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Development and Validation of CAGIB Score for Evaluating the Prognosis of Cirrhosis with Acute Gastrointestinal Bleeding: A Retrospective Multicenter Study

  • Zhaohui Bai
  • Bimin Li
  • Su Lin
  • Bang Liu
  • Yiling Li
  • Qiang Zhu
  • Yunhai Wu
  • Yida Yang
  • Shanhong Tang
  • Fanping Meng
  • Yu Chen
  • Shanshan Yuan
  • Lichun Shao
  • Xingshun QiEmail author
Open Access
Original Research
  • 73 Downloads

Abstract

Introduction

Acute gastrointestinal bleeding (GIB) is a major cause of death in liver cirrhosis. This multicenter study aims to develop and validate a novel and easy-to-access model for predicting the prognosis of patients with cirrhosis and acute GIB.

Methods

Patients with cirrhosis and acute GIB were enrolled and randomly divided into the training (n = 865) and validation (n = 817) cohorts. In the training cohort, the independent predictors for in-hospital death were identified by logistic regression analyses, and then a new prognostic model (i.e., CAGIB score) was established. Area under curve (AUC) of CAGIB score was calculated by receiver operating characteristic curve analysis and compared with Child–Pugh, model for end-stage liver disease (MELD), MELD-Na, and neutrophil–lymphocyte ratio (NLR) scores.

Results

In the training cohort, hepatocellular carcinoma (HCC), diabetes, total bilirubin (TBIL), albumin (ALB), alanine aminotransferase (ALT), and serum creatinine (Scr) were independent predictors of in-hospital death. CAGIB score = diabetes (yes = 1, no = 0) × 1.040 + HCC (yes = 1, no = 0) × 0.974 + TBIL (μmol/L) × 0.005 − ALB (g/L) × 0.091 + ALT (U/L) × 0.001 + Scr (μmol/L) × 0.012 − 3.964. In the training cohort, the AUC of CAGIB score for predicting in-hospital death was 0.829 (95% CI 0.801–0.854, P < 0.0001), which was higher than that of Child–Pugh (0.762, 95% CI 0.732–0.791), MELD (0.778, 95% CI 0.748–0.806), MELD-Na (0.765, 95% CI 0.735–0.793), and NLR (0.587, 95% CI 0.553–0.620) scores. In the validation cohort, the AUC of CAGIB score (0.714, 95% CI 0.682–0.746, P = 0.0006) remained higher than that of Child–Pugh (0.693, 95% CI 0.659–0.725), MELD (0.662, 95% CI 0.627–0.695), MELD-Na (0.660, 95% CI 0.626–0.694), and NLR (0.538, 95% CI 0.503–0.574) scores.

Conclusion

CAGIB score has a good predictive performance for prognosis of patients with cirrhosis and acute GIB.

Keywords

Child–Pugh Cirrhosis Gastrointestinal bleeding MELD Prognosis 

Abbreviations

AKP

Alkaline phosphatase

ALB

Albumin

ALT

Alanine aminotransferase

AUC

Area under curve

CAGIB

Cirrhosis acute gastrointestinal bleeding

CIs

Confidence intervals

GBS

Glasgow–Blatchford score

GGT

Gamma-glutamyl transpeptidase

GIB

Gastrointestinal bleeding

Hb

Hemoglobin

HCC

Hepatocellular carcinoma

HCT

Hematocrit

HE

Hepatic encephalopathy

INR

International normalized ratio

K

Potassium

MELD

Model for end-stage liver disease

Na

Sodium

NLR

Neutrophil–lymphocyte ratio

ORs

Odds ratios

PLT

Platelet

Scr

Serum creatinine

TBIL

Total bilirubin

WBC

White blood cell

Introduction

Acute gastrointestinal bleeding (GIB) is an emergency and critical clinical event [1]. The mortality of acute GIB is 6–20% in patients with cirrhosis [2, 3, 4]. The prognosis seems to be similar between patients with cirrhosis and acute variceal bleeding and those with cirrhosis and peptic ulcer bleeding [5]. It is important to accurately evaluate the prognosis in patients with cirrhosis and acute GIB. Conventional scoring systems for assessing the prognosis of patients with acute GIB mainly include Rockall score [6], Glasgow–Blatchford score (GBS) [7], and AIMS65 score [8]. However, they are not specific for patients with cirrhosis in whom gastroesophageal varices are the most frequent sources of acute GIB [9] and the severity of liver dysfunction is closely associated with patients’ outcomes. On the other hand, Child–Pugh [10], model for end-stage liver disease (MELD) [11], MELD-Na [12], and neutrophil–lymphocyte ratio (NLR) [13] scores have been widely employed for prognostic assessment in general patients with liver cirrhosis. But their predictive performances remain suboptimal in patients with cirrhosis and acute GIB.

