European Journal of Neurology 2014, 21: 1115–1123
doi:10.1111/ene.12441
Tumor necrosis factor a level in cerebrospinal fluid for bacterial and aseptic meningitis: a diagnostic meta-analysis S. Lva*, J. Zhaoa*, J. Zhanga,b, S. Kwonc, M. Hand, R. Biand, H. Fud, Y. Zhangd and H. Pana a
Department of Endocrinology, Key Laboratory of Endocrinology of the Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing; bFirst Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China; cUniversity of Toronto, Toronto, ON, Canada; and dShandong University School of Medicine, Jinan, Shandong, China
Keywords:
aseptic meningitis, bacterial meningitis, cerebrospinal fluid, diagnostic meta-analysis, TNF-a Received 20 January 2014 Accepted 6 March 2014
Background and purpose: In our previous study, tumor necrosis factor a (TNF-a) was identified as an effective target for sepsis patients (Int J Clin Pract, 68, 2014, 520). TNF-a in cerebrospinal fluid (CSF) was also investigated for its utility in the differential diagnosis of bacterial and aseptic meningitis. However, there has been neither definite nor convincing evidence so far. Here the overall diagnostic accuracy of TNF-a in differentiation between bacterial and aseptic meningitis was evaluated through the meta-analysis of diagnostic tests. Methods: The sensitivity, specificity and other measures of accuracy were pooled using random effect models. Summary receiver operating characteristic curves were used to assess overall test performance. Publication bias was evaluated using funnel plots, and sensitivity analysis was also introduced. Results: A total of 21 studies involving bacterial meningitis (678) and aseptic meningitis (694) involved a total of 1372 patients. The pooled sensitivity and specificity for the TNF-a test were 0.83 [95% confidence interval (CI) 0.80–0.86, I2 = 65.1] and 0.92 (95% CI 0.89–0.94, I2 = 61.8), respectively. The positive likelihood ratio was 12.05 (95% CI 7.41–19.60, I2 = 36.5), the negative likelihood ratio was 0.17 (95% CI 0.13–0.24, I2 = 59.4), and TNF-a was significantly associated with bacterial meningitis, with a diagnostic odds ratio of 49.84 (95% CI 28.53–87.06, I2 = 47.9). The overall accuracy of the TNF-a test was very high with the area under the curve 0.9317. Publication bias was absent, and sensitivity analysis suggested that our results were highly stable. Conclusions: Our meta-analysis suggested that TNF-a could be recommended as a useful marker for diagnosis of bacterial meningitis and differential diagnosis between bacterial and aseptic meningitis with high sensitivity and specificity. Thus, hospitals should be encouraged to conduct TNF-a tests in CSF after lumbar puncture.
Introduction Tumor necrosis factor a (TNF-a), an inflammatory mediator, exhibits a capacity for induction of inflammation. Previously, our study revealed that in patients with severe sepsis (before shock) immunotherapy with anti-TNF-a monoclonal antibodies reduced overall Correspondence: H. Pan, Department of Endocrinology, Key Laboratory of Endocrinology of the Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China (tel.: +86 18 611613878; fax: +010 68419898; e-mail: [email protected]). *These authors contributed equally to this work.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
mortality [1], suggesting that TNF-a played a crucial role in inflammation response. The inflammatory response in the central nervous system is activated depending on the recognition and presentation of pathogens, which are responsible for the generation of inflammatory mediators such as inflammatory cytokines TNF-a, interleukin-1 (IL-1) and IL-6 [2]. Of these mediators, TNF-a can be available for detection 2 days following infection, and then recruit or induce leukocytes, microvascular endothelial cells and astrocytes to phagocytize and digest pathogens [3]. However, it should be noted that the type of pathogens, such as bacteria and viruses, can cause differences in cytokine expression [4,5]. Thus,
1115
1116
S. Lv et al.
the different expression of TNF-a in the cerebrospinal fluid (CSF) between bacterial and aseptic meningitis raises concerns [6]. In the context of diagnosis, TNF-a has been recommended as a biomarker in differentiation between bacterial and aseptic meningitis. However, due to the small sample size across studies, definite and convincing evidence was still unclear. Therefore our aim was to examine the sensitivity and specificity of TNF-a and identify the usefulness of TNF-a for differential diagnosis between bacterial and aseptic meningitis.
statistically significant heterogeneity across the studies [8]. Subgroup analyses were performed to explore the potential between-study heterogeneity. All analyses were performed using statistical software Stata version 11 (Stata Corporation, College Station, TX, USA) and Meta-DiSc for Windows (XI Cochrane Colloquium, Barcelona, Spain). Data were expressed as pooled values with 95% confidence intervals (CIs). All statistical tests were two-sided and P < 0.05 was considered statistically significant.
Results Materials and methods Study characteristics Search strategy and study selection
To find relevant studies, searches were performed through Pubmed (Medline), Embase, Cnki, Wanfang and the Cochrane database up to 30 October 2013, using the keywords ‘Tumor necrosis factor-alpha [MESH]’, ‘bacterial and aseptic meningitis’, ‘diagnosis’, ‘ROC’ and ‘accuracy’. A manual search of the references of the eligible articles was conducted subsequently. If possible, conference abstracts and letters to the editor were also included. Two reviewers independently screened the articles for inclusion. Disagreements between the reviewers were resolved by consensus. Data extraction and quality assessment
The final articles included were assessed independently by two reviewers. Data retrieved from the studies included author, publication year, patient country, test method, cut-off value, sensitivity, specificity and methodological quality. To assess the methodology, the articles were reviewed independently by two reviewers and assigned a quality score using the quality assessment for studies of diagnostic accuracy (QUADAS, a putative evidence-based quality assessment system in systematic reviews of diagnostic accuracy studies, maximum score 14) introduced by Revman 5.0 [7]. Statistical analyses
The following factors for test accuracy were computed for each study: sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR). The diagnostic performance power identified for each study was used to plot a summary receiver operating characteristic (SROC) curve. The average sensitivity, specificity and other measures of the studies were calculated using a random effect model. The Spearman correlation coefficient and Moses’ model were used to assess
As shown in Fig. 1, searches generated a total of 578 relevant studies. Amongst these studies, 312 animal studies, 29 duplicates and 12 studies overlapping with databases were eliminated. The 225 left underwent further assessment, where additional studies were excluded because 46 were not related to bacterial meningitis, 51 were not related to aseptic meningitis, 23 were reviews, 20 were other studies, 13 were duplicates, and 35 lacked enough data. Therefore 21 eligible studies with a total of 1372 patients were included. Of 21 studies, 10 studies were conducted in Asian countries. The sample sizes varied from 19 to 151 patients. Of the 1372 patients, the bacterial meningitis group consisted of 678 patients and the aseptic meningitis group consisted of 694 patients. Methods involved enzyme-linked immunosorbent assay (ELISA) in 16 studies, radioimmunoassay (RIA) in three studies, one immunofluorescence (IF) and one reverse transcription polymerase chain reaction
578 studies were initially identified as relevant 29 duplicate studies excluded 312 molecular studies excluded 12 studies excluded for overlap with databases
225 studies proceed to secondary assessment 46 not related to bacterial meningitis 51 not related to aseptic meningitis 23 review 20 other studies 13 duplicates 35 lack of enough data
21 studies included in meta-analysis
Figure 1 Literature search and selection of studies. Twenty-one eligible studies were included according to selection criteria.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
TNF-a for diagnosis of meningitis
(RT-PCR). Details of the characteristics of the included studies are summarized in Table 1.
Table 1 Summary of the studies included in the meta-analysis
Study ID
Country
Cut-off (pg/ml)
Method
B.M.
A.M.
