Advantages of a Novel Model for Predicting Hepatic Fibrosis in Chronic Hepatitis B Virus Carriers Compared with APRI and FIB-4 Scores

doi:10.14218/JCTH.2022.00098

Publications > Journals > Journal of Clinical and Translational Hepatology > Article Full Text

Original Article
OPEN ACCESS

Advantages of a Novel Model for Predicting Hepatic Fibrosis in Chronic Hepatitis B Virus Carriers Compared with APRI and FIB-4 Scores

Na-Ling Kang^1,#,
Qing-Fa Ruan^2,#,
De-Sheng Zhang^3,#,
Xue-Ping Yu^4,5,#,
Zhen-Ting Hu^6,#,
Zhi-Min Lin⁷,
Lu-Ying Wu¹,
Meng-Xin Lin⁵,
Zu-Xiong Huang⁸,
Jia-Ji Jiang¹,
Yu-Rui Liu¹,
Ri-Cheng Mao^4,* and
Da-Wu Zeng^1,*

Author information

Cite

1Department of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China

2Hepatology Center, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian, China

3Department of Pharmacy, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China

4Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China

5Department of Infectious Diseases, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, Fujian, China

6Department of Infectious Disease, The Affiliated Hospital of Putian College, Putian, Fujian, China

7Department of Gastroenterology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China

8Hepatology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China

^#Contributed equally to this work.

*Correspondence to: Ri-Cheng Mao, Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai 200000, China. ORCID: https://orcid.org/0000-0001-5534-8299 . Tel: +86-13482523005, Fax: +86-21-52887940, E-mail: [email protected]; Da-Wu Zeng, Department of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350005, China. ORCID: https://orcid.org/0000-0003-3818-0062 . Tel: +86-15605917968, E-mail: [email protected]

Journal of Clinical and Translational Hepatology 2022;10(3):412-419

doi: 10.14218/JCTH.2022.00098

Abstract

Background and Aims

Aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 index (FIB-4) are widely used to assess liver fibrosis in chronic hepatitis B virus (HBV) infection. Currently, the definition of normal alanine aminotransferase (ALT) is controversial. We aimed to examine the diagnostic value of APRI and FIB-4 in chronic HBV carriers with different upper limits of normal (ULNs) for ALT.

Methods

581 chronic HBV carriers were divided into the following four groups based on different ULNs for ALT: chronic HBV carriers I, II, III, and IV. Furthermore, 106 chronic HBV carriers formed an external validation group. Predictive values of APRI and FIB-4 were elucidated using the area under the curve (AUC). A liver fibrosis-predictive model-GPSA (named for its measure of gamma glutamyl transpeptidase, platelet count, HBsAg and albumin) was developed using multivariate logistic regression analysis.

Results

In chronic HBV carriers I, the AUCs of APRI and FIB-4 were 0.680 and 0.609 for significant fibrosis and 0.678 and 0.661 for cirrhosis, respectively. The AUCs of GPSA for significant fibrosis in the training group, internal group, and external validation group were 0.877, 0.837, and 0.871, respectively. The diagnostic value of GPSA differed among chronic HBV carriers I, II, III, and IV, with AUCs for significant fibrosis being 0.857, 0.853, 0.868, and 0.905 and AUCs for cirrhosis being 0.901, 0.905, 0.886, and 0.913, respectively. GPSA showed a higher diagnostic value than APRI and FIB-4 for predicting significant fibrosis in the four groups.

Conclusions

The GPSA model allows for accurate diagnosis of liver fibrosis in chronic HBV carriers with different ULN for ALT.

Keywords

APRI, FIB-4, Liver fibrosis, Chronic HBV carriers, HBV

Introduction

In chronic hepatitis B virus (HBV) carriers with normal alanine aminotransferase (ALT) levels, liver histopathology is usually normal or minimally affected, and even under such conditions, the antiviral treatment may still not be effective.¹ However, chronic HBV carriers with normal ALT levels can also present with severe histopathology, which may progress into liver cirrhosis or hepatocellular carcinoma (HCC).^2–5 Therefore, early diagnosis of hepatic fibrosis followed by timely administration of antiviral therapy is critical to controlling disease progression and possibly even reversing early liver cirrhosis.⁶

The upper limit of normal (ULN) for ALT levels has long been defined as ≤40 U/L, and the European Association for the Study of the Liver and the Asian Pacific Association for the Study of the Liver (APASL) guidelines recommend this ULN for ALT as the traditional threshold.^7,8 However, the American Association for the Study of Liver Diseases hepatitis B guidlines define the ULN for ALT as ≤35 U/L for men and ≤25 U/L for women,⁹ and the World Health Organization (WHO) guidelines define it as <30 U/L for men and <19 U/L for women.¹⁰ Duan et al.¹¹ found ALT >20 U/L to be an ideal marker to predict moderate liver injury in HBeAg-negative chronic hepatitis B (CHB) patients with normal ALT levels. Several specialists have suggested that chronic HBV-infected patients should undergo invasive or noninvasive liver fibrosis assessment to allow for timely administration of antiviral treatment based on these new ALT standards.¹²

Liver biopsy is the gold standard to assess hepatic fibrosis; however, most chronic HBV-infected patients are extremely reluctant to undergo this invasive procedure. Therefore, aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 index (FIB-4) are extensively used for this assessment; these are based on routine laboratory tests⁸ and are recommended in the WHO and APASL HBV guidelines.^7,10 However, Li et al.¹³ found the WHO-recommended cutoffs of APRI and FIB-4 to have poor diagnostic value for significant fibrosis and cirrhosis in HBeAg-negative CHB patients with ALT ≤2 ULN. Tan et al.¹⁴ found APRI and FIB-4 to have poor accuracy for diagnosing significant fibrosis in a small sample of CHB patients with persistently normal ALT. Recent studies have suggested that APRI and FIB-4 are not optimal noninvasive panels for assessing fibrosis in chronic hepatitis C or CHB patients during long-term antiviral treatment.^15,16 In summary, APRI and FIB-4 are controversial for assessing hepatic fibrosis in CHB patients.

