metricas
covid
Buscar en
Annals of Hepatology
Toda la web
Inicio Annals of Hepatology Different models in predicting the short-term prognosis of patients with hepatit...
Información de la revista
Vol. 11. Núm. 3.
Páginas 311-319 (mayo - junio 2012)
Compartir
Compartir
Descargar PDF
Más opciones de artículo
Visitas
1196
Vol. 11. Núm. 3.
Páginas 311-319 (mayo - junio 2012)
Open Access
Different models in predicting the short-term prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure
Visitas
1196
Wen-Bin Yang*,**, En-Qiang Chen*,**, Hong-Xia Bi*,**, Lang Bai*,**, Xue-Bin Chen***, Ping Feng*,**, Hong Tang
,**,
Autor para correspondencia
htang6198@hotmail.com

Correspondence and reprint request:
* Center of Infectious Diseases, West China Hospital, Sichuan University, Chengdu 610041, China
** Division of Infectious Diseases, State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, China
*** Department of Infectious Diseases, People’s Hospital of Deyang City, Deyang 618000, China
Este artículo ha recibido

Under a Creative Commons license
Información del artículo
Resumen
Texto completo
Bibliografía
Descargar PDF
Estadísticas
Figuras (2)
Tablas (5)
Table 1. Child-Turcotte-Pugh (CTP).
Table 2. The scoring criteria of MELD, MELDNa, iMELD and KCH criteria.
Table 3. Clinical and biochemical characteristics of the 273 HBV-ACLF patients subdivided according to 3-month survival.
Table 4. Predictive accuracy of the six models in predicting HBV-ACLF patients with liver cirrhosis.
Table 5. Predictive accuracy of the six models in predicting HBV-ACLF patients without liver cirrhosis.
Mostrar másMostrar menos
Abstract

Background and aims. Effective assessing the prognosis of patients with end-stage liver disease is always challenging. This study aimed to investigate the accuracy of different models in predicting short-term prognosis of patients with hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF).

Material and methods. We retrospectively evaluated survival of a cohort of patients with at least 3-month follow up. The receiver-operating-characteristic curves (ROC) were drawn for Child-Turcotte-Pugh (CTP) classification, King’s College Hospital (KCH) criteria, model for end-stage liver disease (MELD), MELD combined with serum sodium (Na) concentration (MELDNa), integrated MELD (iMELD) and logistic regression model (LRM).

Results. Of the 273 eligible patients, 152 patients (55.7%) died within 3-month follow up. In cirrhotic patients (n = 101), the AUCs of LRM (0.851), MELDNa (0.849), iMELD (0.845) and MELD (0.840) were all significantly higher than those of KCH criteria (0.642) and CTP (0.625) (all p < 0.05), while the differences among LRM, MELD, MELDNa and iMELD were not significant, and the most predictive cutoff value was 0.5176 for LRM, 30 for MELDNa, 47.87 for iMELD and 29 for MELD, respectively. In non-cirrhotic patients (n = 172), the AUC of LRM (0.897) was significantly higher than that of MELDNa (0.776), iMELD (0.768), MELD (0.758), KCH criteria (0.647) and CTP (0.629), respectively (all p < 0.05), and the most predictive cutoff value for LRM was-0.3264.

Conclusions. LRM, MELD, MELDNa and iMELD are with similar accuracy in predicting the short-term prognosis of HBV-ACLF patients with liver cirrhosis, while LRM is superior to MELD, MELDNa and iMELD in predicting the short-term prognosis of HBV-ACLF patients without liver cirrhosis.

Key words:
Acute-on-chronic liver failure
Hepatitis B virus
Liver cirrhosis
Model for end-stage liver disease
Prognosis
Texto completo
Introduction

Chronic hepatitis B virus (HBV) infection is a global public health problem. There are about 350400 million persons infected worldwide.1 Abnormal liver function or even liver failure occurred in some patients during the long process of the chronic HBV infection.2 Hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF) is characterized by acute deterioration in liver function due to the effects of the precipitating event such as infection/sepsis, reactivation of hepatitis B or hepatotoxic drugs in a patient on the basis of the chronic HBV infection, and accompanied by the clinical syndromes such as jaundice, coagulopathy, ascites and/or hepatic ence-phalopathy (HE), etc.3 ACLF leads to a poor prognosis and a high short-term mortality (50-90%).4 Nowadays, liver transplantation remains the most effective treatment for patients to sustain life, however it is hampered by lack of liver donors world-wide.5 It is important to timely, accurately and objectively assess the disease severity and short-term prognosis of the patients with HBV-ACLF in the face of a critical shortage of liver donors, and the emergent preparation of the liver transplantation for the patients based on the assessment will be helpful to improve the survival and the quality of life of the patients.

