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Platelet Count to Spleen Diameter Ratio for the Diagnosis of Gastroesophageal Varices in Liver Cirrhosis: A Systematic Review and Meta-Analysis.

1. Introduction

Gastroesophageal varices are one of the major complications of liver cirrhosis. Early detection of varices in cirrhotic patients is crucial to ensure timely initiation of prophylactic therapies. Platelet count to spleen diameter ratio (PSR) was first proposed by Giannini et al. to predict the presence of varices in 2003 [1]. An increasing number of studies have since evaluated the accuracy of PSR in the detection of varices, albeit drawing inconsistent conclusions. In two previous meta-analyses regarding the diagnostic accuracy of PSR in predicting the presence of varices, Ying et al. [2] recommended using PSR to identify varices to decrease the use of upper gastrointestinal endoscopy, while Chawla et al. [3] found that PSR has low grade evidence to replace upper gastrointestinal endoscopy as a noninvasive method for varices. The performance of PSR for varices is still not unified at present, which has limited the use of PSR in clinical practice. Thus, we conducted this systematic review and meta-analysis to evaluate the performance of PSR for varices.

2. Methods

Selection, data extraction, and quality assessment of studies were conducted by two investigators (RC and QS) independently. Disagreement between the two investigators was resolved by a consensus.

2.1. Search Strategy and Selection Criteria. PubMed and EMBASE were searched on May 27, 2016. The search terms were as follows: (((((((platelet count to spleen diameter ratio) OR PSR) OR PC/SD)) OR ((((platelet) OR platelet count)) AND ((spleen) OR spleen diameter)))) AND liver cirrhosis) AND varices. Relevant references were also screened. Duplicates, commentaries, reviews, case reports, letters, meta-analyses, booksections, and meeting abstracts were excluded. The inclusion criteria were as follows: (1) participants should be diagnosed with liver cirrhosis; (2) upper gastrointestinal endoscopy should be performed as the reference tests for the diagnosis of varices; (3) PSR should be performed as alternative tests for the diagnosis of varices; (4) diagnostic accuracy data of PSR on the diagnosis of varices were available. The language and publication year were not limited.

2.2. Data Extraction. The following data from each study was extracted: the first author, publication year, region, study design, total number of patients, age, sex, etiology of liver cirrhosis, hepatocellular carcinoma (HCC), Child-Pugh class, location of varices (i.e., esophageal varices [EV] and/or gastric varices [GV]), prevalence of any varices and/or high-risk (or large) varices, cut-off value, true positive (TP) value, false positive (FP) value, false negative (FN) value, and true negative (TN) value. Missing values were calculated using the following formulae: sensitivity = TP/(TP + FN), and specificity = TN/(TN + FP). The raw TP, FN, FP, and TN numbers of included studies were shown in Supplementary Table 1 (see Supplementary Material available online at https://doi.org/10.1155/2017/7407506).

2.3. Quality Assessment. The quality of each study was assessed by QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) tool [4]. This tool comprises four domains: patient selection, index test, reference standard, and flow and timing. The risk of bias in each domain was rated as "low risk," "high risk," and "unclear risk" with signaling questions. The applicability concerns in the first three domains were assessed as "low concern," "high concern," and "unclear concern." If the total number of "low risk" and "high concern" was equal or greater than 6 in a study, the study was considered as high quality.

2.4. Statistical Analysis. The area under the summary receiver operating characteristic curves (AUSROCs) with standard errors (SEs) and Q indexes with SEs, summary sensitivities and specificities with 95% confidence intervals (CIs), summary positive and negative likelihood ratios (PLRs and NLRs) with 95% CIs, and summary diagnostic odds ratios (DORs) with 95% CIs were calculated using statistical software (MetaDisc software version 1.4). We analyzed these data using the random-effects model. The diagnostic threshold was analyzed by Spearman correlation coefficient and p value. p < 0.05 showed a statistically significant diagnostic threshold effect. Therefore, only AUSROCs with SEs and Q indexes with SEs were calculated. The heterogeneity among studies was evaluated by Chi-square test and inconsistency index. A statistically significant heterogeneity was defined as p <0.1 and/or [I.sup.2] > 50%.

To explore the publication bias, we performed Deeks' funnel plot asymmetry test in Stata 12.0 (College Station, TX, USA). Sensitivity analyses were performed via removing each study to evaluate the impact on the pooled results of the removed study.

We calculated the diagnostic accuracy of PSR for various cut-off values in predicting the presence of any and highrisk varices. If there were multiple different cut-off values in the same paper, we selected the optimal cut-off values. PSR for any varices at threshold of 909 was the most generally accepted cut-off value at present. Thus, we performed subgroup analyses using the cut-off value of 909. In addition, we performed subgroup analyses based on the etiology of cirrhosis, region, study design, prevalence of varices, sample size, and study quality for any varices.

3. Results

3.1. Selection of Studies. A total of 345 papers were selected from PubMed (n = 120), EMBASE (n = 218), and manual search (n = 7). The flow diagram of study selection was shown in Figure 1. 112 duplicates and 145 irrelevant papers were excluded. Then, we screened 88 full-text articles for eligibility. 39 papers which lacked relevant diagnostic data were excluded. Among them, 28 papers were applied only with abstracts. Finally, 49 papers [1, 5-52] were included in our study. 42 and 16 papers were about any varices and high-risk varices, respectively.