Titmarsh [9] BociagaJasik [10] Liang [11] Mukai [12] Chang [13] Kleine [2] Jin [14] Tang [15] Pang [16] L opezCortes [17] L opezCortes [18] Yu [19] Li [20] Zhao [21]
Greek Poland
pos 75.8
RT-PCR ELISA
98 42
53 25
China Brazil China Germany China China China Spain
100 100 35 25 50 20 100 200
ELISA ELISA ELISA ELISA ELISA IF ELISA ELISA
53 6 27 40 14 23 18 20
58 13 17 46 30 26 32 22
Spain
150
ELISA
32
46
China China China
366 94.6 15.8 fM 50 60 20 6.7 fM 60 35 32
RIA ELISA RIA
21 22 21
28 47 31
ELISA ELISA ELISA RIA
43 20 41 61
35 22 14 78
ELISA ELISA ELISA
20 38 18
25 15 31
Ceyhan [22] Dulkerian [23] Akalin [24] Glimaker [25]
Turkey USA Japan Sweden
Handa [26] Arditi [27] Nadal [28]
Japan USA Switzerland
Diagnostic accuracy
ELISA, enzyme-linked immunosorbent assay; IF, immunofluorescence; RIA, radioimmunoassay; RT-PCR, reverse transcription polymerase chain reaction; B.M., bacterial meningitis; A.M., aseptic meningitis.
Figure 2 Forest plots of sensitivity and specificity for TNF-a in the differential diagnosis between bacterial and aseptic meningitis. (a) Forest plots exhibit the individual and pooled sensitivity in all 21 studies, and chi-squared and I-squared of heterogeneity. (b) Forest plots exhibit the individual and pooled specificity in all 21 studies with chi-squared and I-squared of heterogeneity. The point estimates of sensitivity and specificity are shown as solid circles. Error bars indicate 95% CI.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
The pooled sensitivity and specificity of these 21 studies concerning TNF-a in the differential diagnosis between bacterial and aseptic meningitis are shown in Fig. 2. The average sample size of the studies was 65 (range 19–151). According to random effect models, the pooled sensitivity was 0.83 and 0.92 with 95% CI 0.8–0.86 and 0.89–0.94, respectively (Table 2). Chisquared values of sensitivity and specificity were 57.37 and 52.30 respectively, with all P < 0.10, suggesting an obvious heterogeneity amongst the studies. To determine the test efficacy of TNF-a, the SROC curve was used to plot the true positive against the false positive rates of the individual studies, and then the Q value (the maximum joint sensitivity and specificity) was also used to evaluate the discriminatory power. In the present meta-analysis, the Spearman correlation coefficient and Moses’ model revealed no diagnostic threshold (P = 0.540, 0.460, respectively), so the symmetrical SROC curve was used. As shown in Fig. 3, the Q value of our study was 0.8671. The area under the curve (AUC) was 0.9317, indicating that the level of overall accuracy was high enough according to the accuracy criteria of AUC: low, 0.5 < AUC ≤ 0.7; moderate, 0.7 < AUC ≤ 0.9; or high, 0.9 < AUC ≤ 1.
Titmarsh [9] Bociaga-Jasik [10] Liang [11] Mukai [12] Chang [13] Kleine [2] Jin [14] Tang [15] Pang [16] López-Cortés [17] López-Cortés [18] Yu [19] Li [20] Zhao [21] Ceyhan [22] Dulkerian [23] Akalin [24] Glimaker [25] Handa [26] Arditi [27] Nadal [28]
Titmarsh [9] Bociaga-Jasik [10] Liang [11] Mukai [12] Chang [13] Kleine [2] Jin [14] Tang [15] Pang [16] López-Cortés [17] López-Cortés [18] Yu [19] Li [20] Zhao [21] Ceyhan [22] Dulkerian [23] Akalin [24] Glimaker [25] Handa [26] Arditi [27] Nadal [28]
0
0.2
1117
0.4 0.6 Sensitivity
0.8
Pooled Sensitivity = 0.83 (0.80 to 0.86) Chi-square = 57.37; df = 20 (P = 0.0000) Inconsistency (l-square) = 65.1%
1 0
0.2
0.4 0.6 Specificity
0.8
Pooled Specificity = 0.92 (0.89 to 0.94) Chi-square = 52.30; df = 20 (P = 0.0001) Inconsistency (l-square) = 61.8%
1
1118
S. Lv et al.
Table 2 Pooled values and 95% CI in the meta-analysis about diagnostic accuracy
Sensitivity Specificity Positive LR Negative LR Diagnostic OR
Pooled value
95% CI
0.83 0.92 12.05 0.17 49.84
0.80–0.86 0.89–0.94 7.41–19.60 0.13–0.24 28.53–87.06
Chisquared
Cochrane Q
I2 (%)
46.82 49.22 38.35
65.1 61.8 36.5 59.4 47.9
57.37 52.30
LR, likelihood ratio; OR, odds ratio.
The PLR was 12.05 (95% CI 7.41–19.60, I2 = 36.5) and the NLR was 0.17 (95% CI 0.13–0.24, I2 = 59.4). The Cochrane Q of PLR and NLR was 46.82 and 49.22, respectively, with all P < 0.001, suggesting a significant heterogeneity between the studies. At the same time, the clinical or patient-relevant utility of a diagnostic test was evaluated using the Fagan plot. Our Fagan plot demonstrated that TNF-a was very informative raising the probability of bacterial meningitis over 12-fold from 20% when positive and lowering the probability of disease to as low as 17% when negative (Fig. 4). In addition, as shown in Fig. 5, the DOR for TNFa in the differential diagnosis between bacterial and aseptic meningitis was 49.84 (95% CI 28.53–87.06), indicating that the TNF-a level was significantly related to bacterial meningitis, further enhancing the clinical implications of the TNF-a test in patients’ CSF.
