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. 2018 Sep 8;13:329–337. doi: 10.1515/med-2018-0050

Diagnostic Efficacy of Serum 1,3-β-D-glucan for Invasive Fungal Infection: An Update Meta-Analysis Based on 37 Case Or Cohort Studies

Lu Xiaoling 2, Tang Tingyu 2, Hu Caibao 3, Zhao Tian 2, Chen Changqin 1,*
PMCID: PMC6132083  PMID: 30211316

Abstract

Objective

The aim of this study was to investigate the diagnostic performance of serum 1,3-β-D-gluan as biomarker for invasive fungal infection through meta-analysis.

Methods

The electronic databases of Medline, Cochrane, Embase, Web of Science, OVID and CNKI were systematic searched to identified the case-control or Cohort studies relevant to diagnostic efficacy of serum 1,3-β-D-glucan for invasive fungal infection. The data of true positive (tp), false positive (fp), false negative (fn) and true negative (tn) patients number were extracted from each of the original included studies. The diagnostic sensitivity, specificity and systematic receiver operating characteristic (SROC) curve were calculated and pooled through random or fixed effect method. The publication bias was evaluated by the Deek’s funnel plot.

Results

Thirty-seven relevant studies were fulfilled the inclusion criteria and included in our present meta-analysis. The combined sensitivity, specificity, positive likely hood ratio (+lr), negative likely hood ratio (-lr) and diagnostic odds ratio(dor) for 1,3-β-D-glucan in diagnosis of invasive fungal infectionwere 0.83 (95%CI:0.38-0.61), 0.81 (95%CI:0.80-0.82), 5.13 (95%CI:3.98-6.62), 0.23 (95%CI:0.18-0.30), and 29.68 (95%CI:18.94-46.52) respectively. The pooled area under the ROC curve (AUC) was 0.91.The Deek’s funnel plot asymmetry test showed there was no publication bias for 1,3-β-D-glucan in diagnosis of invasive fungal infection of the included 37 studies.

Conclusion

Serum 1,3-β-D-glucan assay was a promising biomarker for invasive fungal infection diagnosis.

Keywords: 1,3-β-D-glucan; invasive fungal infection; meta-analysis; diagnosis

1. Introduction

Invasive fungal infections (IFIs) are serious kinds of fungal diseases that invade the body, grow and reproduce in body tissues, organs and blood, and cause inflammation and tissue damage [1]. Due to the application of high strength, immunosuppressive agents and high-dose chemotherapy for organ transplantation, hematopoietic stem cell transplantation, a variety of catheter in interventional, indwelling intubation ventilator have been widely carried out and the increase of AIDS infection, the incidence of IFI increased significantly [2]. However, the diagnosis of invasive fungal infection was not easy in clinical practice [3]. Generally, invasive fungal infection often requires fungal culture or histopathological examination to confirm the diagnosis. However, fungal culture usually takes 2 to 4 days to obtain results, while histological examination is difficult to achieve in most cases. Therefore, it is important to make a rapid and accurate diagnosis of IFI with a simple and convenient method. 1,3-β-D-glucan is a fungal-cell-wall polysaccharide that can released into the peripheral blood in patients with IFIs. And serum 1,3-β-D-glucan were used as serum biomarker for IFIs diagnosis. However, the diagnostic performance of 1,3-β-D-glucan was quite different according to the previously published studies because of different 1,3-β-D-glucan assays and cutoff value [4]. Therefore, we performed this meta-analysis and pooled the diagnostic efficacy to further assess it clinical value.

2. Methods

2.1. Publication search strategy

The electronic databases of Medline, Cochrane, Embase, Web of Science, OVID and CNKI were systematic searched to identified the case-control or Cohort studies relevant to diagnostic efficacy of serum 1,3-β-D-glucan for invasive fungal infection. The below terms were used for electronic databases searching. (1) invasive fungal infection/IFI; (2) invasive fungal disease/IFD; (3) 1,3-β-D-glucan/BG/BDG. The language was limited to English and Chinese. In order to identify additional relevant publications, the reference of the included studies were also screened to find potential suitable studies. For studies without enough data to make the meta-analysis, the corresponding authors were contacted by e-mail to obtain further information, if necessary.

2.2. Data and information extraction

Hu Caibao and Zhao Tian independently read the whole paper and extracted the data and information. Any disagreement was consulted to another investigator (Chen Changqin) for consensus. The extracted data and information included (1) The study type (case-control or Cohort); (2) The language of the publication (English or Chinese); (3) The journal name of the paper published; (4) The year of the paper published; (5) The first and the corresponding authors; (6) The diagnosis of invasive fungal infectionreference standard; (7) The cutoff value of serum 1,3-β-D-glucan in each study; (8) The number of true positive, false positive, false negative and true negative; (9) Invasive fungal infection detection methods; (10) The region the study performed. And the above detail information and data were by Zhao Tian and Chen Changqin respectively and cross checked.