3.2. Characteristics of Studies. The characteristics of studies were shown in Table 1. Our meta-analysis included a total of 6274 patients. 22 papers were prospective studies. 2 papers [10, 20] were published in abstracts and 47 papers were full-texts. The etiologies of cirrhosis were alcohol, schistosomiasis, and viral hepatitis in 1 [12], 2 [5, 32], and 13 [9, 19, 22, 26, 27, 30, 33, 36, 39, 40, 42-44] papers, respectively. Two papers were about EV and GV [8, 12], and the rest of the papers were about EV alone. In two papers, all patients had Child-Pugh A [22, 40]. The diagnostic accuracy of PSR for any varices at threshold of 909 was reported in 19 papers.

3.3. Quality Assessment of Studies. The overall quality of the included studies was not very high (Supplementary Table 2). In the patient selection domain, only 17 papers were rated as "low risk." In the index test and reference standard domains, 10 and 14 papers were rated as "low risk." Most papers had not reported whether investigators were blinded when interpreting the results of index test and reference standard. 2 papers were rated as "high risk" in flow and timing domain as the interval time between index test and reference standard was greater than 3 months. 44 papers have "high concern" in patient selection domain. In addition, all papers were rated as "high concern" in index test and reference standard domains. 11 papers were considered with high quality.

3.4. Overall Results. Significant threshold effect was not found in overall meta-analyses.

Diagnostic accuracy of PSR for the presence of any varices was pooled from 42 papers [1, 5-10, 13, 14, 16-19, 21-34, 36-40, 42-50, 52]. The AUSROC was 0.8719 (Figure 2(a)). The summary sensitivity and specificity were 0.84 (95% CI: 0.83-0.85) and 0.78 (95% CI: 0.76-0.79), respectively (Figure3). The summaryPLR, NLR, and DORwere3.54(95% CI: 2.75-4.56), 0.17 (95% CI: 0.12-0.23), and 25.32 (95% CI: 15.72-40.77), respectively.

Diagnostic accuracy of PSR for the presence of high-risk varices was pooled from 16 papers [6, 7, 11-13, 15, 17, 19, 20, 22, 25, 29, 30, 35, 41, 51]. The AUSROC was 0.8132 (Figure 2(b)). The summary sensitivity and specificity were 0.78 (95% CI: 0.75-0.81) and 0.67 (95% CI: 0.64-0.71), respectively (Figure 4). The summary PLR, NLR, and DOR were 2.54 (95% CI: 1.99-3.24), 0.32 (95% CI: 0.24-0.44), and 9.08 (95% CI: 5.33-15.47), respectively. The diagnostic accuracy of PSR for high-risk varices was lower than PSR for any varices.

3.5. Subgroup Results. The subgroup results were summarized in Table 2. Significant threshold effect was found in the subgroup of South America. Thus, their diagnostic accuracy was not combined.

3.6. Heterogeneity. Significant heterogeneity between papers was found in most analyses except for the subgroup of North America and sample size less than 100.

3.7. Sensitivity Analyses. Sensitivity analysis results were similar to the overall meta-analysis results. The heterogeneity remained significant (data not shown).

3.8. Publication Bias. The publication bias existed in the study (p = 0.007).

4. Discussions

In our study, the AUSROC of PSR for any varices was 0.8719. The summary sensitivity and specificity were 0.84 and 0.78, respectively. The diagnostic accuracy of PSR for high-risk varices was lower than PSR for any varices. The diagnostic accuracy of PSR for varices at threshold of 909 was similar to PSR at various thresholds. While the summary sensitivity (0.92) in viral liver cirrhosis was improved over that of mixed etiologies, they had the same summary specificities (0.78). The subgroup analysis of Asia had the highest AUSROC (0.9195).

The high diagnostic accuracy of PSR for varices can be explained as follows. Varices and hypersplenism are the results of portal hypertension. The platelet count can be influenced by many factors in cirrhotic patients other than hypersplenism. The decreased thrombopoietin production is the reason. Thrombopoietin is mainly produced by hepatocytes and the quantity can be largely reduced when the hepatocytes was damaged. In addition, the shortened platelet mean lifetime and myelotoxic effects of alcohol or hepatitis viruses also reduced the platelet count. Splenomegaly is the clinical manifestation of hypersplenism. Thus, a combined index of platelet count and spleen diameter has much more relevance with portal hypertension and varices than the sole decreased platelet count [1].

In clinic practice, the measure of spleen diameter and platelet count is easily obtainable during the routine ultrasonography and serum examination. PSR is convenient, cheap, and noninvasive. Based on our study, we recommend that those patients whose PSR is less than 909 should undergo upper gastrointestinal endoscopy to evaluate the grade of varices. 80% of patients whose value of PSR is greater than 909 can avoid unnecessary upper gastrointestinal endoscopic examination.

Compared with the two previous studies, our metaanalysis included a greater number of studies without limiting the publication language and cut-off values. Compared with other noninvasive methods, PSR has an upper-middle performance for varies. As previous studies have shown [53-55], serum markers cannot be used to identify varices for the low-moderate diagnostic accuracy. Computer tomography has similar summary sensitivity (0.896) and specificity (0.723) compared to PSR [56]. In addition, the diagnostic accuracy of PSR for varices was slightly higher than spleen stiffness measurement [57] and liver stiffness measurement [58]. Their summary sensitivities were 0.78 and 0.87, respectively. The summary specificities were 0.76 and 0.53, respectively. While the diagnostic accuracy of PSR for varices was slightly lower than capsule endoscopy [59], its summary sensitivity and specificity were 0.85 and 0.84, respectively. Some studies reported that splenoportal index and congestion index have high diagnostic accuracy. Their sensitivities and specificities were both greater than 80% [60, 61]. However, there are no systematic studies to evaluate their performance of varices.

