Use of TBAg/PHA ratio in distinguishing tuberculoma from cancer in solitary pulmonary nodule or mass

Wang, Feng; Hou, Hongyan; Zhou, Hongmin; Wu, Shiji; Mao, Lie; Huang, Min; Yin, Botao; Huang, Jing; Sun, Ziyong

doi:10.1111/crj.12648

Cited by 7 publications

(6 citation statements)

References 30 publications

(54 reference statements)

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“…We have first emphasized that the substrate incubation time, peripheral blood mononuclear cell counting, and the setting of ELISPOT reader parameters (such as exposure time and sensitivity) are the key factors affecting SFC results of antigen and PHA in T-SPOT assay (20,37). In addition, we first put forward the concept of TBAg/PHA ratio and found that the performance of TBAg/PHA ratio is better than directly using T-SPOT antigen results not only in distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI) but also in differential diagnosis of pulmonary and extrapulmonary TB (20,26,(37)(38)(39)(40)(41). We also first put forward that 1 × 10 5 was the optimal number of pleural fluid mononuclear cells for performing pleural fluid T-SPOT and that the mean spot size of ESAT-6 has an adjunctive role in the diagnosis of ATB (42,43).…”

Section: 18%mentioning

confidence: 99%

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

et al. 2021

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

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Section: 18%mentioning

confidence: 99%

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

et al. 2021

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“…The TBAg/PHA ratio is a specific indicator for TB diagnosis. Apart from distinguishing between ATB and LTBI it can also be used to discriminate TB from other respiratory diseases such as lung cancer, bacterial pneumonia and fungal pneumonia (Wang et al, 2018a, Wang et al, 2020. Meanwhile, iron metabolism indexesincluding SI, SF and TFhave also been reported to have potential value in differentiating ATB from other lung illness such as chronic obstructive pulmonary disease and respiratory tract infections (Dai et al, 2019, Jacobs et al, 2016.…”

Section: Discussionmentioning

confidence: 99%

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

Luo

Xue

Lin

et al. 2020

International Journal of Infectious Diseases

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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.

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“…(I) The ratio of TB-specific antigen (TBAg) to phytohaemagglutinin (PHA) (TBAg/PHA): the larger of the ESAT-6/PHA and CFP-10/PHA of T-SPOT assay is defined as the TBAg/PHA ratio [ 78 ]. We have found that calculation of the TBAg/PHA ratio of the T-SPOT assay can increase the specificity of this assay for diagnosis of active TB [ 78 , 79 , 80 , 81 , 82 , 83 ]. The theoretical basis is that TBAg/PHA ratio can eliminate the impact of individual immune variation on a T-SPOT assay.…”

Section: Immunological Diagnosismentioning

confidence: 99%

Review and Updates on the Diagnosis of Tuberculosis

Huang

Lin

Wang

et al. 2022

JCM

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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.

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Use of TBAg/PHA ratio in distinguishing tuberculoma from cancer in solitary pulmonary nodule or mass

Abstract: Calculation of the TBAg/PHA ratio might provide a useful non-invasive tool for distinguishing tuberculoma from cancer in patients with a solitary pulmonary nodule or mass in TB-endemic countries.

Cited by 7 publications

References 30 publications

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

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

Review and Updates on the Diagnosis of Tuberculosis

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