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Journal of Clinical and Experimental Hepatology logoLink to Journal of Clinical and Experimental Hepatology
. 2021 May 12;12(2):519–532. doi: 10.1016/j.jceh.2021.05.002

Performance of Leukocyte Esterase Reagent Strips in the Detection of Spontaneous Bacterial Peritonitis in Cirrhotic Patients: A Systematic Review and Meta-analysis

Kishan P Patel ∗, John P Gallagher ∗, Parker M Korbitz †, Cynthia Schmidt ‡, Thammasin Ingviya §,‖, Tomoki Sempokuya , Wuttiporn Manatsathit ¶,∗
PMCID: PMC9077197  PMID: 35535110

Abstract

Background

Spontaneous bacterial peritonitis (SBP) is a bacterial infection associated with a high mortality rate in cirrhotic patients. The gold standard for the detection of SBP is a manual cell count from ascitic fluid; however, alternative screening methods are under investigation. In particular, leukocyte esterase reagent strips (LERS) has been studied as an alternative method to detect SBP with a low cost and instant turnaround time. Therefore, this study aims to evaluate the performance of LERS in the detection of SBP.

Methods

A literature search was performed for studies evaluating LERS for the detection of SBP on PubMed, Embase, Scopus, Cochrane, and clinical trial registries. Summary sensitivity, specificity, log diagnostic odds ratio (LDOR), and the area under the summary receiver operating curve (AUC) were calculated according to the respective manufacturer.

Results

In total, 31 studies were evaluated. The summary sensitivity of Aution Sticks, Combur, Multistix, Periscreen reagent strips was 0.962 (95% confidence interval [CI] 0.926, 0.998), 0.892 (95% CI 0.846, 0.938), 0.806 (95% CI 0.738, 0.874), and 0.939 (95% CI 0.900, 0.979), respectively. The summary specificity of Aution Sticks, Combur, Multistix, and Periscreen reagent strips was 0.940 (95% CI 0.904, 0.976), 0.922 (95% CI 0.874, 0.970), 0.974 (95% CI 0.962, 0.985), and 0.672 (95% CI 0.381, 0.963), respectively.

Conclusion

LERS appears to have a notable overall performance for the detection of SBP. LERS appeared to be an acceptable alternative to diagnose SBP in facilities without ability to perform cell count. However, there were significant differences in performance between each manufacturer.

Keywords: reagent strips, leukocyte esterase, ascites, spontaneous bacterial peritonitis, cirrhosis

Abbreviations: AUC, area under the summary receiver operating curve; LDOR, log diagnostic odds ratio; LERS, leukocyte esterase reagent strips; PMN, polymorphonuclear; SBP, Spontaneous bacterial peritonitis; SROC, summary receiver operating characteristic

Spontaneous bacterial peritonitis (SBP) is an infection of ascitic fluid, in the absence of an apparent source, such as an intra-abdominal inflammatory focus, contiguous source of infection, or malignancy.1, 2, 3 The prevalence of SBP often varies, ranging from 3.5% to 30% based on the setting of diagnosis (inpatient vs. outpatient).1,2 Although some patients may experience fever or abdominal pain, others are often asymptomatic.1Therefore, early diagnosis is crucially important, as the mortality of untreated SBP is high.2

Currently, the gold standard for diagnosis of SBP is the detection of a polymorphonuclear (PMN) cell count from ascitic fluid ≥250 cells/mm3.3, 4, 5This is performed by a manual cell count, which can often be time-consuming.3Therefore, utilization of rapid, easy to operate, and inexpensive methods to diagnose SBP at the bedside may offer early diagnosis and prompt antibiotic administration.6 Leukocyte esterase reagent strips (LERS) are a commonly used test to diagnose urinary tract infection. This method detects leukocyte esterase activity resulting in a color change of the reagent strip.2,3,7 Apart from urine, LERS has been used to detect the presence of PMNs in various body fluids such as pleural fluid, cerebrospinal fluid, and synovial fluid.8, 9, 10 Several studies within recent years have examined the use of LERS for the bedside diagnosis of SBP. The recommendations that LERS suffice as a stand-alone method for SBP detection have been inconsistent because of a variety of manufacturers and differences in cutoff values. Therefore, the aim of this meta-analysis study is to examine the performance of LERS for the diagnosis of SBP based on manufacturers and cutoff values.

