The performance of the TBAg/PHA ratio in the diagnosis of active TB disease in immunocompromised patients

Immunopathology in human pulmonary tuberculosis: Inflammatory changes in the plasma milieu and impaired host immune cell functions

Host immune response is key for protection in tuberculosis, but the causative agent, Mycobacterium (M.) tuberculosis, manages to survive despite immune surveillance. Key mechanisms of immune protection have been identified, but the role of immunopathology in the peripheral blood of tuberculosis patients remains unclear. Tuberculosis immunopathology in the blood is characterised by patterns of immunosuppression and hyperinflammation. These seemingly contradictory findings and the pronounced heterogeneity made it difficult to interpret the results from previous studies and to derive implications of immunopathology. However, novel approaches based on comprehensive data analyses and revitalisation of an ancient plasma milieu in vitro assay connected inflammation with immunosuppressive factors in tuberculosis. Moreover, interrelations between the aberrant plasma milieu and immune cell pathology were observed. This review provides an overview of studies on changes in plasma milieu and discusses recent findings linking plasma factors to T-cell and monocyte/macrophage pathology in pulmonary tuberculosis patients.

Differences in PPD- and mitogen-induced T-cell activation marker expression characterize immunopathology in acute tuberculosis patients

Impaired T-cell responses to mitogens and high T-cell activation marker (TAM) expression on Mycobacterium tuberculosis-specific T-cells characterize immunopathology in patients with tuberculosis (TB). In a study of patients with TB (n = 60) and asymptomatic contacts (controls, n = 37), we found that TB patients had higher CD38+ T-cell proportions specific for M. tuberculosis protein (PPDMtb), yet total proportions of PPDMtb-specific T-cells were comparable. Notably, both activated (CD38+) and total IFN-γ+ T-cells from TB patients had lower mitogen (phytohemagglutinin, PHA)-induced responses. This impaired mitogen response improved the classification efficacy of the TAM-TB assay, especially employing the PPD/PHA-induced T-cell ratio.

Using immune clusters for classifying Mycobacterium tuberculosis infection

BACKGROUND

The differential diagnosis between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) is still a challenge worldwide.

METHODS

Immune indicators involved in innate, humoral, and cellular immune cells, as well as antigen-specific cells were simultaneously assessed in patients with ATB and LTBI.

RESULTS

Of 54 immune indicators, no indicator could distinguish ATB from LTBI, likely due to an obvious heterogeneity of immune indicators noticed in ATB patients. Cluster analysis of ATB patients identified three immune clusters with different severity. Cluster 1 (42.1 %) was a ''Treg/Th1/Tfh unbalance type" cluster, whereas cluster 2 (42.1 %) was an "effector type'' cluster, and cluster 3 was a ''inhibition type'' cluster (15.8 %) which showed the highest severity. A prediction model based on immune indicators was established and showed potential in classifying Mycobacterium tuberculosis infection.

CONCLUSIONS

We depicted the immune landscape of patients with ATB and LTBI. Three immune subtypes were identified in ATB patients with different severity.

Impaired T-cell response to phytohemagglutinin (PHA) in tuberculosis patients is associated with high IL-6 plasma levels and normalizes early during anti-mycobacterial treatment

PURPOSE

Human tuberculosis is characterized by immunopathology that affects T-cell phenotype and functions. Previous studies found impaired T-cell response to phytohemagglutinin (PHA) in patients with acute tuberculosis. However, the influence of disease severity, affected T-cell subsets, and underlying mechanisms remain elusive.

METHODS

Here we investigated PHA-induced and antigen-specific T-cell effector cytokines in tuberculosis patients (n = 55) as well as in healthy asymptomatic contacts (n = 32) from Ghana. Effects of Mycobacterium (M.) tuberculosis sputum burden and treatment response were analyzed and compared during follow-up. Finally, cytokine characteristics of the aberrant plasma milieu in tuberculosis were analyzed as a potential cause for impaired PHA response.

