Screening for latent tuberculosis infection prior to biologic therapy in patients with chronic immune-mediated inflammatory diseases (IMID): Interferon-gamma release assay (IGRA) versus tuberculin skin test (TST)

The Role of Inflammation in the Pathogenesis of Comorbidity of Chronic Obstructive Pulmonary Disease and Pulmonary Tuberculosis

The comorbid course of chronic obstructive pulmonary disease (COPD) and pulmonary tuberculosis is an important medical and social problem. Both diseases, although having different etiologies, have many overlapping relationships that mutually influence their course and prognosis. The aim of the current review is to discuss the role of different immune mechanisms underlying inflammation in COPD and pulmonary tuberculosis. These mechanisms are known to involve both the innate and adaptive immune system, including various cellular and intercellular interactions. There is growing evidence that immune mechanisms involved in the pathogenesis of both COPD and tuberculosis may jointly contribute to the tuberculosis-associated obstructive pulmonary disease (TOPD) phenotype. Several studies have reported prior tuberculosis as a risk factor for COPD. Therefore, the study of the mechanisms that link COPD and tuberculosis is of considerable clinical interest.

Infection Risk in Biological Disease-Modifying Anti-rheumatic Drugs

Rheumatology patients on biological disease-modifying anti-rheumatic drugs (bDMARDs) have been proposed to be at a higher risk of infections. Our review summarizes the current evidence behind this theory as well as explores which factors predispose patients to various infections, which agents are more likely to cause infections, and which infections are common in these patients. We also aim to explore updated guidelines on infection prevention in patients on bDMARDs.

Epidemiology of Latent Tuberculosis in Rheumatic Immune-Mediated Inflammatory Diseases-Study of 1117 Patients and Descriptive Literature Review

Background/Objectives: Patients with rheumatic immune-mediated diseases (rheumatic-IMID) and latent tuberculosis (LTBI) are at an increased risk of developing active tuberculosis (TB); therefore, screening is recommended before starting biological treatment. The aims of this study were as follows: (i) to assess the prevalence of LTBI, (ii) to determine the importance of using a booster test in TST-negative patients, (iii) to compare the tuberculin skin test (TST) with the interferon-gamma release assay (IGRA), (iv) to perform a review of the prevalence of LTBI. Methods: A cross-sectional hospital study was performed, including patients diagnosed with rheumatic-IMID who underwent a TST and/or IGRA during the period 2016-2020. If the first TST was negative, a new TST (booster) was performed. Results: A total of 1117 patients were included. The overall prevalence of LTBI was estimated to be 31.7% (95% confidence interval, 29.74-33.66). The LTBI prevalence ranged from 38.5% for vasculitis to 14% for sarcoidosis. The booster test was positive in 22.9% of 817 patients with a negative or indeterminate IGRA. The IGRA was positive in 3.8% of 793 patients with a negative booster.The adjusted Cohen's kappa coefficient between TST (+booster) and IGRA was 0.62. Conclusions: LTBI is frequent in patients with rheumatic-IMID. IGRA and TST (+booster) show a moderate, fair grade of agreement. Therefore, performing both tests before biological therapy should be highly recommended.

Assessing the Diagnostic Performance of New Commercial Interferon-γ Release Assays for Mycobacterium tuberculosis Infection: A Systematic Review and Meta-Analysis

BACKGROUND

We compared 6 new interferon-γ release assays (IGRAs; hereafter index tests: QFT-Plus, QFT-Plus CLIA, QIAreach, Wantai TB-IGRA, Standard E TB-Feron, and T-SPOT.TB/T-Cell Select) with World Health Organization (WHO)-endorsed tests for tuberculosis infection (hereafter reference tests).

METHODS

Data sources (1 January 2007-18 August 2021) were Medline, Embase, Web of Science, Cochrane Database of Systematic Reviews, and manufacturers' data. Cross-sectional and cohort studies comparing the diagnostic performance of index and reference tests were selected. The primary outcomes of interest were the pooled differences in sensitivity and specificity between index and reference tests. The certainty of evidence (CoE) was summarized using the GRADE approach.