Subgroup analysis and quality assessment
Subgroup analysis should be conducted owing to no diagnostic threshold. The pooled DOR of studies in Asia populations was significantly different from those in non-Asia populations (P = 0.035). However, age, gender and assay method (ELISA versus non-ELISA) did not affect the significant differences (P = 0.651, 0.856, 0.236, respectively), indicating that the country or population may act as the source of heterogeneity. Of the 21 studies, 17 studies had QUADAS scores >10 (mean 11.8, range 8–14). Due to strict inclusion criteria, patient selection was adequately conducted in all studies. An index test showed unclear results in six studies [11,13,16,18,23,28]. A reference standard was adopted in 16 studies. Flow and timing was mitigated just for two low results [11,16] and seven unclear results. Amongst three items of applicability, an index test affected the entire scores with four unclear results [9,15,16,27] and five low results. Lastly, a minimum score of 9 was achieved by Nadal et al. [28] (Fig. 6). Publication bias
A funnel plot of the diagnostic test was used to assess publication bias in this meta-analysis, which relies on a regression of DOR. As shown in Fig. 7, both the funnel plot with superimposed regression line and the slope coefficient P value 0.15 suggested symmetry in the data and a low likelihood of publication bias, inferring a low risk of publication bias in this metaanalysis. Alternatively, Begg’s and Egger’s tests of
Figure 3 The symmetrical SROC curve of TNF-a in the differential diagnosis of bacterial and aseptic meningitis. The size of each solid circle represents the sample size of each study included in the present meta-analysis. AUC indicates the overall diagnostic accuracy.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
TNF-a for diagnosis of meningitis
(b)
(c)
Titmarsh [9] Bociaga-Jasik [10] Liang [11] Mukai [12] Chang [13] Kleine [2] Jin [14] Tang [15] Pang [16] López-Cortés [17] López-Cortés [18] Yu [19] Li [20] Zhao [21] Ceyhan [22] Dulkerian [23] Akalin [24] Glimaker [25] Handa [26] Arditi [27] Nadal [28]
0.1 0.2 0.3 0.5 0.7 1 2 3 5 7 10 20 30 40 50 60 70 80
Random Effects Model
0.01
Random Effects Model
1 Positive LR
100.0 0.01
1 Negative LR
90 93 95 97 98 99 99.3 99.5 99.7 99.8 99.9
Likelihood Ratio 1000 500 200 100 50 20 10 5 2 1 0.5 0.2 0.1 0.05 0.02 0.01 0.005 0.002 0.001
Post-test Probability (%)
(a)
1119
Prior Prob (%) = 20 LR_Positive = 12 Post_ Prob_Pos (%) = 75 LR_Negative = 0.17 Post_ Prob_Neg (%) = 4
99.9 99.8 99.7 99.5 99.3 99 98 97 95 93 90 80 70 60 50 40 30 20 10 7 5 3 2 1 0.7 0.5 0.3 0.2 0.1
100.0 Fagan Plot
Figure 4 Forest plots of PLR and NLR and Fagan plot for TNF-a in the differential diagnosis between bacterial and aseptic meningitis. (a) Forest plots exhibit the individual and pooled PLR in all 21 studies with chi-squared and I-squared of heterogeneity. (b) Forest plots exhibit the individual and pooled NLR in all 21 studies with chi-squared and I-squared of heterogeneity. (c) The Fagan plot consists of a vertical axis on the left with the prior log-odds, an axis in the middle representing the log-likelihood ratio and a vertical axis on the right representing the posterior log-odds. Lines are then drawn from the prior probability on the left through the likelihood ratios in the center and extend to the posterior probabilities on the right. The point estimates of PLR and NLR are shown as solid circles. Error bars indicate 95% CI. %
Study ID
ES (95% CI)
Weight
1
901.86 (51.79, 15704.84)
2.81
2
4335.00 (83.42, 225264.23)
1.66
3
176.67 (40.11, 778.10)
6.24
4
27.50 (2.00, 378.84)
3.18
5
5.24 (1.41, 19.52)
6.91
6
59.50 (15.53, 228.04)
6.79
7
39.00 (6.26, 243.13)
5.05
8
11.90 (3.09, 45.82)
6.77
9
18.90 (4.37, 81.77)
6.31
10
165.00 (8.30, 3281.20)
2.62
11
43.00 (10.42, 177.50)
6.49
12
32.50 (5.81, 181.84)
5.39
13
14.64 (4.24, 50.63)
7.22
14
64.13 (10.64, 386.38)
5.15
15
58.50 (14.45, 236.82)
6.57
16
66.18 (3.52, 1245.30)
2.69
17
61.22 (3.39, 1104.50)
2.75
18
53.91 (18.05, 160.95)
7.86
19
669.67 (25.86, 17339.10)
2.29
20
188.82 (9.81, 3632.90)
2.66
21
279.00 (13.55, 5745.40)
2.57
Overall (I-squared = 46.7%, P = 0.010)
49.54 (28.55, 85.99)
100.00
NOTE: Weights are from random effects analysis 4.4e–06
1
2.3e+05
Figure 5 Forest plots of DOR for TNF-a in the differential diagnosis between bacterial and aseptic meningitis . The Study ID (1 to 21) is consistent with Fig. 2.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
1120
S. Lv et al.
Figure 6 An overview of quality assessment of the included studies.
DORs of all studies also exhibited significant symmetry with P = 0.546 and 0.462, respectively. Sensitivity analysis
A sensitivity analysis was introduced to test if the results were sensitive to restrictions on the data included. As shown in Fig. 8, the DOR estimates (empty circles) with one study excluded are close to the middle line (pooled DOR). On the whole, no studies significantly affected the combined DOR by sensitivity analysis, suggesting a robust result of the present meta-analysis.
Discussion
Figure 7 Funnel plot with superimposed regression line.
The involvement of inflammatory mediators in the physiopathology of meningitis is notable, such as the cytokines, which initiate the infiltration of polymorphonuclear cells in subarachnoid spaces [29]. On the other hand, the release of TNF-a, IL-1 [30] is well
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
TNF-a for diagnosis of meningitis
Meta-analysis estimates, given named study is omitted Lower CI Limit Estimate
Figure 8 Sensitivity analysis showing that all estimates (empty circles) can surround the middle line. The Study ID (1 to 21) is consistent with Fig. 2.
Upper CI Limit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 25.75 28.55
established in experimental models of bacterial meningitis. Importantly, the diagnostic value of TNF-a was also highlighted in the differential diagnosis between bacterial and aseptic meningitis. Our meta-analysis evaluated the diagnostic accuracy of TNF-a in the differential diagnosis between bacterial and aseptic meningitis, and our data demonstrated that the TNF-a test in CSF exhibited a high sensitivity of 0.83 (95% CI 0.80–0.86, I2 = 65.1) and a high specificity of 0.92 (95% CI 0.89–0.94, I2 = 61.8). It appears that TNF-a would be most applicable in screening for bacterial meningitis as expected. Additionally the AUC (0.9317) of the SROC curve offered robust evidence to confirm the differential diagnosis value of TNF-a. The Q value represents the highest common value of sensitivity and specificity for the test, which is a diagonal line from the left upper corner to the right lower corner of the ROC space. The Q value, as an intersection point, offers an overall measure of the discriminatory power of the TNF-a test. Here a Q value of 0.87 was determined by the SROC curve, suggesting high diagnostic accuracy. In the meta-analysis of diagnostic accuracy, DOR represents an indicator of test accuracy that combines the data from sensitivity and specificity into a single number [31]. A DOR of 1.0 indicates that a test does not discriminate between patients with bacterial meningitis and those without. In our metaanalysis, the mean DOR was 49.84 (95% CI 28.53– 87.06), indicating that the TNF-a level in CSF greatly aided the differential diagnosis between bacterial and aseptic meningitis. Given that likelihood ratios were considered clinically applicable, PLR and NLR were also introduced to measure diagnostic © 2014 The Author(s) European Journal of Neurology © 2014 EAN
1121
49.54
85.99
96.23
accuracy. A PLR value of 12.05 suggested that patients with bacterial meningitis had an approximately 12-fold higher chance of being TNF-a test-positive than those with aseptic meningitis. Furthermore, NLR lowered the probability of bacterial meningitis to as low as 17% which is low enough to rule out bacterial meningitis. However, the meta-analysis had some limitations as well. First, heterogeneity existed amongst the included studies. For example, there were variations in different countries. Secondly, different TNF-a cutoffs in the included studies may have caused differences in effectiveness. Thirdly, other factors in the included studies including age, gender and patient number need to be considered as a potential source of heterogeneity. In this meta-analysis, 17 of 21 studies had QUADAS scores >10 [7], and subgroup analysis indicated that the country or population may act as the underlying source of heterogeneity. In addition, few studies significantly affected the DOR by sensitivity analysis, suggesting our conclusion was convincing. Publication bias, which favors the publication of positive results and the rejection of publications with negative results, can be a major source of bias. The language of publication can also potentially contribute to this bias, as the meta-analysis was limited to studies published in English and Chinese. In this meta-analysis, a funnel plot of DOR was performed, and the superimposed regression line suggested a low risk of publication bias. In conclusion, the results of the present meta-analysis suggest that TNF-a in CSF plays a crucial role in the differential diagnosis between bacterial and aseptic
1122
S. Lv et al.
meningitis, and its diagnostic value is satisfactory. The combination of TNF-a with other indicators in CSF may establish the previous diagnosis of bacterial meningitis, such as a combination of TNF-a and white blood cell count. In practice, the TNF-a test with ELISA can be available in all hospitals, so the TNF-a test is not only a useful assistant in diagnosing bacterial meningitis but can also potentially distinguish between bacterial and aseptic meningitis. Finally, more studies should be conducted to determine the diagnostic threshold of TNF-a, and the TNF-a test can be applied to the diagnosis of more infectious diseases.