To the best of our knowledge, no study has explored the diagnostic value of APRI and FIB-4 for assessing hepatic fibrosis in chronic HBV carriers with different ULN for ALT. In this study, we referred to multicenter and cross-sectional research and retrospectively analyzed the predictive value of APRI and FIB-4 for diagnosing different stages of fibrosis in chronic HBV carriers. Furthermore, we constructed a noninvasive gamma glutamyl transpeptidase, platelet count, hepatitis B surface antigen (HBsAg), and albumin (GPSA) panel to diagnose liver fibrosis in chronic HBV carriers with different ULN for ALT, and then, we compared the predictive performance of GPSA with that of APRI and FIB-4.

Methods

Patients

We retrospectively assessed 581 chronic HBV carriers who underwent liver biopsies from three affiliated hospitals of Fujian Medical University (First Affiliated Hospital, Meng Chao Hepatobiliary Hospital, and The First Hospital of Quanzhou), the Affiliated Hospital of Putian University, and the Xiamen Hospital of Traditional Chinese Medicine between June 2010 and June 2018. We also collected data from 106 chronic HBV carriers from Huashan Hospital Affiliated to Fudan University between October 2008 and December 2015. The patients represented our external validation group. Chronic HBV carriers in our study were defined as patients in the phase of HBeAg-positive chronic HBV infection (immune tolerant) and HBeAg-negative chronic HBV infection (inactive carrier) according to the 2017 European Association for the Study of the Liver guidelines, 2018 American Association for the Study of Liver Diseases hepatitis B guidelines, and the 2019 guidelines of prevention and treatment for chronic hepatitis B.^8,9,17 The inclusion criteria were: (1) patients having been positive for HBsAg for ≥6 months; (2) HBV DNA≥500 IU/mL; (3) ALT≤ULN; and (4) antiviral treatment-naïve patients, i.e. patients having never received antiviral therapy before the liver biopsy. The exclusion criteria were: (1) HCC; (2) human immunodeficiency virus, hepatitis A virus, hepatitis C virus, or hepatitis E virus infection; (3) autoimmune liver disease; (4) hepatolenticular degeneration; (5) drug-induced liver injury; or (6) nonalcoholic fatty liver disease or alcoholic liver disease. To account for differences in the ALT ULN criteria, chronic HBV carriers were defined as: chronic HBV carriers I (the ULN was 40 U/L, n=581), chronic HBV carriers II (the ULN was ≤35 U/L for men and ≤25 U/L for women), n=448), chronic HBV carriers III (the ULN was ≤30 U/L for men and ≤19 U/L for women, n=323], and chronic HBV carriers IV (the ULN was 20 U/L, n=167; Supplementary Fig. 1). The study was approved by the institutional review board of Fujian Medical University. Given the retrospective design of the study, the need for informed consent was waived.

Liver biopsy

Liver tissues were obtained with a disposable 16 gauge aspiration needle (TSK Laboratory, Tochigi, Japan). Liver tissues (length ≥1.5 cm with more than six portal tracts) were obtained, fixed in 4% formalin, embedded in paraffin, and stained with hematoxylin–eosin–saffron and Masson’s trichrome. Pathologists were blinded to patient data and used the METAVIR scoring system to diagnose liver fibrosis. Significant fibrosis was defined as F≥2, advanced fibrosis as F≥3, and cirrhosis as F=4, as previously reported.¹⁸

Serum markers

Routine biochemical parameters were quantified by routine automated analyzers. The HBsAg level was tested using an Elecsys HBsAg II quant assay (Roche Diagnostics, Mannheim, Germany) or an Abbott Architect assay (Abbott Laboratories, Chicago, IL, USA). HBV DNA level was assayed by real-time polymerase chain reaction (PG Company, Shenzhen, China).

Statistical analysis

Student’s t-test was used with normally distributed data and homogeneity of variance. The Mann–Whitney test was used with continuous data with a non-normal distribution. The Spearman test was used to assess correlations of APRI and FIB-4 with liver fibrosis. Univariate or multivariate analysis was used to select predictors linked with F≥2. Predictive accuracy was evaluated using the area under the curve (AUC). Difference between advanced and non-advanced fibrosis stages (DANA) was applied to standard AUCs of fibrosis markers according to the prevalence of fibrosis stages. The Obuchowski index was used to take into account all pair-wise comparisons between different stages of liver fibrosis to reduce the spectrum effect and minimize the need for multiple testing. The Z test was used to compare the AUC of GPSA with those of APRI and FIB-4. The statistical analysis was performed with SPSS v. 23.0 (IBM Corp., Armonk, NY, USA) and MedCalc v. 9.38 for Windows.

Results

Clinical data

The patient characteristics are shown in Table 1. Herein, 581 patients were classified as chronic HBV carriers I, 448 as chronic HBV carriers II, 323 as HBV carriers III, and 167 as HBV carriers IV. We found that the differences in total bilirubin, albumin, ALT, AST, gamma glutamyl transpeptidase (GGT), white blood cells (WBCs), and HBsAg levels, platelet count (PLT), and APRI were statistically significant (p<0.05) among the four groups. No significant differences were found in age, globulin levels, total cholesterol levels, cholinesterase levels, HBV DNA levels, prothrombin time, international normalized ratio (INR), and FIB-4. The proportions of patients with significant fibrosis in chronic HBV carriers I, II, III, and IV were 32.7% (190/581), 30.1% (135/448), 30.7% (99/323), and 29.9% (50/167), respectively. Similarly, the prevalence of liver cirrhosis in the four groups was 3.4% (20/581), 2.9% (13/448), 3.4% (11/323), and 4.8% (8/167), respectively.