Materials and methods

This meta-analysis was conducted in accordance to Cochrane's manual of diagnostic test accuracy, and the manuscript was prepared with consideration to the preferred reporting items for systematic reviews and meta-analysis of diagnostic test accuracy (PRISMA-DTA) guidelines.11,12 A literature search of PubMed, Embase, Scopus, Google Scholar, Cochrane, ClinicalTrials.gov, and the European Clinical Trial Registry was performed from inception through March 2020 by a systemic review specialized librarian (C.S.). Search terms for this meta-analysis included (1) SBP and (2) leukocyte esterase, leukocyte esterase, reagent strip, Aution Sticks, Combur, Multistix, Periscreen, Uriscan, Uri-Plus, Choiceline, Elastase Dip, Combina, Combi, or Nephur. The titles and abstracts of the resulting search were reviewed by two independent authors (P.K. and J.G.). Discrepancies were resolved via discussion between the mentioned two independent authors and their senior author (W.M.). Both independent authors compiled the extracted data from each study, including study characteristics, study population characteristics, and study results. Study characteristics included author, year of publication, start and end dates, country, and the type of study design performed. Study population characteristics included the total number of adult patients, total number of paracenteses performed, mean age of patients, the setting of paracentesis (inpatient vs. outpatients), Child-Pugh scores, and criteria for SBP diagnosis. SBP was defined by a PMN ≥250 cells/mm3 regardless of ascitic fluid culture. Exclusion criteria included other secondary causes of neutrocytic ascites, such as malignancy, different intra-abdominal infection(s), recent abdominal surgeries, or recent exposure to antibiotics. Abstracted data also included the prevalence of SBP, LERS manufacturers in each study, grading systems for each LERS, cutoff values, method of detection (visual comparison vs. analyzer), sensitivity, specificity, true positive, false positive, false negative, and true negative. This information was either obtained directly from the data provided by each study or calculated using other available information. Quality assessment for the studies included was performed using QUADAS-2 by both independent authors (P.K. and J.G.). Any discrepancies noted by both were again resolved through discussion between the independent authors and their senior author (W.M.).

Statistical Analysis

This study used the R version 3.2.4 (R Core team 2013) with Metafor and Mada packages for statistical analysis.13,14 An inter-observer agreement was measured and evaluated by Cohen's Kappa coefficient. The summary sensitivities, specificities, and log diagnostic odds ratios (LDOR) were calculated via bivariate meta-analysis as described by Reitasma.15 The hierarchical summary receiver operating characteristic model was used to calculate the summary receiver operating characteristic (SROC), as outlined by Rutter and Gatsonis.16 Lastly, the area under SROC curves (AUC) was calculated for inclusion in this study. Subgroup analyses were conducted by stratifying the data based on LERS manufacturer, cutoff values, and method of detection (visual vs. analyzer). Study heterogeneity was evaluated by the I2 statistic; a range between 0% and 40%, 30% and 60%, 50% and 90%, and 75% and 100% suggested low, moderate, substantial, and considerable heterogeneity, respectively.17 Publication biases were assessed by Deeks' funnel plot, where a P-value <0.1 suggests publication bias.18

Results

Figure 1 highlights the screening and selection process performed for this meta-analysis. After the removal of duplicate articles, a total of 115 studies were found using the search criteria previously mentioned. Of these, 42 articles were discovered to be relevant. Thereafter, two studies were excluded because of the absence of information regarding LERS manufacturers, and nine studies were excluded because of an inadequate amount of data associated with specific LERS manufacturers for use in this meta-analysis. For example, these included two studies with Medi-Test Combi, a study with Choiceline, a study with Combi-screen 10SL, a study with Fagnos Elastase Dip, a study with Human-Test Combina, a study with Uri-Plus, a study with Uri-Quick, and a study with Uriscan. Ultimately, 31 studies (4446 patients), including four abstracts and 27 full articles, were used in this meta-analysis. Of these, five studies evaluated the performance of Aution Sticks, eight studies evaluated Combur, 20 studies evaluated Multistix, and three studies evaluated Periscreen. Table 1 demonstrates the characteristics of each study evaluating the use of LERS in the detection of SBP, whereas Table 2 demonstrated extracted data.

Figure 1.

Figure 1

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A flowchart demonstrating the study search and selection process for this meta-analysis according to the PRISMA statement. (∗Four studies evaluated two LERS, whereas one study evaluated 3 LERS for the detection of SBP).

Table 1.

Characteristics of Studies Evaluating Reagent Strips for the Detection of SBP.

