BMJ Open...STARD checklist for reporting of studies of diagnostic accuracy (version January 2003) Section and Topic Item # On page # TITLE/ABSTRACT/ KEYWORDS 2 standard.

For peer review only

Development and validation of a novel non-invasive index for predicting fibrosis in patients with chronic hepatitis B

infection: a retrospective cohort study.

Journal: BMJ Open

Manuscript ID: bmjopen-2015-008032

Article Type: Research

Date Submitted by the Author: 25-Feb-2015

Complete List of Authors: Feng, Limin; The First Affiliated Hospital, College of Medicine, Zhejiang University, Laboratory Medicine Sun, Ke; The First Affiliated Hospital, College of Medicine, Zhejiang

University, Pathology Zhang, Jie; The First Affiliated Hospital, College of Medicine, Zhejiang University, Laboratory Medicine Feng, Guofang; Women’s Hospital, School of Medicine, Zhejiang University, Reproductive Endocrinology Zhao, Ying; The First Affiliated Hospital, College of Medicine, Zhejiang University, Laboratory Medicine

<b>Primary Subject Heading</b>:

Diagnostics

Secondary Subject Heading: Gastroenterology and hepatology, Infectious diseases

Keywords: HISTOPATHOLOGY, Hepatology < INTERNAL MEDICINE, INFECTIOUS DISEASES

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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Development and validation of a novel non-invasive index for predicting fibrosis

in patients with chronic hepatitis B infection: a retrospective cohort study.

Limin Feng1, Ke Sun

2, Jie Zhang

1, Guofang Feng

3, Ying Zhao

1*

1Department of Laboratory Medicine, The First Affiliated Hospital, College of

Medicine, Zhejiang University, Hangzhou 310003, China

2Department of Pathology, The First Affiliated Hospital, College of Medicine,

Zhejiang University, Hangzhou 310003, China

3Department of Reproductive Endocrinology, Women’s Hospital, School of Medicine,

Zhejiang University, Hangzhou 310006, China

*Corresponding author:Ying Zhao

Department of Laboratory Medicine, The First Affiliated Hospital, College of

Medicine, Zhejiang University, Qingchun Road 79#, Hangzhou 310003, China

E-mail: [email protected]

Tel.: +86-571-87236380

Fax: +86-571-87236383

Short title: a novel non-invasive index and liver fibrosis

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Abstract

Objective: A liver biopsy is the “reference standard” for diagnosing and staging liver

fibrosis but with many disadvantages. Therefore, developing non-invasive index for

predicting fibrosis is very valuable. We developed and validated a novel non-invasive

index for predicting significant fibrosis in patients with chronic hepatitis B infection.

Design: A retrospective cohort study.

Setting: Chronic Hepatitis B virus infected patients were recruited in the Department

of Infectious Disease in the First Affiliated Hospital of Zhejiang University.

Participants: A total of 506 patients were enrolled, and patients were randomly

divided into estimation (n=253) and validation (n=253) cohorts.

Primary and secondary outcome measures: Chronic Hepatitis B virus infected

patients were studied retrospectively using routine parameters. A novel index was

developed from an estimation cohort and validated in another cohort. Liver histology

was assessed for fibrosis according to Xi’an Meeting Scoring System. The novel

index was compared with ten other indices using receiving operating characteristics

curves. Multivariate forward stepwise regression analysis revealed that alpha fetal

protein and activated partial thromboplastin time were significantly associated with

the Xi’an Meeting Scoring System, and were used to calculate the novel index.

Results: The novel index predicted significant fibrosis with an area under the curve of

0.822, exhibited a significantly higher area compared to the other ten indices in

estimation cohort, and was validated in the validation cohort.

Conclusions: The novel index can be used to predict significant fibrosis, and may

decrease the need of liver biopsy in patients with chronic hepatitis B infection.

Keywords: Chronic Hepatitis B virus; non-invasive index; liver biopsy; Xi’an

Meeting Scoring System

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Strengths and limitations of this study

▪The AA index had an AUROC curve of 0.822, 0.845, and 0.893 respectively for All

patients, patients with ALT< 2×ULN, and patients with HBeAg negative for

predicting significant fibrosis.

▪The AA index exhibited a significantly higher AUROC for the prediction of

significant fibrosis compared with some non-invasive Indices.

▪According to the cutoff values of 0.007 and 0.127, the presence of significant fibrosis

was predicted with high sensitivity (90.0%) and high specificity (88.4%).

▪Hepatic fibrosis was evaluated only using the Xi’an stages.

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Introduction

Chronic hepatitis B virus infection (CHB) poses serious public health problems, and

can progress to liver fibrosis, liver cirrhosis, and hepatocellular carcinoma 1

. The

degree of liver fibrosis is an important parameter in the determination of appropriate

antiviral treatment and prognosis for patients with CHB 2,3

.

The "reference standard" for evaluating the degree of liver fibrosis is liver biopsy 3.

However, liver biopsy has some recognized limitations, such as its invasive nature,

pain, sampling error, inter-observer variability, non-dynamic evaluation of liver

fibrosis, and even a small risk of life-threatening complications 2,3

. Due to these

limitations and risks, it is desirable to investigate novel non-invasive methods to

evaluate liver fibrosis 4. These methods include biological approaches based on serum

biomarkers of fibrosis and physical approaches based on the measurement of liver

stiffness using transient elastography 3. In recent years, some non-invasive indices

based on routine serum biomarkers have been demonstrated to have high diagnostic

accuracy and cost-effectiveness in identifying significant fibrosis and cirrhosis in

patients with CHB and/or hepatitis C 5,6

.

The aim of this study was to develop a novel predictive index based on routine

parameters for predicting significant fibrosis according the Xi’an Meeting Scoring

System 7 in patients with CHB. The diagnostic performance of the new index was

then compared with that of several indirect non-invasive indices, including AST to

ALT ratio (AAR) 8, AST to platelet ratio index (APRI)

9, Forns index

10, platelet count

(PLT), age, AST, and INR index (FIB-4) 11

, fibro-quotient (Fibro Q)12

, AST, platelet,

GGT, and AFP index (APGA) 13

, Platelet, Age, Phosphatase, AFP, and AST index

(PAPAS) 14

, Göteborg University Cirrhosis Index (GUCI) 15

, RDW to platelet ratio

(RPR) 16

, and Globulin-platelet model (GP) 17

.