Table 1

Clinical characteristics of the four groups of chronic HBV carriers

	Chronic HBV carriers I (n=581)	Chronic HBV carriers II (n=448)	Chronic HBV carriers III (n=323)	Chronic HBV carriers IV (n=167)	p-value
Age (years)	37.95±10.58	37.49±10.51	37.40±10.62	38.34±10.18	0.714
Sex
Male	368 (63.3%)	314 (70.4%)	239 (74.0%)	74 (44.3%)	<0.001
Female	213 (36.7%)	134 (29.9%)	84 (26.0%)	93 (55.7%)
Total bilirubin (mmol/L)	13.95±10.02	14.99±8.44	15.15±8.36	14.81±8.43	<0.001
Albumin (g/L)	41.94±5.13	42.66±5.06	42.67±4.96	42.27±4.58	<0.001
Globulin (g/L)	25.53±8.00	25.61±10.98	25.50±8.97	26.21±7.57	0.975
ALT (U/L)	26.84±7.62	23.62±6.46	21.59±5.07	17.40±3.96	<0.001
AST (U/L)	24.86±6.51	22.18±5.79	25.52±5.86	19.91±5.16	<0.001
GGT (U/L)	23.43±14.91	23.09±5.57	22.84±14.60	21.65±15.05	<0.001
TCHO (mmol/L)	4.70±1.08	4.73±1.16	4.72±1.26	4.82±1.56	0.945
CHE (LogU/L)	3.90±0.11	3.90±0.11	3.90±0.10	3.88±0.11	0.487
WBC (10⁹/L)	5.76±1.51	5.77±1.53	5.69±1.55	5.38±1.37	0.025
PLT (10⁹/L)	208.32± 55.27	209.77±55.11	207.39±57.90	218.20±62.77	<0.001
HBsAg (log IU/mL)	3.39±1.02	3.40±1.03	3.35±1.06	3.24±1.12	<0.001
HBV DNA (log IU/mL)	5.52±2.04	5.45±2.03	5.38±2.03	5.18±2.07	0.537
PT (s)	12.82±4.40	12.92±4.96	12.77±1.04	12.73±1.02	0.933
INR	1.02±0.07	1.02±0.07	1.02±0.07	1.02±0.07	0.969
APRI	0.31±0.15	0.29±0.13	0.29±0.14	0.26±0.12	<0.001
FIB-4	0.95±0.56	0.92±0.55	0.95±0.59	0.96±0.61	0.808
Fibrosis stage, n (%) 0.582
F0	77 (13.3)	64 (14.3)	40 (12.4)	26 (15.6)
F1	314 (54.0)	249 (55.6)	184 (56.9)	91 (54.5)
F2	125 (21.5)	91 (20.3)	59 (18.3)	23 (13.7)
F3	45 (7.8)	31 (6.9)	29 (9.0)	19 (11.4)
F4	20 (3.4)	13 (2.9)	11 (3.4)	8 (4.8)

Data are n (%) or mean±SEM. ALT, alanine aminotransferase; AST, aspartate aminotransferase; CHE, cholinesterase; GGT, gamma glutamyl transpeptidase; HBV, hepatitis B virus; INR, international normalized ratio PLT, platelet count; PT, prothrombin time; TCHO, total cholesterol; WBC, white blood cell count.

Correlation of APRI and FIB-4 with fibrosis stages

APRI and FIB-4 revealed a weak positive correlation with hepatic fibrosis in the four groups of chronic HBV carriers with different ULN for ALT (Table 2).

Table 2

Correlation of APRI and FIB-4 with liver fibrosis in the four groups of chronic HBV carriers with different ULN for ALT

Score	Chronic HBV carriers I (n=581)	Chronic HBV carriers II (n=448)	Chronic HBV carriers III (n=323)	Chronic HBV carriers IV (n=167)
APRI
Spearman	0.313	0.301	0.357	0.333
p-value	<0.001	<0.001	<0.001	<0.001
FIB-4
Spearman	0.208	0.191	0.238	0.186
p-value	<0.001	0.001	<0.001	0.017

APRI, Aspartate aminotransferase-to-platelet ratio index; FIB-4, fibrosis-4 index; HBV, hepatitis B virus.

Predictive value of APRI and FIB-4 for significant fibrosis and cirrhosis at cutoffs recommended by the WHO

Table 3 shows that no patients were correctly diagnosed when an APRI of >1.5 and a FIB-4 of >3.25 were used to predict significant fibrosis. In chronic HBV carriers I, only 67.2% and 56.4% of patients with nonsignificant fibrosis were correctly predicted with an APRI of <0.5 and a FIB-4 of <1.45. Patients were not correctly diagnosed even when an APRI of >2.0 was used to predict liver cirrhosis. In summary, significant fibrosis and cirrhosis in chronic HBV carriers I and III were correctly predicted using the cutoff values recommended in the WHO HBV guidelines. Furthermore, a large proportion of nonsignificant fibrosis was correctly predicted.

Table 3

Diagnostic value of APRI and FIB-4 for diagnosing liver fibrosis in chronic HBV carriers I and chronic HBV carriers III at cutoff values recommended by the WHO HBV guidelines

Criteria	Score	Cutoff	Predicted fibrosis stage	Sensitivity %	Specificity %	PPV %	NPV %
Chronic HBV carriers I (n=581)
Significant fibrosis	APRI	>1.5	F2–F4	0 (0/190)	100 (391/391)	0 (0/0)	67.3 (391/581)
		<0.5	F0–F1	94.9 (371/391)	21.6 (41/190)	71.4 (371/520)	67.2 (41/61)
	FIB-4	>3.25	F2–F4	0 (0/190)	100 (391/391)	0 (0/0)	67.3 (391/581)
		<1.45	F0–F1	89.3 (349/391)	27.7 (53/190)	71.8 (349/486)	56.4 (53/94)
Cirrhosis	APRI	>2.0	F4	0 (0/20)	100 (561/561)	0 (0/0)	96.6 (561/581)
		<1.0	F0–F3	100 (561/561)	0 (0/561)	96.6 (561/581)	0 (0/0)
Chronic HBV carriers III (n=323)
Significant fibrosis	APRI	>1.5	F2–F4	0 (0/99)	100 (224/224)	0 (0/0)	69.3 (224/323)
		<0.5	F0–F1	95.1 (213/224)	15.2 (15/99)	71.7 (213/297)	57.7 (15/26)
	FIB-4	>3.25	F2–F4	0 (0/99)	100 (224/224)	0 (0/0)	69.3 (224/323)
		<1.45	F0–F1	89.7 (201/224)	22.2 (22/99)	72.3 (201/278)	48.9 (22/45)
Cirrhosis	APRI	>2.0	F4	0 (0/11)	100 (312/312)	0 (0/0)	96.6 (312/323)
		<1.0	F0–F3	100 (312/312)	0 (0/11)	96.6 (312/323)	0 (0/0)

PPV, positive predictive value; NPV, negative predictive value.

