Skip to main content

Development and external validation of a prognostic nomogram for acute decompensation of chronic hepatitis B cirrhosis



Acute decompensation (AD) has been shown to be associated with a high mortality rate for cirrhosis patients. This study aimed to develop a prognostic nomogram to evaluating the individual prognosis for AD of cirrhosis in chronic hepatitis B (CHB).


The nomogram was developed using data from a retrospective study on 509 patients hospitalized for AD of CHB cirrhosis from October 2008 to February 2014 at the Beijing Ditan Hospital, Capital Medical University. The predictive accuracy, discriminative ability, and clinical net benefit were evaluated by concordance index (C-index), calibration curves, and decision curve analysis (DCA). The results were validated on 620 patients consecutively enrolled from January 2005 to December 2010 at the Renji Hospital, Shanghai Jiao Tong University,.


On multivariate analysis of the derivation cohort, independent factors included in the nomogram were age, previous decompensation, bacterial infection, hepatic encephalopathy, and total bilirubin. The calibration curve for the probability of survival showed good agreement between the nomogram and actual observation. The nomogram had a C-index of 0.897, which was statistically higher than the C-index values of CTP (0.793), MELD (0.821), SOFA (0.868), or the Chronic Liver Failure Consortium AD (CLIF-C AD) (0.716) scores (p <  0.001 for all). Using DCA, the nomogram also demonstrated superior net benefits over other score models. The results were confirmed in the validation cohort.


The proposed nomogram enables more-accurate individualized prediction of survival than MELD, CTP, SOFA, or CLIF-C AD scores for AD of CHB cirrhosis patients.

Peer Review reports


Hepatitis B virus (HBV) infection is a critical global health threat, particularly in Asia. In the natural history of chronic HBV infection, partial patients remain asymptomatic until an episode of acute decompensation (AD), which is featured by the rapid development of one or more major complications of liver disease. AD is the common cause of hospital admission and is associated with a high mortality rate for cirrhosis patients. AD of cirrhosis occurs in as many as 15% of cirrhosis patients each year, and the frequency and severity will increase with the progressively deterioration of liver reserve function. The course of illness during a patient’s early hospital phase has been related to eventual prognosis. Therefore, early recognition and aggressive treatment are important to improving survival [1,2,3,4].

Recently, the CANONIC research team built the Chronic Liver Failure Consortium AD (CLIF-C AD), which was demonstrated to be superior to the Model for End-stage Liver Disease (MELD), MELD-Na, and Child-Turcotte-Pugh (CTP) assessments in predicting mortality due to AD [5]. However, the current score models were built primarily base on populations with alcoholism issues and/or hepatitis C virus infections [5]. To date, no reports of a model have been found to assess the prognosis of AD of chronic hepatitis B (CHB) cirrhosis. CHB cirrhosis is deemed as the leading cause of AD in the Asia-Pacific region [6]. Therefore, it is necessary to create a risk model that can predict the prognosis for AD of CHB cirrhosis.

The nomogram is a graphical calculating and two-dimensional diagram, which could offer more precise and individualized prediction relative to the traditional models for multiple illness [6,7,8,9]. The specific objective of the study was to determine the risk factors for short-term death of AD with CHB cirrhosis, develop a prognostic nomogram to estimate the individual prognosis, and promote the implementation of preventive measures.

Patients and methods

Derivation cohort

In total, 1781 patients hospitalized at Beijing Ditan Hospital (Beijing, China), which is affiliated with the Capital Medical University, between October 2008 and February 2014 with CHB cirrhosis were screened, and 509 patients with AD of CHB cirrhosis were enrolled for model derivation. All patients were limited to age ≥ 18 years in this study, and patients with the following diseases would be excluded: (1) infected with hepatitis virus (including A, C, D, and E), human immunodeficiency virus, or other viruses; (2) autoimmune hepatitis, drug-induced liver injury, alcohol-induced liver disease, Wilson’s disease, liver cancer, or hemolytic diseases; (3) other fatal disease, or gestation; or (4) liver operation or liver transplantation. The Ethics Committee from Beijing Ditan Hospital has approved this study protocol.