RESULTS

Eighty-seven studies were included (44 evaluated the QFT-Plus, 4 QFT-Plus CLIA, 3 QIAreach, 26 TB-IGRA, 10 TB-Feron [1 assessing the QFT-Plus], and 1 T-SPOT.TB/T-Cell Select). Compared to the QFT-GIT, QFT Plus's sensitivity was 0.1 percentage points lower (95% confidence interval [CI], -2.8 to 2.6; CoE: moderate), and its specificity 0.9 percentage points lower (95% CI, -1.0 to -.9; CoE: moderate). Compared to QFT-GIT, TB-IGRA's sensitivity was 3.0 percentage points higher (95% CI, -.2 to 6.2; CoE: very low), and its specificity 2.6 percentage points lower (95% CI, -4.2 to -1.0; CoE: low). Agreement between the QFT-Plus CLIA and QIAreach with QFT-Plus was excellent (pooled κ statistics of 0.86 [95% CI, .78 to .94; CoE: low]; and 0.96 [95% CI, .92 to 1.00; CoE: low], respectively). The pooled κ statistic comparing the TB-Feron and the QFT-Plus or QFT-GIT was 0.85 (95% CI, .79 to .92; CoE: low).

CONCLUSIONS

The QFT-Plus and the TB-IGRA have very similar sensitivity and specificity as WHO-approved IGRAs.

Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System

This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10.

Recent advances in the detection of interferon-gamma as a TB biomarker

Tuberculosis (TB) is one of the main infectious diseases worldwide and accounts for many deaths. It is caused by Mycobacterium tuberculosis usually affecting the lungs of patients. Early diagnosis and treatment are essential to control the TB epidemic. Interferon-gamma (IFN-γ) is a cytokine that plays a part in the body’s immune response when fighting infection. Current conventional antibody-based TB sensing techniques which are commonly used include enzyme-linked immunosorbent assay (ELISA) and interferon-gamma release assays (IGRAs). However, these methods have major drawbacks, such as being time-consuming, low sensitivity, and inability to distinguish between the different stages of the TB disease. Several electrochemical biosensor systems have been reported for the detection of interferon-gamma with high sensitivity and selectivity. Microfluidic techniques coupled with multiplex analysis in regular format and as lab-on-chip platforms have also been reported for the detection of IFN-γ. This article is a review of the techniques for detection of interferon-gamma as a TB disease biomarker. The objective is to provide a concise assessment of the available IFN-γ detection techniques (including conventional assays, biosensors, microfluidics, and multiplex analysis) and their ability to distinguish the different stages of the TB disease.

The impact of the interleukin 12/23 inhibitor ustekinumab on the risk of infections in patients with psoriatic arthritis

Introduction: Psoriatic arthritis (PsA) is characterized by chronic inflammation mediated by pro-inflammatory cytokines, with clinical features resulting from dysfunctional integrated signaling pathways affecting different constituents of the immune system. Increased understanding of the processes responsible for enthesitis, synovial inflammation, joint erosion, and new bone formation during PsA has led to development of biologic therapies targeting these cytokines. There is an increased risk of opportunistic infections in patients with PsA, and this risk is increased further with targeted biologic therapy.Areas covered: This paper reviews the role of the interleukin (IL)-12, IL-23 and IL-17 axis in the pathogenesis of PsA. The data suggest that ustekinumab is associated with a low risk of infections in patients with PsA, including tuberculosis or hepatitis reactivation. No live vaccines can be safely administered; ustekinumab is contraindicated/cannot be given with live vaccines. However, long-term treatment with ustekinumab does not impair the immune response to these vaccines when administered after an appropriate interval.Expert opinion: Ustekinumab is associated with a low risk of serious and opportunistic infections. More research is needed to confirm these findings specifically in patients with PsA, and comparative studies are needed to investigate the relative risk of infection with different biologics.