Acknowledgements This study was supported by Shandong Provincial Natural Science Foundation (ZR2013HM096) and Shandong University Science Technology Innovation Foundation (2013349). We are also very grateful for the valuable advice of Sandy Laboratory staff.
9.
10.
11.
12.
13.
14.
15.
Disclosure of conflicts of interest The authors declare no financial or other conflicts of interest.
References 1. Lv S, Han M, Yi R, et al. Anti-TNF-a therapy for patients with sepsis: a systematic meta-analysis. Int J Clin Pract 2014; 68: 520–528. 2. Kleine TO, Zwerenz P, Z€ ofel P, et al. New and old diagnostic markers of meningitis in cerebrospinal fluid (CSF). Brain Res Bulletin 2003; 61: 287–297. 3. van Deuren M, van der Ven-Jongekrijg J, Demacker PN, et al. Differential expression of proinflammatory cytokines and their inhibitors during the course of meningococcal infections. J Infect Dis 1994; 13: 157– 161. 4. Tauber MG, Moser B. Cytokines and chemokines in meningeal inflammation, biology and clinical implications. Clin Infect Dis 1999; 13: 1–11. 5. van Furth AM, Roord JJ, van Furth R. Roles of proinflammatory and anti-inflammatory cytokines in pathophysiology of bacterial meningitis and effect of adjunctive therapy. Infect Immun 1996; 13: 4883–4890. 6. Michelow IC, Nicol M, Tiemessen C, et al. Value of cerebrospinal fluid leukocyte aggregation in distinguishing the causes of meningitis in children. Pediatr Infect Dis J 2000; 19: 66–72. 7. Whiting P, Rutjes AW, Reitsma JB, et al. The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol 2003; 3: 25. 8. Moses LE, Shapiro D, Littenberg B. Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some
16.
17.
18.
19.
20.
21.
22.
23.
24.
additional considerations. Stat Med 1993; 12: 1293– 1316. Titmarsh CJ, Moscovis SM, Hall S, et al. Comparison of cytokine gene polymorphisms among Greek patients with invasive meningococcal disease or viral meningitis. J Med Microbiol 2013; 62: 694–700. Bociaga-Jasik M, Garlicki A, Ciesla A, et al. The diagnostic value of cytokine and nitric oxide concentrations in cerebrospinal fluid for the differential diagnosis of meningitis. Adv Med Sci 2012; 57: 142–147. Liang Y, Gu XL, Li M, et al. Clinical significances of detecting the tumor necrosis factor-a, lactate dehydrogenase and its isoenzyme in cerebrospinal fluid in patients with meningitis. J Chin Clin Neurol 2010; 23: 174–176. Mukai AO, Krebs VL, Bertoli CJ, et al. TNF-alpha and IL-6 in the diagnosis of bacterial and aseptic meningitis in children. Pediatr Neurol 2006; 34: 25–29. Chang M, Huang ZH. Changes and significance of cerebrospinal fluid IL-2 and TNF-a in children with intracranial infection. Cent Chin Med J 2003; 27: 249–250. Jin YX, Jun L, Yu WQ, et al. Differential diagnostic significance of cytokines in cerebrospinal fluid of children with acute central nervous system infections. Chin J Pediatr 2002; 40: 199–201. Tang RB, Lee BH, Chung RL, et al. Interleukin-l beta and tumor necrosis factor-alpha in cerebrospinal fluid of children with bacterial meningitis. Childs Nerv Syst 2001; 17: 453–456. Pang GX, Tang B, Wang Y, et al. Determination of interleukin-6 and tumor necrosis factor in cerebrospinal fluid in children with acute infection of the central nervous system. Chin J Contemp Pediatr 2000; 2: 15–17. L opez-Cortes LF, Cruz-Ruiz M, G omez-Mateos J, et al. Measurement of levels of tumor necrosis factor-alpha and interleukin-1 beta in the CSF of patients with meningitis of different etiologies: utility in the differential diagnosis. Clin Infect Dis 1993; 16: 534–539. L opez-Cortes LF, Marquez-Arbizu R, Jimenez-Jimenez LM, et al. Cerebrospinal fluid tumor necrosis factoralpha, interleukin-1 beta, interleukin-6, and interleukin-8 as diagnostic markers of cerebrospinal fluid infection in neurosurgical patients. Crit Care Med 2000; 28: 215–219. Yu GY, Wang DM, et al. Clinical significances of the tumor necrosis factor-a test in cerebrospinal fluid in patients with meningitis. J Pract Clin Pediatr 1998; 13: 8–9. Li GQ, Li MR. The value of the tumor necrosis factora test in cerebrospinal fluid in children with meningitis. Chin J Pediatr 1998; 36: 490–491. Zhao B, Zhang WY, Huang J, et al. Analysis of the tumor necrosis factor-a test in cerebrospinal fluid in patients with bacterial and viral meningitis. J Chin Neuroimmun Neurol 1997; 4: 100–103. Ceyhan M, Kanra G, Ecevit Z, et al. Tumor necrosis factor-alpha and interleukin-1 beta levels in children with bacterial, tuberculosis and aseptic meningitis. Turk J Pediatr 1997; 39: 177–184. Dulkerian SJ, Kilpatrick L, Costarino AT Jr, et al. Cytokine elevations in infants with bacterial and aseptic meningitis. J Pediatr 1995; 126: 872–876. Akalin H, Akdis AC, Mistik R, et al. Cerebrospinal fluid interleukin-1 beta/interleukin-1 receptor antagonist balance and tumor necrosis factor-alpha concentrations
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
TNF-a for diagnosis of meningitis
in tuberculous, viral and acute bacterial meningitis. Scand J Infect Dis 1994; 26: 667–674. 25. Glimaker M, Kragsbjerg P, Forsgren M, et al. Tumor necrosis factor-alpha (TNF-alpha) in cerebrospinal fluid from patients with meningitis of different etiologies: high levels of TNF alpha indicate bacterial meningitis. J Infect Dis 1993; 167: 882–889. 26. Handa S. Concentrations of interleukin-1 beta, interleukin-6, interleukin-8 and TNF-alpha in cerebrospinal fluid from children with septic or aseptic meningitis. Kurume Med J 1992; 39: 257–265. 27. Arditi M, Manogue KR, Caplan M, et al. Cerebrospinal fluid cachectin/tumor necrosis factor-alpha and platelet-activating factor concentrations and severity of bacterial meningitis in children. J Infect Dis 1990; 162: 139–147.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
1123
28. Nadal D, Leppert D, Frei K, et al. Tumour necrosis factor-alpha in infectious meningitis. Arch Dis Child 1989; 64: 1274–1279. 29. Waage A, Brandtzaeg P, Halstensen A, et al. The complex pattern of cytokines in serum from patients with meningococcal septic shock. J Exp Med 1989; 169: 333–338. 30. Ostergaard C, Yieng-Kow RV, Benfield T, FrimodtMøller N, Espersen F, Lundgren JD. Inhibition of leukocyte entry into the brain by the selectinbBlocker fucoidin decreases interleukin-1 (IL-1) levels but increases IL-8 levels in cerebrospinal fluid during experimental pneumococcal meningitis in rabbits. Infect Immun 2000; 68: 3153–3157. 31. Glas AS, Lijmer JG, Prins MH, et al. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 2003; 56: 1129–1135.
