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. 2020 Sep 3;11:866. doi: 10.3389/fneur.2020.00866

Diagnostic Accuracy of T-SPOT.TB Assay for Tuberculous Meningitis: An Updated Meta-Analysis

Ying Luo 1, Ying Xue 2, Xueyun Guo 3, Qun Lin 1, Liyan Mao 1, Guoxing Tang 1, Huijuan Song 1, Feng Wang 1,*, Ziyong Sun 1,*
PMCID: PMC7494808  PMID: 33013621

Abstract

Background: The role of T-SPOT.TB (T-SPOT) assay for tuberculous meningitis (TBM) diagnosis has not been fully assessed. Here, we conducted an updated meta-analysis to evaluate the diagnostic accuracy of peripheral blood (PB) T-SPOT and cerebrospinal fluid (CSF) T-SPOT for diagnosing TBM.

Methods: Relevant studies in the PubMed database, EmBase database, Cochrane database, Scopus database, Google Scholar, China National Knowledge Internet, and Wan-Fang database were retrieved from August 1, 2005, to June 22, 2020. Statistical analysis was performed using Stata, Revman, and Meta-Disc software. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), summary receiver operating characteristic curves, and the area under the curve were determined and analyzed.

Results: A total of 27 studies were eligible for inclusion within the meta-analysis. The pooled sensitivity and specificity of PB T-SPOT for TBM diagnosis were 0.78 (95% CI, 0.76–0.81) and 0.68 (95% CI, 0.66–0.71), respectively, whereas the pooled PLR, NLR, and DOR were 2.80 (95% CI, 2.29–3.42), 0.32 (95% CI, 0.27–0.38), and 10.08 (95% CI, 7.21–14.08), respectively. On the other hand, the pooled sensitivity and specificity of CSF T-SPOT on diagnosing TBM were 0.76 (95% CI, 0.72–0.80) and 0.88 (95% CI, 0.85–0.90), respectively, whereas the pooled PLR, NLR, and DOR were 5.92 (95% CI, 4.25–8.25), 0.28 (95% CI, 0.21–0.39), and 29.05 (95% CI, 16.40–51.45), respectively. The area under the summary receiver operating characteristic curve values of PB T-SPOT and CSF T-SPOT for TBM diagnosis were 0.83 (95% CI, 0.80–0.86) and 0.92 (95% CI, 0.89–0.94), respectively.

Conclusions: CSF T-SPOT showed a higher specificity compared with PB T-SPOT for diagnosing TBM. Both two T-SPOT assays have considerable potential in improving the diagnosis of TBM. Furthermore, the standardization of the operating procedure is further needed when performing CSF T-SPOT.

Keywords: tuberculous meningitis, T-SPOT.TB, peripheral blood, cerebrospinal fluid, diagnosis, meta-analysis

Introduction

Tuberculosis (TB) remains a global health problem, with an estimated 10.0 million new cases and 1.5 million deaths resulting from the disease in 2018 (1). Extrapulmonary tuberculosis (EPTB) represents about 15% of all TB cases recognized by the World Health Organization in 2018 (1). Tuberculous meningitis (TBM) is the most lethal and disabling form of EPTB. It was reported that TBM accounted for 2.9–6.8% of extrapulmonary presentations (2–4). The global burden of TBM is estimated to be more than 100,000 new cases per year (5). Rapid and accurate diagnosis of TBM is of paramount importance in reducing morbidity and mortality. Therefore, innovative tools with higher diagnostic efficiency are indispensable for the control and management of TBM.