Predictive value of APRI and FIB-4 for the assessment of significant fibrosis, advanced fibrosis, and cirrhosis

We analyzed the diagnostic value of APRI and FIB-4 models for detecting significant fibrosis, advanced fibrosis, and cirrhosis in the four chronic HBV carrier groups with different ULN of ALT (Table 4). The AUCs of APRI vs. FIB-4 to assess significant fibrosis were: chronic HBV carriers I, 0.680 vs. 0.609 (p=0.060); chronic HBV carriers II, 0.680 vs. 0.602 (p=0.070); chronic HBV carriers III, 0.682 vs. 0.609 (p=0.140); and chronic HBV carriers IV, 0.736 vs. 0.647 (p=0.176). The AUCs of APRI vs. FIB-4 to assess advanced fibrosis were: chronic HBV carriers I, 0.757 vs. 0.698 (p=0.228); chronic HBV carriers II, 0.759 vs. 0.702 (p=0.332); chronic HBV carriers III, 0.788 vs. 0.718 (p=0.240); and chronic HBV carriers IV, 0.852 vs. 0.727 (p=0.062). Similarly, the AUCs of APRI vs. FIB-4 to assess cirrhosis were: chronic HBV carriers I, 0.678 vs. 0.661 (p=0.841); chronic HBV carriers II, 0.692 vs. 0.655 (p=0.692); chronic HBV carriers III, 0.756 vs. 0.709 (p=0.580); and chronic HBV carriers IV, 0.767 vs. 0.628 (p=0.256).

Table 4

AUCs of APRI and FIB-4 to assess significant fibrosis, advanced fibrosis, and cirrhosis

Criteria	Score	Chronic HBV carriers I (n=581)	Chronic HBV carriers II (n=448)	Chronic HBV carriers III (n=323)	Chronic HBV carriers IV (n=167)
Criteria	Score	AUROC 95% CI
Significant fibrosis	APRI	0.680 (0.631–0.729)	0.680 (0.623–0.737)	0.682 (0.615–0.748)	0.736 (0.648–0.824)
	FIB-4	0.609 (0.557–0.661)	0.602 (0.541–0.663)	0.609 (0.539–0.679)	0.647 (0.553–0.741)
	p-value	0.060	0.070	0.140	0.176
Advanced fibrosis	APRI	0.757 (0.694–0.819)	0.759 (0.683–0.835)	0.788 (0.711–0.865)	0.852 (0.777–0.927)
	FIB-4	0.698 (0.627–0.770)	0.702 (0.616–0.787)	0.718 (0.629–0.807)	0.727 (0.620–0.833)
	p-value	0.228	0.332	0.240	0.062
Cirrhosis	APRI	0.678 (0.560–0.796)	0.692 (0.569–0.815)	0.756 (0.645–0.868)	0.767 (0.631–0.902)
	FIB-4	0.661 (0.543–0.783)	0.655 (0.519–0.790)	0.709 (0.586–0.832)	0.628 (0.431–0.825)
	p-value	0.841	0.692	0.580	0.256

APRI, Aspartate aminotransferase-to-platelet ratio index; AUC, area under the curve; FIB-4, fibrosis-4 index; HBV, hepatitis B virus.

Development of a novel model for predicting significant fibrosis

First, we randomly divided chronic HBV carriers into training and internal validation groups and assessed 106 chronic HBV carriers who underwent liver biopsies at Huashan Hospital Affiliated to Fudan University as an external validation group. The clinical characteristics of these three groups are shown in Supplementary Table 1. Second, we analyzed the relationship between clinical data and significant fibrosis (Supplementary Table 2). Using univariate analysis, it was found that PLT, HBV DNA levels, HBsAg levels, GGT levels, AST levels, INR, and albumin levels were different in patients with nonsignificant and significant fibrosis (p<0.05). A novel noninvasive predictive panel named GPSA was constructed to assess significant fibrosis in chronic HBV carriers using multivariate regression: 7.987 + 0.087 × GGT (U/L) − 0.013 × PLT (10⁹/L) − 0.422 × log HBsAg (IU/mL) − 0.159× ALB (g/L). Finally, we evaluated the predictive value of GPSA for assessing significant fibrosis (Table 5). The AUC of GPSA for assessing significant fibrosis was 0.877.

Table 5

GPSA validity in internal and external validation groups

	AUC (95% CI)	AduAUC	Cutoff	Sensitivity %	Specificity %	Youden’s index	PPV %	NPV %
Training group (n=290)	0.877 (0.834–0.920)	0.968	−0.6129	75.3 (61/81)	85.4 (158/185)	0.607	69.3 (61/88)	88.8 (158/178)
Internal validation group (n=291)	0.837 (0.787–0.886)	0.927	−0.8607	75.8 (75/99)	84.2 (154/183)	0.599	72.1 (75/104)	86.5 (154/178)
External validation group (n=106)	0.871 (0.799–0.944)	0.885	−1.9845	90.9 (30/33)	76.7 (56/73)	0.676	63.8 (30/47)	94.9 (56/59)

PPV, positive predictive value; NPV, negative predictive value.

Poynard et al.¹⁹ showed that the AUC of liver fibrosis markers should be standardized according to the prevalence of fibrosis stages in a large-scale cohort. Therefore, we adopted a similar method to minimize the bias of statistical analyses. In the training group, the adjusted uniform AUC (AduAUC) for GPSA was 0.968. In the internal validation group, the AduAUC for GPSA was 0.927. In the external validation group, the AduAUC for GPSA was 0.885. The training group showed no significant differences in the AUCs compared with internal validation group and external validation group (Z=1.201, p=0.230 and Z=0.158, p=0.875, respectively).