2.3. Publication quality evaluation

The general method quality of the included 37 studies was evaluated through the QUADAS tool byTingyu and Hu Caibao independently with eight questionnaires [5, 6]. For each of the question, there was 3 status “yes”, “no” and “unclear”. “yes” represents high quality, “no” represents low quality and “unclear” represents moderate quality.

3. Statistical analysis

The diagnostic efficacy for 1,3-β-D-glucan for invasive fungal infection was pooled by MetaDiSc 1.4 software by the equation of sensitivity=true positive/(true positive+false negative), specificity=true negative/( true negative+ false positive). Subgroup analysis was performed according to different serum 1,3-β-D-glucan cutoff value. The diagnostic parameters were pooled by mixed or random effect model according to the statistical heterogeneity across theincluded studies. The SROC curve was drawn by Stata12.0SE software and the AUC was calculated through sensitivity vs specificity for 1,3-β-D-glucan in diagnosis of invasive fungal infection. P< 0.05 was considered statistical significant.

4. Results

4.1. Main characteristic of the individual 37 publications

As a result of the related electronic databases systematic searching, finally 37 relevant studies were fulfilled the inclusion criteria and included in our present meta-analysis (Figure 1). Among the 37 publications, 15 studies were Cohort designed prospective study and other 22 are case-control publications. The cutoff value for 1,3-β-D-glucan in diagnosis of invasive fungal infection ranged from 7 (pg/mL) to 140 (pg/mL). The general characteristics of the 37 publications were demonstrated in Table 1.

Figure 1.

Figure 1

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The flowchart of publication searching and studies inclusion

Table 1.

The main characters of the recruited 37 publications

Study Year Country Study type Method Cutoff value (pg/mL) Reference tp fp fn tn
Miyazaki [7] 1997 Japan Case-control Fungitec G-test 10 Micobiological culture 17 0 7 36
Obayashi [8] 1995 Japan Case-control Fungitec G-test 20 Autopsy 37 0 4 153
Kawazu [9] 2004 Japan Cohort Wako 11 EORTC/MSG 6 2 5 123
Kondori [10] 2004 Sweden Case-control Fungitec G-test 20 Micobiological culture 14 0 0 19
Odabasi [11] 2004 U.S Cohort Fungitell 80 EORTC/MSG 18 15 2 248
Ostrosky-Zeichner [12] 2005 U.S Case-control Fungitell 60 EORTC/MSG 95 22 22 148
Pazos [13] 2005 Spain Cohort Fungitell 120 EORTC/MSG 7 3 1 26
Pickering [14] 2005 U.S Case-control Fungitell 60 Histopathologic examination 31 10 1 22
Fujita [15] 2006 Japan Case-control Wako 11 Micobiological culture 72 28 4 147
Alam [16] 2007 Kuwait Case-control Fungitell 80 EORTC/MSG 14 0 13 26
Akamatsu [17] 2007 Japan Cohort Fungitec G-test 40 EORTC/MSG 12 26 7 130
Obayashi [18] 2008 Japan Case-control Fungitec G-test 30 Autopsy 39 9 2 98
Senn [19] 2008 Switzerland Cohort Wako 7 EORTC/MSG 20 28 10 85
Persat [20] 2008 France Case-control Fungitell 80 EORTC/MSG 70 39 26 123
Ellis [21] 2008 UAE Cohort Fungitell 80 EORTC/MSG 36 23 2 19
Leon [22] 2009 Europe Cohort Fungitell 75 Histopathologic examination 14 105 4 117
Lunel [23] 2009 Netherlands Cohort Fungitell 60 Micobiological culture 16 12 5 18
Hachem [24] 2009 U.S Cohort Fungitell 80 EORTC/MSG 29 2 16 18
Koo [25] 2009 U.S Case-control Fungitell 80 EORTC/MSG 50 124 23 635
Presterl [26] 2009 Austria Cohort Fungitell 40 Micobiological culture 12 14 11 44
Racil [27] 2010 Czech Republic Case-control Fungitell 80 EORTC/MSG 8 55 1 27
Alexander [28] 2010 U.S Cohort Fungitell 60 EORTC/MSG 8 54 3 5
Hirata [29] 2010 Japan Cohort Wako 8.9 EORTC/MSG 8 2 2 196
Li J [30] 2010 China Case-control Fungitec G-test 20 Micobiological culture 37 16 1 26
Zuo XH (1)[31] 2010 China Case-control Fungitec G-test 20 EORTC/MSG 35 4 5 31
Zuo XH (2) [31] 2010 China Case-control Fungitec G-test 50 EORTC/MSG 29 3 11 32
De Vlieger [32] 2011 Belgium Cohort Fungitell 140 EORTC/MSG 12 10 2 33
Posteraro [33] 2011 Italy Cohort Fungitell 80 EORTC/MSG 15 5 1 74
Acosta [34] 2011 Spain Cohort Fungitell 80 EORTC/MSG 7 7 2 31
Jiang ZM [35] 2011 China Case-control Fungitec G-test 20 Micobiological culture 44 21 2 45
Yang HQ [36] 2011 China Case-control Fungitec G-test 20 Micobiological culture 423 355 34 1027
Jin X [37] 2011 China Case-control Fungitec G-test 10 EORTC/MSG 40 1 5 39
Metan [38] 2012 Turkery Case-control Fungitell 80 EORTC/MSG 33 19 17 59
Liu CH [39] 2012 China Case-control Fungitec G-test 20 EORTC/MSG 36 6 7 51
Ding C [40] 2012 China Case-control Fungitec G-test 20 Micobiological culture 42 1 6 29
Wang QF [41] 2012 China Case-control Fungitec G-test 20 Micobiological culture 47 7 2 75
Yang D [42] 2013 China Case-control Fungitec G-test 20 Micobiological culture 26 16 9 87
Zeng WX [43] 2016 China Case-control Fungitec G-test 20 Micobiological culture 38 3 39 478
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4.2. Quality assessment of the included studies

