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)

Sellami, Mokhtar; Fazaa, A.; Cheikh, M. Ben; Miladi, S.; Ouenniche, K.; Ennaifer, Rym; Abdelghani, K. Ben; Laatar, A.

doi:10.1016/j.ejr.2018.11.003

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…The tuberculin skin test (TST) and sputum test are the two most important diagnostic instruments for tuberculosis (TB) [ 5 ]. Positive results on either the tuberculosis skin test or the tuberculosis blood test indicate that the TB bacteria have been present in the body.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…They were not able to determine the sensitivity and specificity of the ELISPOT assay in their study and their results were not an accurate reflection of latent tuberculosis infection prevalence due to this study being small scale. Furthermore, Sellami et al [ 41 ] performed a study to determine how in accordance the TST and IGRA are with each other in detecting latent tuberculosis infection before undergoing biologic therapy in patients diagnosed with inflammatory disease. They discovered that the two tests were in poor agreement with each other with the sensitivity of the tests each being low, although the sensitivity of IGRA was higher.…”

Section: Conventional Assay Techniquesmentioning

confidence: 99%

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

et al. 2021

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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.

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“…TST has been used routinely for over a century as a guide for screening TB. However, some TST limitations, such as koebnerization phenomena and false-positive results related to prior BCG vaccination or exposure to nontuberculous mycobacteria, maybe the reasons that resulted in the growing acceptance and popularity of the IGRAs in clinical practice in the last decade [7][8][9]. A gold standard test is still not available for establishing LTBI.…”

Section: Introductionmentioning

confidence: 99%

Assessing Latent Tuberculosis Infections Prior to Biologic Therapy in Psoriasis: A New Diagnostic Approach with an Online Interpreter

Metin

Özyurt

Atasoy

et al. 2023

Preprint

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The use of biological agents, particularly anti-TNF-alpha treatments, in psoriasis patients is associated with an elevated risk of tuberculosis (TB). Hence, a comprehensive assessment of latent tuberculosis infection (LTBI) before biologic therapies is imperative. The objective of this study was to evaluate the utility of an online tuberculin skin test (TST)/ interferon-γ release assay (IGRA) interpreter (OI-TST/IGRA) in assessing the risk of LTBI prior to initiating biological therapies in psoriasis patients. One hundred-sixteen psoriasis patients who were previously evaluated for TB by a pulmonologist before being treated with a biologic agent were re-evaluated retrospectively with OI-TST/IGRA (tstin3d.com). Mean positive predictive value (PPV), mean annual risk of development of active tuberculosis (ARDATB), and mean cumulative risk of active tuberculosis (CRATB) values were calculated with OI-TST/IGRA and compared with previous results. Chi-square, Fisher-Freeman-Halton exact tests, Cohen's Kappa, and Mann-Whitney U-test were used in comparisons of groups. The PPV of the LTBI-positive group was significantly higher than the LTBI-negative group. The PPV, ARDATB and CRATB values of the TST size of > 15 mm group were significantly higher than the TST size of 5–9 mm and TST size of 10–15 mm groups. The PPV, ARDATB, and CRATB values of the QuantiFERON-TB Gold In-tube test (QFT-GIT)-positive group were significantly higher than the QFT-GIT-negative group. And the same values of the chest X-ray (CXR)-positive group were significantly higher than the CXR-negative group. The PPV, ARDATB, and CRATB values were positively correlated with QFT-GIT and CXR results. In addition, the PPV was positively correlated with previous LTBI decisions and TSTs. OI-TST/IGRA in which many factors are questioned and PPV, ARDATB, and CRATB values are evaluated together, may be a valuable tool for assessing the risk of active TB in psoriasis patients and preventing overdiagnosis and unnecessary prophylaxis.

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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)

Cited by 6 publications

References 38 publications

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

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

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

Assessing Latent Tuberculosis Infections Prior to Biologic Therapy in Psoriasis: A New Diagnostic Approach with an Online Interpreter

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