Our study has some limitations. (1) 39 papers lacking relevant data were excluded, out of which some reported that PSR had no statistically significant difference in predicting the presence of varices. (2) Most analyses had significant heterogeneity. It may be attributed to the different selection criteria of patients, such as the prevalence of decompensated cirrhosis, etiologies of cirrhosis, and history of variceal bleeding. We have no evidence to support this assumption. The same situation was also found in the previous meta-analyses [2, 3]. (3) The publication bias exists in the study. (4) Most TP, FP, FN, and TN were recalculated using sensitivities and specificities, which may introduce some errors. (5) PSR is not applicable to patients with a history of splenectomy.

In conclusion, PSR can be used to identify varices in liver cirrhosis. PSR had a high sensitivity in viral liver cirrhosis.
Abbreviations

PSR:      Platelet count to spleen diameter ratio
HCC:      Hepatocellular carcinoma
TP:       True positive
FP:       False positive
FN:       False negative
TN:       True negative
AUSROC:   Area under the summary receiver
          operating characteristic curve
SE:       Standard error
CI:       Confidence interval
PLR:      Positive likelihood ratio
NLR:      Negative likelihood ratio
DOR:      Diagnostic odds ratio.


http://dx.doi.org/ 10.1155/2017/7407506

Competing Interests

The authors declare that they have no competing interests.

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Runhua Chen, (1) Han Deng, (2) Xia Ding, (3) Chune Xie, (4) Wei Wang, (5) and Qian Shen (6)

(1) Department of Gastroenterology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China

(2) Postgraduate College, Dalian Medical University, Dalian, China

(3) Department of Hospital Administration, Beijing University of Chinese Medicine, Beijing, China

(4) Department of Personnel, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China

(5) Department of Cardiology, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China

(6) Department of Massage and Physiotherapy, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China

Correspondence should be addressed to Qian Shen; rbd144@163.com

Received 3 September 2016; Accepted 30 November 2016; Published 8 February 2017

Academic Editor: Nianping Feng

Caption: FIGURE 1: Flowchart of study selection.

Caption: FIGURE 2: AUSROCs of PSR for varices in liver cirrhosis. (a) Any size varices; (b) high-risk varices.
TABLE 1: Characteristics of studies.

First                  Regions           Study        Number
author                                  design          of
(year)                                                total
                                                       Pts

Karatzas (2016)        Greece         Prospective       38

Kim (2016)           South Korea     Retrospective     103

Takuma (2016)           Japan         Prospective       60

Xu (2016)               China         Prospective      236

Albreedy (2015)         Egypt         Prospective      100

Cho (2015)           South Korea     Retrospective     219

Stefanescu (2015)      Romania      Cross-sectional     90

Valero (2015)        Philippines     Retrospective     101
(abstract)

Zhao (2015)             China        Retrospective     124

Chiodi (2014)          Uruguay       Retrospective     125

Gonzalez-Ojeda         Mexico           Cross-          91
(2014)                                 sectional

Wang (2014)             China        Retrospective     104

Zafar (2014)          Pakistan        Prospective      215

Calvaruso (2013)        Italy         Prospective       96

Masjedizadeh            Iran          Prospective      140
(2013)

Rajendran (2013)        India       Cross-sectional    101
(abstract)

Saad (2013)             Egypt             NA            32

Sharma (2013)           India             NA           174

Al-Dahshan              Egypt             NA            60
(2012)

Colecchia (2012)        Italy         Prospective      100

Mahassadi (2012)    Cote d'Ivoire    NA (training      111
                                        sample)

                                          NA            91
                                      (validation
                                        sample)

Mangone (2012)          Italy         Prospective       87

Nisar (2012)          Pakistan      Cross-sectional    150

Abu El Makarem          Egypt         Prospective      175
(2011)

Agha (2011)             Italy         Prospective       43

Cherian (2011)          India         Prospective      229

Esmat (2011)            Egypt         Prospective      100

Mosqueira (2011)        Peru         Retrospective      47

Stefanescu (2011)      Romania        Prospective      137

Barikbin (2010)         Iran          Prospective       50

De Mattos (2010)       Brazil             NA           164

Nashaat (2010)          Egypt             NA            50

Sarangapani             India         Prospective      106
(2010)

Schwarzenberger          US          Retrospective     137
(2010)

Agha (2009)           Pakistan        Prospective      311

Barrera (2009)          Chile         Prospective       67

Camma (2009)            Italy         Prospective      104

Sharif (2009)         Pakistan      Cross-sectional    100

Baig (2008)             India         Prospective      150

Parrino (2008)          Italy             NA           158

Tao (2008)              China             NA            69

Yu (2008)               China        Retrospective      89

Lei (2007)              China        Retrospective     326

Giannini (2006)         Italy         Prospective      218

Legasto (2006)       Philippines    Cross-sectional    150

Sethar (2006)         Pakistan      Cross-sectional    113

Giannini (2005)         Italy         Prospective       68

Plestina (2005)        Croatia            NA            99

Giannini (2003)         Italy        Retrospective     145

                                                       121

First                        Age            Male (%)
author                     (year)
(year)

Karatzas (2016)              63              78.9%

Kim (2016)           53.5 [+ or -] 11.8      65.0%

Takuma (2016)         70.8 [+ or -] 9.9      56.7%

Xu (2016)            61.4 [+ or -] 10.2      43.2%

Albreedy (2015)      44.24 [+ or -] 7.05     58.0%

Cho (2015)                 52/50 *           91.8%

Stefanescu (2015)    56.47 [+ or -] 9.38     55.6%
                    /54.98 [+ or -]8.42 *

Valero (2015)             64.8/61.6            NA
(abstract)

Zhao (2015)         51.34 [+ or -] 11.089    57.3%

Chiodi (2014)                54              56.8%

Gonzalez-Ojeda        53.75 [+ or -] 12      54.9%
(2014)