RESULTS

PHA-induced IFN-γ expression was significantly lower in sputum-positive tuberculosis patients as compared to both, contacts and paucibacillary cases, and efficiently discriminated the study groups. T-cell responses to PHA increased significantly early during treatment and this was more pronounced in tuberculosis patients with rapid treatment response. Analysis of alternative cytokines revealed distinct patterns and IL-22, as well as IL-10, showed comparable expression to IFN-γ in response to PHA. Finally, we found that high IL-6 plasma levels were strongly associated with impaired IFN-γ and IL-22 response to PHA.

CONCLUSION

We conclude that impaired T-cell response to PHA stimulation in acute tuberculosis patients (i) was potentially caused by the aberrant plasma milieu, (ii) affected differentially polarized T-cell subsets, (iii) normalized early during treatment. This study shed light on the mechanisms of impaired T-cell functions in tuberculosis and yielded promising biomarker candidates for diagnosis and monitoring of treatment response.

Review and Updates on the Diagnosis of Tuberculosis

Diagnosis of tuberculosis, and especially the diagnosis of extrapulmonary tuberculosis, still faces challenges in clinical practice. There are several reasons for this. Methods based on the detection of Mycobacterium tuberculosis (Mtb) are insufficiently sensitive, methods based on the detection of Mtb-specific immune responses cannot always differentiate active disease from latent infection, and some of the serological markers of infection with Mtb are insufficiently specific to differentiate tuberculosis from other inflammatory diseases. New tools based on technologies such as flow cytometry, mass spectrometry, high-throughput sequencing, and artificial intelligence have the potential to solve this dilemma. The aim of this review was to provide an updated overview of current efforts to optimize classical diagnostic methods, as well as new molecular and other methodologies, for accurate diagnosis of patients with Mtb infection.

A scoring system developed from a nomogram to differentiate active pulmonary tuberculosis from inactive pulmonary tuberculosis

Purpose

This study aimed to develop and validate a scoring system based on a nomogram of common clinical metrics to discriminate between active pulmonary tuberculosis (APTB) and inactive pulmonary tuberculosis (IPTB).

Patients and methods

A total of 1096 patients with pulmonary tuberculosis (PTB) admitted to Wuhan Jinyintan Hospital between January 2017 and December 2019 were included in this study. Of these patients with PTB, 744 were included in the training cohort (70%; 458 patients with APTB, and 286 patients with IPTB), and 352 were included in the validation cohort (30%; 220 patients with APTB, and 132 patients with IPTB). Data from 744 patients from the training cohort were used to establish the diagnostic model. Routine blood examination indices and biochemical indicators were collected to construct a diagnostic model using the nomogram, which was then transformed into a scoring system. Furthermore, data from 352 patients from the validation cohort were used to validate the scoring system.

Results

Six variables were selected to construct the prediction model. In the scoring system, the mean corpuscular volume, erythrocyte sedimentation rate, albumin level, adenosine deaminase level, monocyte-to-high-density lipoprotein ratio, and high-sensitivity C-reactive protein-to-lymphocyte ratio were 6, 4, 7, 5, 5, and 10, respectively. When the cut-off value was 15.5, the scoring system for recognizing APTB and IPTB exhibited excellent diagnostic performance. The area under the curve, specificity, and sensitivity of the training cohort were 0.919, 84.06%, and 86.36%, respectively, whereas those of the validation cohort were 0.900, 82.73, and 86.36%, respectively.

Conclusion

This study successfully constructed a scoring system for distinguishing APTB from IPTB that performed well.