The use of smear microscopy in cerebrospinal fluid (CSF) remains consistently difficult owing to the low numbers of bacilli present in this paucibacillary disease (6). Mycobacterial culture displays an increased sensitivity but is limited by a long turnaround time, which results in delayed clinical decision-making (7). Molecular technologies such as GeneXpert MTB/RIF (manufactured by Cepheid) indeed improve the diagnostic value for TB infection, but these methods still show a limited value according to the paucibacillary nature of the disease (8). In total, the present conventional microbiological methods provide restricted efficiency in TBM diagnosis due to the unsatisfactory sensitivity and are time-consuming and costly (9). For biochemical indicators, the levels of adenosine deaminase (ADA) and interferon-gamma in CSF also display potential diagnostic value, but the performance of these indicators is also not satisfactory (10, 11). Besides, brain imaging has long been part of the diagnostic evaluation of TBM (12). However, few studies have defined the diagnostic performance of the potential features, and the appearance of imaging modalities would be influenced by the status, including age and underlying infection (13). Apart from these technologies, clinical scoring systems have also been introduced to apply in this area (14, 15). The major limitation of these diagnostic rules is that their utility is variable in different settings, and few of these scoring systems have been externally validated. The findings vary according to the population origin, age, and HIV status accounting for much of this variation (15). Also, some emerging approaches, including GeneXpert MTB/RIF Ultra (16, 17), droplet digital PCR (18), and exosome DNA (19), have been developed aiming to address the issue recently, but few have been subject to enough repeated validation.

T-SPOT.TB (T-SPOT), one of two commercially interferon-gamma release assays, has also been developed to diagnose TB infection (20). It was reported that both peripheral blood (PB) and CSF could be used to perform T-SPOT and that PB T-SPOT and CSF T-SPOT showed different diagnostic accuracy in TBM (21). Although a previous meta-analysis was conducted based on studies before 2015 (21), several new pieces of research were noted in recent years. Therefore, this study aims to evaluate the diagnostic accuracy of PB T-SPOT and CSF T-SPOT in TBM and comprehensively compare the efficiency between T-SPOT and other existing methods.

Materials and Methods

This meta-analysis was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (22). Given that the present study was a meta-analysis of published researches, the approval from the institutional ethics committee and patient consent were not available.

Search Strategy

All relevant individual studies performed were searched on human subjects published from August 1, 2005, to June 22, 2020, in PubMed database, EmBase database, Cochrane database, Scopus database, Google Scholar, China National Knowledge Internet, and Wan-Fang database. The search terms were used as the follows: (“tuberculous” or “tuberculosis” or “tubercular” or “TB” or “mycobacterium” or “mycobacterial”) and (“meningeal” or “meningitis” or “meningitides”) and (“enzyme-linked immunospot” or “T-SPOT” or “T-SPOT.TB” or “ELISpot” or “interferon-gamma release assays” or “interferon-gamma assays” or “IGRA” or “interferon” or “interferon-gamma” or “gamma-interferon” or “IFN” or “T cell-based assay” or “T cell assays”). Relevant articles related to the keywords were included.

Study Selection Criteria

To be included in the meta-analysis, studies had to meet the following criteria: (1) original data on the evaluation of diagnostic value; (2) a case–control design and clear diagnosis; and (3) sufficient parameters including at least specificity and sensitivity, together with the corresponding 95% confidence interval (CI), and the number of the included patients. Ying Luo and Ying Xue independently reviewed and assessed study eligibility, and disagreements were resolved by a third author (Ziyong Sun).

Data Extraction and Quality Assessment

Two investigators (Ying Luo and Ying Xue) independently extracted the following information from each study: first author, country, year of publication, study design, numbers of participants, sensitivity, specificity, and values of true-positive, false-positive, true-negative, and false-negative. The methodological quality of the studies included was assessed using the criteria of the Quality Assessment of Diagnostic Accuracy Studies-2 (23).

Statistical Analysis

The analysis was performed using Stata (version 14; Stata Corporation, TX, USA), Revman (version 5.3; The Nordic Cochrane Center, The Cochrane Collaboration, Copenhagen, Denmark), and Meta-Disc (version 1.4; XI Cochrane Colloquium, Barcelona, Spain). Data from individual studies were pooled using a random-effect model and used to generate values for the following measures of test accuracy: sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) with corresponding 95% CI, and a summary receiver operating characteristic (SROC) curve, which was made to present the individual assessment of sensitivity and specificity for each study. Heterogeneity was assessed by the I2 statistic (24), and publication bias was evaluated using Deeks' funnel plot (25).