To evaluate the disease severity and assess the prognosis of patients with end-stage liver disease (ESLD) is a topic always challenging the clinician. Over the years, researchers have been looking for a prognostic model that could objectively and accurately reflect the disease severity and prognosis of patients with (ESLD), and various prognostic models or criteria of liver transplantation have been proposed. Child-Turcotte-Pugh (CTP) classification,6 King’s College Hospital (KCH) criteria,7 model for end-stage liver disease (MELD),8 MELD combined with serum sodium (Na) concentration (MELDNa),9 MELD with the incorporation of Na (MELD-Na),10 MELD to Na ratio index (MESO index)11 and integrated MELD (iMELD)12 are commonly used to assess the prognosis of the patients with ESLD. Among those models, CTP, MELD, MELDNa and MESO index are widely used to evaluate the prognosis of the patients with liver cirrhosis,1318 while KCH criteria is mainly used to assess the disease severity of patients with acute liver failure (ALF).19,20 At present, someone also reported those models also could be applied in assessing the prognosis of the patients with ACLF;2123 however the relevant literatures are comparatively few. Recently, a new logistic regression model (LRM) has been established and applied in predicting prognosis of HBV-ACLF patients.24 However, its prognostic accuracy still need to be validated in large-sample cohorts.

Objective

In this study, our aim was to investigate the accuracy of the six models including CTP, KCH criteria, MELD, MELDNa, iMELD and LRM in predicting the short-term (3-month) prognosis of patients with HBV-ACLF.

Material and MethodsPatients

This is a retrospective single-center study on HBV-ACLF patients admitted to Department of Infectious Disease, West China Hospital of Sichuan University from October 2008 to July 2011. The inclusion criteria were defined as follows: chronic HBV infection (hepatitis B surface antigen positive for at least 6 months); ACLF; known the survival status at follow up after 3-month. Patient who met one of the following criteria was excluded: coexisting hepatitis A, C, E or human immunodeficiency virus (HIV) infection; alcohol abuse; drug-induced liver injury, autoimmune liver disease, or other causes that might lead to liver failure; past or current malignant tumors; liver transplantation; severe systemic or mental diseases (Figure 1).

Figure 1.

A flow diagram of patient selection.

(0.12MB).
Clinical diagnosis and definitions

HBV-ACLF was defined as an acute hepatic insult manifesting as jaundice (serum bilirubin ≥ 5 x upper limit of normal), coagulopathy [international normalized ratio (INR) ≥ 1.5 or prothrombin activity [PTA] < 40%) and complicated within 4 weeks by ascites and/or HE in a patient with chronic HBV infection according to consensus recommendations of the Asian Pacific Association for the Study of the Liver (APASL) in 2009.3

The diagnosis of liver cirrhosis was mainly based on the computed tomography or ultrasonography findings of a nodular liver surface, blunt liver edge, coarsened echogenicity of liver parenchyma, enlarged spleen and combined the following findings (1) medical history (2) the presence of hepatic stigmata, spider angiomas or splenomegaly on physical examination; (3) laboratory abnormalities (e.g. decreased serum albumin, increased serum globulin levels, low platelet count).

HE was classified into I-IV stages according the neuropsychiatric symptoms and physical examina-tion.25

Study design

The HBV-ACLF patients were divided into two groups according to whether the patients had liver cirrhosis or not. Baseline clinical and laboratory data of all the patients (i.e. age, serum bilirubin, creatinine, Na, PT, INR) were collected when patients were diagnosed with HBV-ACLF after the admission and these data were retrospectively analyzed in this study.

All patients were followed up for at least 3-month, and the discharged patients were followed up by telephone. The prognosis of every patient (survival more than 3 months, liver transplantation or death) was recorded. All the patients underwent combined medical treatment.

All prognostic models scores were calculated based on clinical and laboratory results obtained on the day when patients were diagnosed with HBV-as-sociated ACLF after the admission. The scoring criteria of the prognostic models were listed in tables 1 and 2.

Table 1.

Child-Turcotte-Pugh (CTP).

Clinical and biochemical measurements    Score   
 
HE (grade)  Abscent  I and II  III and VI 
Ascites  Abscent  Slight  Moderate 
Prolonged PT (s)  1-4  4-6  > 6 
Albumin (g/L)  > 35  28-35  < 28 
Bilirubin (mg/dl)  1-2  2-3  > 3 
Table 2.

The scoring criteria of MELD, MELDNa, iMELD and KCH criteria.