Wang (2014)                  59              56.7%

Zafar (2014)        46.93 [+ or -] 13.22     42.3%

Calvaruso (2013)      63.2 [+ or -] 9.5      69.8%

Masjedizadeh                57/53            70.0%
(2013)

Rajendran (2013)             NA              93.1%
(abstract)

Saad (2013)           55 [+ or -] 6.6/       62.5%
                     49.5 [+ or -] 4.7/
                     48.9 [+ or -] 4.7 *

Sharma (2013)        49.3 [+ or -] 11.7      88.5%

Al-Dahshan           52.62 [+ or -] 8.22     78.3%
(2012)

Colecchia (2012)             54              71.0%

Mahassadi (2012)             49              70.3%

                             50              64.2%

Mangone (2012)              62.8             58.6%

Nisar (2012)        50.99 [+ or -] 12.99     54.0%

Abu El Makarem               48              65.7%
(2011)

Agha (2011)                  61              70.0%

Cherian (2011)               42              61.6%

Esmat (2011)           49.2 [+ or -] 8       48.0%

Mosqueira (2011)            60.74            50.0%

Stefanescu (2011)            56              56.2%

Barikbin (2010)      52.1 [+ or -] 16.2      82.0%

De Mattos (2010)     56.6 [+ or -] 11.6      56.7%

Nashaat (2010)        49.6 [+ or -] 8.8      74.0%

Sarangapani                  45              67.9%
(2010)

Schwarzenberger              56              64.0%
(2010)

Agha (2009)                  49              55.6%

Barrera (2009)        66 [+ or -] 12.2       43.3%

Camma (2009)          61.4 [+ or -] 9.5      57.7%

Sharif (2009)                NA              56.0%

Baig (2008)                  51              84.0%

Parrino (2008)        66.6 [+ or -] 9.6      63.3%

Tao (2008)                   53              63.8%

Yu (2008)                    53              82.0%

Lei (2007)                   55              77.3%

Giannini (2006)            58/54 *           58.7%

Legasto (2006)             51/57 *           70.7%

Sethar (2006)        37.1 [+ or -] 14.85     69.0%

Giannini (2005)         65 [+ or -]10        63.2%

Plestina (2005)      53.6 [+ or -] 9.76      78.8%

Giannini (2003)              61              71.0%

                             64              65.3%

First                    Etiology of        HCC    Child-Pugh
author                    cirrhosis         (%)    class (%)
(year)

Karatzas (2016)         Alcohol 47.4%        NA     A, 55.3%
                    Viral hepatitis 34.2%           B, 28.9%
                        Others 18.4%                C, 15.8%

Kim (2016)              Alcohol 28.2%        NA        NA
                          HBV 50.5%
                          HCV 8.7%
                       HBV and alcohol
                            7.8%
                         Others 4.9%

Takuma (2016)            Alcohol 10%         NA     A, 68.3%
                          HBV 13.3%                  B, 30%
                          HCV 58.3%                 C, 1.7%
                        Others 18.3%

Xu (2016)           Schistosomiasis 100%     NA        NA

Albreedy (2015)              NA              0%      A, 41%
                                                     B, 33%
                                                     C, 26%

Cho (2015)              Alcohol 100%         0%     A, 59.4%
                                                    B, 36.1%
                                                    C, 4.6%

Stefanescu (2015)       Alcohol 33.3%        0%     A, 62.2%
                          HBV 13.3%                 B, 35.6%
                          HCV 33.3%                 C, 2.2%
                         Others 20%

Valero (2015)                NA              0%        NA
(abstract)

Zhao (2015)               HCV 100%           NA        NA

Chiodi (2014)            Alcohol 40%         0%        NA
                          HBV 3.2%
                          HCV16.8%
                       Autoimmune 12%
                        Others 19.2%
                         Unknown 8.8

Gonzalez-Ojeda          Alcohol 52.7%        0%     A, 18.7%
(2014)                    HCV 26.4%                 B, 40.7%
                         Others 11%                 C, 40.7%
                         Unknown 10%

Wang (2014)             Alcohol 17.3%        NA      A, 24%
                          HBV 66.3%                 B, 35.6%
                      Alcohol and E1BV              C, 40.4%
                            12.5%
                        Unknown 3.8%

Zafar (2014)                 NA              NA        NA

Calvaruso (2013)          HCV 100%           0%     A, 100%

Masjedizadeh            Alcohol 3.6%         NA     A, 43.6%
(2013)                    HBV 36.4%                 B, 42.1%
                          HCV 17.1%                 C, 14.3%
                       Autoimmune 8.6%
                         Others 5.7%
                        Unknown 28.6%

Rajendran (2013)         Alcohol 85%         NA        NA
(abstract)                 HBV 9%
                           HCV 5%

Saad (2013)               HCV 100%           0%     A, 71.9%
                                                    B, 28.1%

Sharma (2013)           Alcohol 44.3%        0%     A, 31.6%
                          HBV 13.2%                 B, 56.9%
                          HCV 16.7%                 C, 11.5%

Al-Dahshan              Unknown 25.9%        0%        NA
(2012)                    HBV 81.7%
                          HCV 18.3%

Colecchia (2012)           HCV100%           0%      A, 68%
                                                     B, 32%

Mahassadi (2012)        Alcohol 20.7%        0%     A, 22.5%
                          HBV 61.3%                 B, 35.1%
                          HCV 12.6%                 C, 42.3%
                         Others 5.4%

                        Alcohol 26.4%        0%     A, 19.8%
                          HBV 58.2%                 B, 52.7%
                          HCV 15.4%                  C, 22%
                                                    Unknown,
                                                      5.5%