Study Type Country Study type Strip type Analyze method Start date End date Mean age (years) Sample size (n) Most common etiology of cirrhosis Location Child-Pugh score
A (n) B (n) C (n)
Ahmed et al.19 Full paper Egypt Prospective Aution Sticks Visual NR NR NR 100 NR IP NR NR NR
Al Sawaf et al.20 Full paper Egypt Prospective Combur 10 Visual Jan 2010 Dec 2012 51 ± 11 168 Hepatitis C IP 0 16 152
Braga et al.21 Full paper Brazil Prospective Combur 10 Visual Mar 2004 Aug 2004 51.7 42 Alcohol IP, OP 0 23 19
Butani et al.22 Full paper USA NR Multistix Visual NR NR NR 75 NR NR NR NR NR
Campillo et al.23 Full paper France NR Multistix Combur 10 Visual Dec 2003 Feb 2005 57 ± 12 116 Alcohol IP 0 38 78
Castellote et al.24 Full paper NR Prospective Aution Sticks Visual NR NR 59 ± 13 128 Alcohol IP 0 46 82
Chinnock et al.25 Full paper India Prospective Peri-screen Visual Apr 2016 Jan 2018 56 ± 10 282 Alcohol ER NR NR NR
Chugh et al.26 Full paper India NR Multistix Visual NR NR 44 ± 15 103 Alcohol NR 0 48 55
De Araujo et al.27 Full paper Brazil Prospective Multistix Visual Jun 2006 Apr 2007 55 ± 11 71 Hepatitis C IP, OP 0 27 39
Gaya et al.28 Full paper United Kingdom Cross-Sectional Multistix Analyzer NR NR 57 ± 11 105 Alcohol IP, OP NR NR 80
Jha et al.29 Full paper India Prospective Multistix Visual Apr 2005 Jun 2007 44 ± 13 100 Alcohol IP 0 27 73
Jothimani et al.30 Abstract India NR Peri-screen Visual June 2016 Mar 2017 52 72 NR NR NR NR NR
Khatwani et al.31 Full paper Pakistan Cross-Sectional Multistix Visual Jan 2009 Jun 2009 45 ± 15 94 Hepatitis C IP NR NR NR
Kim et al.32 Full paper Korea Prospective Multistix Visual Dec 2003 Mar 2004 57 ± 9 53 Hepatitis B IP 0 21 32
Li et al.33 Abstract NR NR Multistix Visual NR NR 46 84 NR IP NR NR NR
Nalmpantidis et al.34 Abstract Greece Prospective Aution Sticks NR NR NR 70 43 NR NR 14 17 12
Nousbaum et al.35 Full paper France Prospective Multistix Visual Jan 2004 May 2004 60 1041 Alcohol IP, OP 0 470 571
Oey et al.36 Full paper Netherlands Prospective Combur 10 Visual Analyzer Jul 2006 Jul 2007 51 ± 10 52 Alcohol IP, OP NR NR NR
Rerknimitr et al. (2006)37 Full paper Thailand NR Combur 10 Visual Jul 2004 Jul 2007 57 ± 13 127 Hepatitis B NR 3 34 90
Rerknimitr et al. (2010)38 Full paper Thailand Prospective Aution Sticks Combur 10 Multistix Visual Sep 2006 Dec 2008 59 ± 12 143 Hepatitis B NR 0 58 85
Ribeiro et al.39 Full paper Brazil Prospective Multistix Visual Apr 2003 Dec 2004 54 ± 12 106 Alcohol IP, OP 10 89 101
Sapey (JGH-1) et al.40 Full paper France, USA Prospective Multistix Visual NR NR 60 ± 11 53 Alcohol OP 3 18 32
Sapey (JGH-2) et al.40 Full paper France, USA Prospective Multistix Visual NR NR 51 ± 13 23 Alcohol IP 0 10 13
Sapey (LI) et al.41 Full paper France Prospective Multistix Visual NR NR 55 ± 11 51 Alcohol IP, OP 6 20 25
Sarwar et al.42 Full paper Pakistan Cross-Sectional Combur 10 Visual Nov 2003 Aug 2004 52 ± 10 214 NR ER, IP, OP NR NR NR
Tellez et al.43 Full paper Mexico Prospective Multistix Visual Mar 2005 Feb 2007 54 138 Hepatitis C ER 4 51 83
Thévenot et al. (2004)44 Full paper France Prospective Combur 10 Multistix Visual Apr 2002 Apr 2003 59 ± 10 31 Alcohol IP, OP 0 10 21
Thévenot et al. (2016)45 Full paper France Prospective Peri-screen Visual Mar 2014 Aug 2015 61 ± 10 649 Alcohol IP, OP NR NR NR
Torun et al.46 Full paper Turkey NR Aution Sticks Visual Apr 2005 Jul 2006 59 ± 12 63 Hepatitis B IP 0 25 38
Vanbiervliet et al.47 Full paper France Prospective Multistix Visual Sep 2000 May 2001 62 ± 14 72 Alcohol IP 0 28 44
Wisniewski et al.48 Abstract France NR Multistix Visual Jun 2003 May 2004 NR 47 NR IP NR NR NR
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ELISA, enzyme-linked immunosorbent assay; ER, emergency room; IP, inpatient; OP, outpatient; NR, not reported; PMN, polymorphonuclear leukocytes; SBP, spontaneous bacterial peritonitis.