Performance of the new panel to predict F≥2, F≥3, and F4

The respective AUCs of GPSA for the prediction of liver fibrosis in chronic HBV carriers I, II, III, and IV were as follows: for F≥2, 0.857, 0.853, 0.868, and 0.905; for F≥3, 0.902, 0.896, 0.892, and 0.926; and for F=4, 0.901, 0.905, 0.886, and 0.913, respectively. There were no significant differences in the AUCs for predicting F≥2, F≥3, and F4 in chronic HBV carriers I, II, III, and IV (all p>0.5; Table 6).

Table 6

Diagnostic value of GPSA in the four groups of chronic HBV carriers

	AUC (95% CI)	Cutoff	Sensitivity %	Specificity %	Youden’s index	PPV %	NPV %
Chronic HBV carriers I (n=581)
F≥2	0.857 (0.824–0.890)	−0.8582	76.1	83.2	0.596	68.8	87.7
F≥3	0.902 (0.873–0.932)	−0.5664	92.2	77.0	0.692	34.7	98.7
F=4	0.901 (0.853–0.949)	−0.2352	85.0	76.7	0.617	12.1	99.3
Chronic HBV carriers II (n=448)
F≥2	0.853 (0.815–0.891)	−0.8635	74.4	83.2	0.576	65.3	88.0
F≥3	0.896 (0.860–0.933)	−0.6373	93.0	76.9	0.699	31.5	99.0
F=4	0.905 (0.846–0.946)	−0.4306	92.3	74.2	0.665	10.3	99.7
Chronic HBV carriers III (n=323)
F≥2	0.868 (0.827–0.908)	−0.8930	77.9	89.2	0.671	67.3	89.2
F≥3	0.892 (0.850–0.993)	−0.6718	92.3	76.5	0.688	36.7	98.5
F=4	0.886 (0.814–0.957)	−0.4276	90.9	72.7	0.636	11.1	99.5
Chronic HBV carriers IV (n=167)
F≥2	0.905 (0.858–0.952)	−1.0354	91.7	82.3	0.740	68.8	95.9
F≥3	0.926 (0.885–0.967)	−0.5846	88.5	80.6	0.691	46.9	97.3
F=4	0.913 (0.830–0.995)	−0.2076	87.5	77.8	0.653	17.1	99.2

PPV, positive predictive value; NPV, negative predictive value.

Comparisons of GPSA with APRI and FIB-4 models for assessing significant fibrosis

The GPSA model showed the highest predictive value among GPSA, APRI, and FIB-4 models (all p<0.001); therefore, GPSA was superior to APRI and FIB-4 in predicting significant fibrosis in the four chronic HBV carrier groups (Fig. 1). To avoid the spectrum effect and the risk of multiple testing, we performed comparisons of diagnostic accuracy of GPSA, APRI, and FIB-4 models for significant fibrosis using the Obuchowski index^20,21 (Table 7). The Obuchowski index of GPSA was also significantly higher than that of APRI and FIB-4 in the four chronic HBV carrier groups (all p<0.001).

Fig. 1 ROC curves of the noninvasive models (GPSA, APRI, and FIB-4) in the four groups of chronic HBV carriers.

APRI, Aspartate aminotransferase-to-platelet ratio index; AUC, area under the curve; FIB-4, fibrosis-4 index; HBV, hepatitis B virus; ROC, receiver operating characteristic curve.

Table 7

Diagnostic value and Obuchowski Indexes of GPSA, APRI, and FIB-4 in the four groups of chronic HBV carriers

	GPSA	APRI	FIB-4
Chronic HBV carriers I (n=581)
AUC (95% CI)	0.857 (0.824–0.890)	0.680 (0.631–0.729)	0.609 (0.557–0.669)
Obuchowski index	0.858 (0.831–0.885)	0.698 (0.660–0.736)	0.634 (0.593–0.674)
Chronic HBV carriers II (n=448)
AUC (95% CI)	0.853 (0.815–0.891)	0.680 (0.623–0.737)	0.602 (0.541–0.663)
Obuchowski index	0.850 (0.818–0.882)	0.699 (0.655–0.744)	0.625 (0.578–0.672)
Chronic HBV carriers III (n=323)
AUC (95% CI)	0.868 (0.827–0.908)	0.682 (0.615–0.748)	0.609 (0.539–0.679)
Obuchowski index	0.866 (0.830–0.902)	0.705 (0.655–0.756)	0.636 (0.582–0.690)
Chronic HBV carriers IV (n=167)
AUC (95% CI)	0.905 (0.858–0.952)	0.736 (0.648–0.824)	0.647 (0.553–0.741)
Obuchowski index	0.910 (0.865–0.954)	0.773 (0.711–0.834)	0.670 (0.598–0.741)

APRI, Aspartate aminotransferase-to-platelet ratio index; AUC, area under the curve; FIB-4, fibrosis-4 index; HBV, hepatitis B virus.

Discussion

Several studies have shown that inactive carriers can still have significant liver disease.^22,23 The major novel findings of this retrospective study of chronic HBV carriers with different ULN for ALT from multiple centers were: (1) The prevalence of significant fibrosis was 29.9–32.7%. (2) APRI and FIB-4 had a weak positive correlations with hepatic fibrosis and had poor diagnostic value in predicting significant liver fibrosis. (3) In this specific chronic HBV-infected population, the WHO-recommended cutoffs were higher than what are required to predict significant fibrosis and cirrhosis, which may lead to an underestimation of the proportion of patients with significant fibrosis and cirrhosis. (4) Finally, the GPSA model had significantly better predictive accuracy than APRI and FIB-4 in diagnosing significant fibrosis. To our knowledge, this is the first effort to construct a novel panel (GPSA) and validate and compare the abilities of GPSA, APRI, and FIB-4 models in assessing significant fibrosis in chronic HBV carriers with different ULN for ALT.