Validation cohort

2610 patients hospitalized with CHB cirrhosis at Renji Hospital, Shanghai Jiao Tong University, from January 2005 to December 2010 were screened, and 620 patients with AD of CHB cirrhosis were enrolled for external validation of the model. Only patients meeting the inclusion and exclusion standard and having sufficient data were registered. The research was supported by the Ethics Committee from Renji Hospital.

Cirrhosis was confirmed by previous hepatic pathology or clinical signs and results of laboratory detection, radiologic and endoscopic presentation.

AD of cirrhosis was defined as the rapid development of one or more major complications (ie, large ascites, hepatic encephalopathy (HE), gastrointestinal hemorrhage and bacterial infection) of liver disease [10,11,12,13,14]. Cirrhosis patients with grade 2 to 3 ascites within 2 weeks could be diagnosed with the acute development of large ascites [10]. Acute HE was characterized by the development of the acute confusional status in patients with previously normal conscious state and no evidence of the neurological deficit [11]. Cirrhosis patients with occurrence of upper or lower gastrointestinal bleeding could be diagnosed with acute gastrointestinal hemorrhage [12]. Bacterial infection was defined to include spontaneous bacterial peritonitis (SBP), pneumonia, cellulitis, urinary system infection and spontaneous bacteremia [13].

Definitions of organ failures were as follows [15]: the definition of liver failure was the total bilirubin (TBil) ≥ 12.0 mg/dL; the definition of kidney failure was the serum creatinine (Cr) ≥ 2.0 mg/dL or the use of renal replacement therapy; cerebral failure was defined by grade III-IV HE; the definition of coagulation failure was an international normalized ratio (INR) ≥ 2.5; the definition of circulatory failure was the use of vasoconstrictors; the definition of respiratory failure was PaO2/FiO2 ≤ 200 or SpO2/FiO2 ≤ 214.

The MELD, CTP, SOFA and CLIF-C AD scores were calculated according to previously published criteria [5, 16,17,18]. All definitions and prognostic scores were applied at baseline.


Standard medical therapies were used for all patients after diagnosis, such as bed rest, liver-protecting therapy, antiviral treatment, nutritional support, vitamins supplements, plasma and albumin transfusion, maintenance treatment of water-electrolyte and acid-base balance, control and prevention of complications.

Clinical predictors

Possible variables associated with short-term survival of AD of CHB cirrhosis were collected, including age; sex; mean arterial pressure; decompensation history; occurrences of organ failures or clinic complications; biochemical parameters, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transpeptidase (GGT), albumin (ALB), TBil, Cr, serum Na, INR, white blood cell (WBC) count, absolute neutrophil count (NC), absolute platelet count (PLT), and HBV deoxyribonucleic acid (DNA) levels. Baseline data were obtained at the first diagnosis of AD of CHB cirrhosis. The 7-, 14- and 28-day survival rates were gained by searching the medical records.

Statistical analysis

Statistical analysise were conducted using SPSS version 20.0 (IBM, Armonk, NY, USA). Patient characteristics were compared by the Fisher’s exact or χ 2 tests (categorical variables) and the Mann-Whitney U or t test (continuous variables). Univariate risk factors that reached p <  0.05 were subjected to Cox regression analysis.

The nomogram was built in terms of results of Cox regression analyses using R version 3.0.2 with the rms package [19]. According to the Akaike information criterion, backward step-down selection process was applied to the selection of final model [20]. The discrimination of the models were assessed in terms of the concordance index (C-index). Bootstraps with 1000 resamples were drawn to correct the C-index. Comparisons between nomogram and other models were carried out by using the rcorrp.cens in the Hmisc package [21]. Calibration curves were also drawn to evaluate the concordance between predicted and observed probabilities. Decision curve analyses (DCA) were used to compare the benefits and improved performance of different models [22]. All tests were two-sided and were considered statistically significant when p <  0.05.