Table 2.

Extracted Data From Studies Evaluating Reagent Strips for the Detection of SBP.

Study Number of paracenteses (n) Prevalence of SBP (%) Cutoff value Grade scale Sensitivity (%) Specificity (%) True positive(n) False positive (n) False negative (n) True negative (n)
Aution Sticks
  • •

    Ahmed et al.

 100 42 ≥3 5 100 91.38 42 5 0 53
  • •

    Castellote et al.

 228 22.81 ≥2 5 96.49 88.89 55 19 2 152
  • •

    Nalmpantidis et al.

 108 13.95 NR 5 81.25 95.65 13 4 3 88
  • •

    Rerknimitr et al. (2010)

 250 12 ≥1 5 90 93.18 27 15 3 205
  • •

    Torun et al.

 63 23.81 ≥2 5 93.33 100 14 0 1 48
Combur 10
  • •

    Al Sawaf et al.

 168 29.17 ≥1 4 89.80 81.51 44 22 5 97
  • •

    Braga et al.

 100 21.43 ≥2 4 100 98.86 12 1 0 87
  • •

    Campillo et al.

 443 21.55 ≥1 4 80.22 90.27 73 32 18 297
  • •

    Oey et al.

 157 7.64 NR 4 75 99.31 9 1 3 144
  • •

    Rerknimitr et al. (2006)

 200 21 ≥1 4 88.10 81.01 37 30 5 128
  • •

    Rerknimitr et al. (2010)

 250 12 ≥1 4 90 93.2 27 15 3 205
  • •

    Sarwar et al.

 214 17.76 ≥2 4 94.74 92.05 36 14 2 162
  • •

    Thevenot et al. (2004)

 100 25.81 ≥2 4 87.50 100 7 0 1 23
Multistix
  • •

    Butani et al.

 136 8.82 ≥2 5 83.33 99.19 10 1 2 123
  • •

    Campillo et al.

 443 21.55 ≥1 5 69.23 94.83 63 17 28 312
  • •

    Chugh et al.

 103 19.42 ≥2 5 95 96.39 19 3 1 80
  • •

    De Araujo et al.

 159 23.94 ≥1 5 80 98.46 20 2 5 128
  • •

    Gaya et al.

 173 8.09 ≥1 5 100 91.19 14 14 0 145
  • •

    Jha et al.

 100 18 ≥2 5 77.77 95.12 16 4 4 88
  • •

    Khatwani et al.

 94 55.32 ≥1 5 92.31 95.24 48 2 4 40
  • •

    Kim et al.

 75 24 ≥3 5 50 100 7 0 7 48
  • •

    Li et al.

 84 29.76 NR 5 92 84.75 23 9 2 50
  • •

    Nalmpantidis et al.

 108 13.95 NR 5 64.3 100 10 0 5 93
  • •

    Nousbaum et al.

 2123 5.51 ≥3 5 45.3 99.25 53 15 64 1991
  • •

    Rerknimitr et al. (2010)

 250 12 ≥1 5 80 94.55 24 12 6 208
  • •

    Ribeiro et al.

 200 13.21 ≥2 5 85.19 95.95 23 7 4 166
  • •

    Sapey et al. (JGH 1)

 151 11.32 ≥1 5 100 100 6 0 0 47
  • •

    Sapey et al. (JGH 2)

 33 21.74 ≥1 5 83 96 4 1 1 17
  • •

    Sapey et al. (LI)

 245 23.53 ≥1 5 64.71 99.56 11 1 6 227
  • •

    Tellez et al.

 229 21.97 ≥3 5 77.55 97.71 38 4 11 171
  • •

    Thevenot et al. (2004)

 100 25.81 ≥3 5 87.5 100 7 0 1 23
  • •

    Vanbiervliet et al.

 144 12.50 ≥1 5 100 100 9 0 0 63
  • •

    Wisniewki et al.

 47 12.77 ≥1 5 83.33 82.93 5 7 1 34
Periscreen
  • •

    Chinnock et al.

 330 7.09 ≥ trace 5 95 48.07 38 309 2 286
  • •

    Jothimani et al.

 NR 20.83 NR 4 100 96.49 15 2 0 55
  • •

    Thevenot et al. (2016)

 1402 5.99 ≥ trace 4 91.67 57.13 77 565 7 753
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NR, no reported; SBP, spontaneous bacterial peritonitis.