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Subjects and Methods

Subjects

Seven hundred eighty-seven consecutive patients with CHB, including 476 male and

311 female aged 18–84 years (40.0±11.4 years) seen by the Department of Infectious

Disease, The First Affiliated Hospital, College of Medicine, Zhejiang University

(China) between July 2010 and December 2013 were considered for inclusion in the

study if they had received liver biopsy and a fasting serum sample collected on their

first admission. A diagnosis of CHB infection required a previous history of hepatitis

B or hepatitis B surface antigen (HBsAg) positivity for >6 months, and persistently

positive HBsAg and/or hepatitis B virus (HBV) DNA 18

. Exclusion criteria included

age under 18 years, concurrent infection with hepatitis C virus, hepatitis D virus,

hepatitis G virus, and/or human immunodeficiency virus, any autoimmune liver

disease, hepatocellular carcinoma, metabolic liver disease, alcoholic liver disease (20

grams per day for females, 30 grams per day for males), liver transplantation, and

decompensated cirrhosis. Two hundred eighty one patients were excluded because the

above reasons. Finally, 506 patients (337 male and 169 female, 37.45±9.60years)

were enrolled retrospectively. Written consent was obtained prior to liver biopsy, and

the trial was approved by the Ethics Committee of the First Affiliated Hospital,

College of Medicine, Zhejiang University, China. After receiving a liver biopsy (as

described below), the cohort was randomly divided into estimation (n=253) and

validation (n=253) cohorts for derivation of the prediction model for significant

fibrosis and subsequent validation.

Data Collection

Patient demographics and laboratory parameters were recorded on the first admission.

These included age, gender, HBsAg and HBeAg status, HBV DNA levels, alanine

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aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL),

triglyceride (TG), total cholesterol (Tch), total protein (TP), albumin (ALB), alkaline

phosphatase (ALP), gamma glutamyl transpeptidase (GGT), fasting plasma glucose

(FPG), alpha fetal protein (AFP), activated partial thromboplastin time (APTT),

D-dimers, fibrinogen, prothrombin time (PT), the international normalized ratio (INR),

hemoglobin (Hgb), red cell distribution width (RDW), White blood cell (WBC), Red

blood cell (RBC), and platelet count (PLT). The upper limit of normal (ULN) of ALT

was 40 U/L in men and 35 U/L in women. The real-time fluorescent polymerase chain

reaction system (7300; Applied Biosystems, Inc., Carlsbad, CA, USA) was used to

detect HBV DNA levels, with a lower limit of detection of 20 IU/ml. The ALT, AST,

TBIL, TG, Tch, TP, ALB, ALP, GGT, and FPG levels were measured on a Hitachi

7600 automatic biochemical analyzer (Hitachi Ltd., Tokyo, Japan) using Roche

Diagnostics GmbH reagents (Roche Diagnostics, Mannheim, Germany). HBsAg,

HBeAg, and AFP levels were measured on an Architect Ci8200 automated

immunoassay analyzer (Abbott Laboratories, Abbott Park, IL, USA) using Abbott

reagents. APTT, PT/INR, and Fbg were measured by a coagulation method using a

Sysmex CA7000 system (Sysmex, Kobe, Japan) and Siemens reagents (Siemens,

Marburg, Germany) 19

. INR was calculated from the PT according to the formula:

INR = (PT/mean normal PT) International Sensitivity Index (ISI)

20

. WBC, RBC, and PLT count

was assessed using a Sysmex XE-2100 automated hematology analyzer (Sysmex

Corp, Kobe, Japan) using Sysmex reagents.

Liver biopsy

Liver biopsy enables the reliable diagnosis of hepatic lesions, and is an important aid

to treatment and prognosis. For patients with HBV, liver biopsy is used for grading,

staging, exclusion of comorbidities, evaluation of the degree of fibrosis and/or

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inflammation and is an important factor in the choice of an antiviral treatment strategy

21. The indications for liver biopsy in the current study were: viral hepatitis,

autoimmune hepatitis, cholestatic liver diseases, storage diseases and metabolic

diseases, acute liver failure, liver transplantation, tumor, and hepatopathy of unknown

cause 21

. All patients underwent percutaneous liver biopsy guided by ultrasonography.

Liver biopsy was performed using 18G BioPince biopsy needles (InterV-MDTech,

Gainesville, Florida). A minimum of 1.5 cm of liver tissue with at least 5 portal tracts

was required for appropriate diagnosis. The specimens were fixed, paraffin-embedded,

and stained with hematoxylin and eosin (HE). The histologic staging of liver fibrosis

(S0 to S4) of liver biopsy specimens were analyzed according to the Xi’an Meeting

Scoring System 7 by a single pathologist who was unaware of patient characteristics.

Hepatic fibrosis was assessed using the Xi’an Meeting Scoring System as follows: S0,

no fibrosis; S1, fibrosis confined to portal tracts, periportal spaces, and perisinusoidal

spaces, or fibrous scar in the hepatic lobule; hepatic lobular structure integrity; S2,

bridging fibrosis, mainly caused by bridging necrosis; most of hepatic lobular

structure integrity; S3, a lot of fibrous septa are separated and/or involve the hepatic

lobule with distortion of the lobular structure, but without obvious cirrhosis; possible

with portal hypertension and esophageal varices; S4, early cirrhosis, liver parenchyma

is damaged extensively, with diffuse fiber hyperplasia, liver cells are in various

degrees of regeneration, and false flocculus is formed 7. S0 and S1 were considered to

indicate no fibrosis, while S2, S3, and S4 were considered to indicate significant liver

fibrosis.

Published Non-invasive Indices for Predicting Significant Liver Fibrosis

Some published non-invasive indices for significant fibrosis were calculated for each

patient based on previously described formulas, which have been summarized in

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Table 1. The indices included AAR 8, APRI

9, Forns index

10, FIB-4

11, FibroQ

12,

APGA index 13

, PAPAS index 14

, GUCI 15

, RPR 16

, and GP 17

.