Eight items (questions) original from QUADAS (Quality Assessment of Diagnostic Accuracy Studies) were used to assessed the general quality of the included studies. For the QUADAS quality assessment, most of the 37 studies fulfilled the items of “Clear description of study selection criteria “and” acceptable reference standard”. However, most of the included studies didn’t clearly addressed the item of “ withdraw reports”. The generally quality of the included 37 publications were showed in Figure 2.

Figure 2.

Figure 2

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The general quality of the included 37 studies evaluated by QUADAS

4.3. The pooled diagnostic sensitivity

The I2 test of the 37-publication indicated significant heterogeneity (I2=83.5%). The diagnostic sensitivity was pooled by random effect model. The combined sensitivity for 1,3-β-D-glucan in diagnosis of invasive fungal infection was 0.83 (95%CI:0.38-0.61), Figure 3.

Figure 3.

Figure 3

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Pooled forest plot of sensitivity of 1,3-β-D-glucan in diagnosis of invasive fungal infection.

4.4. The pooled diagnostic specificity

Significant statistical heterogeneity was found in the aspect of pooling the specificity through the I2 test (I2=95.5%). The pooled diagnostic specificity was 0.81 (95%CI:0.80-0.82) by random effect model, Figure 4.

Figure 4.

Figure 4

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Pooled forest plot of specificity of 1,3-β-D-glucan in diagnosis of invasive fungal infection

4.5. The pooled +lr and –lr

The statistical heterogeneity for effect size +lr and –lr were evaluated through I2 test. And the results indicated statistical significant heterogeneity (P=0.000). Therefore, the data was pooled through random effect. The pooled +lr and –lr were 5.13 (95%CI:3.98-6.62), Figure 5 and 0.23 (95%CI:0.18-0.30), Figure 6.

Figure 5.

Figure 5

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Pooled forest plot of +lr for 1,3-β-D-glucan in diagnosis of invasive fungal infection

Figure 6.

Figure 6

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Pooled forest plot of -lr for 1,3-β-D-glucan in diagnosis of invasive fungal infection

4.6. Pooled diagnostic odds ratio(dor)

Because of significant statistical heterogeneity across the included 37 studies (I2=80.9%), the dor was calculated through random effect model. The combined dor was 29.68 (95%CI:18.94-46.52), Figure 7.

Figure 7.

Figure 7

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Pooled forest plot of dor for 1,3-β-D-glucan in diagnosis of invasive fungal infection

4.7. The pooled summary ROC and AUC

The combined receiver operating characteristic (ROC) curve was calculated by Stata12.0SE software (Figure 8). The area under the ROC curve was 0.91.

Figure 8.

Figure 8

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The AUC of the SROC for 1,3-β-D-glucan in diagnosis of invasive fungal infection

4.8. Diagnostic efficacy changes according to cutoff value

The diagnostic sensitivity, specificity, +lr, -lr and odds ratio changes according to cutoff value were demonstrated in Figure 9. The diagnostic sensitivity, specificity and –lrdid not change a lot for different cutoff value of serum 1,3-β-D-glucan (Figure 9a, Figure 9b, Figure 9d).

Figure 9.

Figure 9

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The scatter plot of diagnostic efficacy changes according to cutoff value

However, the +lr and dor changed significantly for different cutoff value especially for <20 (pg/mL) group (Figure 9c and Figure 9e).