Results

Characteristics of the Included Studies

A total of 3,026 citations were found for patients with TBM diagnosed by T-SPOT (Figure 1). After independent reviews, 27 studies met the inclusion criteria and were finally included into meta-analysis (Table 1 and Figure 1). The PB-based T-SPOT was performed in 25 studies (26–35, 37–42, 44–52), and the CSF-based T-SPOT was used in 16 studies (26–29, 31, 32, 36–39, 41, 43, 46, 48, 50, 51). Head-to-head comparisons of the diagnostic accuracies of PB T-SPOT against CSF T-SPOT were found in 14 studies. The characteristics of the 27 eligible studies, such as country, TB burden, and patient number, are presented in Table 1. The patients of these studies came from six countries and three continents. Of the 27 articles, 21 studies reported data from areas with high TB burden. The amount of cerebrospinal fluid mononuclear cells (CSFMCs) added per well in 11 studies was 2.5 × 105, and the amount of CSFMCs' in the remaining five studies was lower than 2.5 × 105 (Table 1). The total sample size in studies performed on PB was 2,312, comprising 989 patients with TBM and 1,323 non-TBM controls, and sample size in studies based on CSF was 1,252, comprising 538 patients with TBM and 714 non-TBM controls.

Figure 1.

Figure 1

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Flowchart diagram for study selection and inclusion.

Table 1.

Summary characteristics of studies included in meta-analysis.

Study number References Country Continent TB burden* Study design Samples TBM/non-TBM patients recruited PBMCs or CSFMCs/well Test results
TP FP FN TN
1 Kim et al. (26) Korea Asia Intermediate Prospective Peripheral blood 11/24 2.5 × 105 10 9 1 15
Cerebrospinal fluid 4/12 2.5 × 105 3 3 1 9
2 Thomas et al. (27) India, UK, Germany Asia and Europe High† Prospective Peripheral blood 11/8 2.5 × 105 9 2 2 6
Cerebrospinal fluid 10/7 2.5 × 105 9 0 1 7
3 Kim et al. (28) Korea Asia Intermediate Prospective Peripheral blood 31/53 2.5 × 105 22 23 9 30
Cerebrospinal fluid 22/28 2.5 × 105 13 3 9 25
4 Patel et al. (29) South Africa Africa High Prospective Peripheral blood 37/50 2.5 × 105 33 33 4 17
Cerebrospinal fluid 38/48 3 × 104 -2.5 × 105 32 13 6 35
5 Cho et al. (30) Korea Asia Intermediate Prospective Peripheral blood 35/87 2.5 × 105 26 47 9 40
6 Park et al. (31) Korea Asia Intermediate Prospective Peripheral blood 25/57 2.5 × 105 22 24 3 33
Cerebrospinal fluid 25/57 2.5 × 105 18 12 7 45
7 Zhang et al. (32) China Asia High Retrospective Peripheral blood 30/30 2.5 × 105 23 4 7 26
Cerebrospinal fluid 30/30 1 × 104 28 1 2 29
8 Chen et al. (33) China Asia High Retrospective Peripheral blood 40/18 2.5 × 105 27 2 13 16
9 Lu et al. (34) China Asia High Retrospective Peripheral blood 30/39 2.5 × 105 21 5 9 34
10 Lv et al. (35) China Asia High Retrospective Peripheral blood 32/27 2.5 × 105 23 4 9 23
11 Mou et al. (36) China Asia High Retrospective Cerebrospinal fluid 47/45 2.5 × 105 38 5 9 40
12 Qin et al. (37) China Asia High Prospective Peripheral blood 12/28 2.5 × 105 10 5 2 23
Cerebrospinal fluid 12/28 ≤2.5 × 105 11 2 1 26
13 Cui et al. (38) China Asia High Retrospective Peripheral blood 40/66 2.5 × 105 29 9 11 57
Cerebrospinal fluid 40/66 1 × 104 38 5 2 61
14 Li et al. (39) China Asia High Retrospective Peripheral blood 53/36 2.5 × 105 48 9 5 27
Cerebrospinal fluid 53/36 2.5 × 105 38 3 15 33
15 Lu et al. (40) China Asia High Retrospective Peripheral blood 30/39 2.5 × 105 21 5 9 34
16 Park et al. (41) Korea Asia Intermediate Prospective Peripheral blood 46/159 2.5 × 105 38 66 8 93
Cerebrospinal fluid 38/109 2.5 × 105 28 16 10 93
17 Wang et al. (42) China Asia High Retrospective Peripheral blood 54/34 2.5 × 105 45 10 9 24
18 Li et al. (43) China Asia High Retrospective Cerebrospinal fluid 52/44 2.5 × 105 51 10 1 34
19 Lu et al. (44) China Asia High Retrospective Peripheral blood 61/85 2.5 × 105 38 23 23 62
20 Ma et al. (45) China Asia High Retrospective Peripheral blood 74/80 2.5 × 105 56 33 18 47
21 Pan et al. (46) China Asia High Prospective Peripheral blood 53/37 2.5 × 105 48 9 5 28
Cerebrospinal fluid 51/36 1 × 105-2.5 × 105 31 1 20 35
22 Wang et al. (47) China Asia High Retrospective Peripheral blood 29/36 2.5 × 105 26 9 3 27
23 Song et al. (48) China Asia High Retrospective Peripheral blood 76/57 2.5 × 105 59 9 17 48
Cerebrospinal fluid 76/57 2.5 × 105 48 1 28 56
24 Wu et al. (49) China Asia High Retrospective Peripheral blood 55/76 2.5 × 105 45 18 10 58
25 Yang et al. (50) China Asia High Retrospective Peripheral blood 30/66 2.5 × 105 28 15 2 51
Cerebrospinal fluid 30/66 2.5 × 105 20 8 10 58
26 Kwon et al. (51) Korea Asia Intermediate Prospective Peripheral blood 10/45 2.5 × 105 5 10 5 35
Cerebrospinal fluid 10/45 2.5 × 105 3 4 7 41
27 Xu et al. (52) China Asia High Retrospective Peripheral blood 84/86 2.5 × 105 63 35 21 51
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*