Statistical Analysis

Statistical analysis was performed using SPSS16.0 (SPSS Inc., IL, USA) and

MedCalc v.9.3 software (MedCalc, Mariakerke, Belgium). Data are presented as

mean ± SD or median (range), and categorical data are presented as percentages. For

continuous variables, the differences between two groups were assessed with an

independent samples t-test or the Mann-Whitney U test, as appropriate. Categorical

variables were analyzed using the chi-square test. Spearman's rank correlation test

was used for correlation analysis. Multivariate forward stepwise regression analysis

was used to assess the association between the clinical/ laboratory parameters and the

Xi’an Meeting Scoring System fibrosis stages and to develop an index for predicting

significant fibrosis. A predictive index was constructed by modeling the values of

independent variables and their coefficient of regression. The receiver operating

characteristic (ROC) curve was used to assess the diagnostic performance of the novel

index. Differences between the diagnostic performance of the novel index and other

non-invasive indices were compared by using ROC curves, and the area under the

ROC curves (AUROC). Statistical significance was defined at two sides as P<0.05.

Results

Baseline Characteristics of the Patients

The enrolled CHB patients were divided into two cohorts: the estimation cohort and

the validation cohort. The demographic, laboratory and histological characteristics of

the estimation cohort, validation cohort, and entire cohort are shown in Table 2. There

were no significant differences in demographic and laboratory parameters between the

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estimation and validation cohorts, except that the ALT levels were significantly

higher in estimation cohort than those in the validation cohort.

Development of a Novel Index for Predicting Significant Fibrosis

The relevant variables of the estimation cohort based on the Xi’an Meeting Scoring

System (S0-1 and S2-4) are shown in Table 3. The RDW, GGT, ALP, INR, PT, APTT,

and AFP levels were significantly higher in S2-4 group than those in the S0-1 group,

while the PLT, TP, ALB, TG, and Tch levels were significantly lower. Other

demographic and laboratory parameters were not significantly different between the

S2-4 and the S0-1 groups. Among these variables, AFP (P=0.039) and APTT

(P<0.001) were identified as independent predictors for significant fibrosis based on

multivariate forward stepwise logistic regression analysis. The relationship between

the Xi’an Meeting Scoring System stages and AFP and APTT separately are

displayed in Figure 1. It was clear from Figure 1 that as the fibrosis progressed, the

AFP and APTT levels increased. The Spearman correlation coefficient for AFP and

APTT, and the Xi’an Meeting Scoring System stages (Xi’an stages) were 0.305

(P=0.001) and 0.289 (P<0.001), respectively. A novel index (denoted the AA index)

for predicting significant fibrosis was constructed and expressed by a formula

consisting of AFP and APTT:

Log index = - 9.164 + 0.114 × AFP + 0.236 × APTT.

The chi-square Hosmer Lemershore test was 4.215 (P=0.837), and the Spearman

correlation coefficient for the new index and Xi’an stages was 0.416 (P<0.001).

Diagnostic Performance of the AA Index

The diagnostic performance of the AA index for predicting significant fibrosis was

assessed using the ROC curve. It was found that the AA index had an AUROC curve

of 0.822 (standard error (SE) =0.055; 95% CI, 0.714–0.930; P<0.001) for predicting

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significant fibrosis in the estimation cohort.

The AA index was compared with ten other published non-invasive indices, as shown

in Table 1. The AA, FIB-4, APRI, Forns, Fibro Q, APGA, GUCI, RPR, and GP

indices were all correlated with significant fibrosis (r=0.465, 0.247, 0.229, 0.253,

0.182, 0.367, 0.261, 0.272, and 0.253, respectively [all P<0.05]) in the estimation

cohort, except AAR (r=0.039, P=0.566) and PAPAS (r=0.183, P=0.063). The

AUROC of the AA and other indices for predicting significant fibrosis in all patients,

patients with ALT levels lower than the 2-fold ULN (ALT< 2×ULN), and patients

with hepatitis B e antigen (HBeAg) negativity in the estimation cohort are shown in

Table 4. The ROC curves for AA and these ten indices for all patients, patients with

ALT< 2×ULN, and HBeAg negative patients are shown in Figures 2a, 2b and 2c,

respectively. The AA index exhibited a significantly higher AUROC for the

prediction of significant fibrosis compared to AAR (P=0.003) and PAPAS (P=0.033).

No significant differences were observed between the AUROCs of FIB-4 (P=0.141),

Forns (P=0.123), APGA (P=0.444), GP (P=0.101), APRI (P=0.177), Fibro Q

(P=0.078), GUCI (P=0.262), and RPR (P=0.262) indices and AA index in the

prediction of significant fibrosis.

Definition Cut-off Values

We selected low (0.007) and high (0.127) cutoff values that achieved an excess of

90% for both sensitivity and specificity in the diagnosis of significant fibrosis in the

estimation cohort. The sensitivity, specificity, positive predictive value (PPV),

negative predictive value (NPV), positive likelihood ratio (+LR), and negative

likelihood ratio (-LR) of AA were 91.3%, 50.0%, 29.0%, 96.3%, 1.83%, and 0.17%,

respectively using a low cutoff value of 0.007, 65.2%, 90.0%, 58.8%, 92.1%, 6.52%,

and 0.39% using a low cutoff value of 0.127, respectively.

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Validation Cohort

Applying the new AA index to the validation cohort, the AUROC for predicting

significant fibrosis was 0.773 (SE=0.053; 95% CI, 0.669–0.877; P<0.001). The

AUROC in the total cohort was 0.795 (SE=0.038; 95% CI, 0.720–0.871; P<0.001).

There were no significant differences in the AUCs between the estimation and

validation cohorts (Z=0.642, P=0.521). The AA, FIB-4, APRI, Forns, Fibro Q, APGA,

PAPAS, GUCI, RPR, and GP indices all correlated well with significant fibrosis

(r=0.358, 0.247, 0.271, 0.285, 0.168, 0.323, 0.211, 0.291, 0.274, and 0.256,

respectively [all P<0.05]) in validation cohort, except AAR (r=-0.023, P=0.736). The

AUROCs of AAR, FIB-4, APRI, Forns, Fibro Q, APGA, PAPAS, GUCI, RPR, and

GP indices were 0.412, 0.670, 0.707, 0.708, 0.592, 0.745, 0.672, 0.722, 0.689, and

0.716, respectively. The AA index exhibited a significantly higher AUC in the

prediction of significant fibrosis compared to AAR (P<0.001) and Fibro Q (P=0.008).