Using TBAg/PHA Ratio for Monitoring TB Treatment: A Prospective Multicenter Study

The way to monitor tuberculosis (TB) treatment is extremely lacking in clinical practice. The aim of the study is to assess the role of the TBAg/PHA ratio in the treatment monitoring of TB. TB patients were followed up for 6 months and serial T-SPOT.TB (T-SPOT) assays were performed. In patients with successful treatment outcomes, the ESAT-6 sfc, CFP-10 sfc, and TBAg/PHA ratio all showed a decreased trend after the initiation of treatment. Conversely, PHA sfc showed an increased trend after 2 months of treatment. However, these indicators had moderate performance in distinguishing between before and after 6 months of treatment, and the AUC ranged from 0.702 to 0.839. Notably, the TBAg/PHA ratio in patients without risk factors was of important value in differentiation between before and after treatment. The optimal AUC of TBAg/PHA ratio reached up to 0.890. Patients with unsuccessful treatment outcomes showed persistently high levels of TBAg/PHA ratio. The TBAg/PHA ratio in patients after 6 months of treatment showed a certain potential in distinguishing between patients with successful and unsuccessful treatment outcomes. A further calculation of the TBAg/PHA ratio in T-SPOT assay has potential value in the treatment monitoring of TB, but further confirmation is needed.

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

Background

The differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM.

Methods

Patients with TBM or BM were recruited between January 2017 and January 2021 at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The detection for indicators involved in cerebrospinal fluid (CSF) and T-SPOT assay were performed simultaneously. Multivariate logistic regression was used to create a diagnostic model.

Results

A total of 174 patients (76 TBM and 98 BM) and another 105 cases (39 TBM and 66 BM) were enrolled from Qiaokou cohort and Caidian cohort, respectively. Significantly higher level of CSF lymphocyte proportion while significantly lower levels of CSF chlorine, nucleated cell count, and neutrophil proportion were observed in TBM group when comparing with those in BM group. However, receiver operating characteristic (ROC) curve analysis showed that the areas under the ROC curve (AUCs) produced by these indicators were all under 0.8. Meanwhile, tuberculosis-specific antigen/phytohemagglutinin (TBAg/PHA) ratio yielded an AUC of 0.889 (95% CI, 0.840–0.938) in distinguishing TBM from BM, with a sensitivity of 68.42% (95% CI, 57.30%–77.77%) and a specificity of 92.86% (95% CI, 85.98%–96.50%) when a cutoff value of 0.163 was used. Consequently, we successfully established a diagnostic model based on the combination of TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion for discrimination between TBM and BM. The established model showed good performance in differentiating TBM from BM (AUC: 0.949; 95% CI, 0.921–0.978), with 81.58% (95% CI, 71.42%–88.70%) sensitivity and 91.84% (95% CI, 84.71%–95.81%) specificity. The performance of the diagnostic model obtained in Qiaokou cohort was further validated in Caidian cohort. The diagnostic model in Caidian cohort produced an AUC of 0.923 (95% CI, 0.867–0.980) with 79.49% (95% CI, 64.47%–89.22%) sensitivity and 90.91% (95% CI, 81.55%–95.77%) specificity.

Conclusions

The diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.

Past and Present Approaches to Diagnosis of Active Pulmonary Tuberculosis

Tuberculosis disease continues to contribute to the mortality burden globally. Due to the several shortcomings of the available diagnostic methods, tuberculosis disease continues to spread. The difficulty to obtain sputum among the very ill patients and the children also affects the quick diagnosis of tuberculosis disease. These challenges warrant investigating different sample types that can provide results in a short time. Highlighted in this review are the approved pulmonary tuberculosis diagnostic methods and ongoing research to improve its diagnosis. We used the PRISMA guidelines for systematic reviews to search for studies that met the selection criteria for this review. In this review we found out that enormous biosignature research is ongoing to identify host biomarkers that can be used as predictors of active PTB disease. On top of this, more research was also being done to improve already existing diagnostic tests. Host markers required more optimization for use in different settings given their varying sensitivity and specificity in PTB endemic and non-endemic settings.

Combination of Blood Routine Examination and T-SPOT.TB Assay for Distinguishing Between Active Tuberculosis and Latent Tuberculosis Infection

Background

Distinguishing between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging.