Patient characteristics and outcomes

In total, 509 and 620 patients from the derivation and external validation cohorts, respectively, were included for analyses. The baseline data of enrolled patients were summarized in Table 1. In both sets, the average age was 51–52 years, and the patients were predominantly men. 410 patients (80.6%) were treated with nucleotide analogs (NUCs) and 99 patients (19.4%) didn’t received NUCs after hospital admission in the derivation cohort. 372 patients (60.0%) were treated with NUCs and 248 patients (40.0%) didn’t receive antiviral therapy in the validation cohort. The most common complication was ascites, followed by HE (I-IV) and gastrointestinal hemorrhage at admission. Liver failure was the most frequent organ failure, which was followed by coagulation and cerebral failure.

Table 1 Baseline characteristics in enrolled acute decompensation of CHB cirrhosis patients in the derivation and external validation cohort

When comparing the demographic and clinical characteristics between the derivation and validation sets, we found that patients in the derivation set had lower rates of previous decompensation and hyponatremia, and higher rates of complications (ascites, bacterial infection, gastrointestinal hemorrhage, HE) at the time of admission (p <  0.05). Moreover, the validation set had lower Na levels and higher ALT, AST, GGT, TBil, Cr, INR, WBC, and HBV DNA levels (p <  0.05). The validation set had higher 7-, 14- and 28-day risk of death than those of the derivation set (p <  0.05).

Univariate and multivariate analyses

Univariate analysis showed that age, male sex, previous decompensation, hyponatremia, ascites, bacterial infection, gastrointestinal hemorrhage, HE III-IV, SBP, hepatorenal syndrome, organ failure (liver, kidney, cerebral, coagulation, circulatory, and lung), AST, ALB, TBil, Cr, Na, INR, WBC, and NC were significantly associated with poor prognosis of AD of CHB cirrhosis in the derivation set (p <  0.05, Table 2).

Table 2 Univariate and multivariate Cox regression analyses in patients with acute decompensation of CHB cirrhosis from the derivation cohort (n = 509)

These variables were subjected to the Cox regression analyses. The results showed that only age (HR = 1.057, 95% confidence interval [CI]: 1.032–1.082, p <  0.001), previous decompensation (HR = 2.449, 95% CI: 1.351–4.438, p = 0.002), bacterial infection (HR = 5.325, 95% CI: 3.015–9.405, p <  0.001), HE III–IV (HR = 4.660, 95% CI: 3.115–6.972, p <  0.001), and TBil (HR = 1.053, 95% CI: 1.030–1.078, p <  0.001) were independent risk factors for outcomes (Table 2).

Derivation of the prognostic nomogram

The nomogram was established on the basis of the coefficients gained from multivariate analysis, which included age, previous decompensation, bacterial infection, HE, and TBil (Fig. 1). Each value of the factors was allocated the score in the point scale axis. By summing each single score and using that value in the total point scale axis, the total score could be easily calculated to assign the probability of survival for individual patients.

Fig. 1

Acute decompensation of CHB cirrhosis survival nomogram. To use the nomogram, the value of an individual patient is located on each variable axis, and a line is drawn upward to determine the number of points received for the value of each variable. The sum of these numbers is located on the total point axis, and a line is drawn downward to the survival axes to determine the likelihood of 7-, 14-, and 28-day survivals

Validation of the prognostic nomogram

The C-index of the nomogram was 0.897 (95% CI: 0.850–0.943) in the derivation cohort. To more effectively validate the practicability of the nomogram, we adopted an external cohort with AD of CHB cirrhosis for model validation. When the validation set was estimated by the established nomogram, the C-index was 0.839 (95% CI: 0.811–0.867), suggesting that the nomogram is suitable for estimating the short-term outcome for AD of CHB cirrhosis.