A meta-analysis reported the AUCs of APRI and FIB-4 for diagnosing significant fibrosis as 0.7407 and 0.7844 and for as 0.7268 and 0.8448 for diagnosing cirrhosis in CHB patients.⁶ Tan et al.¹⁴ found that the AUCs of APRI and FIB-4 to predict significant fibrosis were lower in patients with persistently normal ALT than in patients with ALT within 1–2×ULN and in those with ALT >2×ULN. The results indicate that APRI and FIB-4 had poor predictive value for liver fibrosis in CHB patients with normal ALT when compared with those with abnormal ALT.

When the WHO-recommended cutoffs of an APRI >1.5 and a FIB-4 of >3.25 were used to predict significant fibrosis in chronic HBV carriers I, all chronic HBV carriers I having significant fibrosis were misclassified as not having significant fibrosis, which limits the use of APRI and FIB-4 models for predicting significant liver fibrosis in chronic HBV carriers I before liver biopsy, thus affecting antiviral therapy. PPV was 0 when an APRI >2.0 was used for predicting cirrhosis in chronic HBV carriers I, which means that the APRI score of all patients with cirrhosis was <2. Therefore, if these patients with cirrhosis cannot be diagnosed in time, the risk of their cirrhosis progressing into HCC is likely to increase. We considered the cutoff values recommended by the WHO as mainly suitable for CHB patients with abnormal ALTs.

We herein developed GPSA, which is a novel panel that combines GGT, HBsAg, PLT, and albumin for assessing hepatic fibrosis in chronic HBV carriers. Low serum albumin is common in decompensated cirrhosis and is associated with an adverse prognosis.^24,25 Decreased liver synthesis of albumin and progression of liver disease both lead to hypoalbuminemia.²⁶ GGT is reportedly related to hepatocyte growth factor and HBV-related fibrosis.²⁷ Early cholestasis increases the production of epidermal growth factor, which may explain the correlation between the increase in GGT levels and the severity of liver fibrosis.²⁸ Because of decreased production of thrombopoietin by the liver in the presence of liver fibrosis or cirrhosis,²⁹ PLT can be used as a potential noninvasive marker to assess liver fibrosis. Zeng et al.^30,31 found a significantly negative correlation between serum HBsAg and liver fibrosis. HBsAg is modulated by both virus and host immunity; it is speculated that the immune-mediated response to HBV infection results in liver damage and that the retention of HBsAg within hepatocytes results in the reduction of HBsAg levels. In this study, all AUC values were found to be >0.8 when GPSA was used to assess different liver fibrosis stages in chronic HBV carriers. GPSA was also found to have good predictive value on external validation. On further comparison of the AUC of the GPSA model with APRI and FIB-4 models, the GPSA model had a significantly higher AUC value. We demonstrated that GPSA would be a better non-invasive tool to facilitate clinicians’ decision-making regarding antiviral treatment.

Several studies have recommended revision of the ULN for ALT. Thus, based on different standards of ALT levels,^11,32,33 we divided chronic HBV carriers into chronic HBV carriers I, II, III, and IV. Our analyses showed that APRI and FIB-4 had poor predictive value in assessing significant fibrosis in the four chronic HBV carrier groups. All AUCs of GPSA for predicting F≥2, F≥3, and F4 were >0.8, and GPSA was more accurate than APRI and FIB-4 in predicting significant fibrosis in the four chronic HBV carrier groups according to the Obuchowski index. We demonstrated that the GPSA model has good diagnostic value in identifying different liver fibrosis stages in chronic HBV carriers with different ULN for normal ALT.

Our study has several limitations. First, several studies have shown that liver stiffness measurement (LSM) can accurately diagnose liver fibrosis in CHB patients with normal ALTs.^34,35 Our study was a multicenter, cross-sectional study. Unfortunately, as we have insufficient valid data because of the lack of LSM in some centers, we could not compare the performance of GPSA with LSM, such as transient elastography or shear wave elastography, in detecting fibrosis. In the future, we will cooperate with centers that can perform LSM to expand the sample size for the next research. Second, few patients with advanced fibrosis (45, 7.8%) and cirrhosis (20, 3.4%) were included, which could have led to a statistical bias. Although we used the AduAUC to standardize the prevalence of different fibrosis stages in our study patients to minimize the bias in statistical analysis, large, multicenter cohort studies including patients with more advanced fibrosis and cirrhosis are needed for further investigation.

In summary, when compared with APRI and FIB-4, the GPSA model had increased diagnostic value for assessing liver fibrosis in chronic HBV carriers with different ULN for ALT, which can be beneficial for accurate and timely assessment of liver fibrosis and for reducing disease progression of chronic HBV infection.

Supporting information

Supplementary Table 1

Demographic characteristics and clinical features in our patient cohort.

(DOCX)

Click here for additional data file.

Supplementary Table 2

Clinical parameters associated with significant fibrosis in the training group (290 patients).

(DOCX)

Click here for additional data file.

Supplementary Fig. 1

Flow diagram of the study population. ALT, alanine aminotransferase; HAV, hepatitis A virus; HBV, hepatitis B virus; HCV, hepatitis C virus; HDV, hepatitis D virus; HEV; hepatitis E virus, ULN, upper limit of normal.

(DOCX)

Click here for additional data file.

Abbreviations

ALT:: alanine aminotransferase

APRI:: Aspartate aminotransferase-to-platelet ratio index

AUC:: area under the curve

CHB:: chronic hepatitis B

CHE:: cholinesterase

FIB-4:: fibrosis-4 index

GGT:: glutamyl transpeptidase

HBsAg:: hepatitis B surface antigen

HBV:: hepatitis B virus

HCC:: hepatocellular carcinoma

INR:: international normalized ratio

PLT:: platelet count

TCHO:: total cholesterol

ULN:: upper limits of normal

WBC:: white blood cell count

Declarations

Ethical statement

The study was approved by the institutional review board of Fujian Medical University. Given the retrospective design of the study, the need for informed consent was waived.

Data sharing statement

All data are available upon request.

Funding

This work was supported by Fujian Provincial Health Technology Project (No. 2019-ZQN-60), Natural Science Fundation of Fujian Province(No. 2019J01432), and National Natural Science Foundation of China (No. 81670528).

Conflict of interest

The authors have no conflict of interests related to this publication.