No significant difference was observed between the AUROCs of FIB-4 (P=0.079),

Forns (P=0.154), APGA (P=0.366), GP (P=0.209), APRI (P=0.181), PAPAS

(P=0.113), GUCI (P=0.249), and RPR (P=0.132) indices and the AA index in the

prediction of significant fibrosis. The AUROCs of AA, AAR, FIB-4, APRI, Forns,

Fibro Q, APGA, PAPAS, GUCI, RPR, and GP indices were 0.792, 0.187, 0.448,

0.556, 0.606, 0.390, 0.683,0.564, 0.633, 0.579, and 0.595, respectively, for HBeAg

negative patients, and were 0.762, 0.407, 0.665, 0.696, 0.677, 0.618, 0.698, 0.570,

0.692, 0.693, and 0.698, respectively, for patients with ALT< 2×ULN. According to

the cutoff values of 0.007 and 0.127, the presence of significant fibrosis was predicted

with high sensitivity (90.0%) and high specificity (88.4%) in the validation cohort.

Discussion

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Many studies have indicated that non-invasive indices containing simple serum

markers are valuable in the evaluation of liver fibrosis in patients with chronic liver

diseases 8-17

. Most of the non-invasive methods were developed in patients with

chronic hepatitis C (CHC) virus infections 9-12,15

. In recent years, these indices have

been used to evaluate CHB patients 13,14,16,17

. In CHB, evaluation of liver fibrosis is

crucial since HBV cannot be eradicated completely from the patient by treatment for

the persistence of covalently closed circular DNA in the nucleus of infected

hepatocytes 22,23

. European Association for the Study of the Liver (EASL) guidelines

have stated that non-invasive evaluation of fibrosis would be of interest in CHB 24

.

Although these methods cannot replace liver biopsy in chronic liver diseases, they

narrow the group who really need biopsy and provide an evaluation of liver damage

without biopsy 24

. Many clinicians have already used these tests for patients with

CHB in the same way as for CHC 21

.

The AA index was based on two routine serum parameters: AFP and APTT. The

Spearman correlation found AFP and APTT were significantly correlated with the

Xi’an stages. The addition of other variables in our study did not further improve the

accuracy of the index. AFP has been shown in previous studies to be associated with

significant fibrosis in CHB 13,14,17,25,26

. AFP is related to hepatic impairment and

chronic fibrosis and can aid in the differential diagnosis of hepatic diseases 25

. APTT

measures the intrinsic pathway of coagulation, and the APTT values are in accordance

with the fact that the degree of impairment of clotting factors is related to the severity

of liver damage 27

. There were some non-routine parameters used in some indices for

predicting fibrosis, including hyaluronic acid, a-2 macroglobulin, haptoglobin,

apolipoprotein A1, and Golgi protein 73, however, use of these parameters in

predictive models might hinder widespread use of these indices 22,25,28,29

.

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Liver biopsy also has its limitations. The AUROC in evaluating non-invasive indices

of fibrosis never reached a perfect value of 1.0, and barely reached 0.90 30,31

. Indices

such as FIB-4, GUCI, APRI, FibroQ, and Forns were developed based on patients

with CHC 9-12,15

, while APGA, PAPAS, RPR, and GP indices were developed based

on patients with CHB 13,14,16,17

. Even with using the same indices, a different study

population will lead to different results; only the APGA, RPR, and GP indices were

based on a Chinese population. The AUROC of FIB-4, GUCI, APRI, Fibro Q, and

Forns indices for CHB patients in our estimation cohort (0.691, 0.729, 0.707, 0.661,

0.687, respectively) and in our validation cohort (0.448, 0.633, 0.556, 0.390, 0.606,

respectively) were all lower than those of previous studies (0.765, 0.720, 0.880, 0.783,

0.860, respectively) with CHC patients. The one exception was that the AUROC of

GUCI in our estimation cohort was similar to that of previous studies 9-12,15

. A study

by Erdogan et al. 32

also evaluated the AUROC of FIB-4, GUCI, APRI, FibroQ, and

Forns indices in CHB patients and found that the AUROC (0.701, 0.670, 0.670, 0.588,

0.680, respectively) was lower than those in CHC patients 9-12,15

, which is similar to

the current results. The AUROC of APGA, PAPAS, RPR, and GP indices in CHB

patients in our estimation cohort (0.758, 0.630, 0.723, 0.671, respectively) and in our

validation cohort (0.683, 0.564, 0.579, 0.595, respectively) were all lower than those

of previous studies (0.850, 0.776, 0.825, 0.762, respectively) in CHB patients 13,14,16,17

.

The pathogenesis of liver fibrosis in CHB is different from that of CHC 33-36

. First, the

total amount of liver fibrosis reflected by the fibrosis area is significantly lower in

CHB than in CHC 34

. Second, hepatitis B patients tend to progress to cirrhosis with

larger nodules (macronodular cirrhosis) than hepatitis C patients 34

. Third, bridging

necrosis is the main pathogenic predictors of fibrosis progression in CHB. However,

CHC has a more progressive natural history with persistent inflammation associated

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with liver fibrosis and cirrhosis 33

. Fourth, hepatic stellate cells (HSCs) have a key role

in the development of fibrosis in chronic liver disease, and activated HSCs synthesize

and secrete chemokines, extracellular matrix proteins, and other factors, which all

contribute to remodel liver fibrosis 35,36

. HBV Dane particles and X and C protein may

induce HSC proliferation through the platelet-derived growth factor-B/PDGF

receptor-β signal pathway 36

. However, HCV core protein may directly activate

hepatic fibrogenesis a by a toll-like receptor 2-dependent manner 35

. Fifth, the liver

biopsies in these studies were assessed for fibrosis stages according to different

staging systems 9-12,14-16

, except that APGA was based on liver stiffness

measurements13

. The Scheuer 10

, Ishak 9,14,15

, and METAVIR 11,12,16

staging systems

are often used in clinical pathology, but the Xi’an stages 7

are widely used in China.

These staging systems all have advantages and disadvantages 37

. A comparison of the

four staging systems for chronic hepatitis fibrosis stages are listed in Table 5; the

selection of staging system depends on the comfort of the pathologist and the needs of

the involved clinicians 37,38

. Compared with FIB-4, GUCI, APRI, FibroQ, and Forns,

which were developed based on patients with CHC by ROC analyses 9-12,15

, the AA

index is more suitable for patients with CHB, FIB-4, GUCI, APRI, FibroQ; Forns is

more suitable for patients with CHC. The APGA, PAPAS, RPR, and GP indices were

developed based on patients with CHB 13,14,16,17

. However, the APGA was based on

liver stiffness measurements, and not on liver biopsy, and the study population of the

PAPAS index was not Chinese.