The calibration curves were plotted showing that good agreements between the nomogram predictions and observed probabilities for the 7-, 14-, and 28-day outcomes in the primary (Figs. 2A-C) and external validation cohort (Figs. 2D-F).

Fig. 2

The calibration curve of overall survival at 7, 14, and 28 days for the derivation cohort (a-c) and the external validation cohort (d-f). Nomogram-predicted probability of survival is plotted on the x-axis, and the actual survival is plotted on the y-axis. Dashed lines along the 45-degree line through the point of origin represent the perfect calibration models in which the predicted probabilities are identical to the actual probabilities

Performance of the nomogram compared with other models

Futhermore, we compared the C-indexes for evaluating the concordance of these models. The corrected C-index of our nomogram was 0.897, which was significantly higher than that of the MELD (0.820), CTP (0.793), SOFA (0.868), and CLIF-C ADs (0.716) in the primary set (p <  0.001, Table 3). Meanwhile, the nomogram also had the highest C-index (0.839) in the validation set, with statistical significance in comparison with MELD (0.826), CTP (0.741), SOFA (0.837), and CLIF-C ADs (0.569) (p <  0.001 for all).

Table 3 The predictive discrimination ability of the nomogram compared to the MELD, CTP, and SOFA score systems in the primary and validation cohorts

On DCA, our nomogram provided superior net benefit and improved performance for the 7-, 14-, and 28-day prognostic evaluation in the primary (Figs. 3A-C) and validation (Figs. 3D-F) cohorts relative to MELD, CTP, SOFA, and CLIF-C AD score models. This represents superior clinical usefulness of the nomogram over other score models.

Fig. 3

Decision curve analysis at 7, 14, and 28 days for the derivation cohort (a-c) and the external validation cohort (d-f). Decision curve analyses depict the clinical net benefit in pairwise comparisons across the different models. The horizontal solid black line represents the assumption that no patients will experience the event, and the solid gray line represents the assumption that all patients will relapse. On decision curve analysis, the nomogram showed superior net benefit compared with other models across a range of threshold probabilities


In the present study, a novel and easy-to-use nomogram was constructed to evaluate individual prognosis for AD of CHB cirrhosis. This nomogram demonstrated superior predictive capability and clinical usefulness relative to the current prognostic score models, including MELD, CTP, SOFA, and CLIF-C ADs.

In our derivation cohort, 11.0% (56/509) of patients with AD of CHB cirrhosis died within 4 weeks—a result that was similar to that of another study [23]. A total of 13.0% (66/509) of the patients died within 90 days, and 56 of 66 patients died in the first 4 weeks; thus, we set up the 28-day statistical prognostic score model for patients with AD to find out those at the highest risk for death, which would enable us to take effective treatment measures as soon as possible. In addition, most of studies on prognosis factors for liver decompensation cirrhosis did not strictly distinguish the etiology, such as viral hepatitis, alcohol, drugs, or other factors [17, 24]. Our study only observed patients with AD of CHB cirrhosis as subjects; both blood biochemical tests and clinical complications were part of our prognosis factors. We also excluded the influence of liver transplantation, considering this has less application in our country. Moreover, to effectively evaluate the predictive ability of our nomogram versus those of established models, we adopted the external validation cohort to reduce the influence of drugs, treatments, and other factors on the results.

The proposed nomogram included age, two liver function indices (previous decompensation, TBil), and two complications (bacterial infection and HE). Many studies have suggested the correlation between age and prognosis in many diseases, and that old age is an important factor in poor prognosis for cirrhosis [25, 26]. Recently, the CANONIC study team built two models for ACLF (CLIF-C ACLFs) and AD (CLIF-C ADs) patients, where old age was considered an important indicator of poor outcomes [5, 15]. This may be explained by loss of immune function or decline in tissue regeneration and repair [27, 28]. Serum TBil is commonly used as an indicator of the degree of liver damage and reserve function; it is also the main parameter of the CTP and MELD score systems [16, 17]. Previous decompensation could better reflect the underlying disease before the onset of AD. Bacterial infection represent the particularly important cause of liver failure and other complications [29]. The end-organ damaging effect of bacterial infection is more serious in liver cirrhosis and often culminates in newly developed liver and extrahepatic organ failures, which account for significant morbidity and mortality [4, 30]. HE is a frequent and serious complication of cirrhosis [31, 32]. Once HE occurs in patients with cirrhosis, the prognosis is poor, and liver transplant should be performed as soon as possible in order to increase the survival rate [33, 34].