The present work aimed to develop and validate a novel model for assessing the prognosis of patients with cirrhosis and acute GIB on the basis of the data obtained from a multicenter study.

Methods

The present study was based on the TORCH study (NCT03846180), which was an investigator-initiated multicenter study across 13 centers from eight provinces or municipalities in China. It was carried out following the rules of the Declaration of Helsinki and was approved by the Medical Ethical Committee of the General Hospital of Northern Theater Command (formerly General Hospital of Shenyang Military Area), which is the principal affiliation of this study. The ethical approval number was k (2019) 20. The requirement for informed written consent was waived because of the nature of this study. Briefly, we enrolled the patients with cirrhosis who were admitted because of acute GIB from January 2010 to December 2018. Age, gender, and comorbidities were not limited.

The following data were collected: age; gender; etiology of liver cirrhosis; history of GIB, diabetes, and hepatocellular carcinoma (HCC); ascites; hepatic encephalopathy (HE); and laboratory tests at admission, mainly including hemoglobin (Hb), hematocrit (HCT), white blood cell (WBC), platelet (PLT), total bilirubin (TBIL), albumin (ALB), alanine aminotransferase (ALT), alkaline phosphatase (AKP), gamma-glutamyl transpeptidase (GGT), serum creatinine (Scr), potassium (K), sodium (Na), and international normalized ratio (INR); and in-hospital death. Child–Pugh [10], MELD [11], MELD-Na [12], and NLR [13] scores were calculated.

Random sampling was used to divide patients into training and validation cohorts with an approximate percentage of 50%. Continuous variables were expressed as mean ± standard deviation and median (range), and categorical variables were expressed as frequency (percentage). Difference between training and validation cohorts was compared by the non-parametric Mann–Whitney U test and the chi-square test. In the training cohort, logistic regression analyses were performed to identify the independent predictors associated with in-hospital death. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. An equation for predicting the death of patients with cirrhosis and acute GIB was established by merging the independent predictors with their regression coefficients. Then, receiver operating characteristic curve (ROC) analysis was performed to evaluate the predictive performance of the new equation. The area under curve (AUC) and the best cutoff value with sensitivity and specificity were calculated. The predictive performance of the new equation was also compared with other established scores (Child–Pugh [10], MELD [11], MELD-Na [12], and NLR [13] scores). All statistical analyses were performed using SPSS software version 20.0 (IBM Corp, Armonk, NY, USA) and MedCalc software version 11.4.2.0 (MedCalc Software, Mariakerke, Belgium). P < 0.05 was considered statistically significant.

Results

Patient Selection

A total of 1682 patients with cirrhosis and acute GIB were included, of whom 865 and 817 patients were enrolled into the training and validation cohorts, respectively. Characteristics of patients are summarized in Table 1. All but the percentage of HCC were not statistically significantly different between the training and validation cohorts (Table 1).
Table 1

Characteristics of patients in training and validation cohorts

Variables

No. pts

Training cohort

No. pts

Validation cohort

P value

Age (years)

865

56.00 (20.00–88.00)

56.19 ± 12.31

817

57.00 (18.00–91.00)

57.06 ± 12.06

0.1410

Sex (male) (%)

865

615 (71.10%)

817

568 (69.50%)

0.4800

Hepatic B virus (%)

865

442 (51.10%)

817

433 (53.00%)

0.4360

Hepatic C virus (%)

865

60 (6.90%)

817

48 (5.90%)

0.3750

Alcohol abuse (%)

865

221 (25.50%)

817

199 (24.40%)

0.5730

Autoimmune liver diseases (%)

865

47 (5.40%)

817

35 (4.30%)

0.2740

History of GIB (%)

865

482 (55.70%)

817

461 (56.40%)

0.7710

History of diabetes (%)