doi:10.1111/ene.12441
Tumor necrosis factor a level in cerebrospinal fluid for bacterial and aseptic meningitis: a diagnostic meta-analysis S. Lva*, J. Zhaoa*, J. Zhanga,b, S. Kwonc, M. Hand, R. Biand, H. Fud, Y. Zhangd and H. Pana a
Department of Endocrinology, Key Laboratory of Endocrinology of the Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing; bFirst Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China; cUniversity of Toronto, Toronto, ON, Canada; and dShandong University School of Medicine, Jinan, Shandong, China
Keywords:
aseptic meningitis, bacterial meningitis, cerebrospinal fluid, diagnostic meta-analysis, TNF-a Received 20 January 2014 Accepted 6 March 2014
Background and purpose: In our previous study, tumor necrosis factor a (TNF-a) was identified as an effective target for sepsis patients (Int J Clin Pract, 68, 2014, 520). TNF-a in cerebrospinal fluid (CSF) was also investigated for its utility in the differential diagnosis of bacterial and aseptic meningitis. However, there has been neither definite nor convincing evidence so far. Here the overall diagnostic accuracy of TNF-a in differentiation between bacterial and aseptic meningitis was evaluated through the meta-analysis of diagnostic tests. Methods: The sensitivity, specificity and other measures of accuracy were pooled using random effect models. Summary receiver operating characteristic curves were used to assess overall test performance. Publication bias was evaluated using funnel plots, and sensitivity analysis was also introduced. Results: A total of 21 studies involving bacterial meningitis (678) and aseptic meningitis (694) involved a total of 1372 patients. The pooled sensitivity and specificity for the TNF-a test were 0.83 [95% confidence interval (CI) 0.80–0.86, I2 = 65.1] and 0.92 (95% CI 0.89–0.94, I2 = 61.8), respectively. The positive likelihood ratio was 12.05 (95% CI 7.41–19.60, I2 = 36.5), the negative likelihood ratio was 0.17 (95% CI 0.13–0.24, I2 = 59.4), and TNF-a was significantly associated with bacterial meningitis, with a diagnostic odds ratio of 49.84 (95% CI 28.53–87.06, I2 = 47.9). The overall accuracy of the TNF-a test was very high with the area under the curve 0.9317. Publication bias was absent, and sensitivity analysis suggested that our results were highly stable. Conclusions: Our meta-analysis suggested that TNF-a could be recommended as a useful marker for diagnosis of bacterial meningitis and differential diagnosis between bacterial and aseptic meningitis with high sensitivity and specificity. Thus, hospitals should be encouraged to conduct TNF-a tests in CSF after lumbar puncture.
Introduction Tumor necrosis factor a (TNF-a), an inflammatory mediator, exhibits a capacity for induction of inflammation. Previously, our study revealed that in patients with severe sepsis (before shock) immunotherapy with anti-TNF-a monoclonal antibodies reduced overall Correspondence: H. Pan, Department of Endocrinology, Key Laboratory of Endocrinology of the Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China (tel.: +86 18 611613878; fax: +010 68419898; e-mail: [email protected]). *These authors contributed equally to this work.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
mortality [1], suggesting that TNF-a played a crucial role in inflammation response. The inflammatory response in the central nervous system is activated depending on the recognition and presentation of pathogens, which are responsible for the generation of inflammatory mediators such as inflammatory cytokines TNF-a, interleukin-1 (IL-1) and IL-6 [2]. Of these mediators, TNF-a can be available for detection 2 days following infection, and then recruit or induce leukocytes, microvascular endothelial cells and astrocytes to phagocytize and digest pathogens [3]. However, it should be noted that the type of pathogens, such as bacteria and viruses, can cause differences in cytokine expression [4,5]. Thus,
1115
1116
S. Lv et al.
the different expression of TNF-a in the cerebrospinal fluid (CSF) between bacterial and aseptic meningitis raises concerns [6]. In the context of diagnosis, TNF-a has been recommended as a biomarker in differentiation between bacterial and aseptic meningitis. However, due to the small sample size across studies, definite and convincing evidence was still unclear. Therefore our aim was to examine the sensitivity and specificity of TNF-a and identify the usefulness of TNF-a for differential diagnosis between bacterial and aseptic meningitis.
statistically significant heterogeneity across the studies [8]. Subgroup analyses were performed to explore the potential between-study heterogeneity. All analyses were performed using statistical software Stata version 11 (Stata Corporation, College Station, TX, USA) and Meta-DiSc for Windows (XI Cochrane Colloquium, Barcelona, Spain). Data were expressed as pooled values with 95% confidence intervals (CIs). All statistical tests were two-sided and P < 0.05 was considered statistically significant.
Results Materials and methods Study characteristics Search strategy and study selection
To find relevant studies, searches were performed through Pubmed (Medline), Embase, Cnki, Wanfang and the Cochrane database up to 30 October 2013, using the keywords ‘Tumor necrosis factor-alpha [MESH]’, ‘bacterial and aseptic meningitis’, ‘diagnosis’, ‘ROC’ and ‘accuracy’. A manual search of the references of the eligible articles was conducted subsequently. If possible, conference abstracts and letters to the editor were also included. Two reviewers independently screened the articles for inclusion. Disagreements between the reviewers were resolved by consensus. Data extraction and quality assessment
The final articles included were assessed independently by two reviewers. Data retrieved from the studies included author, publication year, patient country, test method, cut-off value, sensitivity, specificity and methodological quality. To assess the methodology, the articles were reviewed independently by two reviewers and assigned a quality score using the quality assessment for studies of diagnostic accuracy (QUADAS, a putative evidence-based quality assessment system in systematic reviews of diagnostic accuracy studies, maximum score 14) introduced by Revman 5.0 [7]. Statistical analyses
The following factors for test accuracy were computed for each study: sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR). The diagnostic performance power identified for each study was used to plot a summary receiver operating characteristic (SROC) curve. The average sensitivity, specificity and other measures of the studies were calculated using a random effect model. The Spearman correlation coefficient and Moses’ model were used to assess
As shown in Fig. 1, searches generated a total of 578 relevant studies. Amongst these studies, 312 animal studies, 29 duplicates and 12 studies overlapping with databases were eliminated. The 225 left underwent further assessment, where additional studies were excluded because 46 were not related to bacterial meningitis, 51 were not related to aseptic meningitis, 23 were reviews, 20 were other studies, 13 were duplicates, and 35 lacked enough data. Therefore 21 eligible studies with a total of 1372 patients were included. Of 21 studies, 10 studies were conducted in Asian countries. The sample sizes varied from 19 to 151 patients. Of the 1372 patients, the bacterial meningitis group consisted of 678 patients and the aseptic meningitis group consisted of 694 patients. Methods involved enzyme-linked immunosorbent assay (ELISA) in 16 studies, radioimmunoassay (RIA) in three studies, one immunofluorescence (IF) and one reverse transcription polymerase chain reaction
578 studies were initially identified as relevant 29 duplicate studies excluded 312 molecular studies excluded 12 studies excluded for overlap with databases
225 studies proceed to secondary assessment 46 not related to bacterial meningitis 51 not related to aseptic meningitis 23 review 20 other studies 13 duplicates 35 lack of enough data
21 studies included in meta-analysis
Figure 1 Literature search and selection of studies. Twenty-one eligible studies were included according to selection criteria.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
TNF-a for diagnosis of meningitis
(RT-PCR). Details of the characteristics of the included studies are summarized in Table 1.
Table 1 Summary of the studies included in the meta-analysis
Study ID
Country
Cut-off (pg/ml)
Method
B.M.
A.M.