Models  Description 
MELD  3.78 × loge [total bilirubin (mg/dL)] + 11.2 × loge (INR) + 9.57 × loge [creatinine (mg/dL)] + 6.43 × (etiology: 0 if alcoholic or cholestatic; 1 otherwise). Creatinine and INR were calculated as 1.0 when they were < 1.0. The value of creatinine was calculated as 4 when it was > 4 mg/dL. The score is rounded to the nearest integer. 
MELDNa  MELD - Na - [0.025 × MELD × (140 - Na)] + 140. Na concentration is bound between 125 and 140 mmol/L. The score is rounded to the nearest integer. 
iMELD  MELD + (age × 0.3) - (0.7 × Na) + 100. 
LRM  −1.343 + 0.772 × HE + 2.279 × HRS + 0.85 × LC + 1.026 × HBeAg - 2.117 × PAT/age. 
KCH criteria  PT > 100 s or any 3 of the following: age < 10 years old or > 40 years old; non-A and non-B hepatitis, drug-induced liver disease, halothane hepatitis; jaundice to encephalopathy time > 7 days; PT > 50 s; bilirubin > 300 μmol/L. 

The study protocol was conducted in accordance with the standards of the Declaration of Helsinki and current ethical guidelines.

Data analysis

Statistical analyses were performed using Med-Calc statistical software (MedCalc Software, Ma-riakerke, Belgium) and SPSS software version16.0 (SPSS Inc., Chicago, USA). Data expressed as mean ± standard deviation (SD) or median and interquartile range for continuous variables, and frequency with percentage for categorical variables. Continuous variables were compared by Student’s t-test or Mann-Whitney U test. Categorical variables were compared by Pearson Chi-squared test.

Receiver-operating-characteristic (ROC) curves were used to measure the performance of the models in predicting 3-month mortality of patients with HBV-ACLF, and validity of the model was measured by means of the area under the receiver-operating-characteristic curve (AUC). For a prognostic model, the AUC may ranges from 0 to 1. An AUC between 0.8 and 0.9 indicates excellent diagnostic accuracy and > 0.7 is generally considered useful, while an AUC of 0.9 or greater is seldom seen,8 and the comparison of AUCs was done with the method of Hanley & McNeil. The sensitivity (SS), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) were calculated according to the most predictive cutoff that had the best discriminative ability to predict mortality. For all analyses, a p value < 0.05 was considered statistically significant.

ResultsThe basic clinical features of the 273 eligible patients

A total of 273 eligible patients (251 male and 22 female, age ranged from 15 to 78 years, mean age: 40.8 ± 10.9 years) were recruited, and 101 patients had evidence of liver cirrhosis. Of those patients with HBV-ACLF, the most common complication was ascites (249 patients; 91.2%), followed by HE (102 patients; 37.4%), and HRS (84 patients; 30.8%). The overall 3-month mortality was 55.7% (152/273), and the majority of them died of the complications of ACLF such as HE, upper gastrointestinal bleeding, hepatorenal syndrome (HRS) and multi-organ failure, etc. The morality rate of HBV-ACLF patients with liver cirrhosis (60.4%) was higher than that of the HBV-ACLF patients without liver cirrhosis (52.9%), however there was no significant difference (p > 0.05).

Clinical and biochemical characteristics of the 273 eligible patients subdivided according to 3-month survival

The patients were further divided into the survival and death groups according to the 3-month follow up results. The characteristics of the patients who survived and who died are shown in table 3.

Table 3.

Clinical and biochemical characteristics of the 273 HBV-ACLF patients subdivided according to 3-month survival.

Clinical dataCirrhotic patients (n = 101)Non-cirrhotic patients (n = 172)
Survival group (n = 40)  Death group (n = 61)  Survival group (n = 81)  Death group (n=91) 
Age (years)  40.4 ± 10.1  44.7 ± 10.2 a  37.1 ± 10.0  41.8 ± 11.4 b 
Gender (female/male)  2/38  7/54 c  6/75  7/84 c 
Total bilirubin (mg/dL)  22.5 ± 5.6  26.3 ± 6.4 b  23.7 ± 5.3  25.5 ± 5.9 a 
ALT (IU/L)  133.5 (13, 3309)  130.0 (12, 1947) c  345.0 (25, 2485)  272.0 (22, 3133) c 
AST (IU/L)  215.5 (31, 2975)  205.0 (36, 4895) c  226.0 (44, 1815)  281.0 (58, 3157) c 
Albumin (g/L)  31.3 ± 4.0  29.5 ± 4.2 a  31.4 ± 4.7  30.0 ± 4.4 a 
Creatinine (mg/dL)  1.0 ± 0.2  1.5 ± 1.3 b  0.9 ± 0.2  1.5 ± 1.2 b 
Na (mmol/L)  136.7 ± 6.3  131.6 ± 6.8 b  136.9 ± 4.8  133.7 ± 5.6 b 
PT  24.4 ± 3.4  30.0 ± 8.4 b  28.7 ± 9.7  37.4 ± 20.2 b 
PTA (%)  31.8 ± 5.8  25.7 ± 7.9 b  27.6 ± 8.3  22.6 ± 9.6 b 
INR  2.2 ± 0.3  2.7 ± 0.8 b  2.5 ± 0.9  3.2 ± 1.6 b 
Ascites (%)  36 (90.0)  57 (93.4) c  71 (87.7)  85 (93.4) c 
HE (%)  6 (15.0)  31 (50.8) b  18 (22.2)  47 (51.6) b 
HRS (%)  2 (5.0)  31 (50.8) b  1 (1.2)  50 (54.9) b 
HBeAg positivity rate (%)  9 (22.5)  24 (39.3) c  28 (34.6)  41 (45.10 c 
CTP  11.6 ± 1.1  12.2 ± 0.9 b  11.6 ± 0.9  12.0 ± 0.9 b 
KCH (%)  5 (12.5)  25(41.0) b  10(12.3)  38(41.8) b 
MELD  27.2 ± 1.9  32.3 ±5.2 b  28.6 ± 3.0  33.6 ± 6.7 b 
MELDNa  28.5 ± 2.1  33.5 ± 4.4b  29.5 ± 3.1  34.4 ± 5.7 b 
iMELD  43.6 ± 4.8  53.6 ± 9.0 b  43.8 ± 5.6  52.6 ± 10.2 b 
LRM  −0.5060  1.7669 b  −0.5920  0.9204 
  (−0.5206, 2.5389)  (−0.5134, 3.5749)  (−1.3706, 1.9403)  (−1.3559, 3.5772) b 