865

164 (19.00%)

817

166 (20.30%)

0.4830

Hepatocellular carcinoma (%)

865

127 (14.70%)

817

153 (18.70%)

0.0260*

Ascites (%)

865

452 (55.30%)

817

513 (59.30%)

0.0990

Hepatic encephalopathy (%)

865

36 (4.20%)

817

40 (4.90%)

0.4690

Hemoglobin (g/L)

865

76.00 (16.00–152.00)

79.11 ± 24.60

816

76.00 (23.00–170.00)

78.21 ± 24.08

0.5090

Hematocrit (%)

865

23.60 (2.74–45.90)

24.29 ± 6.93

814

23.40 (8.70–47.00)

23.95 ± 6.78

0.3380

White blood cell (109/L)

865

5.81 (0.98–68.00)

6.75 ± 4.73

815

5.63 (0.74–51.00)

6.69 ± 4.80

0.3360

Platelet (109/L)

865

77.00 (4.00–827.00)

88.93 ± 61.38

814

77.00 (2.00–846.00)

95.42 ± 83.36

0.4890

Total bilirubin (μmol/L)

863

23.70 (4.20–518.00)

38.03 ± 51.14

816

22.80 (2.40–449.00)

34.01 ± 42.19

0.0680

Albumin (g/L)

846

29.00 (11.70–49.80)

29.07 ± 5.98

797

28.80 (10.10–47.20)

28.64 ± 5.90

0.2160

Alanine aminotransferase (U/L)

862

28.00 (3.00–2651.00)

52.21 ± 147.23

815

26.00 (4.00–1575.00)

41.51 ± 86.36

0.0880

Aspartate aminotransferase (U/L)

804

37.00 (6.00–3182.00)

78.33 ± 216.66

768

35.14 (6.00–1993.00)

64.46 ± 120.45

0.2770

Alkaline phosphatase (U/L)

843

79.78 (18.00–2344.00)

110.71 ± 122.35

782

80.00 (18.90–1320.00)

104.32 ± 95.26

0.3730

Gamma-glutamyl transpeptidase (U/L)

840

39.20 (2.80–2996.00)

93.36 ± 190.59

781

41.00 (5.00–1494.90)

85.74 ± 132.51

0.5520

Serum creatinine (μmol/L)

865

65.50 (7.00–372.80)

70.96 ± 31.13

817

65.00 (11.20–303.00)

70.99 ± 30.20

0.6680

Potassium (mmol/L)

864

4.10 (2.25–6.71)

4.18 ± 0.63

815

4.10 (1.85–7.37)

4.21 ± 0.69

0.4830

Sodium (mmol/L)

860

137.95 (115.00–153.90)

137.08 ± 4.69

816

137.85 (105.00–161.60)

137.19 ± 5.23

0.6360

International normalized ratio

860

1.35 (0.79–7.96)

1.45 ± 0.42

804

1.34 (0.91–4.99)

1.43 ± 0.37

0.3480

Child–Pugh score

841

8.00 (5.00–15.00)

7.91 ± 1.81

784

8.00 (5.00–13.00)

7.82 ± 1.78

0.4670

MELD score

858

7.99 (− 13.30 to 38.79)

8.85 ± 5.91

803

7.75 (− 8.13 to 33.49)

8.45 ± 5.53

0.3940

NLR score

864

5.07 (0.40–72.92)

6.36 ± 5.33

812

4.86 (0.51–179.80)

6.41 ± 7.73

0.4350

In-hospital death (%)

865

29 (3.40%)

817

23 (2.80%)

0.5240

Pts patients, GIB gastrointestinal bleeding, MELD model for end-stage liver disease, NLR neutrophil to lymphocyte ratio

*Statistically significant at P < 0.05

Univariate and Multivariate Analyses in the Training Cohort

Univariate logistic regression analyses demonstrated that HCC, diabetes, hepatic C virus infection, ascites, HE, WBC, TBIL, ALB, ALT, Scr, and INR were significantly associated with in-hospital death (Table 2). Multivariate logistic regression analyses showed that HCC, diabetes, TBIL, ALB, ALT, and Scr were independently associated with in-hospital death (Table 2).
Table 2

Univariate and multivariate analyses of predictors associated with the in-hospital mortality of acute GIB in training cohort