In this study, the nomogram model performed well in predicting survival, as supported by the C-index (0.897 and 0.839 for the primary and validation cohorts respectively) and the calibration curve. Compared with MELD, CTP, SOFA, and CLIF-C ADs, the nomogram showed superior predictive capability for outcomes. Furthermore, the nomogram demonstrated better net benefit and improved performance for 7-, 14-, and 28-day prognostic evaluation in the derivation and validation cohorts compared with MELD, CTP, SOFA, and CLIF-C AD score models.

In current study, some patients didn’t received antiviral therapy. One of the reasons is that patients with HE III-IV and gastrointestinal hemorrhage are unable to take drugs. Another reason is that the antiviral therapy was administered not only according to HBV replication levels, but also the willingness of the patient. So, considering the bad financial condition, many patients have to abandon the use of antiviral drugs. Besides, more patients didn’t received the antiviral therapy in the validation cohort. The major reason is that the enrollment time of patients in validation cohort is from January 2005 to December 2010, four years ahead of the derivation cohort. Antiviral drugs are more widespread with the development of healthcare in China. This reflects the clinical treatment situation of China objectively.

Our study has several limitations. First, our nomogram only included basic laboratory data. However, this study aimed to construct a reliable prognostic model. To avoid inevitable bias, subjective variables have not been included to construct our nomogram. Second, the nomogram was built on the basis of a retrospective cohort, and selection bias may exist. However, we have validated the model with data from another institution. The results consistently demonstrated the very good performance of our established nomogram.


To our knowledge, the nomogram model here is the first model developed to predict the individual prognosis of AD of cirrhosis in CHB patients to date. This provided better performance than MELD, CTP, SOFA, and CLIF-C AD scores, and it offers a foundation for individualized counseling and clinical treatment.



acute decompensation




alkaline phosphatase


alanine aminotransferase


aspartate aminotransferase


chronic hepatitis B


confidence intervals


Chronic Liver Failure Consortium Acute Decompensation




decision curve analysis




hepatitis B virus


hepatic encephalopathy


hazard ratios


international normalized ratio


Model for End-stage Liver Disease


absolute neutrophil count


absolute platelet count


Sequential Organ Failure Assessment


total bilirubin


white blood cell


  1. 1.

    Dienstag JL, Ghany MG, Morgan TR, Di Bisceglie AM, Bonkovsky HL, Kim HY, et al. A prospective study of the rate of progression in compensated, histologically advanced chronic hepatitis C. Hepatology. 2011;54:396–405.

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Jepsen P, Ott P, Andersen PK, Sørensen HT, Vilstrup H. Clinical course of alcoholic liver cirrhosis: a Danish population-based cohort study. Hepatology. 2010;51:1675–82.

    Article  PubMed  Google Scholar 

  3. 3.

    D'Amico G, Garcia-Tsao G, Pagliaro L. Natural history and prognostic indicators of survival in cirrhosis: a systematic review of 118 studies. J Hepatol. 2006;44:217–31.

    Article  PubMed  Google Scholar 

  4. 4.

    Moreau R, Jalan R, Gines P, Pavesi M, Angeli P, Cordoba J, et al. Acute-on-chronic liver failure is a distinct syndrome that develops in patients with acute decompensation of cirrhosis. Gastroenterology. 2013;144:1426–37 1437.e1–9.

    Article  PubMed  Google Scholar 

  5. 5.