Mangone (2012)           Alcohol 8%          NA     A, 90.8%
                          HBV 10.5%                 B, 6.9%
                          HCV 63.2%                 Unknown,
                        Others 18.3%                  2.3%

Nisar (2012)                 NA              NA        NA

Abu El Makarem            HCV 100%           NA     A, 26.3%
(2011)                                              B, 33.7%
                                                     C, 40%

Agha (2011)         Schistosomiasis 100%     NA        NA

Cherian (2011)          Alcohol 42.4%        0%     A, 18.3%
                          HBV 15.3%                 B, 55.5%
                           HCV 10%                  C, 26.2%
                        Others 12.7%
                        Unknown 19.7%

Esmat (2011)              HCV 100%           NA      A, 20%
                                                     B, 31%
                                                     C, 49%

Mosqueira (2011)        Alcohol 25.5%        NA        NA
                          HBV 2.1%
                          HCV 14.9%
                       Autoimmune 8.5%
                        Unknown 48.9%

Stefanescu (2011)     Alcohol/HCV 100%       NA     A, 64.9%
                                                    B, 28.4%
                                                    C, 6.8%

Barikbin (2010)          Alcohol 4%          0%      A, 10%
                           HBV 38%                   B, 28%
                           HCV 14%                   C, 62%
                          Others 6%
                         Unknown 38%

De Mattos (2010)        Alcohol 29.3%        NA     A, 57.6%
                    Viral hepatitis 43.9%           B, 37.7%
                     Viral hepatitis and            C, 4.6%
                        alcohol 10.4%
                        Others 16.5%

Nashaat (2010)             HBV 20%           NA      A, 38%
                           HCV 70%                   B, 42%
                       HBV and HCV 10%               C, 20%

Sarangapani             Alcohol 58.5%        0%        NA
(2010)                    HBV 21.7%
                        Others 19.8%

Schwarzenberger          Alcohol 18%         NA        NA
(2010)                     HBV 23%
                           HCV 34%
                         Others 17%
                          Unknown 9

Agha (2009)               HCV 100%           0%     A, 25.8%
                                                    B, 58.6%
                                                    C, 15.6%

Barrera (2009)          Alcohol 26.9%        NA     A, 46.2%
                    Viral hepatitis 7.5%            B, 38.8%
                          PBC 14.9%                  C, 15%
                        Others 26.8%
                        Unknown 26.9%

Camma (2009)              HCV 100%           0%     A, 100%

Sharif (2009)              HBV 29%           NA        NA
                           HCV 71%

Baig (2008)             Alcohol 48.7%        NA     A, 64.7%
                           HBV 26%                  B, 21.3%
                          HCV 9.3%                   C, 14%
                         Others 7.3%
                        Unknown 8.7%

Parrino (2008)               NA              0%      A, 64%
                                                     B, 31%
                                                     C, 5%

Tao (2008)                 HBV100%           NA     A, 20.3%
                                                    B, 63.8%
                                                    C, 15.9%

Yu (2008)                  HBV100%           NA     A, 29.2%
                                                    B, 42.7%
                                                    C, 28.1%

Lei (2007)              Alcohol 4.3%         NA     A, 14.7%
                          HBV 90.8%                 B, 67.5%
                      HBV and HBV 1.5%              C, 17.8%
                         Others 3.4%

Giannini (2006)         Alcohol 18.8%       8.7%    A, 50.9%
                    Viral hepatitis 48.2%           B, 34.4%
                          PBC 16.1%                 C, 14.7%
                     Viral hepatitis and
                        alcohol 12.4%
                         Others 4.6%

Legasto (2006)           Alcohol 90%         0%        NA
                           HBV 10%

Sethar (2006)             HBV 33.6%          NA     A, 13.3%
                          HCV 52.2%                 B, 60.2%
                       HBV + HCV 7.1%               C, 26.6%
                         Others 7.1%

Giannini (2005)         Viral, 77.9%         NA     A, 35.3%
                       Nonviral, 22.1%              B, 41.2%
                                                    C, 23.5%

Plestina (2005)         HCV/HBV 13.1%        0%     A, 20.2%
                        Alcohol 83.9%               B, 53.5%
                           PBC 3%                   C, 25.3%
                                                    Unknown,
                                                       1%

Giannini (2003)         Alcohol 16.6%        NA      A, 37%
                           HBV11%                    B, 36%
                          HCV 53.8%                  C, 27%
                        Others 18.6%

                         Alcohol 24%         NA     A, 41.3%
                           HBV 5%                   B, 42.1%
                          HCV 63.6%                 C, 16.5%
                         Others 7.4%

First                  Location      Prevalence   Prevalence
author                of varices     of varices    of high-
(year)                                  (%)       risk/large
                                                  varices (%)

Karatzas (2016)        EV + GV         63.2%         10.5%

Kim (2016)                EV           38.8%         12.6%

Takuma (2016)             EV           40.0%         26.7%

Xu (2016)                 EV           40.3%          NA

Albreedy (2015)           EV           66.0%         47.0%

Cho (2015)          EV [+ or -] GV       NA          33.3%

Stefanescu (2015)         EV           81.1%         52.2%

Valero (2015)             EV           85.1%          NA
(abstract)

Zhao (2015)               EV           51.6%          NA

Chiodi (2014)             EV           63.2%         42.4%

Gonzalez-Ojeda            EV           80.2%         57.1%
(2014)

Wang (2014)               EV           99.0%         51.9%

Zafar (2014)              EV           60.9%          NA

Calvaruso (2013)          EV           56.3%         27.1%

Masjedizadeh              EV           85.0%         33.6%
(2013)

Rajendran (2013)          EV           95.0%         65.0%
(abstract)

Saad (2013)               EV           62.5%         31.3%

Sharma (2013)             EV           71.3%         44.8%

Al-Dahshan                EV           66.7%          NA
(2012)