Refer to Global Tuberculosis Report 2019; Most participants were enrolled in India. TB, tuberculosis; TBM, tuberculous meningitis; PBMCs, peripheral blood mononuclear cells; CSFMCs, cerebrospinal fluid mononuclear cells; TP, true positive; FP, false positive; FN, false negative; TN, true negative.

Quality Assessment

All studies provided detailed diagnostic standards used to define TBM. There was no low risk of bias in all studies (Figure 2). The risk of bias for the index test domain largely resulted from a lack of information on blinding.

Figure 2.

Figure 2

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Quality evaluation of articles included.

Pooled Diagnostic Accuracy in Peripheral Blood and Cerebrospinal Fluid T-SPOT

Data from 25 studies based on PB T-SPOT were subjected to a meta-analysis to generate pooled values for diagnostic accuracy parameters, as follows: the sensitivities varied from 0.50 to 0.93, with a pooled estimate of 0.78 (95% CI, 0.76–0.81). The specificities varied from 0.34 to 0.87, with a pooled estimate of 0.68 (95% CI, 0.66–0.71) (Figure 3). The pooled estimates for PLR, NLR, and DOR were 2.80 (95% CI, 2.29–3.42), 0.32 (95% CI, 0.27–0.38), and 10.08 (95% CI, 7.21–14.08), respectively (Figures 3, 5A), and area under the SROC curve, 0.83 (95% CI, 0.80–0.86) (Figure 6A). Data from the studies showed various heterogeneity for these accuracy parameters, based on I2 values of 49.1% for sensitivity, 80.6% for specificity, 78.0% for PLR, 43.1% for NLR, and 56.1% for DOR (Figures 3, 5A).

Figure 3.

Figure 3

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Forest plot showing estimates of sensitivity, specificity, PLR, and NLR for PB T-SPOT for the diagnosis of TBM. Solid dots represent the point estimates of sensitivity, specificity, PLR, and NLR from each study. Size of solid dots reflects the total number of cases and controls. Error bars indicate 95% CI. Pooled results are shown as diamonds. PLR, positive likelihood ratio; NLR, negative likelihood ratio; PB, peripheral blood; TBM, tuberculous meningitis; CI, confidence interval.