    Jalan R, Pavesi M, Saliba F, Amorós A, Fernandez J, Holland-Fischer P, et al. The CLIF consortium acute decompensation score (CLIF-C ADs) for prognosis of hospitalised cirrhotic patients without acute-on-chronic liver failure. J Hepatol. 2015;62:831–40.

    Article  PubMed  Google Scholar 

  6. 6.

    Touijer K, Scardino PT. Nomograms for staging, prognosis, and predicting treatment outcomes. Cancer. 2009;115:3107–11.

    Article  PubMed  Google Scholar 

  7. 7.

    Kattan MW, Scardino PT. Evidence for the usefulness of nomograms. Nat Clin Pract Urol. 2007;4:638–9.

    Article  PubMed  Google Scholar 

  8. 8.

    Wan G, Gao F, Chen J, Li Y, Geng M, Sun L, et al. Nomogram prediction of individual prognosis of patients with hepatocellular carcinoma. BMC Cancer. 2017;17:91.

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Sternberg CN. Are nomograms better than currently available stage groupings for bladder cancer. J Clin Oncol. 2006;24:3819–20.

    Article  PubMed  Google Scholar 

  10. 10.

    Moore KP, Wong F, Gines P, Bernardi M, Ochs A, Salerno F, et al. The management of ascites in cirrhosis: report on the consensus conference of the international ascites Club. Hepatology. 2003;38:258–66.

    Article  PubMed  Google Scholar 

  11. 11.

    Blei AT, Córdoba J. Hepatic encephalopathy. Am J Gastroenterol. 2001;96:1968–76.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Garcia-Tsao G, Bosch J. Management of varices and variceal hemorrhage in cirrhosis. N Engl J Med. 2010;362:823–32.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Gustot T, Durand F, Lebrec D, Vincent JL, Moreau R. Severe sepsis in cirrhosis. Hepatology. 2009;50:2022–33.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Arvaniti V, D'Amico G, Fede G, Manousou P, Tsochatzis E, Pleguezuelo M, et al. Infections in patients with cirrhosis increase mortality four-fold and should be used in determining prognosis. Gastroenterology. 2010;139:1246–56 1256.e1–5.

    Article  PubMed  Google Scholar 

  15. 15.

    Jalan R, Saliba F, Pavesi M, Amoros A, Moreau R, Ginès P, et al. Development and validation of a prognostic score to predict mortality in patients with acute-on-chronic liver failure. J Hepatol. 2014;61:1038–47.

    Article  PubMed  Google Scholar 

  16. 16.

    Kamath PS, Kim WR. The model for end-stage liver disease (MELD). Hepatology. 2007;45:797–805.

    Article  PubMed  Google Scholar 

  17. 17.

    Pugh RN, Murray-Lyon IM, Dawson JL, Pietroni MC, Williams R. Transection of the oesophagus for bleeding oesophageal varices. Br J Surg. 1973;60:646–9.

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al. The SOFA (Sepsis-related organ failure assessment) score to describe organ dysfunction/failure. On behalf of the working group on Sepsis-related problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22:707–10.

    CAS  Article  Google Scholar 

  19. 19.

    Frank E, Harrell J. Rms: Regression Modeling Strategies. R Package version 3.4–0.

  20. 20.

    Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–87.

    Article  PubMed  Google Scholar 

  21. 21.

    Frank E, Harrell Jr. Harrell Miscellaneous. R Package version 3.9–2.

  22. 22.

    Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26:565–74.

    Article  Google Scholar 

  23. 23.

    McPhail MJ, Shawcross DL, Abeles RD, Chang A, Patel V, Lee GH, et al. Increased survival for patients with cirrhosis and organ failure in liver intensive care and validation of the chronic liver failure-sequential organ failure scoring system. Clin Gastroenterol Hepatol. 2015;13:1353–60 e8.

    Article  PubMed  Google Scholar 

  24. 24.

    Malinchoc M, Kamath PS, Gordon FD, Peine CJ, Rank J, ter Borg PC. A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. Hepatology. 2000;31:864–71.