a

p < 0.05.

b

p < 0.01.

c

p > 0.05 vs. survival group.

In the cirrhotic patients:

  • The death group had significantly higher age (44.7 ± 10.2 vs. 40.4 ± 10.1).

  • Total bilirubin (26.3 ± 6.4 vs. 22.5 ± 5.6).

  • Creatinine (1.5 ± 1.3 vs. 1.0 ± 0.2).

  • PT (30.0 ± 8.4 vs. 24.4 ± 3.4).

  • INR (2.7 ± 0.8 vs. 2.2 ± 0.3).

  • CTP score (12.2 ± 0.9 vs. 11.6 ± 1.1).

  • MELD score (32.3 ± 5.2 vs. 27.2 ± 1.9).

  • MELDNa score (33.5 ± 4.4 vs. 28.5 ± 2.1).

  • iMELD score (53.6 ± 9.0 vs. 43.6 ± 4.8), and

  • LRM score (1.7669 vs. −0.5060).

And rate of patients met the KCH criteria (41.0 vs. 12.5%) than the survival group (all p < 0.05), while the death group had significantly lower Na (131.6 ± 6.8 vs. 136.7 ± 6.3) and PTA (0.257 ± 0.079 vs. 0.318 ± 0.058) (all p < 0.05). However, AST, ALT, and HBeAg positivity rate did not differ statistically between the survival group and death group.

In the non-cirrhotic patients:

  • The death group had significantly higher age (41.8 ± 11.4 vs. 37.1 ± 10.0).

  • Total bilirubin (25.5 ± 5.9 vs. 23.7 ± 5.3).

  • Creatinine (1.5 ± 1.2 vs. 0.9 ± 0.2).

  • PT (37.4 ± 20.2 vs. 28.7 ± 9.7).

  • INR (3.2 ± 1.6 vs. 2.5 ± 0.9).

  • CTP score (12.0 ± 0.9 vs. 11.6 ± 0.9).

  • MELD score (33.6 ± 6.7 vs. 28.6 ± 3.0).

  • MELDNa score (34.4 ± 5.7 vs. 29.5 ± 3.1).

  • iMELD score (52.6 ± 10.2 vs. 43.8 ± 5.6), and

  • LRM score (0.9204 vs. −0.5920).

And rate of patients met the KCH criteria (41.8 vs. 12.3%) than the survival group (all p < 0.05), while the death group had significantly lower Na (133.7 ± 5.6 vs. 136.9 ± 4.8) and PTA (0.226 ± 0.096 vs. 0.276 ± 0.083) (all p < 0.05). However, AST, ALT and HBeAg positivity rate did not differ statistically between the survival group and death group.

Predictive accuracy of the prognostic models

The AUC was:

  • 0.851 (95%CI: 0.766-0.914) for LRM.

  • 0.849 (95%CI: 0.764-0.912) for MELDNa.

  • 0.845 (95%CI: 0.760-0.910) for iMELD.

  • 0.840 (95%CI: 0.753-0.905) for MELD.

  • 0.642 (95%CI: 0.541-0.735) for KCH criteria, and

  • 0.625 (95%CI: 0.523-0.719) for CTP in evaluating the short-term prognosis of the HBV-ACLF patients with liver cirrhosis, respectively.

The AUCs of LRM, MELD, MELDNa and iMELD were all significantly higher than those of CTP and KCH criteria (all p < 0.05), while the differences among LRM, MELD, MELDNa and iMELD were not significant.

The best predictive cutoff value was (Table 4, Figure 2A):

Table 4.

Predictive accuracy of the six models in predicting HBV-ACLF patients with liver cirrhosis.