Methods

Between 2013 and 2019, 2,059 (1,097 ATB and 962 LTBI) and another 883 (372 ATB and 511 LTBI) participants were recruited based on positive T-SPOT.TB (T-SPOT) results from Qiaokou (training) and Caidian (validation) cohorts, respectively. Blood routine examination (BRE) was performed simultaneously. Diagnostic model was established according to multivariate logistic regression.

Results

Significant differences were observed in all indicators of BRE and T-SPOT assay between ATB and LTBI. Diagnostic model built on BRE showed area under the curve (AUC) of 0.846 and 0.850 for discriminating ATB from LTBI in the training and validation cohorts, respectively. Meanwhile, TB-specific antigens spot-forming cells (SFC) (the larger of early secreted antigenic target 6 and culture filtrate protein 10 SFC in T-SPOT assay) produced lower AUC of 0.775 and 0.800 in the training and validation cohorts, respectively. The diagnostic model based on combination of BRE and T-SPOT showed an AUC of 0.909 for differentiating ATB from LTBI, with 78.03% sensitivity and 90.23% specificity when a cutoff value of 0.587 was used in the training cohort. Application of the model to the validation cohort showed similar performance. The AUC, sensitivity, and specificity were 0.910, 78.23%, and 90.02%, respectively. Furthermore, we also assessed the performance of our model in differentiating ATB from LTBI with lung lesions. Receiver operating characteristic analysis showed that the AUC of established model was 0.885, while a threshold of 0.587 yield a sensitivity of 78.03% and a specificity of 85.69%, respectively.

Conclusions

The diagnostic model based on combination of BRE and T-SPOT could provide a reliable differentiation between ATB and LTBI.

The TBAg/PHA ratio in T-SPOT.TB assay has high prospective value in the diagnosis of active tuberculosis: a multicenter study in China

Background

The positive rate of pathogenic examination about tuberculosis is low. It is still difficult to achieve early diagnosis for some TB patients. The value of Interferon-gamma release assays (IGRA) in the diagnosis of active tuberculosis remains controversial. The purpose of this multicenter prospective study was to verify and validate the role of TBAg/PHA ratio (TB-specific antigen to phytohaemagglutinin) of T-SPOT.TB assay in diagnosing ATB.

Methods

We prospectively enrolled 2390 suspected pulmonary tuberculosis patients with positive T-SPOT assay results from three tertiary hospitals.

Results

A total of 1549 ATB (active tuberculosis) patients (including 1091 confirmed and 458 probable ATB) and 724 non-tuberculosis (non-TB) patients with positive T-SPOT results were included. The results of this study showed that ESAT-6 and CFP-10 in the T-SPOT.TB assay were significantly higher in the ATB group compared with the non-TB group, while PHA was lower in the ATB group. Results of ESAT-6, CFP-10 and PHA show a certain diagnostic performance, but moderate sensitivity and specificity. The TBAg/PHA ratio, a further calculation of ESAT-6, CFP-10 and PHA in T-SPOT.TB assay showed improved performance in the diagnosis of active Tuberculosis. If using the threshold value of 0.2004, the specificity and sensitivity of TBAg/PHA ratio in distinguishing ATB from non-TB were 92.3% and 74.4%, PPV was 95.4, PLR was 9.6.

Conclusion

By recalculating the results of T-SPOT.TB Assay, the TBAg/PHA ratio shows high prospect value in the diagnosis of active tuberculosis in high prediction areas.

Gastrointestinal and Abdominal Tuberculosis

Gastrointestinal (GI) tuberculosis (TB) remains a significant problem worldwide, and may involve the luminal GI tract from oral cavity to perianal area in addition to associated viscera and peritoneum. Although GI TB more commonly affects immunocompromised hosts, it can also occur in immunocompetent people. Diagnosis is difficult because it usually mimics a malignancy or inflammatory bowel disease. A high index of clinical suspicion and appropriate use of combined investigative methods help in early diagnosis, and reduce morbidity and mortality. Anti-TB therapy is the same as for pulmonary disease, and invasive and specialized interventions are reserved for selected complications.