Titmarsh [9] BociagaJasik [10] Liang [11] Mukai [12] Chang [13] Kleine [2] Jin [14] Tang [15] Pang [16] L opezCortes [17] L opezCortes [18] Yu [19] Li [20] Zhao [21]
Greek Poland
pos 75.8
RT-PCR ELISA
98 42
53 25
China Brazil China Germany China China China Spain
100 100 35 25 50 20 100 200
ELISA ELISA ELISA ELISA ELISA IF ELISA ELISA
53 6 27 40 14 23 18 20
58 13 17 46 30 26 32 22
Spain
150
ELISA
32
46
China China China
366 94.6 15.8 fM 50 60 20 6.7 fM 60 35 32
RIA ELISA RIA
21 22 21
28 47 31
ELISA ELISA ELISA RIA
43 20 41 61
35 22 14 78
ELISA ELISA ELISA
20 38 18
25 15 31
Ceyhan [22] Dulkerian [23] Akalin [24] Glimaker [25]
Turkey USA Japan Sweden
Handa [26] Arditi [27] Nadal [28]
Japan USA Switzerland
Diagnostic accuracy
ELISA, enzyme-linked immunosorbent assay; IF, immunofluorescence; RIA, radioimmunoassay; RT-PCR, reverse transcription polymerase chain reaction; B.M., bacterial meningitis; A.M., aseptic meningitis.
Figure 2 Forest plots of sensitivity and specificity for TNF-a in the differential diagnosis between bacterial and aseptic meningitis. (a) Forest plots exhibit the individual and pooled sensitivity in all 21 studies, and chi-squared and I-squared of heterogeneity. (b) Forest plots exhibit the individual and pooled specificity in all 21 studies with chi-squared and I-squared of heterogeneity. The point estimates of sensitivity and specificity are shown as solid circles. Error bars indicate 95% CI.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
The pooled sensitivity and specificity of these 21 studies concerning TNF-a in the differential diagnosis between bacterial and aseptic meningitis are shown in Fig. 2. The average sample size of the studies was 65 (range 19–151). According to random effect models, the pooled sensitivity was 0.83 and 0.92 with 95% CI 0.8–0.86 and 0.89–0.94, respectively (Table 2). Chisquared values of sensitivity and specificity were 57.37 and 52.30 respectively, with all P < 0.10, suggesting an obvious heterogeneity amongst the studies. To determine the test efficacy of TNF-a, the SROC curve was used to plot the true positive against the false positive rates of the individual studies, and then the Q value (the maximum joint sensitivity and specificity) was also used to evaluate the discriminatory power. In the present meta-analysis, the Spearman correlation coefficient and Moses’ model revealed no diagnostic threshold (P = 0.540, 0.460, respectively), so the symmetrical SROC curve was used. As shown in Fig. 3, the Q value of our study was 0.8671. The area under the curve (AUC) was 0.9317, indicating that the level of overall accuracy was high enough according to the accuracy criteria of AUC: low, 0.5 < AUC ≤ 0.7; moderate, 0.7 < AUC ≤ 0.9; or high, 0.9 < AUC ≤ 1.
Titmarsh [9] Bociaga-Jasik [10] Liang [11] Mukai [12] Chang [13] Kleine [2] Jin [14] Tang [15] Pang [16] López-Cortés [17] López-Cortés [18] Yu [19] Li [20] Zhao [21] Ceyhan [22] Dulkerian [23] Akalin [24] Glimaker [25] Handa [26] Arditi [27] Nadal [28]
Titmarsh [9] Bociaga-Jasik [10] Liang [11] Mukai [12] Chang [13] Kleine [2] Jin [14] Tang [15] Pang [16] López-Cortés [17] López-Cortés [18] Yu [19] Li [20] Zhao [21] Ceyhan [22] Dulkerian [23] Akalin [24] Glimaker [25] Handa [26] Arditi [27] Nadal [28]
0
0.2
1117
0.4 0.6 Sensitivity
0.8
Pooled Sensitivity = 0.83 (0.80 to 0.86) Chi-square = 57.37; df = 20 (P = 0.0000) Inconsistency (l-square) = 65.1%
1 0
0.2
0.4 0.6 Specificity
0.8
Pooled Specificity = 0.92 (0.89 to 0.94) Chi-square = 52.30; df = 20 (P = 0.0001) Inconsistency (l-square) = 61.8%
1
1118
S. Lv et al.
Table 2 Pooled values and 95% CI in the meta-analysis about diagnostic accuracy
Sensitivity Specificity Positive LR Negative LR Diagnostic OR
Pooled value
95% CI
0.83 0.92 12.05 0.17 49.84
0.80–0.86 0.89–0.94 7.41–19.60 0.13–0.24 28.53–87.06
Chisquared
Cochrane Q
I2 (%)
46.82 49.22 38.35
65.1 61.8 36.5 59.4 47.9
57.37 52.30
LR, likelihood ratio; OR, odds ratio.
The PLR was 12.05 (95% CI 7.41–19.60, I2 = 36.5) and the NLR was 0.17 (95% CI 0.13–0.24, I2 = 59.4). The Cochrane Q of PLR and NLR was 46.82 and 49.22, respectively, with all P < 0.001, suggesting a significant heterogeneity between the studies. At the same time, the clinical or patient-relevant utility of a diagnostic test was evaluated using the Fagan plot. Our Fagan plot demonstrated that TNF-a was very informative raising the probability of bacterial meningitis over 12-fold from 20% when positive and lowering the probability of disease to as low as 17% when negative (Fig. 4). In addition, as shown in Fig. 5, the DOR for TNFa in the differential diagnosis between bacterial and aseptic meningitis was 49.84 (95% CI 28.53–87.06), indicating that the TNF-a level was significantly related to bacterial meningitis, further enhancing the clinical implications of the TNF-a test in patients’ CSF.