Study Characteristics

Across the included studies evaluating Aution sticks, there were 749 patients with a total of 749 paracenteses performed. The overall prevalence of SBP was 22.91%, with individual studies ranging from 12% to 42%. Of the five studies, three included only inpatient data, whereas two studies did not have information regarding the setting of paracentesis. Four studies used visual comparison and one study did not specify the analysis method.

Among the studies evaluating Combur, there were 1544 patients with a total of 1632 paracenteses performed. The overall prevalence of SBP was 19.55%, with individual studies ranging from 7.64% to 29.17%. Of the eight studies, two included inpatient data only, four included both inpatient and outpatient samples, and two did not specify the location paracentesis was performed.

Of the studies evaluating Multistix, there were 2581 patients with a total of 5179 paracenteses performed. The overall prevalence of SBP was 19.50%, with individual studies ranging from 5.51% to 55.32%. Of the 19 studies, eight included inpatient data only, six included both inpatient and outpatient samples, one included emergency room samples only, one included outpatient data only, and three did not specify the location paracentesis was performed.

Among the studies evaluating Periscreen, there were 2111 patients with a total of 1732 paracenteses performed. The overall prevalence of SBP was 19.50%, with individual studies ranging from 5.99% to 20.83%. Of the three studies analyzing Periscreen, one included both inpatient and outpatient, one included emergency room samples only, and one study did not specify the location paracentesis was performed.

Performance of Each LERS in the Detection of SBP

Aution Sticks

Summary sensitivity, specificity, and LDOR were 0.962 (95% confidence interval [CI] 0.926, 0.998), 0.940 (95% CI 0.904, 0.976), 4.963 (95% CI 4.230, 5.696), and 0.962, respectively (see Table 3). Figure 2A and B highlights a graphical representation of summary sensitivity and specificity across Aution Stick studies. SROC is shown in Supplementary Figure 1 (AUC 0.962). The level of heterogeneity for summary sensitivity, specificity, and LDOR were low (26.86%), considerable (74.04%), and low (0%), respectively. By visual detection method, sensitivity, specificity, LDOR, and AUC were 0.973 (95% CI 0.945, 1.008), 0.935 (95% CI 0.890, 0.981), 5.518 (95% CI 4.320, 5.997), and 0.964, respectively. The level of heterogeneity for sensitivity, specificity, and LDOR by visual detection were low (8.27%), considerable (79.59%), and low (0%), respectively.

Table 3.

Results of Summary Statistic From Meta-analyses.

Index tests and subgroup analyses Number of studies (n) Sensitivity (95% CI) I2 (%) Specificity (95% CI) I2 (%) LDOR (95% CI) I2 (%) AUC
Aution Sticks
  • •

    Overall

 5 0.962 (0.926, 0.998) 26.86 0.940 (0.904, 0.976) 74.04 4.963 (4.230, 5.696) 0 0.962
  • •

    Visual

 4 0.973 (0.945, 1.008) 8.27 0.935 (0.890, 0.981) 79.59 5.518 (4.320, 5.997) 0 0.964
Combur 10
  • •

    Overall

 8 0.892 (0.846, 0.938) 34.89 0.922 (0.874, 0.970) 94.60 4.247 (3.579, 4.914) 15.54 0.903
  • •

    Cutoff ≥ 1

 4 0.866 (0.815, 0.917) 18.79 0.871 (0.811, 0.931) 86.07 3.741 (3.288, 4.195) 0.65 0.915
  • •

    Cutoff ≥ 2

 6 0.814 (0.689, 0.939) 86.78 0.970 (0.949, 0.991) 71.25 4.715 (3.976, 5.454) 0 0.964
Multistix
  • •

    Overall

 20 0.806 (0.738, 0.874) 78.84 0.938 (0.962, 0.985) 81.39 4.577 (4.204, 4.950) 0 0.943
  • •

    Visual

 18 0.801 (0.729, 0.872) 78.27 0.976 (0.965, 0.986) 74.56 4.546 (4.159, 4.933) 0 0.942
  • •

    Cutoff ≥ 1

 10 0.847 (0.771, 0.923) 64.98 0.964 (0.944, 0.985) 81.51 4.549 (3.826, 5.273) 6.13 0.933
  • •

    Cutoff ≥ 2

 5 0.775 (0.598, 0.952) 88.78 0.976 (0.962, 0.990) 39.09 4.766 (3.778, 5.754) 0 0.974
  • •

    Cutoff ≥ 3

 7 0.622 (0.492, 0.753) 76.92 0.992 (0.989, 0.996) 0 4.789 (4.305, 5.272) 0 0.971
Periscreen
  • •

    Overall

 3 0.939 (0.900, 0.979) 0 0.672 (0.381, 0.963) 99.45 3.229 (1.814, 4.644) 46.63 0.936
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AUC, area under the summary receiver operating curve; CI, confidence interval; LDOR, log diagnostic odds ratio.