Lymphocyte Non-Specific Function Detection Facilitating the Stratification of Mycobacterium tuberculosis Infection

Background

Inadequate tuberculosis (TB) diagnostics, especially for discrimination between active TB (ATB) and latent TB infection (LTBI), are major hurdle in the reduction of the disease burden. The present study aims to investigate the role of lymphocyte non-specific function detection for TB diagnosis in clinical practice.

Methods

A total of 208 participants including 49 ATB patients, 64 LTBI individuals, and 95 healthy controls were recruited at Tongji hospital from January 2019 to October 2020. All subjects were tested with lymphocyte non-specific function detection and T-SPOT assay.

Results

Significantly positive correlation existed between lymphocyte non-specific function and phytohemagglutinin (PHA) spot number. CD4⁺ T cell non-specific function showed the potential for differentiating patients with negative T-SPOT results from those with positive T-SPOT results with an area under the curve (AUC) of 0.732 (95% CI, 0.572-0.893). The non-specific function of CD4⁺ T cells, CD8⁺ T cells, and NK cells was found significantly lower in ATB patients than in LTBI individuals. The AUCs presented by CD4⁺ T cell non-specific function, CD8⁺ T cell non-specific function, and NK cell non-specific function for discriminating ATB patients from LTBI individuals were 0.845 (95% CI, 0.767-0.925), 0.770 (95% CI, 0.683-0.857), and 0.691 (95% CI, 0.593-0.789), respectively. Application of multivariable logistic regression resulted in the combination of CD4⁺ T cell non-specific function, NK cell non-specific function, and culture filtrate protein-10 (CFP-10) spot number as the optimally diagnostic model for differentiating ATB from LTBI. The AUC of the model in distinguishing between ATB and LTBI was 0.939 (95% CI, 0.898-0.981). The sensitivity and specificity were 83.67% (95% CI, 70.96%-91.49%) and 90.63% (95% CI, 81.02%-95.63%) with the threshold as 0.57. Our established model showed superior performance to TB-specific antigen (TBAg)/PHA ratio in stratifying TB infection status.

Conclusions

Lymphocyte non-specific function detection offers an attractive alternative to facilitate TB diagnosis. The three-index diagnostic model was proved to be a potent tool for the identification of different events involved in TB infection, which is helpful for the treatment and management of patients.

Combination of prealbumin and tuberculosis-specific antigen/phytohemagglutinin ratio for discriminating active tuberculosis from latent tuberculosis infection

BACKGROUND

Given 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.

METHODS

Participants 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.

RESULTS

A 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.

CONCLUSIONS

The diagnostic model based on combination of prealbumin and TBAg/PHA ratio is a rapid and accurate tool for discriminating ATB from LTBI.

Identification of a novel biomarker based on lymphocyte count, albumin level, and TBAg/PHA ratio for differentiation between active and latent tuberculosis infection in Japan

Data from China have shown that the ratio of Mycobacterium tuberculosis-specific antigen (TBAg) spots obtained from the T-SPOT.TB test to the number of positive control phytohemagglutinin (PHA) spots (TBAg/PHA ratio) could help distinguish between active tuberculosis infection (ATBI) and latent tuberculosis infection (LTBI). As the applicability of the T-SPOT.TB test may differ according to region and race, we retrospectively verified the utility of the TBAg/PHA ratio in distinguishing between ATBI and LTBI in Japan. The TBAg/PHA ratio was significantly lower in the LTBI group than in the ATBI group. Area under the receiver operating characteristic curve (AUC) analysis between ATBI and LTBI according to the TBAg/PHA ratio was 0.76, with a sensitivity of 65.8% and a specificity of 75.6%. The best AUC was obtained when the TBAg/PHA ratio was divided by both lymphocyte count and albumin levels. Our results demonstrate that, in Japan, the TBAg/PHA ratio is superior to TBAg alone for distinguishing between ATBI and LTBI. In addition, the sensitivity and specificity were improved by combining the TBAg/PHA ratio with lymphocyte count and albumin levels.