Defects in the design of diagnostic studies include problems with the study population

and bias. Despite the fact that ROC analysis is widely used in diagnostic test

evaluations, a proper design with a broad study population and avoidance of bias are

required to obtain valid and reliable conclusions in the assessment of diagnostic test

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evaluations 39

. A broad study population is required to evaluate the accuracy and

specificity. Bias can manifest in many different ways, including different diagnosis

procedures and a non-blind design. Bias can lead to a false low or high

sensitivity/specificity and, therefore, a false low or high AUC 39

. In the current study,

we included varying degrees of fibrosis (S0-S4). Although the

prevalence of severe fibrosis (S4) was low, hepatic fibrosis was assessed using the

Xi’an staging system, which led to a limitation in the comparison of AUCs. Low

(0.007, absence of fibrosis) and high (0.127, presence of fibrosis) cutoff values that

achieved high sensitivity and specificity for the diagnosis of significant fibrosis were

selected. For patients with an AA index < 0.007, significant fibrosis could be ruled out

and, for patients with an AA index > 0.127, significant fibrosis could be ruled in. A

clinician may choose not to perform a liver biopsy with AA index < 0.007 or > 0.127,

avoiding biopsy associated risks and costs, and may choose clinical follow-up instead.

International guidelines of Chronic Hepatitis B suggest that CHB patients with ALT >

2×ULN should be treated. However, recent reports have suggested that CHB patients

with persistently normal ALT levels may experience severe histologic liver damage

29,40. HBeAg negative CHB patients are usually asymptomatic for the first 30-40 years

41. The characteristics, therapy and prognosis of HBeAg negative CHB are different

from positive ones, spontaneous recovery of HBeAg negative CHB is rare, and the

long-term prognosis is poor with rapid evolution to cirrhosis and Hepatocellular

Carcinoma 41,42

. Therefore, the degree of hepatic fibrosis can guide treatment

decisions and monitor progress in patients with ALT < 2×ULN and that are HBeAg

negative 43,44

Only a few studies have addressed ALT < 2×ULN and HBeAg negative

CHB patients with hepatic fibrosis 43,44

. Our study found that the AUCs of the AA

index in our study were higher than other indices, and may be useful to predict

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significant fibrosis in CHB patients with ALT < 2×ULN who are HBeAg negative.

The present study has several limitations. First, hepatic fibrosis was evaluated only

using the Xi’an stages. Second, some liver tissue had only 5 portal tracts in the

liver biopsy. As this is less than the recommended 11 portal tracts 45

, this is a clear

limitation of the current study. Third, the AA index was developed for patients with

CHB, and the prevalence of severe fibrosis in the Chinese CHB population was low;

thus, the AA index may not be applicable to patients with CHC or other causes of

hepatic fibrosis and other ethnic populations. Fourth, some non-routine parameters

used in some indices for predicting fibrosis, including hyaluronic acid, a-2

macroglobulin, haptoglobin, apolipoprotein A1, and Golgi protein 73, were not

available for use in the current study 22,25,28,29

. Fibrotest (Fibrosure in the USA) is a

non-invasive index for predicting significant fibrosis in patients with CHC or CHB.

The laboratory parameters for calculating Fibrotest include α2-macroglobulin,

apolipoprotein A1, haptoglobin, γ-GT, and total bilirubin obtained on the same day as

liver biopsy. However, α2-macroglobulin and haptoglobin were not routinely

available in our hospital. Therefore, it was not possible to perform Fibrotest, and

compare the AUROC of the AA index for predicting significant fibrosis with that of

Fibrotest. The AUROC of the Fibrotest index for predicting significant fibrosis was

reported by Leroy et al. 22

to be 0.77 (95%CI:0.71-0.83), which was smaller than that

of our study. In contrast, Kim et al. 46

found the AUROC was 0.903 (0.838–0.968),

which was larger than that of our study. Morever, FibroScan can calculate liver

elasticity using a low frequency elastic wave transmitted through the liver, and has

been considered the most accurate non-invasive model to assess liver fibrosis among

patients with chronic liver diseases due to various etiologies 47

. While Fibroscan

certainly has value as a non-invasive measure of liver fibrosis, the instrument was not

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available in our institution. Therefore, we were unable to compare results. Therefore,

we were unable to compare our index with these indices. Fifth, although the AA index

can be accurately used to predict significant fibrosis, it cannot truly replace

histological fibrosis staging. The AA index is likely to be most useful as a supplement

to liver biopsy. Sixth, larger sample sizes as well as multi-center and multi-ethnic

studies will be necessary to validate the clinical application of the AA index. These

non-invasive indices were used to diagnose significant fibrosis (F2) and/or cirrhosis

(F4) using various AUROC and cutoff values. However, if only a small number of

patients with advanced fibrosis were included in the studies, the accuracy of the

non-invasive indices for higher stages of fibrosis (F3 and F4) can influence the

validity of the serum markers investigated 5,15,16

. A small number of patients with

advanced fibrosis is a limitation of the current study. In conclusion, we found that an

index (AA) containing AFP and APTT can accurately predict significant fibrosis in

CHB patients with ALT< 2×ULN that are HBeAg negative. The AA index is more

accurate than the AAR, APRI, Forns, FIB-4, FibroQ, APGA, PAPAS, GUCI, RPR,

and GP indices. The parameters used in the AA index are widely available, and can be

used as non-invasive index to predict significant HBV-related fibrosis. Use of the AA

index may decrease the need of liver biopsy in patients with CHB.

Contributors YZ and LF contributed to the study conception/design. LF, KS, JZ, and

GF contributed to the data collection. YZ and LF conducted the data analysis,

critically revised the article and reviewed the draft of the article.

Funding This work was supported by grants from the Department of Education

Foundation of Zhejiang Province, China (Nos. Y201017380 and Y201330146). The

funders had no role in study design, data collection and analysis, decision to publish,

or preparation of the manuscript.

Competing interests None.

Patient consent Obtained.

Ethics approval This study was approved by the ethics committee of the First

Affiliated Hospital of Zhejiang University School of Medicine, China.