Figure 2.

Figure 2

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Forest plots demonstrating individual and summary sensitivity and specificity of each study evaluating Aution Sticks for the detection of SBP: sensitivity of Aution sticks (A) and specificity of Aution Sticks (B).

Combur

Summary sensitivity, specificity, and LDOR were 0.892 (95% CI 0.846, 0.938), 0.922 (95% CI 0.874, 0.970), and 4.247 (95% CI 3.579, 4.914), respectively (see Table 3). Figure 3A and B highlights a graphical representation of summary sensitivity and specificity across Combur studies. SROC is shown in Supplementary Figure 2 (AUC 0.903). The level of heterogeneity for summary sensitivity, specificity, and LDOR were low (34.89%), considerable (94.60%), and low (15.54%), respectively. At the cutoff value of ≥1, sensitivity, specificity, LDOR, and AUC were 0.866 (95% CI 0.815, 0.917), 0.871 (95% CI 0.811, 0.931), 3.741 (95% CI 3.288, 4.195), and 0.915, respectively. At the cutoff value of ≥2, sensitivity, specificity, LDOR, and AUC were 0.814 (95% CI 0.689, 0.939), 0.970 (95% CI 0.949, 0.991), 4.715 (95% CI 3.976, 5.454), and 0.964, respectively. The heterogeneity for different cutoff values is outlined in Table 3.

Figure 3.

Figure 3

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Forest plots demonstrating individual and summary sensitivity and specificity of each study evaluating Combur for the detection of SBP: sensitivity of Combur (A) and specificity of Combur (B).

Multistix

Summary sensitivity, specificity, and LDOR were 0.806 (95% CI 0.738, 0.874), 0.974 (95% CI 0.962, 0.985), and 4.577 (95% CI 4.204, 4.950), respectively (see Table 3). Figure 4A and B highlights a graphical representation of summary sensitivity and specificity across Multistix studies. SROC is shown in Supplementary Figure 3 (AUC 0.943). The level of heterogeneity for summary sensitivity, specificity, and LDOR were considerable (78.84%), considerable (81.39%), and low (0%), respectively. For visual detection method, sensitivity, specificity, LDOR, and AUC were 0.801 (95% CI 0.729, 0.872), 0.976 (95% CI 0.965, 0.986), 4.546 (95% CI 4.159, 4.933), and 0.942, respectively. The level of heterogeneity for sensitivity, specificity, and LDOR by visual detection were considerable (78.27%), considerable (74.56%), and low (0%), respectively. At the cutoff value of ≥1, sensitivity, specificity, LDOR, and AUC were 0.847 (95% CI 0.771, 0.923), 0.964 (95% CI 0.944, 0.985), 4.549 (95% CI 3.826, 5.273), and 0.933, respectively. At the cutoff value of ≥2, sensitivity, specificity, LDOR, and AUC were 0.775 (95% CI 0.598, 0.952), 0.976 (95% CI 0.962, 0.990), 4.766 (95% CI 3.778, 5.754), and 0.974, respectively. At the cutoff value of ≥3, sensitivity, specificity, LDOR, and AUC were 0.622 (95% CI 0.492, 0.753), 0.992 (95% CI 0.989, 0.996), 4.789 (95% CI 4.305, 5.272), and 0.971, respectively. The heterogeneity for different cutoff values is also outlined in Table 3.

Figure 4.

Figure 4

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Forest plots demonstrating individual and summary sensitivity and specificity of each study evaluating Multistix for the detection of SBP: sensitivity of Multistix (A) and specificity of Multistix (B).

Periscreen

Summary sensitivity, specificity, and LDOR were 0.939 (95% CI 0.900, 0.979), 0.672 (95% CI 0.381, 0.963), and 3.229 (95% CI 1.814, 4.644), respectively (see Table 3). Figure 5A and B highlights a graphical representation of summary sensitivity and specificity across Periscreen studies. SROC is shown in Supplementary Figure 4 (AUC 0.936). The level of heterogeneity for summary sensitivity, specificity, and LDOR was low (0%), considerable (99.45%), and moderate (46.63%), respectively.

Figure 5.

Figure 5

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Forest plots demonstrating individual and summary sensitivity and specificity of each study evaluating Periscreen for the detection of SBP: sensitivity of Periscreen (A) and specificity of Periscreen (B).