Variables

No. pts (all = 865)

Univariate

Multivariate

OR

95% CI

P value

OR

95% CI

P value

Age (years)

865

1.029

0.998–1.061

0.0670

   

Sex (female vs. male)

865

1.288

0.543–3.054

0.5660

   

Hepatic B virus (yes vs. no)

865

1.124

0.536–2.358

0.7570

   

Hepatic C virus (yes vs. no)

865

3.778

1.476–9.670

0.0060*

2.794

0.917–8.512

0.0710

Alcohol abuse (yes vs. no)

865

1.325

0.594–2.954

0.4920

   

Autoimmune (yes vs. no)

865

1.630

0.217–12.250

0.6800

   

History of GIB (yes vs. no)

865

1.573

0.747–3.311

0.2330

   

History of diabetes (yes vs. no)

865

2.728

1.263–5.894

0.0110*

2.824

1.127–7.079

0.0270*

Hepatocellular carcinoma (yes vs. no)

865

2.738

1.218–6.158

0.0150*

2.647

1.022–6.859

0.0450*

Ascites (yes vs. no)

865

2.707

1.091–6.718

0.0320*

1.995

0.713–5.586

0.1880

Hepatic encephalopathy (yes vs. no)

865

4.020

1.321–12.235

0.0140*

2.147

0.562–8.210

0.2640

Hemoglobin (g/L)

865

0.995

0.980–1.011

0.5280

   

Hematocrit (%)

865

0.965

0.912–1.020

0.2040

   

White blood cell (109/L)

865

1.054

1.005–1.104

0.0290*

1.020

0.964–1.078

0.5000

Platelet (109/L)

865

1.003

0.999–1.007

0.1580

   

Total bilirubin (μmol/L)

863

1.008

1.004–1.011

< 0.0001*

1.005

1.001–1.009

0.0200*

Albumin (g/L)

846

0.874

0.815–0.936

< 0.0001*

0.912

0.840–0.989

0.0260*

Alanine aminotransferase (U/L)

862

1.002

1.001–1.003

0.0040*

1.001

1.000–1.002

0.0490*

Aspartate aminotransferase (U/L)b

804

1.001

1.000–1.002

0.0060*

   

Alkaline phosphatase (U/L)

843

1.001

1.000–1.003

0.0750

   

Gamma-glutamyl transpeptidase (U/L)

840

1.000

0.999–1.002

0.6560

   

Serum creatinine (μmol/L)

865

1.012

1.005–1.018

< 0.0001*

1.012

1.004–1.020

0.0040*

Potassium (mmol/L)

864

1.323

0.765–2.289

0.3160

   

Sodium (mmol/L)

860

0.940

0.877–1.007

0.0790

   

International normalized ratio

860

2.320

1.310–4.110

0.0040*

1.311

0.737–2.335

0.3570

Child–Pugh scorea

841

1.652

1.358–2.009

< 0.0001*

   

MELD scorea

858

1.149

1.095–1.205

< 0.0001*

   

NLR scorea

864

1.042

0.998–1.088

0.0640

   

Pts patients, GIB gastrointestinal bleeding, MELD model for end-stage liver disease, NLR neutrophil to lymphocyte ratio

*Statistically significant at P < 0.05

aChild–Pugh score, MELD score, and NLR score are complex variables composed of many clinically significant variables, so they were not included in the multivariate analysis

bAspartate aminotransferase and alanine aminotransferase had a potential collinearity for assessing liver dysfunction, so we excluded the aspartate aminotransferase in multivariate analysis

Development of CAGIB Score

A prognostic model called CAGIB (Cirrhosis Acute GastroIntestinal Bleeding) was established. CAGIB = Diabetes (yes = 1, no = 0) × 1.040 + HCC (yes = 1, no = 0) × 0.974 + TBIL (μmol/L) × 0.005 − ALB (g/L) × 0.091 + ALT (U/L) × 0.001 + Scr (μmol/L) × 0.012 − 3.964. It had an AUC of 0.829 (95% CI 0.801–0.854, P < 0.0001), and its best cutoff value was greater than − 4.6646 with a sensitivity of 78.57% and a specificity of 75.52% (Fig. 1). The AUCs of Child–Pugh, MELD, MELD-Na, and NLR scores were 0.762 (95% CI 0.732–0.791), 0.778 (95% CI 0.748–0.806), 0.765 (95% CI 0.735–0.793), and 0.587 (95% CI 0.553–0.620), respectively (Fig. 2). The difference was statistically significant between CAGIB and NLR score (P = 0.0001), but not between CAGIB and Child–Pugh, MELD, or MELD-Na score.
Fig. 1