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Figure Legends

Figure 1. Box-plots displaying the values of alpha fetal protein and activated partial

thromboplastin time based on Xi’an Meeting Scoring System.

The top and bottom of each box represents the 25th and 75th percentile interval. The

line through the box in the median and the error bars are the 5th and 95th percentile

intervals. AFP, alpha fetal protein; APTT, activated partial thromboplastin time.

Figure 2. Receiver operating characteristic curves for prediction of significant fibrosis

in the estimation cohort using the new index in comparison with several other

calculated indices.

a: all patients, b: patients with ALT< 2×ULN, c: patients HBeAg negative. AA, the

new index consisted of alpha fetal protein and activated partial thromboplastin time;

ROC, receiver operating characteristic; ALT, alanine aminotransferase; ULN, upper

limit of normal; HBeAg, hepatitis B e antigen.

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Table 1. Summary of Non-invasive Indices for Predicting Significant Liver Fibrosis

Index Formula

AAR [8] AST/ALT

FIB-4 [11] (Age×AST)/ (PLT (109/L)×ALT

1/2]

Forns index[10] 7.811–3.131×LN(PLT)+0.781×LN(GGT)+3.467×LN(age)-0.014×Tch

APRI [9] [(AST/ULN)/PLT (109/L)] ×100

Fibro Q [12] (10×age×AST×PT INR)/(PLT×ALT)

APGA [13] Log(index)=1.44+0.1490×log(GGT)+0.3308×log(AST)-0.5846×log(PL

T)+0.1148×log (AFP+1)

PAPAS [14] Log(index+1)=0.0255+0.0031×age+0.1483×log(ALP)-0.004×log(AST)

+0.0908×log(AFP+1)-0.028×log (PLT)

GUCI [15] [(AST/ULN) ×prothrombin-INR] ×100/ PLT

RPR [16] RDW/PLT

GP [17] GLOB×100/PLT

ALT, alanine aminotransferase; AST, aspartate aminotransferase; PLT, platelet count; GGT,

gamma glutamyl transpeptidase; Tch, total cholesterol; INR, the international normalized ratio;

AFP, alpha fetal protein; ALP, alkaline phosphatase; ULN, upper limit of normal; RDW, red cell

distribution width; GLOB, globulin. Units of AST, ALT, GGT, and ALP: U/L; Units of age, Tch,

AFP, GLOB, PLT, and RDW: years, mmol/L, ng/mL, g/dL, 109/L, and %, respectively.

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Table 2. Baseline Characteristics of the Patients in the Estimation and Validation

Cohorts

Variable All patients

(n=506)

Estimation cohort

(n=253)

Validation cohort

(n=253)

P value

Age (years) 37.45±9.6 37.51±10.0 37.39±9.1 0.886

Male, n (%) 337 (66.6) 166 (65.6) 171 (68.0) 0.637#

BMI (kg/m2) 22.5±3.51 22.5±3.60 22.5±3.49 0.957

WBC (109/L) 5.51±1.60 5.47±1.61 5.55±1.49 0.558

RBC (1012

/L) 4.81±0.51 4.80±0.52 4.81±0.50 0.817

Hgb (g/L) 148±18 147±19 148±17 0.501

RDW (%) 13.0±1.0 13.1±1.2 13.0±0.8 0.092

PLT (109/L) 178±53 181±55 175±51 0.191

TP (g/L) 72.2±6.7 72.5±6.7 71.8±6.7 0.268

ALB (g/L) 45.4±5.2 45.2±5.2 45.6±5.1 0.482

TBIL (µmol/L) 13 (3-436) 13 (4-436) 14 (3-280) 0.748*

AST (U/L) 30 (14-479) 31 (14-479) 30 (14-358) 0.478*

ALT (U/L) 40 (8-631) 45 (8-569) 38 (10-631) 0.019*

GGT (U/L) 25 (6-586) 25 (7-586) 25 (6-456) 0.653*

ALP (U/L) 68 (22-292) 68 (29-184) 69 (22-292) 0.871*

FPG (mmol/L) 4.72±1.41 4.64±0.90 4.80±1.80 0.268

TG (mmol/L) 0.97 (0.38-9.41) 0.99 (0.39-9.41) 0.94 (0.38-7.67) 0.780*

Tch (mmol/L) 4.47±1.04 4.46±1.03 4.47±1.05 0.917

INR 1.03±0.13 1.03±0.17 1.03±0.08 0.666

PT (s) 11.8±1.6 11.8±2.1 11.8±0.9 0.752

APTT (s) 28.2±4.8 28.2±5.4 28.2±4.2 0.952

Fbg (g/L) 2.31±0.61 2.31±0.64 2.31±0.57 0.918

AFP (ng/mL) 3.4 (0.8-644.3) 3.4 (0.8-644.3) 3.4 (1.1-259.8) 0.811*

HBeAg status, n (%) 285 (56.3) 142 (56.1) 143 (56.5) 0.929#

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HBV DNA detectable (%) 351 (69.4) 182 (71.9) 169 (66.8) 0.210#

anti-HBV therapy (%) 47 (9.3) 23 (9.1) 24 (9.5) 0.878#

Fibrosis stage, n (%) 0.933#

S0 251 (49.6) 123 (48.6) 128 (50.6)

S1 167 (33.0) 84 (33.2) 83 (32.8)

S2 48 (9.5) 26 (10.2) 22 (8.7)

S3 22 (4.3) 10 (4.0) 12 (4.7)

S4 18 (3.6) 10 (4.0) 8 (3.2)

WBC, white blood cell; RBC, red blood cell; Hgb, hemoglobin; RDW, red cell distribution width;

PLT, platelet count; TP, total protein; ALB, albumin; TBIL, total bilirubin; AST, aspartate

aminotransferase; ALT, alanine aminotransferase; GGT, gamma glutamyl transpeptidase; ALP,

alkaline phosphatase; FPG, fasting plasma glucose; TG, triglyceride; Tch, total cholesterol; INR,

the international normalized ratio; PT, prothrombin time; APTT, activated partial thromboplastin

time; AFP, alpha fetal protein. P values are comparisons between the estimation cohort and

validation cohort using independent samples t-test, except: #: using Chi-squared test, *: using

Mann-Whitney U test.