One the other hand, data based on 16 studies showed the pooled sensitivity and specificity for CSF T-SPOT were 0.76 (95% CI, 0.72–0.80) and 0.88 (95% CI, 0.85–0.90), respectively (Figure 4). The PLR, NLR and DOR of CSF T-SPOT in TBM diagnosis were 5.92 (95% CI, 4.25–8.25), 0.28 (95% CI, 0.21–0.39), and 29.05 (95% CI, 16.40–51.45) (Figures 4, 5B), respectively. The area under the SROC curve for CSF T–SPOT on diagnosing TBM was 0.92 (95% CI, 0.89–0.94) (Figure 6B). Data from the studies showed various heterogeneity for these accuracy indexes, based on I2 values of 78.6% for sensitivity, 60.7% for specificity, 52.1% for PLR, 74.3% for NLR, and 53.3% for DOR (Figures 4, 5B).

Figure 4.

Figure 4

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Forest plot showing estimates of sensitivity, specificity, PLR, and NLR for CSF T-SPOT for the diagnosis of TBM. Solid dots represent the point estimates of sensitivity, specificity, PLR, and NLR from each study. Size of solid dots reflects the total number of cases and controls. Error bars indicate 95% CI. Pooled results are shown as diamonds. PLR, positive likelihood ratio; NLR, negative likelihood ratio; CSF, cerebrospinal fluid; TBM, tuberculous meningitis; CI, confidence interval.

Figure 5.

Figure 5

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Forest plot of estimates of DOR for (A) PB T-SPOT and (B) CSF T-SPOT for the diagnosis of TBM. Point estimates of DOR from each study are shown as solid dots whose size reflects the total number of cases and controls. Error bars show 95% CI. DOR, diagnostic odds ratio; PB, peripheral blood; CSF, cerebrospinal fluid; TBM, tuberculous meningitis.

Figure 6.

Figure 6

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SROC curves for T-SPOT in the (A) PB and (B) CSF for TBM diagnosis. Each study included in the meta-analysis is shown as a solid dot. Numbers indicate the included numbers of studies. Regression SROC curves summarize the overall diagnostic accuracy. PB, peripheral blood; CSF, cerebrospinal fluid; TBM, tuberculous meningitis; SROC, summary receiver operating characteristic.

In total, the pooled sensitivity of PB T-SPOT for TBM diagnosis was slightly higher than that of CSF T-SPOT (0.78 vs. 0.76). However, the pooled specificity of PB T-SPOT was obviously lower than that of CSF T-SPOT (0.68 vs. 0.88).

Multiple Regression Analysis

The regression analysis was performed for CSF T-SPOT on heterogeneous sources. It was found that experimental design, TB burden, number of patients, and CSFMCs added to per well did not significantly affect the diagnostic utility of CSF T-SPOT for TBM (Tables 2, 3).

Table 2.

Subgroup analyses for exploration of factors influencing heterogeneity in CSF T-SPOT assay.

Variables Category (number of studies) Pooled sensitivity (95% CI) I2 Pooled specificity (95% CI) I2 Pooled diagnostic odds ratio (95% CI)
Design Prospective (9) 0.70 (0.64–0.77) 60.1% 0.85 (0.81–0.89) 57.4% 14.88 (8.86–24.99)
Retrospective (7) 0.80 (0.75–0.84) 86.5% 0.90 (0.87–0.93) 60.6% 61.29 (24.53–153.10)
TB burden High TB prevalence (11) 0.78 (0.74–0.82) 82.1% 0.89 (0.86–0.92) 67.6% 51.27 (25.47–103.20)
Intermediate TB prevalence (5) 0.66 (0.55–0.75) 46.2% 0.85 (0.80–0.89) 5.9% 11.25 (6.35–19.93)
Sample >60 (10) 0.76 (0.72–0.80) 81.3% 0.87 (0.84–0.90) 70.2% 29.20 (15.81–53.93)
≤60 (6) 0.76 (0.66–0.85) 77.4% 0.91 (0.86–0.95) 12.1% 30.29 (7.03–130.54)
CSFMCs/well 2.5 × 105 (11) 0.73 (0.68–0.78) 76.4% 0.87 (0.84–0.90) 52.1% 19.51 (11.39–33.44)
≤2.5 × 105 (5) 0.82 (0.75–0.87) 82.6% 0.89 (0.84–0.93) 75.9% 84.63 (20.43–350.66)
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CSF, cerebrospinal fluid; CSFMCs, cerebrospinal fluid mononuclear cells; TB, tuberculosis; CI, confidence interval.