Subgroup analysis and quality assessment
Subgroup analysis should be conducted owing to no diagnostic threshold. The pooled DOR of studies in Asia populations was significantly different from those in non-Asia populations (P = 0.035). However, age, gender and assay method (ELISA versus non-ELISA) did not affect the significant differences (P = 0.651, 0.856, 0.236, respectively), indicating that the country or population may act as the source of heterogeneity. Of the 21 studies, 17 studies had QUADAS scores >10 (mean 11.8, range 8–14). Due to strict inclusion criteria, patient selection was adequately conducted in all studies. An index test showed unclear results in six studies [11,13,16,18,23,28]. A reference standard was adopted in 16 studies. Flow and timing was mitigated just for two low results [11,16] and seven unclear results. Amongst three items of applicability, an index test affected the entire scores with four unclear results [9,15,16,27] and five low results. Lastly, a minimum score of 9 was achieved by Nadal et al. [28] (Fig. 6). Publication bias
A funnel plot of the diagnostic test was used to assess publication bias in this meta-analysis, which relies on a regression of DOR. As shown in Fig. 7, both the funnel plot with superimposed regression line and the slope coefficient P value 0.15 suggested symmetry in the data and a low likelihood of publication bias, inferring a low risk of publication bias in this metaanalysis. Alternatively, Begg’s and Egger’s tests of
Figure 3 The symmetrical SROC curve of TNF-a in the differential diagnosis of bacterial and aseptic meningitis. The size of each solid circle represents the sample size of each study included in the present meta-analysis. AUC indicates the overall diagnostic accuracy.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
TNF-a for diagnosis of meningitis
(b)
(c)
Titmarsh [9] Bociaga-Jasik [10] Liang [11] Mukai [12] Chang [13] Kleine [2] Jin [14] Tang [15] Pang [16] López-Cortés [17] López-Cortés [18] Yu [19] Li [20] Zhao [21] Ceyhan [22] Dulkerian [23] Akalin [24] Glimaker [25] Handa [26] Arditi [27] Nadal [28]
0.1 0.2 0.3 0.5 0.7 1 2 3 5 7 10 20 30 40 50 60 70 80
Random Effects Model
0.01
Random Effects Model
1 Positive LR
100.0 0.01
1 Negative LR
90 93 95 97 98 99 99.3 99.5 99.7 99.8 99.9
Likelihood Ratio 1000 500 200 100 50 20 10 5 2 1 0.5 0.2 0.1 0.05 0.02 0.01 0.005 0.002 0.001
Post-test Probability (%)
(a)
1119
Prior Prob (%) = 20 LR_Positive = 12 Post_ Prob_Pos (%) = 75 LR_Negative = 0.17 Post_ Prob_Neg (%) = 4
99.9 99.8 99.7 99.5 99.3 99 98 97 95 93 90 80 70 60 50 40 30 20 10 7 5 3 2 1 0.7 0.5 0.3 0.2 0.1
100.0 Fagan Plot
Figure 4 Forest plots of PLR and NLR and Fagan plot for TNF-a in the differential diagnosis between bacterial and aseptic meningitis. (a) Forest plots exhibit the individual and pooled PLR in all 21 studies with chi-squared and I-squared of heterogeneity. (b) Forest plots exhibit the individual and pooled NLR in all 21 studies with chi-squared and I-squared of heterogeneity. (c) The Fagan plot consists of a vertical axis on the left with the prior log-odds, an axis in the middle representing the log-likelihood ratio and a vertical axis on the right representing the posterior log-odds. Lines are then drawn from the prior probability on the left through the likelihood ratios in the center and extend to the posterior probabilities on the right. The point estimates of PLR and NLR are shown as solid circles. Error bars indicate 95% CI. %
Study ID
ES (95% CI)
Weight
1
901.86 (51.79, 15704.84)
2.81
2
4335.00 (83.42, 225264.23)
1.66
3
176.67 (40.11, 778.10)
6.24
4
27.50 (2.00, 378.84)
3.18
5
5.24 (1.41, 19.52)
6.91
6
59.50 (15.53, 228.04)
6.79
7
39.00 (6.26, 243.13)
5.05
8
11.90 (3.09, 45.82)
6.77
9
18.90 (4.37, 81.77)
6.31
10
165.00 (8.30, 3281.20)
2.62
11
43.00 (10.42, 177.50)
6.49
12
32.50 (5.81, 181.84)
5.39
13
14.64 (4.24, 50.63)
7.22
14
64.13 (10.64, 386.38)
5.15
15
58.50 (14.45, 236.82)
6.57
16
66.18 (3.52, 1245.30)
2.69
17
61.22 (3.39, 1104.50)
2.75
18
53.91 (18.05, 160.95)
7.86
19
669.67 (25.86, 17339.10)
2.29
20
188.82 (9.81, 3632.90)
2.66
21
279.00 (13.55, 5745.40)
2.57
Overall (I-squared = 46.7%, P = 0.010)
49.54 (28.55, 85.99)
100.00
NOTE: Weights are from random effects analysis 4.4e–06
1
2.3e+05
Figure 5 Forest plots of DOR for TNF-a in the differential diagnosis between bacterial and aseptic meningitis . The Study ID (1 to 21) is consistent with Fig. 2.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
1120
S. Lv et al.
Figure 6 An overview of quality assessment of the included studies.
DORs of all studies also exhibited significant symmetry with P = 0.546 and 0.462, respectively. Sensitivity analysis
A sensitivity analysis was introduced to test if the results were sensitive to restrictions on the data included. As shown in Fig. 8, the DOR estimates (empty circles) with one study excluded are close to the middle line (pooled DOR). On the whole, no studies significantly affected the combined DOR by sensitivity analysis, suggesting a robust result of the present meta-analysis.
Discussion
Figure 7 Funnel plot with superimposed regression line.
The involvement of inflammatory mediators in the physiopathology of meningitis is notable, such as the cytokines, which initiate the infiltration of polymorphonuclear cells in subarachnoid spaces [29]. On the other hand, the release of TNF-a, IL-1 [30] is well
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
TNF-a for diagnosis of meningitis
Meta-analysis estimates, given named study is omitted Lower CI Limit Estimate
Figure 8 Sensitivity analysis showing that all estimates (empty circles) can surround the middle line. The Study ID (1 to 21) is consistent with Fig. 2.
Upper CI Limit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 25.75 28.55
established in experimental models of bacterial meningitis. Importantly, the diagnostic value of TNF-a was also highlighted in the differential diagnosis between bacterial and aseptic meningitis. Our meta-analysis evaluated the diagnostic accuracy of TNF-a in the differential diagnosis between bacterial and aseptic meningitis, and our data demonstrated that the TNF-a test in CSF exhibited a high sensitivity of 0.83 (95% CI 0.80–0.86, I2 = 65.1) and a high specificity of 0.92 (95% CI 0.89–0.94, I2 = 61.8). It appears that TNF-a would be most applicable in screening for bacterial meningitis as expected. Additionally the AUC (0.9317) of the SROC curve offered robust evidence to confirm the differential diagnosis value of TNF-a. The Q value represents the highest common value of sensitivity and specificity for the test, which is a diagonal line from the left upper corner to the right lower corner of the ROC space. The Q value, as an intersection point, offers an overall measure of the discriminatory power of the TNF-a test. Here a Q value of 0.87 was determined by the SROC curve, suggesting high diagnostic accuracy. In the meta-analysis of diagnostic accuracy, DOR represents an indicator of test accuracy that combines the data from sensitivity and specificity into a single number [31]. A DOR of 1.0 indicates that a test does not discriminate between patients with bacterial meningitis and those without. In our metaanalysis, the mean DOR was 49.84 (95% CI 28.53– 87.06), indicating that the TNF-a level in CSF greatly aided the differential diagnosis between bacterial and aseptic meningitis. Given that likelihood ratios were considered clinically applicable, PLR and NLR were also introduced to measure diagnostic © 2014 The Author(s) European Journal of Neurology © 2014 EAN
1121
49.54
85.99
96.23
accuracy. A PLR value of 12.05 suggested that patients with bacterial meningitis had an approximately 12-fold higher chance of being TNF-a test-positive than those with aseptic meningitis. Furthermore, NLR lowered the probability of bacterial meningitis to as low as 17% which is low enough to rule out bacterial meningitis. However, the meta-analysis had some limitations as well. First, heterogeneity existed amongst the included studies. For example, there were variations in different countries. Secondly, different TNF-a cutoffs in the included studies may have caused differences in effectiveness. Thirdly, other factors in the included studies including age, gender and patient number need to be considered as a potential source of heterogeneity. In this meta-analysis, 17 of 21 studies had QUADAS scores >10 [7], and subgroup analysis indicated that the country or population may act as the underlying source of heterogeneity. In addition, few studies significantly affected the DOR by sensitivity analysis, suggesting our conclusion was convincing. Publication bias, which favors the publication of positive results and the rejection of publications with negative results, can be a major source of bias. The language of publication can also potentially contribute to this bias, as the meta-analysis was limited to studies published in English and Chinese. In this meta-analysis, a funnel plot of DOR was performed, and the superimposed regression line suggested a low risk of publication bias. In conclusion, the results of the present meta-analysis suggest that TNF-a in CSF plays a crucial role in the differential diagnosis between bacterial and aseptic
1122
S. Lv et al.
meningitis, and its diagnostic value is satisfactory. The combination of TNF-a with other indicators in CSF may establish the previous diagnosis of bacterial meningitis, such as a combination of TNF-a and white blood cell count. In practice, the TNF-a test with ELISA can be available in all hospitals, so the TNF-a test is not only a useful assistant in diagnosing bacterial meningitis but can also potentially distinguish between bacterial and aseptic meningitis. Finally, more studies should be conducted to determine the diagnostic threshold of TNF-a, and the TNF-a test can be applied to the diagnosis of more infectious diseases.
