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Application of ImmunoScore Model for the Differentiation between Active Tuberculosis and Latent Tuberculosis Infection as Well as Monitoring Anti-tuberculosis Therapy.

ABSTRACT: Tuberculosis (TB) is a leading global public health problem. To achieve the end TB strategy, non-invasive markers for diagnosis and treatment monitoring of TB disease are urgently needed, especially in high-endemic countries such as China. Interferon-gamma release assays (IGRAs) and tuberculin skin test (TST), frequently used immunological methods for TB detection, are intrinsically unable to discriminate active tuberculosis (ATB) from latent tuberculosis infection (LTBI). Thus, the specificity of these methods in the diagnosis of ATB is dependent upon the local prevalence of LTBI. The pathogen-detecting methods such as acid-fast staining and culture, all have limitations in clinical application. ImmunoScore (IS) is a new promising prognostic tool which was commonly used in tumor. However, the importance of host immunity has also been demonstrated in TB pathogenesis, which implies the possibility of using IS model for ATB diagnosis and therapy monitoring. In the present study, we focused on the performance of IS model in the differentiation between ATB and LTBI and in treatment monitoring of TB disease. We have totally screened five immunological markers (four non-specific markers and one TB-specific marker) and successfully established IS model by using Lasso logistic regression analysis. As expected, the IS model can effectively distinguish ATB from LTBI (with a sensitivity of 95.7% and a specificity of 92.1%) and also has potential value in the treatment monitoring of TB disease.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC5670161 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Application of ImmunoScore Model for the Differentiation between Active Tuberculosis and Latent Tuberculosis Infection as Well as Monitoring Anti-tuberculosis Therapy.

Zhou Yu Y Du Juan J Hou Hong-Yan HY Lu Yan-Fang YF Yu Jing J Mao Li-Yan LY Wang Feng F Sun Zi-Yong ZY

Frontiers in cellular and infection microbiology 20171030

Tuberculosis (TB) is a leading global public health problem. To achieve the end TB strategy, non-invasive markers for diagnosis and treatment monitoring of TB disease are urgently needed, especially in high-endemic countries such as China. Interferon-gamma release assays (IGRAs) and tuberculin skin test (TST), frequently used immunological methods for TB detection, are intrinsically unable to discriminate active tuberculosis (ATB) from latent tuberculosis infection (LTBI). Thus, the specificity ...[more]

PMID: 29164066

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Project description:BackgroundThe differential diagnosis of active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging in clinical practice. We aimed to evaluate the diagnostic accuracy of the IFN-γ/TNF-α FluoroSpot assay for differentiating ATB from LTBI.MethodsWe conducted a pilot study of case-control design, using the FluoroSpot assay to simultaneously detect IFN-γ and TNF-α secretion at the single-cell level. The frequencies of antigen-specific single TNF-α-, total TNF-α-, single IFN-γ-, total IFN-γ- and dual IFN-γ/TNF-α-secreting T cells were detected. The optimal cutoffs value of frequencies for differentiating ATB from LTBI were determined according to receiver operating characteristic curve analysis. The sensitivity, specificity, predictive values (PV) and likelihood ratios (LR) of the FluoroSpot assay were calculated.ResultsThirty patients diagnosed microbiologically with ATB, 36 healthcare workers with LTBI and 36 healthy controls were enrolled. After stimulated by ESAT-6 or CFP-10 peptides, the median frequencies of single TNF-α-, total TNF-α-, single IFN-γ-, total IFN-γ- and dual IFN-γ/TNF-α-secreting T cells in ATB patients were all significantly higher than those in LTBI and HC groups (P < 0.01). The frequencies of total IFN-γ-secreting T cells detected by FluoroSpot assay correlated significantly with those of T-SPOT.TB (r = 0.910 for ESAT-6, P < 0.001, r = 0.845 for CFP-10, P < 0.001). After stimulated by ESAT-6 peptides, with total TNF-α-secreting T cells frequencies at a cut off value of 21 iSFCs/250,000 PBMCs, the sensitivity, specificity, PLR, NLR, PPV, NPV of IFN-γ/TNF-α FluoroSpot assay in differentiating ATB from LTBI were 96.7% (95%CI, 82.8-99.9%), 94.3% (95%CI, 80.8-99.3%), 16.92 (95%CI, 4.40-65.08), 0.04 (95%CI, 0.01-0.24), 93.6% (95%CI,78.6-99.2%) and 97.1% (95%CI, 84.7-99.9%), respectively. With the frequencies of total TNF-α- and total IFN-γ-secreting T cells stimulated by ESAT-6 peptides combined, the specificity was increased to 97.1%, and the positive likelihood ratio to 31.5. The combination with CFP-10 might not improve the diagnostic accuracy of the ESAT-6 for differentiating ATB from LTBI.ConclusionsIFN-γ/TNF-α FluoroSpot assay might have potential to help differentiate ATB from LTBI, but the findings need to be further verified by cross-sectional or prospective cohort studies.

Project description:BackgroundGiven that there is no rapid and effective method for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI), the discrimination between these two statuses remains challenging. This study sought to investigate the value of nutritional indexes and tuberculosis-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) for distinguishing ATB from LTBI.MethodsParticipants were consecutively recruited based on positive T-SPOT.TB results between January 2018 and January 2020. ATB was diagnosed by positive mycobacterial culture and/or positive GeneXpert MTB/RIF, with clinical symptoms and radiological characteristics suggestive of ATB. Individuals with positive T-SPOT.TB but without the evidence of ATB were defined as LTBI. Patients younger than 17 years and undergoing anti-TB treatment were excluded.ResultsA total of 709 (312 ATB and 397 LTBI) and another 309 (120 ATB and 189 LTBI) subjects were respectively recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The level of prealbumin was significantly lower in ATB than in LTBI. With a cut-off value of 139 mg/L, the sensitivity and specificity of prealbumin in distinguishing ATB from LTBI were 50.96% (45.41%-56.51%) and 91.69% (88.97%-94.40%). Meanwhile, TBAg/PHA ratio was found statistically higher in ATB compared with LTBI. If using the threshold of 0.29, the sensitivity and specificity of TBAg/PHA ratio were 65.71% (60.44%-70.97%) and 90.93% (88.11%-93.76%), respectively. Moreover, the combination of prealbumin and TBAg/PHA ratio (obtaining by diagnostic model) yielded better specificity (90.18%, [87.25%-93.10%]) and sensitivity (87.18%, [83.47%-90.89%]), while the clinical utility index (CUI) positive and CUI negative were respectively 0.76 and 0.81. After anti-TB treatment, TBAg/PHA ratio was declined while the level of prealbumin was restored (Wilcoxon test, P < 0.001). Furthermore, the performance of diagnostic model obtained in Qiaokou cohort was confirmed in Caidian cohort.ConclusionsThe diagnostic model based on combination of prealbumin and TBAg/PHA ratio is a rapid and accurate tool for discriminating ATB from LTBI.

Project description:Background The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status. Methods T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms. Results A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively. Conclusions Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07954-7. Highlights The first study to establish 28 models using machine learning for TB diagnosis. The first TB diagnostic model based on routine, TB-specific and non-specific tests. Cforest model presented excellent performance in discriminating ATB from LTBI. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07954-7.

Dataset Information

Application of ImmunoScore Model for the Differentiation between Active Tuberculosis and Latent Tuberculosis Infection as Well as Monitoring Anti-tuberculosis Therapy.

Publications

Application of ImmunoScore Model for the Differentiation between Active Tuberculosis and Latent Tuberculosis Infection as Well as Monitoring Anti-tuberculosis Therapy.

Similar Datasets

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