  CTP  KCH criteria  MELD  MELDNa  iMELD  LRM 
AUC  0.625  0.642  0.840  0.849  0.845  0.851 
95%CI  0.523-0.719  0.541-0.735  0.753-0.905  0.764-0.912  0.760-0.910  0.766-0.914 
SS (%)  34.4 (21/61)  41.0 (25/61)  67.2 (41/61)  77.0 (47/61)  77.0 (47/61)  70.5 (43/61) 
SP (%)  82.5 (33/40)  87.5 (35/40)  87.5 (35/40)  87.5 (35/40)  85.0 (34/40)  90.0 (36/40) 
PPV (%)  75.0 (21/28)  83.3 (2530)  89.1 (41/46)  90.4 (47/52)  88.7 (47/53)  91.5 (43/47) 
NPV (%)  45.2 (33/73)  49.3 (35/71)  63.6 (35/55)  71.4 (35/49)  70.8 (34/48)  66.7 (36/54) 
Cutoff values  12  29  30  47.87  0.5176 

In cirrhotic patients: the AUCs of LRM, MELD, MELDNa and iMELD were all significantly higher than those of CTP and KCH criteria (all p < 0.05), while the differences among LRM, MELD, MELDNa and iMELD were not significant (all p < 0.05).

Figure 2.

ROC curves of six models in predicting the 3-month mortality of HBV-ACLF patients with cirrhosis (A), without cirrhosis (B).

(0.15MB).

  • 0.5176 (SS = 70.5%; SP = 90.0%; PPV =91.5%; NPV = 66.7%) for LRM.

  • 30 (SS = 77.0%; SP = 87.5%; PPV = 90.4%; NPV =71.4%) for MELDNa.

  • 47.87 (SS = 77.0%; SP = 85.0%; PPV = 88.7%; NPV = 70.8%) for iMELD, and

  • 29 (SS = 67.2%; SP = 87.5%; PPV =89.1%; NPV = 63.6%) for MELD, respectively.

The AUC was:

  • 0.897 (95%CI: 0.841-0.938) for LRM.

  • 0.776 (95%CI: 0.707-0.836) for MELDNa.

  • 0.768 (95%CI: 0.689-0.829) for iMELD.

  • 0.758 (95%CI: 0.687-0.820) for MELD.

  • 0.647 (95%CI: 0.571-0.718) for KCH criteria, and

  • 0.629 (95%CI: 0.552-0.701) for CTP in evaluating the short-term prognosis of the HBV-ACLF patients without liver cirrhosis, respectively.

The AUC of LRM was significantly higher than that of MELDNa, iMELD, MELD, KCH criteria and CTP, respectively (all p < 0.05) and the best predictive cutoff value for LRM was-0.3264 (SS = 76.9%; SP = 95.1%; PPV = 94.6%; NPV = 78.6%) for LRM (Table 5, Figure 2B).

Table 5.

Predictive accuracy of the six models in predicting HBV-ACLF patients without liver cirrhosis.

  CTP  KCH criteria  MELD  MELDNa  iMELD  LRM 
AUC  0.629  0.647  0.758  0.776  0.768  0.897 
95%CI  0.552-0.701  0.571-0.718  0.687-0.820  0.707-0.836  0.698-0.829  0.841-0.938 
SS (%)  71.4 (65/91)  41.8 (38/91)  62.6 (57/91)  75.8 (69/91)  78.0 (71/91)  76.9 (70/91) 
SP (%)  49.4 (40/81)  87.7 (71/81)  81.5 (66/81)  70.4 (57/81)  66.7 (54/81)  95.1 (77/81) 
PPV (%)  61.3 (65/106)  79.2 (38/48)  79.2 (57/72))  74.2 (69/93)  72.4 (71/98)  94.6 (70/74) 
NPV (%)  60.6 (40/66)  57.3 (71/124)  66.0 (66/100)  72.2 (57/79)  73.0 (54/74)  78.6 (77/98) 
Cutoff values  11  30  30  45.05  −0.3264 

In non-cirrhotic patients: the AUC of LRM was significantly higher than that of CTP, KCH criteria, MELD, MELDNa and iMELD, respectively (all p < 0.05).

Discussion

HBV-ACLF is one of the most dangerous ESLDs with a poor prognosis and high short-term mortality. An objective, accurate, simple and convenient prognostic model can help the doctors to evaluate the disease severity and short-term prognosis and select an appropriate treatment program such as combined medical treatment or liver transplantation.