Acknowledgements This study was supported by Shandong Provincial Natural Science Foundation (ZR2013HM096) and Shandong University Science Technology Innovation Foundation (2013349). We are also very grateful for the valuable advice of Sandy Laboratory staff.
9.
10.
11.
12.
13.
14.
15.
Disclosure of conflicts of interest The authors declare no financial or other conflicts of interest.
References 1. Lv S, Han M, Yi R, et al. Anti-TNF-a therapy for patients with sepsis: a systematic meta-analysis. Int J Clin Pract 2014; 68: 520–528. 2. Kleine TO, Zwerenz P, Z€ ofel P, et al. New and old diagnostic markers of meningitis in cerebrospinal fluid (CSF). Brain Res Bulletin 2003; 61: 287–297. 3. van Deuren M, van der Ven-Jongekrijg J, Demacker PN, et al. Differential expression of proinflammatory cytokines and their inhibitors during the course of meningococcal infections. J Infect Dis 1994; 13: 157– 161. 4. Tauber MG, Moser B. Cytokines and chemokines in meningeal inflammation, biology and clinical implications. Clin Infect Dis 1999; 13: 1–11. 5. van Furth AM, Roord JJ, van Furth R. Roles of proinflammatory and anti-inflammatory cytokines in pathophysiology of bacterial meningitis and effect of adjunctive therapy. Infect Immun 1996; 13: 4883–4890. 6. Michelow IC, Nicol M, Tiemessen C, et al. Value of cerebrospinal fluid leukocyte aggregation in distinguishing the causes of meningitis in children. Pediatr Infect Dis J 2000; 19: 66–72. 7. Whiting P, Rutjes AW, Reitsma JB, et al. The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol 2003; 3: 25. 8. Moses LE, Shapiro D, Littenberg B. Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some
16.
17.
18.
19.
20.
21.
22.
23.
24.
additional considerations. Stat Med 1993; 12: 1293– 1316. Titmarsh CJ, Moscovis SM, Hall S, et al. Comparison of cytokine gene polymorphisms among Greek patients with invasive meningococcal disease or viral meningitis. J Med Microbiol 2013; 62: 694–700. Bociaga-Jasik M, Garlicki A, Ciesla A, et al. The diagnostic value of cytokine and nitric oxide concentrations in cerebrospinal fluid for the differential diagnosis of meningitis. Adv Med Sci 2012; 57: 142–147. Liang Y, Gu XL, Li M, et al. Clinical significances of detecting the tumor necrosis factor-a, lactate dehydrogenase and its isoenzyme in cerebrospinal fluid in patients with meningitis. J Chin Clin Neurol 2010; 23: 174–176. Mukai AO, Krebs VL, Bertoli CJ, et al. TNF-alpha and IL-6 in the diagnosis of bacterial and aseptic meningitis in children. Pediatr Neurol 2006; 34: 25–29. Chang M, Huang ZH. Changes and significance of cerebrospinal fluid IL-2 and TNF-a in children with intracranial infection. Cent Chin Med J 2003; 27: 249–250. Jin YX, Jun L, Yu WQ, et al. Differential diagnostic significance of cytokines in cerebrospinal fluid of children with acute central nervous system infections. Chin J Pediatr 2002; 40: 199–201. Tang RB, Lee BH, Chung RL, et al. Interleukin-l beta and tumor necrosis factor-alpha in cerebrospinal fluid of children with bacterial meningitis. Childs Nerv Syst 2001; 17: 453–456. Pang GX, Tang B, Wang Y, et al. Determination of interleukin-6 and tumor necrosis factor in cerebrospinal fluid in children with acute infection of the central nervous system. Chin J Contemp Pediatr 2000; 2: 15–17. L opez-Cortes LF, Cruz-Ruiz M, G omez-Mateos J, et al. Measurement of levels of tumor necrosis factor-alpha and interleukin-1 beta in the CSF of patients with meningitis of different etiologies: utility in the differential diagnosis. Clin Infect Dis 1993; 16: 534–539. L opez-Cortes LF, Marquez-Arbizu R, Jimenez-Jimenez LM, et al. Cerebrospinal fluid tumor necrosis factoralpha, interleukin-1 beta, interleukin-6, and interleukin-8 as diagnostic markers of cerebrospinal fluid infection in neurosurgical patients. Crit Care Med 2000; 28: 215–219. Yu GY, Wang DM, et al. Clinical significances of the tumor necrosis factor-a test in cerebrospinal fluid in patients with meningitis. J Pract Clin Pediatr 1998; 13: 8–9. Li GQ, Li MR. The value of the tumor necrosis factora test in cerebrospinal fluid in children with meningitis. Chin J Pediatr 1998; 36: 490–491. Zhao B, Zhang WY, Huang J, et al. Analysis of the tumor necrosis factor-a test in cerebrospinal fluid in patients with bacterial and viral meningitis. J Chin Neuroimmun Neurol 1997; 4: 100–103. Ceyhan M, Kanra G, Ecevit Z, et al. Tumor necrosis factor-alpha and interleukin-1 beta levels in children with bacterial, tuberculosis and aseptic meningitis. Turk J Pediatr 1997; 39: 177–184. Dulkerian SJ, Kilpatrick L, Costarino AT Jr, et al. Cytokine elevations in infants with bacterial and aseptic meningitis. J Pediatr 1995; 126: 872–876. Akalin H, Akdis AC, Mistik R, et al. Cerebrospinal fluid interleukin-1 beta/interleukin-1 receptor antagonist balance and tumor necrosis factor-alpha concentrations
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
TNF-a for diagnosis of meningitis
in tuberculous, viral and acute bacterial meningitis. Scand J Infect Dis 1994; 26: 667–674. 25. Glimaker M, Kragsbjerg P, Forsgren M, et al. Tumor necrosis factor-alpha (TNF-alpha) in cerebrospinal fluid from patients with meningitis of different etiologies: high levels of TNF alpha indicate bacterial meningitis. J Infect Dis 1993; 167: 882–889. 26. Handa S. Concentrations of interleukin-1 beta, interleukin-6, interleukin-8 and TNF-alpha in cerebrospinal fluid from children with septic or aseptic meningitis. Kurume Med J 1992; 39: 257–265. 27. Arditi M, Manogue KR, Caplan M, et al. Cerebrospinal fluid cachectin/tumor necrosis factor-alpha and platelet-activating factor concentrations and severity of bacterial meningitis in children. J Infect Dis 1990; 162: 139–147.
© 2014 The Author(s) European Journal of Neurology © 2014 EAN
1123
28. Nadal D, Leppert D, Frei K, et al. Tumour necrosis factor-alpha in infectious meningitis. Arch Dis Child 1989; 64: 1274–1279. 29. Waage A, Brandtzaeg P, Halstensen A, et al. The complex pattern of cytokines in serum from patients with meningococcal septic shock. J Exp Med 1989; 169: 333–338. 30. Ostergaard C, Yieng-Kow RV, Benfield T, FrimodtMøller N, Espersen F, Lundgren JD. Inhibition of leukocyte entry into the brain by the selectinbBlocker fucoidin decreases interleukin-1 (IL-1) levels but increases IL-8 levels in cerebrospinal fluid during experimental pneumococcal meningitis in rabbits. Infect Immun 2000; 68: 3153–3157. 31. Glas AS, Lijmer JG, Prins MH, et al. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 2003; 56: 1129–1135.