ROC curve of CAGIB score for predicting the in-hospital death of patients with cirrhosis and acute GIB in the training cohort

Fig. 2

Comparison of predictive performance of CAGIB score with Child–Pugh, MELD, MELD-Na and NLR scores in the training cohort. Brown line refers to the CAGIB score, red line refers to the Child–Pugh score, green line refers to the MELD score, purple line refers to the MELD-Na score, and orange line refers to the NLR score

Validation of CAGIB Score

In the validation cohort, the CAGIB score had an AUC of 0.714 (95% CI 0.682–0.746, P = 0.0006) (Fig. 3). The AUCs of Child–Pugh, MELD, MELD-Na, and NLR scores were 0.693 (95% CI 0.659–0.725), 0.662 (95% CI 0.627–0.695), 0.660 (95% CI 0.626–0.694), and 0.538 (95% CI 0.503–0.574), respectively (Fig. 4). The difference was statistically significant between CAGIB and NLR score (P = 0.0165), but not between CAGIB and Child–Pugh, MELD, or MELD-Na score.
Fig. 3

ROC curve of CAGIB score for predicting the in-hospital death of patients with cirrhosis and acute GIB in the validation cohort

Fig. 4

Comparison of predictive performance of CAGIB score with Child–Pugh, MELD, MELD-Na and NLR scores in the validation cohort. Brown line refers to the CAGIB score, red line refers to the Child–Pugh score, green line refers to the MELD score, purple line refers to the MELD-Na score, and orange line refers to the NLR score

Discussion

Our study developed a new model (CAGIB score) for assessing the prognosis of patients with cirrhosis and acute GIB. Our study has the following notable features: (1) the data was obtained from multiple institutions in China; (2) a large number of patients were included; (3) the variables used for this model were readily available in clinical practice; (4) CAGIB score had a greater predictive performance than other conventional models in both training and validation cohorts; and (5) the predictive performance of CAGIB score was further validated.

CAGIB score includes two clinical variables (i.e., diabetes and HCC). Diabetes is a worldwide pandemic with a prevalence of 9.4% in the USA [14] and 11.6% in China [15]. Increasing evidence suggests a close relationship between diabetes and outcomes of liver disease. Diabetes increased the risks of liver cancer and chronic liver diseases [16, 17, 18] and was also associated with an increased risk of mortality in patients with cirrhosis [19]. Our previous single-center study also showed that diabetes was significantly associated with the prognosis of patients with cirrhosis and acute upper GIB, which is consistent with our current study [20]. On the other hand, HCC is one of the most common causes of cancer-related death [21]. And 80% of HCC patients have liver cirrhosis [22]. HCC can further aggravate portal pressure due to tumor compression and tumor thrombus formation and is considered as the independent predictor of death and re-bleeding in patients with cirrhosis and GIB [9, 23, 24, 25].

CAGIB score also includes four laboratory variables (i.e., TBIL, ALB, Scr, and ALT). Inclusion of TBIL, ALB, and Scr into this new model is easily understood, because they are important components of conventional scoring systems (i.e., MELD and Child–Pugh scores). Notably, a rapid increase in Scr level is often an acute critical condition indicating decreased kidney perfusion in patients with cirrhosis developing an acute GIB episode. Indeed, regardless of acute GIB, renal failure increases the mortality sevenfold in patients with cirrhosis [26]. In patients with cirrhosis and acute GIB, acute kidney injury is also an independent predictor for death [5, 27]. Besides, our study found that an increased ALT level was another independent predictor. In patients with cirrhosis and massive GIB, nearly all organs, including liver, are in an ischemic state after acute blood loss [28]. Hypoxic hepatitis, which is characterized by a rapid rise in serum aminotransferases due to liver cell necrosis by mitochondrial damage and DNA fragmentation [29], can be frequently observed in patients with cirrhosis and variceal bleeding [30] and negatively influence the patients’ outcomes [31].