A combination of iron metabolism indexes and tuberculosis-specific antigen/phytohemagglutinin ratio for distinguishing active tuberculosis from latent tuberculosis infection

BACKGROUND

Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. This study aimed to investigate a diagnostic model based on a combination of iron metabolism and the TB-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) in T-SPOT.TB assay for differentiation between ATB and LTBI.

METHODS

A total of 345 participants with ATB (n=191) and LTBI (n=154) were recruited based on positive T-SPOT.TB results at Tongji hospital between January 2017 and January 2020. Iron metabolism analysis was performed simultaneously. A diagnostic model for distinguishing ATB from LTBI was established according to multivariate logistic regression.

RESULTS

The TBAg/PHA ratio showed 64.00% sensitivity and 90.10% specificity in distinguishing ATB from LTBI when a threshold of 0.22 was used. All iron metabolism biomarkers in the ATB group were significantly different from those in the LTBI group. Specifically, serum ferritin and soluble transferrin receptor in ATB were significantly higher than LTBI. On the contrary, serum iron, transferrin, total iron binding capacity, and unsaturated iron binding capacity in ATB were significantly lower than LTBI. The combination of iron metabolism indicators accurately predicted 60.00% of ATB cases and 91.09% of LTBI subjects, respectively. Moreover, the combination of iron metabolism indexes and TBAg/PHA ratio resulted in a sensitivity of 88.80% and specificity of 90.10%. Furthermore, the performance of models established in the Qiaokou cohort was confirmed in the Caidian cohort.

CONCLUSIONS

The data suggest that the combination of iron metabolism indexes and TBAg/PHA ratio could serve as a biomarker to distinguish ATB from LTBI in T-SPOT-positive individuals.

Combination of mean spot sizes of ESAT-6 spot-forming cells and modified tuberculosis-specific antigen/phytohemagglutinin ratio of T-SPOT.TB assay in distinguishing between active tuberculosis and latent tuberculosis infection

OBJECTIVES

Distinguishing between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging.

METHODS

The modified T-SPOT.TB assay was performed in 499 participants (243 ATB and 256 LTBI) and another 322 participants (162 ATB and 160 LTBI) who were diagnosed in Qiaokou (training) and Caidian (validation) cohort respectively.

RESULTS

The mean spot sizes (MSS) of early secreted antigenic target 6 (ESAT-6) spot-forming cells (SFC) of T-SPOT.TB assay in ATB patients was significantly higher than that in LTBI individuals. 1.0 × 10⁵ was the optimal number of cells added to phytohaemagglutinin (PHA) well for obtaining more accurate TB-specific antigen to phytohaemagglutinin (TBAg/PHA) ratio. The area under the curve of the diagnostic model by combination of ESAT-6 SFC MSS and modified TBAg/PHA ratio in distinguishing ATB from LTBI was 0.959 in training cohort, with a sensitivity of 90.12% and a specificity of 91.02% when a cutoff value of 0.46 was used. This diagnostic model showed similar performance in the validation cohort. The area under the curve, sensitivity, and specificity were 0.962, 93.21%, and 90.00%, respectively. Further flow cytometry analysis showed that ESAT-6 stimulation induced a significantly higher mean fluorescence intensity of IFN-γ⁺ cells in lymphocytes compared with culture filtrate protein 10 (CFP-10) stimulation. In contrast, CFP-10 stimulation induced a significantly higher percentage of IFN-γ⁺ cells in lymphocytes compared with ESAT-6 stimulation.

CONCLUSIONS

The combination of the MSS of ESAT-6 SFC and the modified TBAg/PHA ratio of T-SPOT.TB assay showed great value in discriminating ATB from LTBI.