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Schiodt FV, Chung RT, Schilsky ML, Hay JE, Christensen E, Lee WM. Outcome of acute liver failure in the elderly. Liver Transpl. 2009;15:1481–7.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Faber W, Stockmann M, Schirmer C, Möllerarnd A, Denecke T, Bahra M, et al. Significant impact of patient age on outcome after liver resection for HCC in cirrhosis. Eur J Surg Oncol. 2014;40:208–13.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Zeeh J, Platt D. The aging liver: structural and functional changes and their consequences for drug treatment in old age. Gerontology. 2002;48:121–7.

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Wynne HA, Cope LH, Mutch E, Rawlins MD, Woodhouse KW, James OF. The effect of age upon liver volume and apparent liver blood flow in healthy man. Hepatology. 1989;9:297–301.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Jalan R, Fernandez J, Wiest R, Schnabl B, Moreau R, Angeli P, et al. Bacterial infections in cirrhosis: a position statement based on the EASL special conference 2013. J Hepatol. 2014;60:1310–24.

    Article  PubMed  Google Scholar 

  30. 30.

    Bajaj JS, O’Leary JG, Reddy KR, Wong F, Biggins SW, Patton H, et al. Survival in infection-related acuteon-chronic liver failure is defined by extrahepatic organ failures. Hepatology. 2014;60:250–6.

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Ferenci P, Lockwood A, Mullen K, Tarter R, Weissenborn K, Blei AT. Hepatic encephalopathy-definition, nomenclature, diagnosis, and quantification: final report of the working party at the 11th world congresses of gastroenterology, Vienna, 1998. Hepatology. 2002;35:716–21.

    Article  PubMed  Google Scholar 

  32. 32.

    Häussinger D, Schliess F. Pathogenetic mechanisms of hepatic encephalopathy. Gut. 2008;57:1156–65.

    Article  CAS  PubMed  Google Scholar 

  33. 33.

    Orr JG, Morgan CL, Jenkins-Jones S, Hudson M, Conway P, Radwan A, et al. Resource use associated with hepatic encephalopathy in patients with liver disease. J Hepatol. 2014;60:S228–9.

    Article  Google Scholar 

  34. 34.

    Cordoba J, Ventura-Cots M, Simón-Talero M, Amorós À, Pavesi M, Vilstrup H, et al. Characteristics, risk factors, and mortality of cirrhotic patients hospitalized for hepatic encephalopathy with and without acute-on-chronic liver failure (ACLF). J Hepatol. 2014;60:275–81.

    Article  PubMed  Google Scholar 

Download references


We gratefully recognize the patients who participated in this study. We thank Yan Sang for her help with the data.


This study was supported by grants from the National Natural Science Foundation of China (No. 81473641) and the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (ZYLX201707). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

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

Author information




XBW and HL were responsible for the conception and design of the study. FYG, XSL, GW, YXL, QZ, YL and HML were responsible for the acquisition and analysis of data. FYG, XSL and GW drafted the manuscript. All authors participated in interpretation of the findings. XBW revised and commented on the draft, and all authors read and approved the final version of the manuscript. All authors confirm that the content has not been published elsewhere and does not overlap with or duplicate their previously published work. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Hai Li or Xianbo Wang.

Ethics declarations

Ethics approval and consent to participate

The study protocol was in accordance with the ethical guidelines of the Declaration of Helsinki and was approved by the ethics committee of the Beijing Ditan Hospital (Beijing, China) and Renji Hospital, Shanghai Jiao Tong University. All patients provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflicts of interest with regard to the publication of this research report.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted 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. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gao, F., Li, X., Wan, G. et al. Development and external validation of a prognostic nomogram for acute decompensation of chronic hepatitis B cirrhosis. BMC Gastroenterol 18, 179 (2018).

Download citation


  • Chronic liver disease
  • Chronic hepatitis B
  • Nomogram
  • Prognosis