Table 3.

Weighted meta-regression to assess the effects of various factors on the diagnostic accuracy of CSF T-SPOT assay.

Covariate Coefficient RDOR (95% CI) P
Design
  Prospective (9) −1.528 0.22 (0.03–1.51) 0.11
  Retrospective (7)
TB burden
  High TB prevalence (11) −0.045 0.96 (0.10–9.06) 0.97
Intermediate TB prevalence (5)
Sample
  >60 (10) −0.537 0.58 (0.14–2.38) 0.41
  ≤60 (6)
CSFMCs/well
  2.5 × 105 (11) −1.302 0.27 (0.04–1.75) 0.15
  ≤2.5 × 105 (5)
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CSF, cerebrospinal fluid; RDOR, relative diagnostic odds ratio; TB, tuberculosis; CSFMCs, cerebrospinal fluid mononuclear cells; CI, confidence interval.

Publication Bias

Deeks' funnel plot asymmetry test was used to assess the publication bias. The P-values were 0.10 for PB T-SPOT and 0.53 for CSF T-SPOT, which indicated a low risk for publication bias among all included studies (Figure 7).

Figure 7.

Figure 7

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Deeks' funnel plot asymmetry test for the evaluation of potential publication bias in (A) PB T-SPOT and (B) CSF T-SPOT studies. PB, peripheral blood; CSF, cerebrospinal fluid.

Diagnostic Value of T-SPOT for Tuberculous Meningitis When Comparing Microbiologically Confirmed Tuberculous Meningitis With Non-tuberculous Meningitis

We analyzed the included literature and identified the T-SPOT results in patients with microbiologically confirmed TBM and non-TBM in three studies (Table 4) (31, 41, 46). The total sample size in studies performed on PB was 319, comprising 66 patients with confirmed TBM and 253 non-TBM; the sample size in studies based on CSF was 262, comprising 60 patients with confirmed TBM and 202 non-TBM. The data showed the pooled sensitivity and specificity of PB T-SPOT were 0.92 (95% CI, 0.83–0.97) and 0.62 (95% CI, 0.56–0.68), respectively (Figure 8A). Besides, the pooled sensitivity and specificity of CSF T-SPOT were 0.77 (95% CI, 0.64–0.87) and 0.86 (95% CI, 0.80–0.90), respectively (Figure 8B).

Table 4.

Summary characteristics of studies comparing confirmed TBM with non-TBM.

References Country Continent TB burden* Study design Samples TBM/non-TBM patients recruited PBMCs or CSFMCs/well Test results
TP FP FN TN
Park et al. (31) Korea Asia Intermediate Prospective Peripheral blood 17/57 2.5 × 105 14 24 3 33
Cerebrospinal fluid 17/57 2.5 × 105 14 12 3 45
Park et al. (41) Korea Asia Intermediate Prospective Peripheral blood 28/159 2.5 × 105 27 66 1 93
Cerebrospinal fluid 22/109 2.5 × 105 19 16 3 93
Pan et al. (46) China Asia High Prospective Peripheral blood 21/37 2.5 × 105 20 6 1 31
Cerebrospinal fluid 21/36 1 × 105 -2.5 × 105 13 1 8 35
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*

Refer to Global Tuberculosis Report 2019; TB, tuberculosis; TBM, tuberculous meningitis; PBMCs, peripheral blood mononuclear cells; CSFMCs, cerebrospinal fluid mononuclear cells; TP, true positive; FP, false positive; FN, false negative; TN, true negative.

Figure 8.

Figure 8

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Forest plot showing estimates of sensitivity and specificity for PB T-SPOT (A) and CSF T-SPOT (B) for the diagnosis of TBM when comparing microbiologically confirmed TBM with non-TBM. Solid dots represent the point estimates of sensitivity and specificity from each study. Size of solid dots reflects the total number of cases and controls. Error bars indicate 95% CI. Pooled results are shown as diamonds. PB, peripheral blood; CSF, cerebrospinal fluid; TBM, tuberculous meningitis; CI, confidence interval.

