon-γ Release Assays Be Useful for Monitoring the Response to Anti-tuberculosis Treatment?: A Systematic Review and Meta-analysis

Evaluating the diagnostic accuracy of QIAreach QuantiFERON-TB compared to QuantiFERON-TB Gold Plus for tuberculosis: a systematic review and meta-analysis

Accurate tuberculosis (TB) diagnosis remains challenging, especially in resource-limited settings. This study aims to assess the diagnostic performance of the QIAreach QuantiFERON-TB (QFT) assay, with a specific focus on comparing its diagnostic performance with the QuantiFERON-TB Gold Plus (QFT-Plus). We systematically reviewed relevant individual studies on PubMed, Scopus, and Web of Science up to January 20, 2024. The focus was on evaluating the diagnostic parameters of the QIAreach QFT assay for TB infection, which included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and concordance with the QFT-Plus assay. QIAreach QFT demonstrated strong diagnostic performance with a pooled sensitivity of 99% (95% CI 95-100%) and specificity of 94% (95% CI 85-97%). Additionally, it showed a PLR of 15.6 (95% CI 6.5-37.5) and NLR of 0.01 (95% CI 0-0.03). The pooled PPV and NPV were 88% (95% CI 70-98%) and 100% (95% CI 99-100%), respectively. Concordance analysis with QFT-Plus revealed a pooled positive percent agreement of 98% (95% CI 88-100%) and pooled negative percent agreement of 91% (95% CI 81-97%), with a pooled overall percent agreement of 92% (95% CI 83-98). In conclusion, QIAreach QFT has shown promising diagnostic performance, with a strong concordance with QFT-Plus. However, further studies are needed to comprehensively evaluate its diagnostic performance in the context of TB infection.

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.

Current status and future landscape of diagnosing tuberculosis infection

Interferon-γ release assays (IGRAs), such as QuantiFERON-TB Gold (QFT) or T-SPOT.TB, are frequently used as tools for the diagnosis of tuberculosis (TB) infection in the 21st century. QFT-Plus recently emerged as the fourth generation of QFT assays and has replaced QFT In-Tube. However, IGRAs have several problems regarding the identification of active, latent, and cured TB infection, and the time-consuming diagnosis of TB infection because of the overnight incubation of clinical specimens or complexity of measuring the level of interferon (IFN)-γ. To easily diagnose TB infection and quickly compare it with conventional IGRAs, many in vitro tests are developed based on assays other than enzyme-linked immunosorbent assay or enzyme-linked immunospot, such as the fluorescent lateral flow assay that requires less manual operation and a shorter time. Simplified versions of IGRAs are emerging, including QIAreach QuantiFERON-TB. On the other hand, to distinguish active TB from latent or cured TB infection, new immunodiagnostic biomarkers beyond IFN-γ are evaluated using QFT supernatants. While IFN-γ or IFN-γ-related chemokine such as IFN-γ induced protein 10 is a potential biomarker in patients with active TB, interleukin-2 or latency-associated antigen such as heparin-binding hemagglutinin may be useful to distinguish active TB from latent or cured TB infection. There are no potential biomarkers to fully distinguish the time-phase of TB infection at present. It is necessary to discover new immunodiagnostic biomarkers to facilitate decisions on treatment selection for active or latent TB infection.

Role of Interferon-Gamma Release Assay for the Diagnosis and Clinical Follow up in Ocular Tuberculosis

PURPOSE

To assess the performance of interferon-gamma release assay (IGRA) associated with tuberculosis skin test (TST) for ocular tuberculosis (OTB) diagnosis and therapeutic decision making.

METHOD

One hundred and ninety-one patients with ocular inflammation were prospectively followed-up. Patients with clinical signs highly suspected of OTB, TST≥10 mm, and/or IGRA≥0.35 IU/mL received antitubercular therapy (ATT). Sensitivity (Se), specificity (Sp), and area under the curve (AUC) were assessed.

RESULTS

Seventy-two (37.7%) patients received ATT for presumed OTB. Combining TST and IGRA had Se=89.6%, Sp=99.2%, and AUC (0.98) significantly higher compared to TST (0.85, Z=6.3, p<.001) or IGRA (0.95, Z=2.5, p=.01). Prior history of corticosteroids or immunosuppressant with concomitantly oral prednisone and baseline IGRA> 2.0 IU/mL was associated significantly with more recurrences in ATT patients (p=.01)      .

CONCLUSION

Considering TST and IGRA together was more effective in assessing OTB diagnosis. The real value of the IGRA test to predict recurrences needs further studies.

Interferon-gamma release assays in tuberculous uveitis: a comprehensive review

Tuberculous uveitis (TBU) comprises a broad clinical spectrum of ocular manifestations, making its diagnosis challenging. Ophthalmologists usually require evidence from investigations to confirm or support a clinical diagnosis of TBU. Since direct isolation of the causative organism from ocular specimens has limitations owing to the small volume of the ocular specimens, resultant test positivities are low in yield. Immunodiagnostic tests, including the tuberculin skin test and interferon-gamma release assays (IGRAs), can help support a clinical diagnosis of TBU. Unlike the tuberculin skin test, IGRAs are in vitro tests that require a single visit and are not affected by prior Bacillus Calmette-Guerin vaccination. Currently, available IGRAs consist of different techniques and interpretation methods. Moreover, newer generations have been developed to improve the sensitivity and ability to detect active tuberculosis. This narrative review collates salient practice points as a reference for general ophthalmologists, such as evidence for the utilization of IGRAs in patients with suspected TBU, and summarizes basic knowledge and details of clinical applications of these tests in a clinical setting.

Assessment of CD27 expression on T-cells as a diagnostic and therapeutic tool for patients with smear-negative pulmonary tuberculosis

Background

There is a global focus on illness diagnosis in smear-negative and latent tuberculosis infectious populations (SN-TB and LTBI). CD27 has been suggested to play a direct role in active TB. Little is known about smear-negative individuals. Here, we tried to investigate whether it has a role in smear-negative populations. The expression of CD27 and MTB-specific CD27 in CD4⁺ T cells (“CD27⁻CD4⁺” and “CD27⁻IFN-γ⁺CD4⁺”) was evaluated in MTB-unexposed controls (HC), TB contacts (TB-C) and SN-TB individuals by flow cytometry. The sensitivity, specificity and AUC (area under curve) of “CD27⁻IFN-γ⁺CD4⁺” cells to distinguish SN-TBs from HCs and TB-Cs were determined by receiver operating characteristic (ROC) curve analysis. The clinical index was selected from the clinical laboratory and evaluated for correlation with “CD27⁻IFN-γ⁺CD4⁺” cells by Spearman statistical analysis.

Results

We observed that the percentages of “CD27⁻IFN-γ⁺CD4⁺” cells were significantly increased in the SN-TB group compared with the HC and TB-C groups (AUC was 0.88, sensitivity was 82.14%, specificity was 80.00%, and P < 0.0001). The percentage of “CD27⁻IFN-γ⁺CD4⁺” cells was negatively correlated with WBC (white blood cell count) (r = − 0.3019, P = 0.0182) and positively correlated with IgE (immunoglobulin E) (r = 0.2805, P = 0.0362). Furthermore, “CD27⁻IFN-γ⁺CD4⁺” cells were significantly decreased, especially in the > 50 years group, after clinical treatment.

Conclusion

The present results demonstrated that the percentage of “CD27⁻IFN-γ⁺CD4⁺” cells might be a conceivable molecular indicator in the diagnosis of SN-TB and was influenced by its outcome of therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12865-021-00430-y.