The CTP classification was first proposed by Child and Turoctte and then modified by Pugh.6 Because the five variables (bilirubin, albumin, PT, as-cites and HE) are easy to obtain and calculation of the CTP scores is simple, it has become the most common model in assessing the reserve liver function capacity and the prognosis of patients with liver cirrhosis.26,27 However, over many years, the limitations came out gradually during the application, such as subjective judgments on HE and ascites and limited discriminant ability.28 In order to overcome these limitations of CTP, Kamath, et al.8 proposed a new scoring system (MELD) which was initially developed and validated to assess the short-term prognosis of cirrhotic patients undergoing the transjugular intrahepatic portosystemic shunt (TIPS). Compared with the CTP, The variables (bili-rubin, creatinine and INR) of the MELD scoring system were objective, and the renal function was taken as an independent factor influencing the prognosis of patients with liver disease. Meanwhile, MELD scores were continuous, which could distinguish the patient’s disease severity better. MELD also could reflect the pathological changes of hyper-bilirubinemia, coagulation disorders, kidney failure and other pathological changes better. In recent year, MELD has been widely applied to predict mortality across a broad spectrum of liver diseases in many studies. Since February 2002, MELD has been a tool used to predict mortality risk and to assess disease severity so as to determine organ allocation priorities by the United Network for Organ Sharing (UNOS) in the United States.29 However, there is still controversy about whether MELD is better than CTP.3032

Na has been considered to be an important independent factor that affects the short-term prognosis of patients with ESLD. Hyponatremia is associated with complications of liver cirrhosis, such as ascites and HRS.10 Kim, et al.9combined the MELD and Na, and proposed the new scoring system (MELD-Na) on the basis of MELD, and showed that it could enhance the predictive accuracy of MELD. iMELD was generated on the basis of 3 clinical variables (MELD, age and Na), and the predictive accuracy of the iMELD was reported in another independent report, which showed that it had better predictive accuracy than that of the MELD in evaluating the prognosis of patients with liver cirrhosis.13 LRM was developed to assess the short-term prognosis of HBV-ACLF patients and Zheng, et al.24 reported that LRM was with greater accuracy than MELD and CTP in predicting the prognosis of patients with HBV-ACLF, however its usefulness still need to be validated in other cohorts. The KCH criteria are mainly used for listing patients with ALF for liver transplantation, however KCH criteria are rarely used to predict the prognosis of patients with ACLF. In our study, we compare the accuracy of the six models in predicting the short-term prognosis of 273 patients with HBV-ACLF. Moreover, whether the incorporation of age and /or Na (MELDNa and iMELD) to MELD could enhance accuracy of MELD in predicting the patients with HBV-ACLF is also investigated. Firstly, LRM, MELD, MELDNa and iMELD are all superior to CTP and KCH criteria in predicting the short-term prognosis of the HBV-ACLF patients. LRM, MELD, MELDNa and iMELD are with similar predictive accuracy in predicting the short-term prognosis of HBV-ACLF patients with liver cirrhosis, while LRM is superior to MELD, MELDNa and iMELD in predicting the short-term prognosis of HBV-ACLF patients without liver cirrhosis. Secondly, our result also shows that incorporation age and/or Na into the MELD (MELDNa and iMELD) in the AUC analysis did not improve the predictive accuracy of the MELD in patients with HBV-ACLF. Thirdly, From the ROC analysis, we have determined the best predictive cutoffs for the models in predicting the short-term prognosis of the HBV-ACLF patients. MELD, MELDNa, iMELD and LRM had a consistently high SS (67.2-77.0%), SP (85.0-90.0%), PPV (88.7-91.5%) and NPV (63.6-71.4%) in predicting the short-term prognosis of HBV-ACLF patients with liver cirrhosis and LRM had a consistently high SS (76.9%), SP (95.1%), PPV (94.6%) and NPV (78.6%) in predicting the short-term prognosis of HBV-ACLF patients without liver cirrhosis. Therefore, it might be helpful to doctors to evaluate the disease severity and prognosis of patients with HBV-ACLF, for example, when a HBV-ACLF patient with liver cirrhosis had a iMELD score 40.0 (< 47.87), the patient would have a 70.8% chance of survival in 3 months without liver transplantation, while with a iMELD score 50 (> 47.87), the patient would have a 88.7% chance of death in 3 months without liver transplantation. So according to the assessment, it might be helpful to doctors to choose an appropriate treatment program (for example, combined medical treatment, artificial liver treatment or liver transplantation). In addition, the morality rate of HBV-ACLF patients with liver cirrhosis (60.4%) was higher than that of the HBV-ACLF patients without liver cirrhosis (52.9%), however there was no significant difference. It indicated that the liver cirrhosis might not affect the short-term prognosis of patients with HBV-ACLF.

Although the parameters in MELD, MELDNa and iMELD were relatively objective, which excluded the potential deviation due to the subjective judgments such as HE and ascites in CTP, we should also realize the potential limitations of the three models. For example, some important factors (i.e. HE, HRS, upper gastrointestinal bleeding) affecting the prognosis of patients with HBV-ACLF have not been taken into consideration in the three mo-dels.24,33,34 In addition, treatment such as diuretics or artificial liver treatment would affect the Na, bilirubin and creatinine. Therefore, in practice, we might consider these factors.