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Table 3. Variables Associated with Significant Fibrosis in the Estimation Cohort

Variable S0-1 (n=207) S2-4 (n=46) P value

Age (years) 37.2±9.8 38.9±10.8 0.288

Male, n (%) 132 (63.8) 34 (73.9) 0.190#

BMI (kg/m2) 22.6±3.26 22.2±4.28 0.316

WBC (109/L) 5.46±1.49 5.53±2.10 0.773

RBC (1012

/L) 4.81±0.50 4.76±0.59 0.536

Hgb (g/L) 148±19 144±19 0.238

RDW (%) 13.0±1.1 13.5±1.5 0.021

PLT (109/L) 189±53 150±55 <0.001

TP (g/L) 73.1±6.3 70.0±7.7 0.006

ALB (g/L) 45.9±4.8 42.5±6.2 <0.001

TBIL (µmol/L) 13 (4-69) 14 (4-436) 0.857*

AST (U/L) 30 (14-429) 31 (16-479) 0.458*

ALT (U/L) 45 (8-379) 40 (8-569) 0.759*

GGT (U/L) 24 (7-175) 37 (10-586) <0.001*

ALP (U/L) 68 (29-169) 75 (36-184) 0.044*

FPG (mmol/L) 4.60±0.52 4.83±1.77 0.159

TG (mmol/L) 1.03 (0.42-9.41) 0.88 (0.39-2.88) 0.032*

Tch (mmol/L) 4.62±0.95 3.79±1.10 <0.001

INR 1.00±0.07 1.12±0.4 <0.001

PT (s) 11.5±0.8 12.9±4.4 <0.001

APTT (s) 27.4±4.3 31.7±8.1 <0.001

Fbg (g/L) 2.34±0.58 2.17±0.82 0.103

AFP (ng/mL) 3.2 (1.0-20.7) 4.8 (0.8-644.3) 0.034*

HBeAg status, n (%) 117 (56.5) 25 (54.3) 0.788#

WBC, white blood cell; RBC, red blood cell; Hgb, hemoglobin; RDW, red cell distribution width;

PLT, platelet count; TP, total protein; ALB, albumin; TBIL, total bilirubin; AST, aspartate

aminotransferase; ALT, alanine aminotransferase; GGT, gamma glutamyl transpeptidase; ALP,

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alkaline phosphatase; FPG, fasting plasma glucose; TG, triglyceride; Tch, total cholesterol; INR,

the international normalized ratio; PT, prothrombin time; APTT, activated partial thromboplastin

time; AFP, alpha fetal protein. P values are of comparisons between S0-1 and S2-4 using

independent samples t-test, except: #: using Chi-squared test, *: using Mann-Whitney U test.

Table 4. Area under curve of AA and Other Non-invasive Indices in the Estimation Cohort

Index All patients Patients with ALT< 2×ULN Patients with HBeAg negative

AUC (95% CI) SE AUC (95% CI) SE AUC (95% CI) SE

AA 0.822 (0.714-0.930)* 0.055 0.845

(0.718-0.971)*

0.064 0.893 (0.779-0.999) * 0.058

AAR 0.554 (0.411-0.697) 0.073 0.566 (0.406-0.727) 0.082 0.638 (0.447-0.829) 0.098

FIB-4 0.691 (0.555-0.827)* 0.070 0.643 (0.470-0.815) 0.088 0.614 (0.398-0.829) 0.110

Forns 0.687 (0.554-0.820)* 0.068 0.690

(0.534-0.847)*

0.080 0.636 (0.417-0.856) 0.112

APRI 0.707 (0.579-0.834)* 0.065 0.690

(0.546-0.835)*

0.074 0.685 (0.484-0.886) 0.103

Fibro Q 0.661 (0.517-0.804)* 0.073 0.667

(0.494-0.839)*

0.088 0.675 (0.469-0.882) 0.105

APGA 0.758 (0.635-0.881)* 0.063 0.748

(0.605-0.892)*

0.073 0.695 (0.502-0.887) 0.098

PAPAS 0.630 (0.491-0.769) 0.071 0.621 (0.462-0.781) 0.081 0.565 (0.344-0.786) 0.113

GUCI 0.729 (0.607-0.851)* 0.062 0.719

(0.583-0.855)*

0.069 0.714 (0.521-0.908) * 0.099

RPR 0.723 (0.589-0.858)* 0.069 0.703

(0.531-0.875)*

0.088 0.682 (0.466-0.897) 0.110

GP 0.671 (0.526-0.817)* 0.074 0.710

(0.546-0.874)*

0.084 0.685 (0.476-0.894) 0.107

AUC: area under curve, 95% CI: 95% confidence interval, SE: standard error, * : P < 0.05.

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Table 5. Four stage systems for chronic hepatitis fibrosis stages Fibrosis

stage

Scheuer stages Ishak stages Metavir stages Xi’an stages

0 No fibrosis No fibrosis No fibrosis No fibrosis

1 Enlarged, fibrotic

portal tracts

Portal fibrosis,

with or without

short fibrous septa

Portal fibrosis

without septa

fibrosis confined to

portal tracts, periportal

spaces, and

perisinusoidal spaces

2 Periportal or

portal-portal

septa, but intact

architecture

Fibrous septa portal fibrosis

with rare septa

bridging fibrosis, with

fibrous septa

3 Fibrosis with

architectural

distortion, but no

obvious cirrhosis

Transition to

cirrhosis

portal fibrosis

with many

septa

a lot of fibrous septa

separate without

obvious cirrhosis

4 Probable or

definite cirrhosis

Probable or

definite cirrhosis

cirrhosis early cirrhosis

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27x11mm (300 x 300 DPI)

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37x12mm (300 x 300 DPI)

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STARD checklist for reporting of studies of diagnostic accuracy

(version January 2003)

Section and Topic Item

#

On page #

TITLE/ABSTRACT/

KEYWORDS

1 Identify the article as a study of diagnostic accuracy (recommend MeSH

heading 'sensitivity and specificity').

1-2

INTRODUCTION 2 State the research questions or study aims, such as estimating diagnostic

accuracy or comparing accuracy between tests or across participant

groups.

4

METHODS

Participants 3 The study population: The inclusion and exclusion criteria, setting and

locations where data were collected.

5

4 Participant recruitment: Was recruitment based on presenting symptoms,

results from previous tests, or the fact that the participants had received

the index tests or the reference standard?