Combination of Xpert MTB/RIF and TBAg/PHA Ratio for Prompt Diagnosis of Active Tuberculosis: A Two-Center Prospective Cohort Study

The prompt diagnosis of active tuberculosis (ATB) is still a challenge in clinical practice, especially in TB-endemic countries. We prospectively enrolled consecutive patients with suspected pulmonary TB from two tertiary hospitals. Acid-fast staining (AFS), Xpert MTB/RIF (Xpert), Mycobacterium tuberculosis culture, and T-SPOT.TB were simultaneously performed. 226 ATB and 348 non-TB patients were diagnosed in Tongji hospital (test cohort), and 86 ATB and 110 non-TB patients were diagnosed in Guangzhou Chest Hospital (validation cohort). Using ATB as patient group and non-TB as control group, for diagnosis of ATB in Tongji Hospital, the sensitivity of AFS was 17.70% (95% CI: 13.08–23.44%). The sensitivity of Xpert and culture were 53.54% (95% CI: 46.81–60.14%) and 46.46% (95% CI: 39.86–53.19%), respectively. The sensitivity of T-SPOT.TB was 81.42% (95% CI: 75.60–86.14%), but the specificity was 71.55% (95% CI: 66.60–76.04%). Calculation of the ratio of TB-specific antigen to phytohaemagglutinin (TBAg/PHA) of T-SPOT.TB assay increased the specificity but with a loss of sensitivity. Combination of Xpert and culture slightly increased the sensitivity compared to using these methods separately. Combination of Xpert and TBAg/PHA ratio (defined as Xpert positive or TBAg/PHA ≥ 0.2) increased diagnostic accuracy, and the sensitivity and specificity of combination of them were 85.84% (95% CI: 80.45–89.98%) and 95.98% (95% CI: 93.36–97.59%), respectively. The diagnostic model was also established based on combination of Xpert and TBAg/PHA ratio. The area under the curve of the diagnostic model was 0.952 (95% CI: 0.932–0.973) for diagnosis of ATB, with a sensitivity of 88.05% (95% CI: 83.10–91.98%) and a specificity of 96.26% (95% CI: 93.70–98.00%) when a cutoff value of 0.44 was used in Wuhan cohort. The performance of combination of Xpert and TBAg/PHA ratio was similar in Guangzhou Chest Hospital. Our data suggest that combination of Xpert and TBAg/PHA ratio may be a good algorithm for prompt diagnosis of ATB in high endemic areas.

Diagnostic Performance of a 5-Marker Predictive Model for Differential Diagnosis Between Intestinal Tuberculosis and Crohn's Disease

BACKGROUND

The differentiation between intestinal tuberculosis (ITB) and Crohn's disease (CD) is a challenge. The aim of this study was to investigate a predictive model for differential diagnosis between ITB and CD.

METHODS

A total of 268 patients who were suspected of having ITB or CD were prospectively recruited between January 2013 and September 2016. The clinical, laboratory, radiological, endoscopic, and histological features were investigated and subjected to univariate and multivariate analyses. The final predictive model was developed based on the regression coefficients of multivariate logistic regression. To validate the model, the same regression equation was tested on the other group.

RESULTS

A total of 239 patients had a final diagnosis, including 86 ITB and 153 CD. Five variables (perianal disease, pulmonary involvement, longitudinal ulcer, left colon, and ratio of tuberculosis-specific antigen to phytohaemagglutinin) were selected for the predictive model to discriminate between ITB and CD. In the predictive model of the training data set, the area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy, with a cutoff level of 0.29, were 0.975 (95% confidence interval [CI], 0.939-0.993), 96.7%, 90.7%, and 92.8%, respectively. Application of the predictive model to the validation data set showed similar performance in distinguishing ITB from CD. The area under the ROC curve, sensitivity, specificity, and accuracy were 0.950 (95% CI, 0.871-0.987), 88.5%, 93.5%, and 91.7%, respectively.

CONCLUSIONS

This 5-marker predictive model could be conveniently used by clinicians to draw a reliable differential diagnosis between ITB and CD in clinical practice. 10.1093/ibd/izy154_video1izy154.video15790725497001.

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

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.