Comparison of the Diagnostic Accuracy of Various Approaches for Tuberculous Meningitis

In this study, we also compared the diagnostic value of T-SPOT with other tests from meta-analyses. GeneXpert MTB/RIF was reported to have higher specificity than both PB and CSF T-SPOT for diagnosing TBM. However, this molecular tool was not sensitive enough to rule out non-TBM cases (53, 54). GeneXpert MTB/RIF Ultra, the next generation of GeneXpert MTB/RIF, showed a slightly higher sensitivity than both PB and CSF T-SPOT (55). Meanwhile, the specificity of this new technology was high. Besides, the overall accuracy of ADA was superior to PB T-SPOT but comparable with CSF T-SPOT (11, 56, 57) (Table 5).

Table 5.

Meta-analyses assessing the performance of various methods for diagnosing TBM.

Methods References TBM/non-TBM patients Included studies AUC (95% CI) Sensitivity (95% CI) Specificity (95% CI) PLR (95% CI) NLR (95% CI) DOR (95% CI)
GeneXpert MTB/RIF Kohli et al. (54) 433/3,341 29 NA 0.71 (0.60–0.80) 0.98 (0.97–0.99) NA NA NA
Chen et al. (53) 884/3,211 14 0.76 (NA) 0.63 (0.59–0.66) 0.98 (0.97–0.99) 20.9 (12.7–52.8) 0.40 (0.32–0.50) 71.49 (32.64–156.56)
GeneXpert MTB/RIF Ultra Donovan et al. (55) 128/321 4 NA 0.81 (0.73–0.88) 1.00 (0.99–1.00) NA NA NA
ADA Xu et al. (56) 356/1,083 10 0.92 (NA) 0.79 (0.75–0.83) 0.91 (0.89–0.93) 6.85 (4.11–11.41) 0.29 (0.19–0.44) 26.93 (12.73–56.97)
Tuon et al. (57) 380/712 13 0.91 (NA) 0.74 (0.69–0.79) 0.87 (0.84–0.89) 5.61 (3.10–10.30) 0.30 (0.18–0.47) 24.22 (9.23–63.64)
Pormohammad et al. (11) 741/1,169 20 0.96 (NA) 0.89 (0.84–0.92) 0.91 (0.87–0.93) 9.4 (7–12.8) 0.12 (0.09–0.17) 77 (45–132)
PB T-SPOT Luo et al., this study 989/1,323 25 0.83 (0.80–0.86) 0.78 (0.76–0.81) 0.68 (0.66–0.71) 2.80 (2.29–3.42) 0.32 (0.27–0.38) 10.08 (7.21–14.08)
CSF T-SPOT Luo et al., this study 538/714 16 0.92 (0.89–0.94) 0.76 (0.72–0.80) 0.88 (0.85–0.90) 5.92 (4.25–8.25) 0.28 (0.21–0.39) 29.05 (16.40–51.45)
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ADA, adenosine deaminase; PB, peripheral blood; CSF, cerebrospinal fluid; TBM, tuberculous meningitis; AUC, area under the curve; PLR, positive likelihood ratio; NLR, negative likelihood ratio; DOR, diagnostic odds ratio; CI, confidence interval; NA, not applicable.

Discussion

The central nervous system disease caused by Mycobacterium tuberculosis (MTB) is highly devastating (58). TBM is the most common form of central nervous system TB (59). Appropriate diagnosis and treatment are paramount tasks to control and end TB (60, 61). Delayed diagnosis is associated with a poor prognosis in TBM, and improved diagnosis has the potential to improve national and international surveillance of numbers of cases and trends in incidence. However, the development in this area has been sluggish in recent years. Some progress has been made but has not yet translated into outcome benefits in clinical practice (62). Further investigations should always be sought. The diagnosis of TBM remains difficult to ascertain due to no specific clinical features and the poor efficiency of the current assays, so the disease might be underreported. Thus, rapid, sensitive, and affordable diagnostic tests that can be used at the point of care are crucially needed, particularly in the regions with a high TB burden.