Colecchia (2012)          EV           53.0%         49.0%

Mahassadi (2012)          EV           76.6%         70.3%

                          EV           79.1%         65.9%

Mangone (2012)            EV           35.6%          NA

Nisar (2012)              EV           68.0%          NA

Abu El Makarem            EV           74.9%          NA
(2011)

Agha (2011)               EV           72.1%         44.2%

Cherian (2011)            EV           77.7%         35.4%

Esmat (2011)              EV           82.0%         60.0%

Mosqueira (2011)          EV           74.5%         46.8%

Stefanescu (2011)         EV           84.9%         44.0%

Barikbin (2010)           EV           74.0%         62.0%

De Mattos (2010)          EV           73.2%         40.8%

Nashaat (2010)            EV           88.0%          NA

Sarangapani               EV           72.6%         48.1%
(2010)

Schwarzenberger           EV           55.5%         18.2%
(2010)

Agha (2009)               EV           49.5%         12.9%

Barrera (2009)            EV           85.1%         49.3%

Camma (2009)              EV           60.6%          NA

Sharif (2009)             EV           50.0%          NA

Baig (2008)               EV           70.7%         46.7%

Parrino (2008)            EV           74.1%         19.6%

Tao (2008)                EV           78.3%          NA

Yu (2008)                 EV           75.3%         29.2%

Lei (2007)                EV           41.7%         15.6%

Giannini (2006)           EV           54.1%         21.6%

Legasto (2006)            EV           46.0%          NA

Sethar (2006)             EV           58.4%          NA

Giannini (2005)           EV           40.0%         5.9%

Plestina (2005)           EV           100.0%        53.5%

Giannini (2003)           EV           61.0%         20.0%

                          EV           58.7%         15.7%

First               Cut-off of
author               varices
(year)

Karatzas (2016)      1310.597
                    (optimal)
                       909

Kim (2016)             860

Takuma (2016)          3.36

Xu (2016)              1004
                    (optimal)
                       909

Albreedy (2015)       979.9

Cho (2015)              NA

Stefanescu (2015)       NA

Valero (2015)          1.86
(abstract)

Zhao (2015)            909

Chiodi (2014)         1010%

Gonzalez-Ojeda         884
(2014)

Wang (2014)             NA

Zafar (2014)           909

Calvaruso (2013)       800

Masjedizadeh           663
(2013)

Rajendran (2013)        NA
(abstract)

Saad (2013)            545

Sharma (2013)         1023.2
                    (optimal)
                       909

Al-Dahshan             1023
(2012)

Colecchia (2012)       1883
                    (optimal)
                       513

Mahassadi (2012)       868

                       868

Mangone (2012)       936.364
                    (optimal)

Nisar (2012)           909

Abu El Makarem        939.7
(2011)

Agha (2011)            885

Cherian (2011)         666

Esmat (2011)          1326.6
                    (optimal)
                       909

Mosqueira (2011)       909

Stefanescu (2011)      1068

Barikbin (2010)        921

De Mattos (2010)       909

Nashaat (2010)         820

Sarangapani            NA%
(2010)

Schwarzenberger        909
(2010)

Agha (2009)            909

Barrera (2009)          NA

Camma (2009)           792

Sharif (2009)          2200

Baig (2008)            1014
                    (optimal)
                       909

Parrino (2008)         1300

Tao (2008)             909

Yu (2008)              909

Lei (2007)             0.55
                    (optimal)
                       0.42

Giannini (2006)        909

Legasto (2006)         160
                    (optimal)
                       909

Sethar (2006)          1445

Giannini (2005)        909

Plestina (2005)         NA

Giannini (2003)        909

                       909

EV, esophageal varices; GV, gastric varices; HBV, hepatitis B virus;
HCC, hepatocellular carcinoma; HCV, hepatitis C virus; NA, not
available; PBC, primary biliary cirrhosis; Pts, patients. Notes. *,
age of patients was applied according to grade of varices or severity
of cirrhosis.

TABLE 2: Results of meta-analyses in subgroups for any varices.

Groups                 AUSROC   Sensitivity (95% CI)

Threshold of 909       0.8867     0.84 (0.82-0.86)
Patients with viral    0.8675     0.92 (0.90-0.94)
  hepatitis
High quality studies   0.876      0.84 (0.81-0.87)
Prospective studies    0.8748     0.86 (0.84-0.88)
Region
  Europe               0.8289     0.83 (0.8-0.86)
  Asia                 0.9195     0.86 (0.84-0.87)
  Africa               0.8537     0.87 (0.84-0.9)
  North America          NA       0.82 (0.75-0.88)
Sample size
  <100                 0.7895     0.81 (0.77-0.84)
  [greater than or     0.9012     0.85 (0.83-0.86)
    equal to] 100
Prevalence ofvarices
  <50%                 0.8804     0.91 (0.88-0.93)
  [greater than or     0.8633     0.83 (0.81-0.84)
   equal to] 50%

Groups                 Specificity (95% CI)     PLR (95% CI)

Threshold of 909         0.80 (0.78-0.82)     3.95 (2.66-5.86)
Patients with viral      0.78 (0.74-0.81)     3.80 (2.04-7.08)
  hepatitis
High quality studies     0.77 (0.74-0.8)      3.64 (2.11-6.3)
Prospective studies      0.76 (0.73-0.79)     3.59 (2.39-5.39)
Region
  Europe                 0.65 (0.61-0.7)      2.58 (1.81-3.69)
  Asia                   0.86 (0.84-0.88)     5.18 (3.5-7.65)
  Africa                 0.71 (0.64-0.77)     3.28 (1.74-6.16)
  North America          0.67 (0.56-0.77)     2.45 (1.76-3.4)
Sample size
  <100                   0.7 (0.65-0.75)      2.57 (2.01-3.3)
  [greater than or       0.79 (0.77-0.81)     4.1 (2.92-5.77)
    equal to] 100
Prevalence ofvarices
  <50%                   0.86 (0.83-0.88)     5.29 (3.03-9.23)
  [greater than or       0.73 (0.7-0.75)      3.15 (2.45-4.05)
   equal to] 50%