Authors’ contributions

Study concept and design (DWZ, NLK, QFR, DSZ, XPY), acquisition of data (NLK, QFR, DSZ, XPY, ZTH, LYW), analysis and interpretation of data (YRL, ZML, MXL, ZXH), administrative, technical, or material support (DWZ, JJJ, NLK, YRL, RCM), drafting of the manuscript (DWZ, NLK, QFR, DSZ), and study supervision (RCM, DWZ). All authors reviewed and commented on the manuscript and approved the final version.

References

1	Tseng KC, Chen CY, Tsai HW, Chang TT, Chuang WL, Hsu PI, et al. Efficacy of entecavir in chronic hepatitis B patients with persistently normal alanine aminotransferase: randomized, double-blind, placebo-controlled study. Antivir Ther 2014;19(8):755-764 View Article PubMed/NCBI

2	Weemhoff JL, Woolbright BL, Jenkins RE, Mcgill MR, Sharpe MR, Olson JC, et al. Plasma Biomarkers to Study Mechanisms of Liver Injury in Patients with Hypoxic Hepatitis. Liver Int 2017;37(3):377-384 View Article PubMed/NCBI

3	Gong Y, Liu Z, Liao Y, Mai C, Chen T, Tang H, et al. Effectiveness of ω-3 Polyunsaturated Fatty Acids Based Lipid Emulsions for Treatment of Patients after Hepatectomy: A Prospective Clinical Trial. Nutrients 2016;8(6):357 View Article PubMed/NCBI

4	Chen CJ, Yang HI, Su J, Jen CL, You SL, Lu SN, et al. Risk of hepatocellular carcinoma across a biological gradient of serum hepatitis B virus DNA level. JAMA 2006;295(1):65-73 View Article PubMed/NCBI

5	Kelleni MT, Ibrahim SA, Abdelrahman AM. Effect of captopril and telmisartan on methotrexate-induced hepatotoxicity in rats: impact of oxidative stress, inflammation and apoptosis. Toxicol Mech Methods 2016;26(5):371-377 View Article PubMed/NCBI

Xiao G, Yang J, Yan L. Comparison of diagnostic accuracy of aspartate aminotransferase to platelet ratio index and fibrosis-4 index for detecting liver fibrosis in adult patients with chronic hepatitis B virus infection: a systemic review and meta-analysis. Hepatology 2015;61(1):292-302 View Article PubMed/NCBI

7	Sarin SK, Kumar M, Lau GK, Abbas Z, Chan HL, Chen CJ, et al. Asian-Pacific clinical practice guidelines on the management of hepatitis B: a 2015 update. Hepatol Int 2016;10(1):1-98 View Article PubMed/NCBI

8	European Association for the Study of the Liver. Electronic address eee, European Association for the Study of the Liver. EASL 2017 Clinical Practice Guidelines on the management of hepatitis B virus infection. J Hepatol 2017;67(2):370-398 View Article PubMed/NCBI

9	Terrault NA, Lok AS, McMahon BJ, Chang KM, Hwang JP, Jonas MM, et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Clin Liver Dis (Hoboken) 2018;12(1):33-34 View Article PubMed/NCBI

10	Guidelines for the Prevention, Care and Treatment of Persons with Chronic Hepatitis B Infection. Geneva: World Health Organization; 2015 PubMed/NCBI

11	Duan M, Chi X, Xiao H, Liu X, Zhuang H. High-normal alanine aminotransferase is an indicator for liver histopathology in HBeAg-negative chronic hepatitis B. Hepatol Int 2021;15(2):318-327 View Article PubMed/NCBI

12	Keeffe EB, Dieterich DT, Han SH, Jacobson IM, Martin P, Schiff ER, et al. A treatment algorithm for the management of chronic hepatitis B virus infection in the United States: 2008 update. Clin Gastroenterol Hepatol 2008;6(12):1315-1341 View Article PubMed/NCBI

13	Li Q, Ren X, Lu C, Li W, Huang Y, Chen L. Evaluation of APRI and FIB-4 for noninvasive assessment of significant fibrosis and cirrhosis in HBeAg-negative CHB patients with ALT </= 2 ULN: A retrospective cohort study. Medicine (Baltimore) 2017;96(12):e6336 View Article PubMed/NCBI

Tan YW, Zhou XB, Ye Y, He C, Ge GH. Diagnostic value of FIB-4, aspartate aminotransferase-to-platelet ratio index and liver stiffness measurement in hepatitis B virus-infected patients with persistently normal alanine aminotransferase. World J Gastroenterol 2017;23(31):5746-5754 View Article PubMed/NCBI

15	Kim WR, Berg T, Asselah T, Flisiak R, Fung S, Gordon SC, et al. Evaluation of APRI and FIB-4 scoring systems for non-invasive assessment of hepatic fibrosis in chronic hepatitis B patients. J Hepatol 2016;64(4):773-780 View Article PubMed/NCBI

16	Itakura J, Kurosaki M, Setoyama H, Simakami T, Oza N, Korenaga M, et al. Applicability of APRI and FIB-4 as a transition indicator of liver fibrosis in patients with chronic viral hepatitis. J Gastroenterol 2021;56(5):470-478 View Article PubMed/NCBI

17	Chinese Society of Infectious Diseases, Chinese Medical Association; Chinese Society of Hepatology, Chinese Medical Association. The guidelines of prevention and treatment for chronic hepatitis B (2019 version). Zhonghua Gan Zang Bing Za Zhi 2019;27(12):938-961 View Article PubMed/NCBI

18	European Association For The Study Of The Liver. EASL Clinical Practice Guidelines: management of chronic hepatitis B. J Hepatol 2009;50(2):227-242 View Article PubMed/NCBI

19	Poynard T, Halfon P, Castera L, Munteanu M, Imbert-Bismut F, Ratziu V, et al. Standardization of ROC curve areas for diagnostic evaluation of liver fibrosis markers based on prevalences of fibrosis stages. Clin Chem 2007;53(9):1615-1622 View Article PubMed/NCBI