Our study demonstrated that CSF T-SPOT is slightly less sensitive but obviously more specific than PB T-SPOT for diagnosing TBM, which indicated that CSF T-SPOT could assist in TBM diagnosis. The overall efficiency of CSF T-SPOT shows some but not an obvious advantage over that of PB T-SPOT, which is consistent with the conclusion of a previous meta-analysis (21). These results support that TB-specific cells would accumulate in infection sites in TB (63). However, our data show that the performance of CSF T-SPOT is not as good as that of other body fluid T-SPOT, such as pleural fluid T-SPOT (64), which indicates that enough number of TB-specific lymphocytes may not always be noted in the CSF of patients with TBM. This may lead to a decrease in sensitivity for CSF T-SPOT. The possible reason for this phenomenon could be the protective effect of the blood–brain barrier. Although TB-inflamed blood–brain barrier with increased permeability allows some lymphocyte migration, the number of TB-specific lymphocytes in the subarachnoid cavity is far lower than that in the pleural cavity. Moreover, another important point that should be mentioned is that the results of CSF T-SPOT may depend on the number of CSFMCs added to the well, as the high background was reported in some studies (41, 64). As a result, it is essential to standardize technical parameters, including the number of cells, time of incubation, and the criteria of result interpretation to achieve consistent results in clinical practice. Also, QuantiFERON-TB Gold in Tube, another kind of commercially available interferon-gamma release assays, was rarely reported in diagnosing TBM. More data are needed to determine this issue in the future.

When comparing with other methods, CSF T-SPOT has no advantages over ADA in TBM diagnosis. Considering the commonly increased level of ADA in many infectious diseases, the diagnostic advantages and disadvantages of these two methods need to be further verified. On the other hand, because of lower cost and easier assessment of CSF ADA, it is undeniable that the clinical application of T-SPOT is limited. Although some studies demonstrated a relatively high sensitivity of GeneXpert MTB/RIF, it should be noticed that the use of the absolute gold standard of microbiological confirmation is likely to lead to overestimates of diagnostic sensitivity in these studies (54). In other words, GeneXpert MTB/RIF lacks enough sensitivity to distinguish TBM from non-TBM reliably and is prone to producing false-negative results because sufficient bacilli-containing CSF samples are difficult to obtain, especially from children and individuals coinfected with HIV (65, 66). Furthermore, GeneXpert MTB/RIF Ultra was reported to have higher sensitivity than GeneXpert MTB/RIF, suggesting that this new assay can provide more evidence of MTB in CSF than current bacteriological and molecular tests (67). However, given the current lack of research in this area, this new method still needs further validation. Diagnostic problems usually require holistic solutions, and further use of comprehensive criterion based on a combination of clinical and laboratory results may be indispensable to solve this problem. Also, high-quality and well-designed studies are required, as small-scale studies without validation are insufficient to establish a new diagnostic algorithm.

Our study has some limitations. First, the included studies in our meta-analysis included clinically diagnosed cases. Some diagnostic errors may have existed, which may affect the results. Second, regarding that most participants in the included studies are adult patients, the utility of T-SPOT in children with TBM is ignored, although they are the major sufferers for EPTB. Finally, there is substantial heterogeneity between the included studies. However, no potential factor was found to contribute significantly to heterogeneity on the multivariate meta-regression modeling, highlighting the need for larger, more rigorous studies.

In summary, our study suggests that both PB T-SPOT and CSF T-SPOT could severe as auxiliary tools with moderate accuracy in TBM diagnosis. Specificity is enhanced when the assay is used on CSF, but large volumes are required to get adequate CSFMCs, which hinders its clinical use.

Data Availability Statement

All datasets presented in this study are included in the article/supplementary material.

Author Contributions

YL and YX conceived and designed the study, analyzed the data, wrote the paper, prepared figures and tables, and reviewed drafts of the paper. XG, QL, LM, GT, and HS contributed to data analysis. FW and ZS reviewed drafts of the paper. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We are indebted to the authors of the primary studies included in this meta-analysis.

Footnotes

Funding. This work was supported by research grants from the National Mega Project on Major Infectious Disease Prevention (2017ZX10103005-007) and the National Natural Science Foundation of China (81401639).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All datasets presented in this study are included in the article/supplementary material.

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