A major limitation was that CAGIB score could not be compared with conventional scoring systems for GIB, such as Rockall, GBS, and AIMS65 scores, because not all patients underwent endoscopy. Second, for some patients, the source of GIB was unclear due to lack of the relevant endoscopy data. Thus, the association of sources of acute GIB with the mortality was not explored in the current study. Third, the stage of HCC was not extracted in our study. Fourth, the potential heterogeneity in the treatment selection among the participating centers should be acknowledged.

Conclusions

We developed and validated the CAGIB score to predict the in-hospital death of patients with cirrhosis and acute GIB. A CAGIB score of greater than − 4.6646 suggested a high risk of in-hospital death in liver cirrhosis with acute GIB. On the basis of the CAGIB score, physicians may also pay attention to the management of diabetes, improvement of liver and renal function, and supplementation of human albumin solution for patients with cirrhosis and acute GIB.

Notes

Acknowledgements

We thank the participants of the study.

Funding

No funding or sponsorship was received for this study or publication of this article.

Authorship

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Authorship Contributions

Zhaohui Bai: collected the data, analyzed the data, performed the statistical analysis, and drafted manuscript. Bimin Li, Su Lin, Bang Liu, Yiling Li, Qiang Zhu, Yunhai Wu, Yida Yang, Shanhong Tang, Fanping Meng, Yu Chen, Shanshan Yuan, and Lichun Shao: collected the data, analyzed the data, and gave critical comments. Xingshun Qi: conceived the work, wrote the study protocol, reviewed the literature, gave critical comments, and revised the manuscript. All authors reviewed and approved the paper.

Disclosures

All authors, including Zhaohui Bai, Bimin Li, Su Lin, Bang Liu, Yiling Li, Qiang Zhu, Yunhai Wu, Yida Yang, Shanhong Tang, Fanping Meng, Yu Chen, Shanshan Yuan, Lichun Shao, and Xingshun Qi, have nothing to disclose.

Compliance with Ethics Guidelines

The present study was carried out following the rules of the Declaration of Helsinki and approved by the Medical Ethical Committee of the General Hospital of Northern Theater Command (formerly General Hospital of Shenyang Military Area), which is the principal affiliation of this study. The ethical approval number was k(2019) 20. Considering the nature of this observational study, the patients’ written informed consent was waived by the ethics committee.

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Zhaohui Bai
    • 1
  • Bimin Li
    • 2
  • Su Lin
    • 3
  • Bang Liu
    • 4
  • Yiling Li
    • 5
  • Qiang Zhu
    • 6
  • Yunhai Wu
    • 7
  • Yida Yang
    • 8
  • Shanhong Tang
    • 9
  • Fanping Meng
    • 10
  • Yu Chen
    • 11
  • Shanshan Yuan
    • 12
  • Lichun Shao
    • 13
  • Xingshun Qi
    • 1
    Email author
  1. 1.Department of GastroenterologyGeneral Hospital of Northern Theater Command (formerly called General Hospital of Shenyang Military Area)ShenyangChina
  2. 2.Department of GastroenterologyFirst Affiliated Hospital of Nanchang UniversityNanchangChina
  3. 3.Liver Research CenterFirst Affiliated Hospital of Fujian Medical UniversityFuzhouChina
  4. 4.Department of Hepatobiliary Disease900 Hospital of the Joint Logistics Team (formerly called Fuzhou General Hospital)FuzhouChina
  5. 5.Department of GastroenterologyFirst Affiliated Hospital of China Medical UniversityShenyangChina
  6. 6.Department of GastroenterologyShandong Provincial Hospital Affiliated to Shandong UniversityJinanChina
  7. 7.Department of Critical Care MedicineSixth People’s Hospital of ShenyangShenyangChina
  8. 8.State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouChina
  9. 9.Department of GastroenterologyGeneral Hospital of Western Theater CommandChengduChina
  10. 10.Department of Biological TherapyFifth Medical Center of PLA General HospitalBeijingChina
  11. 11.Difficult and Complicated Liver Diseases and Artificial Liver Center, Beijing Youan HospitalCapital Medical UniversityBeijingChina
  12. 12.Department of GastroenterologyXi’an Central HospitalXi’anChina
  13. 13.Department of GastroenterologyAir Force Hospital of Northern Theater CommandShenyangChina

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