4.9. Subgroup analysis

In order to minimize the clinical heterogeneity, we performed subgroup analysis according to invasive fungal infection detection method, study type and gold diagnostic reference. However, the diagnostic efficacy didn’t significant changed for different subgroups (Table 2).

Table 2.

The subgroup analysis of 1,3-β-D-glucan for invasive fungal infection

Subgroup No. of study Sensitivity Specificity AUC
Method
Fungitell 18 0.76(0.72-0.79) 0.76(0.74-0.78) 0.86
Fungitec G-test 16 0.87(0.84-0.89) 0.83(0.82-0.85) 0.95
Wako 3 0.83(0.76-0.89) 0.90(0.88-0.92) 0.93
Study type
 Cohort 14 0.75(0.70-0.80) 0.79(0.77-0.81) 0.85
 Case-control 23 0.84(0.82-0.86) 0.82(0.81-0.83) 0.93
Reference
 EORTC/MSG 21 0.76(0.73-0.79) 0.83(0.81-0.84) 0.87
Micobiological culture 12 0.87(0.84-0.89) 0.81(0.80-0.83) 0.94
Others 4 0.92(0.86-0.96) 0.76(0.82-0.80) 0.97
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4.10. Publication bias evaluation

The publication bias was evaluate by the Deek’s funnel plot (Figure 10) and line regression test. The Deek’s funnel plot asymmetry test showed there was no publication bias for 1,3-β-D-glucan in diagnosis of invasive fungal infection of the included 37 studies.

Figure 10.

Figure 10

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The funnel plot of publication bias for 1,3-β-D-glucan in diagnosis of invasive fungal infection

5. Discussion

Statistical studies demonstrated that invasive fungal infection(IFI) has significantly increased because of the extensive application of high-dose chemotherapy, glucocorticoids, broad-spectrum antibiotics and immunosuppressive agents, as well as the extensive development of solid organ transplantation and hematopoietic stem cell transplantation [44, 45]. Generally, appropriate clinical antifungal treatment is often not timely, which leading to deterioration of the patient’s condition, thereafter resulting in high mortality. Therefore, rapid and accurate diagnosis of IFI is particularly important for patients with IFI.

1,3-β-D-glucan is a polysaccharide component of fungal cell wall, which is fungi specific and can’t find in other microorganisms infection disease such as bacteria, viruses and mycoplasma.1,3-β-D-glucan assay was widely used clinically for IFI diagnosis with good clinical practice value. Previously studies showed 1,3-β-D-glucan was continuously released into the peripheral blood in patients with IFI. Generally, 1,3-β-D-glucan concentration was very low in serum of healthy people, usually less than 10 pg /mL. However, it serum level elevated significant when invasive fungal infection occurred in patients, which was generally greater than 20 pg /mL. Theoretically, 1,3-β-D-glucan detection may be the ideal method for rapid diagnosis of invasive fungal infections in early stage. However, the sensitivity and specificity of these findings are not completely consistent according to the previously published studies. In order to explicated its diagnostic efficacy, we performed this meta-analysis with 37 case-control or cohort studies and pooled the diagnostic efficacy. In our present, meta-analysis, we included 37 relevant studies with the combined sensitivity, specificity, positive likelihood ratio(+lr), negative likelyhood ratio(-lr) and diagnostic odds ratio(dor) of 0.83 (95%CI:0.38-0.61), 0.81 (95%CI:0.80-0.82), 5.13 (95%CI:3.98-6.62), 0.23 (95%CI:0.18-0.30), and 29.68 (95%CI:18.94-46.52). And the pooled AUC was 0.91. The results indicated that serum 1,3-β-D-glucan assay was a promising biomarker for invasive fungal infection diagnosis.

ROC curve is an accurate and comprehensive method for evaluation diagnostic tests. According to the results of Swets [46], if the area under the ROC curve (AUC) less than 0.5, there is no diagnostic value. The diagnostic value of low with limited clinical value when the AUC between 0.5 ~ 0.7. However, the diagnostic accuracy is high when the AUC is more than 0.7. In this present meta-analysis, we found the pooled AUC was 0.91 which indicated that the diagnostic accuracy is high for serum 1,3-β-D-glucan assay in diagnosis of IFIs.

However, there are several limitations in this study. Firstly, there are some clinical heterogeneity which can lead to unstable results. Secondly, statistical heterogeneity was also existed in pooling the data, which may decrease the statistical power. Thirdly, BDG is present in numerous fungi and is therefore non-specific. However, it could probably be a helpful tool for ruling out an IFI.

6. Conclusion

Serum 1,3-β-D-glucan assay was a promising biomarker for invasive fungal infection diagnosis. However, because of the non-specificity and heterogeneity across the included studies, it should be further evaluated by high quality multicenter prospective diagnostic studies which can provided more strong evidence.

Footnotes

Conflict of interest: The authors report no conflicts of interest in this work.

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