20	Obuchowski NA, Goske MJ, Applegate KE. Assessing physicians’ accuracy in diagnosing paediatric patients with acute abdominal pain: measuring accuracy for multiple diseases. Stat Med 2001;20(21):3261-3278 View Article PubMed/NCBI

21	Lambert J, Halfon P, Penaranda G, Bedossa P, Cacoub P, Carrat F. How to measure the diagnostic accuracy of noninvasive liver fibrosis indices: the area under the ROC curve revisited. Clin Chem 2008;54(8):1372-1378 View Article PubMed/NCBI

22	Wang H, Ru GQ, Yan R, Zhou Y, Wang MS, Cheng MJ. Histologic Disease in Chinese Chronic Hepatitis B Patients With Low Viral Loads and Persistently Normal Alanine Aminotransferase Levels. J Clin Gastroenterol 2016;50(9):790-796 View Article PubMed/NCBI

23	Liao B, Wang Z, Lin S, Xu Y, Yi J, Xu M, et al. Significant fibrosis is not rare in Chinese chronic hepatitis B patients with persistent normal ALT. PLoS One 2013;8(10):e78672 View Article PubMed/NCBI

24	Salerno F, Borroni G, Moser P, Badalamenti S, Cassarà L, Maggi A, et al. Survival and prognostic factors of cirrhotic patients with ascites: a study of 134 outpatients. Am J Gastroenterol 1993;88(4):514-519 PubMed/NCBI

25	Zaccherini G, Bernardi M. The role and indications of albumin in advanced liver disease. Acta Gastroenterol Belg 2019;82(2):301-308 PubMed/NCBI

26	Arroyo V, Fernandez J. Pathophysiological basis of albumin use in cirrhosis. Ann Hepatol 2011;10(Suppl 1):S6-14 PubMed/NCBI

27	Myers RP, Tainturier MH, Ratziu V, Piton A, Thibault V, Imbert-Bismut F, et al. Prediction of liver histological lesions with biochemical markers in patients with chronic hepatitis B. J Hepatol. 2003;39(2):222-230 View Article PubMed/NCBI

28	Ozdoğan O, Ratip S, Ahdab YA, Dane F, Ahdab HA, Imeryüz N, et al. Causes and risk factors for liver injury following bone marrow transplantation. J Clin Gastroenterol 2003;36(5):421-426 View Article PubMed/NCBI

29	Peck-Radosavljevic M. Thrombocytopenia in chronic liver disease. Liver Int 2017;37(6):778-793 View Article PubMed/NCBI

30	Zeng DW, Zhang JM, Liu YR, Dong J, Jiang JJ, Zhu YY. A Retrospective Study on the Significance of Liver Biopsy and Hepatitis B Surface Antigen in Chronic Hepatitis B Infection. Medicine (Baltimore) 2016;95(8):e2503 View Article PubMed/NCBI

Zeng DW, Huang ZX, Lin MX, Kang NL, Lin X, Li YN, et al. A novel HBsAg-based model for predicting significant liver fibrosis among Chinese patients with immune-tolerant phase chronic hepatitis B: a multicenter retrospective study. Therap Adv Gastroenterol 2021;14:17562848211010675 View Article PubMed/NCBI

32	Lee JK, Shim JH, Lee HC, Lee SH, Kim KM, Lim YS, et al. Estimation of the healthy upper limits for serum alanine aminotransferase in Asian populations with normal liver histology. Hepatology 2010;51(5):1577-1583 View Article PubMed/NCBI

33	Prati D, Taioli E, Zanella A, Della Torre E, Butelli S, Del Vecchio E, et al. Updated definitions of healthy ranges for serum alanine aminotransferase levels. Ann Intern Med 2002;137(1):1-10 View Article PubMed/NCBI

Danis N, Salih Akarca U, Turan I, Karasu Z, Ersoz G, Yilmaz F, et al. Predictive value of FibroScan in detecting liver fibrosis in HBeAg negative patients with chronic hepatitis B whose HBV DNA 2000-20000 IU/ml with ALT 1-2 times the upper limit of normal and those with HBV DNA >20000 IU/ml and normal ALT. North Clin Istanb 2021;8(6):568-574 View Article PubMed/NCBI

Copyright © 2022 Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 License (CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Journal of Clinical and Translational Hepatology
pISSN 2225-0719
eISSN 2310-8819

Article Options

Article History

Metrics

Cite this Article

Kang NL, Ruan QF, Zhang DS, Yu XP, Hu ZT, Lin ZM, et al. Advantages of a Novel Model for Predicting Hepatic Fibrosis in Chronic Hepatitis B Virus Carriers Compared with APRI and FIB-4 Scores. J Clin Transl Hepatol. 2022;10(3):412-419. doi: 10.14218/JCTH.2022.00098.

Publications > Journals > Journal of Clinical and Translational Hepatology > Article Full Text

Advantages of a Novel Model for Predicting Hepatic Fibrosis in Chronic Hepatitis B Virus Carriers Compared with APRI and FIB-4 Scores

Abstract

Background and Aims

Methods

Results

Conclusions

Keywords

Introduction

Methods

Patients

Liver biopsy

Serum markers

Statistical analysis

Results

Clinical data

Correlation of APRI and FIB-4 with fibrosis stages

Predictive value of APRI and FIB-4 for significant fibrosis and cirrhosis at cutoffs recommended by the WHO

Predictive value of APRI and FIB-4 for the assessment of significant fibrosis, advanced fibrosis, and cirrhosis

Development of a novel model for predicting significant fibrosis

Performance of the new panel to predict F≥2, F≥3, and F4

Comparisons of GPSA with APRI and FIB-4 models for assessing significant fibrosis

Discussion

Supporting information

Supplementary Table 1

Supplementary Table 2

Supplementary Fig. 1

Abbreviations

Declarations

Ethical statement

Data sharing statement

Funding

Conflict of interest

Authors’ contributions

References

Article Options

Article History

Metrics

Table of Contents

Cite this Article

Share this Article

Advantages of a Novel Model for Predicting Hepatic Fibrosis in Chronic Hepatitis B Virus Carriers Compared with APRI and FIB-4 Scores