Conclusion

LRM, MELD, MELDNa and iMELD are with similar predictive accuracy in predicting the short-term prognosis of HBV-ACLF patients with liver cirrhosis, while LRM is superior to MELD, MELD-Na and iMELD in predicting the short-term prognosis of HBV-ACLF patients without liver cirrhosis. However, our findings also need to be verified in large sample size clinical trials in future.

Abbreviations

  • ACLF: acute-on-chronic liver failure.

  • HBV: hepatitis B virus.

  • HCV: hepatitis C virus.

  • HEV: hepatitis E virus.

  • HIV: human immunodeficiency virus.

  • ESLD: end-stage liver disease.

  • CTP: Child-Turcotte-Pugh.

  • KCH criteria: King’s College Hospital criteria.

  • MELD: model for end-stage liver disease.

  • MELDNa: MELD combined with serum sodium concentration.

  • iMELD: integrated MELD.

  • MESO index: MELD to serum sodium ratio index.

  • LRM: logistic regression model.

  • ROC: receiver-operating-characteristic curve.

  • AUC: area under the receiver-operating-characteristic curve.

  • INR: international normalized ratio.

  • SS: sensitivity.

  • SP: specificity.

  • PPV: positive predictive value.

  • NPV: negative predictive value.

  • PT: prothrombin time.

  • PTA: prothrombin activity.

  • CI: confidence interval.

  • TIPS: transjugular intrahepatic portosystemic shunt.

  • LC: liver cirrhosis.

  • HE: hepatic encephalopathy.

  • HBeAg: hepatitis B e antigen.

  • HRS: hepatorenal syndrome.

Conflict of Interest

None.

Acknowledgements

This study was supported by National Natural Science Foundation of China (No. 30972622) and National science and Technology major project of China (No. 2012ZX10002007 and 2008ZX10002-006). We thank all patients and his families who participated in this study.