Groups                   NLR (95% CI)         DOR (95% CI)

Threshold of 909       0.21 (0.13-0.32)   25.06 (11.84-53.03)
Patients with viral    0.11 (0.06-0.23)   37.76 (14.43-98.84)
  hepatitis
High quality studies   0.15 (0.07-0.33)    23.79 (10.35-54.7)
Prospective studies    0.12 (0.07-0.22)   33.85 (15.67-73.15)
Region
  Europe               0.2 (0.11-0.38)     15.46 (7.07-33.79)
  Asia                 0.11 (0.06-0.2)    55.48 (24.27-126.81)
  Africa               0.16 (0.08-0.34)    23.31 (9.2-59.09)
  North America        0.27 (0.18-0.39)    9.06 (4.73-17.36)
Sample size
  <100                 0.25 (0.15-0.4)     12.58 (6.34-24.97)
  [greater than or     0.14 (0.09-0.22)    34.51 (18.84-63.2)
    equal to] 100
Prevalence ofvarices
  <50%                 0.11 (0.04-0.3)    54.16 (14.29-205.25)
  [greater than or     0.19 (0.13-0.26)   21.12 (12.85-34.71)
    equal to] 50%

FIGURE 3: Summary sensitivity and specificity of PSR for any size
varices in liver cirrhosis. (a) Summary sensitivity; (b) summary
specificity.

(a)

                                Sensitivity (95% Cl)

Karatzas et al. (2016)            0.79 (0.58-0.93)
Kim et al. (2016)                 0.63 (0.46-0.77)
Takuma et al. (2016)              0.96 (0.79-1.00)
Xu et al. (2016)                  0.85 (0.77-0.92)
Albreedy (2015)                   1.00 (0.95-1.00)
Valero and Olympia (2015)         0.86 (0.77-0.93)
Zhao et al. (2015)                0.73 (0.61-0.84)
Chiodi et al. (2014)              0.75 (0.64-0.84)
Gonzalez-Ojeda et al. (2014)      0.84 (0.73-0.91)
Zafar and Latif (2014)            0.95 (0.89-0.98)
Calvaruso et al. (2013)           0.74 (0.60-0.85)
Masjedizadeh et al. (2013)        0.47 (0.38-0.56)
Saad et al. (2013)                0.85 (0.62-0.97)
Sharma et al. (2013)              0.76 (0.67-0.83)
Al-Dahshan (2012)                 0.80 (0.64-0.91)
Colecchia et al. (2012)           0.98 (0.90-1.00)
Mahassadi et al. (2012) a         0.82 (0.73-0.90)
Mahassadi et al. (2012) b         0.56 (0.43-0.67)
Mangone et al. (2012)             0.65 (0.45-0.81)
Nisar et al. (2012)               0.96 (0.90-0.99)
Abu El Makarem et al. (2011)      1.00 (0.97-1.00)
Agha et al. (2011)                1.00 (0.89-1.00)
Cherian et al. (2011)             0.66 (0.59-0.73)
Esmat and Rashid (2011)           0.96 (0.90-0.99)
Mosqueira et al. (2011)           0.40 (0.24-0.58)
Stefanescu et al. (2011)          0.78 (0.69-0.85)
Barikbin et al. (2010)            0.97 (0.86-1.00)
De Mattos et al. (2010)           0.78 (0.69-0.85)
Nashaat et al. (2010)             0.84 (0.70-0.93)
Schwarzenberger et al. (2010)     0.80 (0.70-0.89)
Agha et al. (2009)                1.00 (0.98-1.00)
Camma et al. (2009)               0.83 (0.71-0.91)
Sharif et al. (2009)              0.98 (0.89-1.00)
Baig et al. (2008)                0.98 (0.93-1.00)
Parrino et al. (2008)             0.51 (0.42-0.61)
Tao et al. (2008)                 1.00 (0.93-1.00)
Yu et al. (2008)                  0.90 (0.80-0.96)
Lei and Huang (2007)              0.97 (0.93-0.99)
Giannini et al. (2006)            0.92 (0.85-0.96)
Legasto et al. (2006)             0.88 (0.78-0.95)
Sethar et al. (2006)              1.00 (0.95-1.00)
Giannini et al. (2005)            1.00 (0.87-1.00)
Giannini et al. (2003) a          1.00 (0.96-1.00)
Giannini et al. (2003) b          1.00 (0.95-1.00)

Pooled sensitivity = 0.84 (0.83 to 0.85)
[chi square] = 671.49; df = 43 (p = 0.0000)
Inconsistency ([I.sup.2]) = 93.6%

(b)

                                Specificity (95% CI)