Quality Assessment and Publication Bias

Generally, the level of heterogeneity was low to considerable throughout for summary sensitivity but moderate to considerably high for summary specificity. At the time of article screening and the selection process, there was a high degree of agreement between the two authors, as demonstrated by Cohen's kappa coefficient of 0.93. A simplified QUADAS-2 for each study investigating LERS is shown in Table 4, whereas a complete QUADAS-2 is shown in Supplementary Table 1. Overall, the concern for patient selection bias, general applicability, and the ability to conduct or interpret the index test was thought to be low. Using Deeks' funnel plot, no publication bias was found among studies evaluating Aution Sticks, Combur, Multistix, and Periscreen (P = 0.40, 0.48, 0.58, and 0.25, respectively; see Figure 6A–D).

Table 4.

Simplified QUADAS Form.

Study
 Patient selection
 Index test
 Reference standard
 Flow and timing
Could the selection of patients have introduced bias? Is there concern the included patients do not match the review question? Could the conduct or interpretation of the index test have introduced bias? Is there concern that the index test, its conduct, or interpretation differ from the review question? Could the reference standard, its conduct, or its, interpretation have introduced bias? Is there concern the target condition as defined by the reference standard does not match the review question? Could the patient flow have introduced bias?
Ahmed et al. L L L L L L L
Al Sawaf et al. L L L L L L L
Braga et al. L L L L L L L
Butani et al. L L L L L L L
Campillo et al. L L L L L L L
Castellote et al. L L L L L L L
Chinnock et al. L L L L L L L
Chugh et al. L L L L L L L
De Araujo et al. L L L L L L L
Gaya et al. L L L L L L L
Jha et al. L L L L L L L
Jothimani et al. L L L L L L L
Khatwani et al. L L L L L L L
Kim et al. L L L L L L L
Li et al. H L H L H L H
Nalmpantidis et al. L L L L L L L
Nousbaum et al. L L L L L L L
Oey et al. L L L L L L L
Rerknimitr et al. (2006) L L L L L L L
Rerknimitr et al. (2010) L L L L L L L
Ribeiro et al. L L L L L L L
Sapey et al. (JGH 1 & 2) L L L L L L L
Sapey et al. (LI) L L L L L L L
Sarwar et al. L L L L L L L
Tellez et al. L L L L L L L
Thevenot et al. (2004) L L L L L L L
Thevenot et al. (2016) L L L L L L L
Torun et al. L L L L L L L
Vanbiervliet et al. L L L L L L L
Wisniewki et al. L L L L L L L
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H, high; L, low, N, no; R, random; U, unclear; Y, yes.

Figure 6.

Figure 6

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Deeks' funnel plots of studies evaluating Aution Sticks (A), Combur (B), Multistix (C), and Periscreen (D). ESS, effective sample size.

Discussion

Our study evaluated the performance of various types of LERS for ascitic fluid testing in the detection of SBP from a total of 31 studies consisting of 4446 patients. The Aution Sticks, Combur, Multistix, and Periscreen had a modest overall performance, as reflected by AUC of 0.962, 0.903, 0.943, and 0.936, respectively. The summary sensitivity of Aution Sticks, Combur, Multistix, and Periscreen was 0.962, 0.892, 0.806, and 0.939, respectively. The summary specificity of Aution Sticks, Combur, Multistix, and Periscreen was 0.940, 0.922, 0.922, and 0.672, respectively.

Currently, SBP is diagnosed by the presence of PMNs ≥250 cells/mm3 in an ascitic fluid regardless of culture. Although automated cell count has been studied, the manual cell count method remains the standard practice and is universally available.49 Furthermore, the manual cell count is laborious, time-consuming, and relatively expensive. In addition, both automated and manual cell counts are also subject to errors from cell lysis and specimen handling. Unfortunately, medical facilities in rural areas or developing countries may have limited access to laboratories capable of performing cell count. More frequently, the cell count is often not routinely possible in the setting of therapeutic paracentesis. Therefore, scientists have searched for an instant, convenient, and inexpensive method to diagnose SBP in these previously mentioned circumstances. In the past decade, previous studies have suggested that LERS may be a potential solution. However, the reported accuracy of LERS for the detection of SBP has been inconsistent, with sensitivities ranging from 45% to 100% and specificities ranging from 90% to 100%.3 Across 17 studies, Koulaouzidis et al. supported a potential for screening for SBP by noting a negative predictive value between 87% and 100%.50 In addition, Nguyen-Khac et al. described that the diagnosis of SBP via LERS solely is not a surrogate to the gold standard but may serve a purpose in the detection and management of SBP.51

In this present study, all types of LERS had a good performance for the detection of SBP with AUC varying from 0.903 to 0.962. However, there were notable differences in performance between each manufacturer. Regarding sensitivity, overall sensitivity was good ranging from 0.806 to 0.962; however, Aution Sticks and Periscreen appeared to be significantly superior to Multistix (0.962 [95% CI 0.926, 0.998] and 0.939 [95% CI 0.90, 0.979] vs. 0.806 [95% CI 0.738, 0.874]). Similarly, the overall specificity of LERS was good, ranging from 0.922 to 0.940 except for Periscreen, which had a specificity of 0.672, although this was not statistically significant.