References
[1.]
Lee W.M..
Hepatitis B virus infection..
N Engl J Med, 337 (1997), pp. 1733-1745
[2.]
Merle P., Trepo C., Zoulim F..
Current management strategies for hepatitis B in the elderly..
Drugs & aging, 18 (2001), pp. 725-735
[3.]
Sarin S.K., Kumar A., Almeida J.A., et al.
Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific Association for the study of the liver (APASL)..
Hepatol Int, 3 (2009), pp. 269-282
[4.]
Katoonizadeh A., Laleman W., Verslype C., et al.
Early features of acute-on-chronic alcoholic liver failure: a prospective cohort study..
Gut, 59 (2010), pp. 1561-1569
[5.]
Graziadei I.W..
The clinical challenges of acute on chronic liver failure..
[6.]
Pugh R.N., Murray-Lyon I.M., Dawson J.L., Pietroni M.C., Williams R..
Transection of the oesophagus for bleeding oesophageal varices..
Br J Surg, 60 (1973), pp. 646-649
[7.]
O’Grady J.G., Alexander G.J., Hayllar K.M., Williams R..
Early indicators of prognosis in fulminant hepatic failure..
Gas-troenterology, 97 (1989), pp. 439-445
[8.]
Kamath P.S., Wiesner R.H., Malinchoc M., et al.
A model to predict survival in patients with end-stage liver disease..
Hepatology, 33 (2001), pp. 464-470
[9.]
Kim W.R., Biggins S.W., Kremers W.K., et al.
Hyponatremia and mortality among patients on the liver-transplant waiting list..
N Engl J Med, 359 (2008), pp. 1018-1026
[10.]
Biggins S.W., Kim W.R., Terrault N.A., et al.
Evidence-based incorporation of serum sodium concentration into MELD..
Gastroenterology, 130 (2006), pp. 1652-1660
[11.]
Huo T.I., Wang Y.W., Yang Y.Y., et al.
Model for end-stage liver disease score to serum sodium ratio index as a prognostic predictor and its correlation with portal pressure in patients with liver cirrhosis..
[12.]
Luca A., Angermayr B., Bertolini G., et al.
An integrated MELD model including serum sodium and age improves the prediction of early mortality in patients with cirrhosis..
Liver Transpl, 13 (2007), pp. 1174-1180
[13.]
Huo T.I., Lin H.C., Huo S.C., et al.
Comparison of four model for end-stage liver disease-based prognostic systems for cirrhosis..
Liver Transpl, 14 (2008), pp. 837-844
[14.]
Zhang J.Y., Zhang F.K., Wang B.E., Jia J.D., Zhang S.T..
The prognostic value of end-stage liver disease model in liver cirrhosis..
Zhonghua Nei Ke Za Zhi, 44 (2005), pp. 822-824
[15.]
Botta F., Giannini E., Romagnoli P., et al.
MELD scoring system is useful for predicting prognosis in patients with liver cirrhosis and is correlated with residual liver function: a European study..
Gut, 52 (2003), pp. 134-139
[16.]
Cho H.C., Jung H.Y., Sinn D.H., et al.
Mortality after surgery in patients with liver cirrhosis: comparison of Child-Turcotte-Pugh, MELD and MELDNa score..
Eur J Gastroenterol Hepa-tol, 23 (2011), pp. 51-59
[17.]
Boursier J., Cesbron E., Tropet A.L., Pilette C..
Comparison and improvement of MELD and Child-Pugh score accuracies for the prediction of 6-month mortality in cirrhotic patients..
J Clin Gastroenterol, 43 (2009), pp. 580-585
[18.]
Jiang M., Liu F., Xiong W.J., Zhong L., Chen X.M..
Comparison of four models for end-stage liver disease in evaluating the prognosis of cirrhosis..
World J Gastroenterol, 14 (2008), pp. 6546-6550
[19.]
Shakil A.O., Kramer D., Mazariegos G.V., Fung J.J., Rakela J..
Acute liver failure: clinical features, outcome analysis, and applicability of prognostic criteria..
Liver Transpl, 6 (2000), pp. 163-169
[20.]
Anand A.C., Nightingale P., Neuberger J.M..
Early indicators of prognosis in fulminant hepatic failure: an assessment of the King’s criteria..
J Hepatol, 26 (1997), pp. 62-68
[21.]
Sun Q.F., Ding J.G., Xu D.Z., et al.
Prediction of the prognosis of patients with acute-on-chronic hepatitis B liver failure using the model for end-stage liver disease scoring system and a novel logistic regression model..
J Viral Hepat, 16 (2009), pp. 464-470
[22.]
Radha Krishna Y., Saraswat V.A., Das K., et al.
Clinical features and predictors of outcome in acute hepatitis A and hepatitis E virus hepatitis on cirrhosis..
[23.]
He W.P., Hu J.H., Tong J.J., Liu F.F., Wang H.F..
Comparison of MELD-Na versus MELDNa in the prediction of short-term prognosis for acute-on-chronic hepatitis B liver failure..
Zhonghua Yi Xue Za Zhi, 91 (2011), pp. 1173-1177
[24.]
Zheng M.H., Shi K.Q., Fan Y.C., et al.
A model to determine 3-month mortality risk in patients with acute-on-chronic hepatitis B liver failure..
Clin Gastroenterol Hepatol, 9 (2011), pp. 351-356
[25.]
Riordan S.M., Williams R..
Treatment of hepatic encephalopathy..
N Engl J Med, 337 (1997), pp. 473-479
[26.]
Christensen E., Schlichting P., Fauerholdt L., et al.
Prognostic value of Child-Turcotte criteria in medically treated cirrhosis..
Hepatology, 4 (1984), pp. 430-435
[27.]
Infante-Rivard C., Esnaola S., Villeneuve J.P..
Clinical and statistical validity of conventional prognostic factors in predicting short-term survival among cirrhotics..
Hepatology, 7 (1987), pp. 660-664
[28.]
Li Q., Wang B.E., Jia J.D..
The evolution and characteristics of liver function classification: from Child to MELD..
Zhong-hua Gan Zang Bing Za Zhi, 12 (2004), pp. 319-320
[29.]
Freeman R.B. Jr., Wiesner R.H., Harper A., et al.
The new liver allocation system: moving toward evidence-based transplantation policy..
Liver Transpl, 8 (2002), pp. 851-858
[30.]
Cholongitas E., Papatheodoridis G.V., Vangeli M., Terreni N., Patch D., Burroughs A.K..
Systematic review: The model for end-stage liver disease–should it replace Child-Pugh’s classification for assessing prognosis in cirrhosis?.
Alimentary pharmacology & therapeutics, 22 (2005), pp. 1079-1089
[31.]
Durand F., Valla D..
Assessment of the prognosis of cirrhosis: Child-Pugh versus MELD..
J Hepatol, 42 (2005), pp. S100-S107
[32.]
Cholongitas E., Marelli L., Shusang V., et al.
A systematic review of the performance of the model for end-stage liver disease (MELD) in the setting of liver transplantation..
Liver Transpl, 12 (2006), pp. 1049-1061
[33.]
Liu X.Y., Hu J.H., Wang H.F..
Analysis of prognostic factors for patients with acute-on-chronic liver failure..
Zhonghua Gan Zang Bing Za Zhi, 17 (2009), pp. 607-610
[34.]
Huang K., Hu J.H., Wang H.F., et al.
Survival and prognostic factors in hepatitis B virus-related acute-on-chronic liver failure..
World J Gastroenterol, 17 (2011), pp. 3448-3452

p < 0.05.

p < 0.01.

p > 0.05 vs. survival group.

Copyright © 2012. Fundación Clínica Médica Sur, A.C.
Descargar PDF
Opciones de artículo
es en pt

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?

Você é um profissional de saúde habilitado a prescrever ou dispensar medicamentos