Karatzas et al. (2016)            0.36 (0.13-0.65)
Kim et al. (2016)                 0.83 (0.71-0.91)
Takuma et al. (2016)              0.78 (0.61-0.90)
Xu et al. (2016)                  0.83 (0.76-0.89)
Albreedy (2015)                   0.29 (0.15-0.47)
Valero and Olympia (2015)         0.40 (0.16-0.68)
Zhao et al. (2015)                0.78 (0.66-0.88)
Chiodi et al. (2014)              0.72 (0.57-0.84)
Gonzalez-Ojeda et al. (2014)      0.72 (0.47-0.90)
Zafar and Latif (2014)            0.95 (0.88-0.99)
Calvaruso et al. (2013)           0.69 (0.53-0.82)
Masjedizadeh et al. (2013)        0.76 (0.53-0.92)
Saad et al. (2013)                0.83 (0.52-0.98)
Sharma et al. (2013)              0.70 (0.55-0.82)
Al-Dahshan (2012)                 0.70 (0.46-0.88)
Colecchia et al. (2012)           0.26 (0.14-0.40)
Mahassadi et al. (2012) a         0.77 (0.56-0.91)
Mahassadi et al. (2012) b         0.79 (0.54-0.94)
Mangone et al. (2012)             0.64 (0.50-0.77)
Nisar et al. (2012)               0.94 (0.83-0.99)
Abu El Makarem et al. (2011)      0.86 (0.73-0.95)
Agha et al. (2011)                0.92 (0.62-1.00)
Cherian et al. (2011)             0.80 (0.67-0.90)
Esmat and Rashid (2011)           0.83 (0.59-0.96)
Mosqueira et al. (2011)           0.75 (0.43-0.95)
Stefanescu et al. (2011)          0.71 (0.48-0.89)
Barikbin et al. (2010)            0.69 (0.39-0.91)
DeMattos et al. (2010)            0.45 (0.30-0.61)
Nashaat et al. (2010)             0.50 (0.12-0.88)
Schwarzenberger et al. (2010)     0.66 (0.52-0.77)
Agha et al. (2009)                0.97 (0.93-0.99)
Camma et al. (2009)               0.61 (0.45-0.76)
Sharif et al. (2009)              0.88 (0.76-0.95)
Baig et al. (2008)                0.89 (0.75-0.96)
Parrino et al. (2008)             0.93 (0.80-0.98)
Tao et al. (2008)                 0.53 (0.27-0.79)
Yu et al. (2008)                  0.82 (0.60-0.95)
Lei and Huang (2007)              0.92 (0.87-0.96)
Giannini et al. (2006)            0.67 (0.57-0.76)
Legasto et al. (2006)             0.80 (0.70-0.88)
Sethar et al. (2006)              1.00 (0.92-1.00)
Giannini et al. (2005)            0.73 (0.57-0.86)
Giannini et al. (2003) a          0.93 (0.83-0.98)
Giannini et al. (2003) b          0.42 (0.28-0.57)

Pooled specificity = 0.78 (0.76 to 0.79)
[chi square] = 361.20; df =43 (p = 0.0000)
Inconsistency ([I.sup.2]) = 88.1%

FIGURE 4: Summary sensitivity and specificity of PSR for high-risk
varices in liver cirrhosis. (a) Summary sensitivity; (b)
summary specificity.

(a)

                            Sensitivity (95% CI)

Kim et al. (2016)             0.85 (0.55-0.98)
Takuma et al. (2016)          0.94 (0.70-1.00)
Albreedy (2015)               1.00 (0.92-1.00)
Cho et al. (2015)             0.70 (0.58-0.80)
Stefanescu et al. (2015)      0.68 (0.53-0.81)
Chiodi et al. (2014)          0.62 (0.48-0.75)
Wang et al. (2014)            0.83 (0.71-0.92)
Calvaruso et al. (2013)       0.73 (0.52-0.88)
Rajendran et al. (2013)       0.85 (0.74-0.92)
Saad et al. (2013)            0.90 (0.55-1.00)
Mahassadi et al. (2012) a     0.85 (0.75-0.92)
Mahassadi et al. (2012) b     0.63 (0.50-0.75)
Esmat and Rashid (2011)       0.93 (0.84-0.98)
Mosqueira et al. (2011)       0.50 (0.28-0.72)
Sarangapani et al. (2010)     0.88 (0.76-0.96)
Barrera et al. (2009)         0.76 (0.58-0.89)
Plestina et al. (2005)        0.70 (0.56-0.82)

Pooled sensitivity = 0.78 (0.75 to 0.81)
[chi square] = 75.98; df =16 (p = 0.0000)
Inconsistency ([I.sup.2]) = 78.9%

(b)

                            Specificity (95% CI)

Kim et al. (2016)             0.80 (0.70-0.88)
Takuma et al. (2016)          0.84 (0.70-0.93)
Albreedy (2015)               0.53 (0.29-0.76)
Cho et al. (2015)             0.49 (0.40-0.57)
Stefanescu et al. (2015)      0.65 (0.49-0.79)
Chiodi et al. (2014)          0.63 (0.50-0.74)
Wang et al. (2014)            0.78 (0.64-0.88)
Calvaruso et al. (2013)       0.64 (0.52-0.75)
Rajendran et al. (2013)       0.97 (0.85-1.00)
Saad et al. (2013)            0.82 (0.60-0.95)
Mahassadi et al. (2012) a     0.64 (0.45-0.80)
Mahassadi et al. (2012) b     0.74 (0.55-0.88)
Esmat and Rashid (2011)       0.40 (0.25-0.57)
Mosqueira et al. (2011)       0.76 (0.55-0.91)
Sarangapani et al. (2010)     0.84 (0.71-0.92)
Barrera et al. (2009)         0.74 (0.56-0.87)
Plestina et al. (2005)        0.59 (0.43-0.73)

Pooled specificity = 0.67 (0.64 to 0.71)
[chi square] = 87.21; df =16 (p = 0.0000)
Inconsistency ([I.sup.2]) = 81.7%
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Author:Chen, Runhua; Deng, Han; Ding, Xia; Xie, Chune; Wang, Wei; Shen, Qian
Publication:Gastroenterology Research and Practice
Article Type:Report
Date:Jan 1, 2017
Words:8475
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