In the subgroup analyses of different cutoffs, only Combur 10 and Multistix had adequate data for the analyses. The threshold effect was apparent in these analyses, as evident by a decrease in sensitivity and an increase in specificity as cutoff increases. However, specificity at the lowest cutoff value (≥1) remained good (0.871 [95% CI 0.811 0.931] and 0.964 [0.944, 0.985]). These findings suggest that LERS is a potential alternative for the cell count method despite the differences between manufacturers. In addition, LERS can serve as a screening tool for SBP as it has good overall sensitivity at the lowest cutoff without sacrificing specificity. Other advantages of LERS include its low cost, simplicity, and instant turnaround time; therefore, LERS could be a perfect tool in facilities without the capability of cell count or when there is suspicion of SBP during a therapeutic paracentesis.

In this study, we also attempted to evaluate the effect of the visual and analyzer method; however, there was only one study in the Multistix group, which used the analyzer method, and one study in the Aution Sticks group without specification of the comparison method. Regardless, when analyzing the studies using visual comparison alone, there was no significant difference in the performance. Although the analyzer method could potentially eliminate human error during visual comparison of LERS, the performance of LERS using the visual method appeared to good. Moreover, analyzer machines are costly and not universally available, which defeats the purpose of LERS.

Despite the potential clinical application of LERS as suggested by the data in this meta-analysis, there are several notable limitations. Firstly, most of the studies in this meta-analysis evaluated patients in the inpatient or emergency room setting, which has a much higher prevalence of SBP than the outpatient setting or in the setting of therapeutic paracentesis. Therefore, this should be considered when applying these findings in clinical practice. Unfortunately, subgroup analysis according to these settings was not possible because of inadequate data. Secondly, the differences in the manufacturers and grading scale of LERS may impact the performance of LERS in clinical practice. Finally, heterogeneity of specificity appeared to be universally high across all types of LERS. Although the heterogeneity of sensitivity appeared to be universally low, the heterogeneity of sensitivity for Multistix appeared to be substantial to considerable.

In summary, our data suggested an excellent overall performance of all LERS. Considering a good sensitivity, LERS has the potential to become a rapid screening tool for diagnosing SBP alternative to the cell count method. LERS is especially useful in certain circumstances with limited resources such as rural settings, developing countries, or facilities with no capacity for the cell count. A standardization of LERS for manufacturers and grading scales may help improve its performance.

Availability of data and material

This study includes all additional materials used in the supplementary section for data transparency purposes.

Code availability

R version 3.2.4 (R Core team 2013) with Metafor and Mada packages was used for the statistical analysis in this study.

CRediT authorship contribution statement

Kishan P. Patel: Conceptualization, Investigation, Writing – original draft, Writing – review & editing. John P. Gallagher: Conceptualization, Investigation, Writing – original draft, Writing – review & editing. Parker M. Korbitz: Conceptualization, Investigation, Writing – original draft, Writing – review & editing. Cynthia Schmidt: Investigation, Writing – original draft, Writing – review & editing. Thammasin Ingviya: Writing – original draft, Writing – review & editing. Tomoki Sempokuya: Writing – original draft, Writing – review & editing. Wuttiporn Manatsathit: Conceptualization, Methodology, Validation, Formal analysis, Supervision.

Conflicts of interest

The authors have none to declare.

Acknowledgement

The authors report no acknowledgements for this study.

Funding

None.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jceh.2021.05.002.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

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Supplementary Figure 1.

Supplementary Figure 1

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Summary receiver operating characteristic (SROC) curves of studies evaluating Aution Sticks for the detection of SBP.

Supplementary Figure 2.

Supplementary Figure 2

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Summary receiver operating characteristic (SROC) curves of studies evaluating Combur for the detection of SBP.

Supplementary Figure 3.

Supplementary Figure 3

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Summary receiver operating characteristic (SROC) curves of studies evaluating Multistix for the detection of SBP.

Supplementary Figure 4.

Supplementary Figure 4

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Summary receiver operating characteristic (SROC) curves of studies evaluating Periscreen for the detection of SBP.

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Associated Data

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Supplementary Materials

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Data Availability Statement

This study includes all additional materials used in the supplementary section for data transparency purposes.

Articles from Journal of Clinical and Experimental Hepatology are provided here courtesy of Elsevier

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