5

5 Participant sampling: Was the study population a consecutive series of

participants defined by the selection criteria in item 3 and 4? If not,

specify how participants were further selected.

5

6 Data collection: Was data collection planned before the index test and

reference standard were performed (prospective study) or after

(retrospective study)?

5-6

Test methods 7 The reference standard and its rationale. 6-7

8 Technical specifications of material and methods involved including how

and when measurements were taken, and/or cite references for index

tests and reference standard.

9 Definition of and rationale for the units, cut-offs and/or categories of the

results of the index tests and the reference standard.

5-8

10 The number, training and expertise of the persons executing and reading

the index tests and the reference standard.

5

11 Whether or not the readers of the index tests and reference standard

were blind (masked) to the results of the other test and describe any

other clinical information available to the readers.

7

Statistical methods 12 Methods for calculating or comparing measures of diagnostic accuracy,

and the statistical methods used to quantify uncertainty (e.g. 95%

confidence intervals).

8

13 Methods for calculating test reproducibility, if done. no

RESULTS

Participants 14 When study was performed, including beginning and end dates of

recruitment.

8-9,table 2

15 Clinical and demographic characteristics of the study population (at least

information on age, gender, spectrum of presenting symptoms).

table 2

16 The number of participants satisfying the criteria for inclusion who did or

did not undergo the index tests and/or the reference standard; describe

why participants failed to undergo either test (a flow diagram is strongly

recommended).

55

Test results 17 Time-interval between the index tests and the reference standard, and

any treatment administered in between.

8-9,table 2

18 Distribution of severity of disease (define criteria) in those with the target

condition; other diagnoses in participants without the target condition.

9

19 A cross tabulation of the results of the index tests (including

indeterminate and missing results) by the results of the reference

standard; for continuous results, the distribution of the test results by the

results of the reference standard.

9-10, table

2

20 Any adverse events from performing the index tests or the reference

standard.

no

Estimates 21 Estimates of diagnostic accuracy and measures of statistical uncertainty

(e.g. 95% confidence intervals).

9-11

22 How indeterminate results, missing data and outliers of the index tests

were handled.

no

23 Estimates of variability of diagnostic accuracy between subgroups of

participants, readers or centers, if done.

10

24 Estimates of test reproducibility, if done. no

DISCUSSION 25 Discuss the clinical applicability of the study findings. 12-17

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A novel noninvasive index using AFP and APTT is associated with liver fibrosis in patients with chronic hepatitis B

infection: A retrospective cohort study

Journal: BMJ Open

Manuscript ID: bmjopen-2015-008032.R1

Article Type: Research

Date Submitted by the Author: 18-Aug-2015

Complete List of Authors: Feng, Limin; The First Affiliated Hospital, College of Medicine, Zhejiang University, Laboratory Medicine Sun, Ke; The First Affiliated Hospital, College of Medicine, Zhejiang

University, Pathology Zhang, Jie; The First Affiliated Hospital, College of Medicine, Zhejiang University, Laboratory Medicine Feng, Guofang; Women’s Hospital, School of Medicine, Zhejiang University, Reproductive Endocrinology Zhao, Ying; The First Affiliated Hospital, College of Medicine, Zhejiang University, Laboratory Medicine

<b>Primary Subject Heading</b>:

Diagnostics

Secondary Subject Heading: Gastroenterology and hepatology, Infectious diseases

Keywords: HISTOPATHOLOGY, Hepatology < INTERNAL MEDICINE, INFECTIOUS DISEASES


BMJ Open on S

eptember 30, 2020 by guest. P

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/B

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1

A novel noninvasive index using AFP and APTT is associated with liver fibrosis 1

in patients with chronic hepatitis B infection: A retrospective cohort study 2

Limin Feng1, Ke Sun

2, Jie Zhang

1, Guofang Feng

3, Ying Zhao

1* 3

4

5 1Department of Laboratory Medicine, The First Affiliated Hospital, College of 6

Medicine, Zhejiang University, Hangzhou 310003, China 7

2Department of Pathology, The First Affiliated Hospital, College of Medicine, 8

Zhejiang University, Hangzhou 310003, China 9

3Department of Reproductive Endocrinology, Women’s Hospital, School of Medicine, 10

Zhejiang University, Hangzhou 310006, China 11

12

*Corresponding author: Ying Zhao 13

Department of Laboratory Medicine, The First Affiliated Hospital, College of 14

Medicine, Zhejiang University, Qingchun Road 79#, Hangzhou 310003, China 15

E-mail: [email protected] 16

Tel.: +86-571-87236380 17

Fax: +86-571-87236383 18

19

Short title: a novel non-invasive index and liver fibrosis 20

21

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Abstract 22

Objective: A liver biopsy is the “reference standard” for diagnosing and staging liver 23

fibrosis but with many disadvantages. Therefore, developing non-invasive index for 24

predicting fibrosis is very valuable. We developed and validated a novel non-invasive 25

index for predicting significant fibrosis in patients with chronic hepatitis B infection. 26

Design: A retrospective cohort study. 27

Setting: Chronic Hepatitis B virus infected patients were recruited in the Department 28

of Infectious Disease in the First Affiliated Hospital of Zhejiang University. 29

Participants: A total of 506 patients were enrolled, and patients were randomly 30

divided into estimation (n=253) and validation (n=253) cohorts. 31

Primary and secondary outcome measures: Chronic Hepatitis B virus infected 32

patients were studied retrospectively using routine parameters. A novel index was 33

developed from an estimation cohort and validated in another cohort. Liver histology 34

was assessed for fibrosis according to Xi’an Meeting Scoring System. The novel 35

index using AFP and APTT (denoted AA index) was compared with ten other indices 36

using receiving operating characteristics curves. Multivariate forward stepwise 37

regression analysis revealed that alpha fetal protein and activated partial 38

thromboplastin time were significantly associated with the Xi’an Meeting Scoring 39

System, and were used to calculate the AA index (Log index = - 9.164 + 0.114 × AFP 40

+ 0.236 × APTT). 41

Results: The AA index predicted significant fibrosis with an area under the curve of 42

0.822, exhibited a significantly higher area compared to the other ten indices in 43

estimation cohort, and was validated in the validation cohort. 44

Conclusions: The AA index can be used to predict significant fibrosis, and may 45

decrease the need of liver biopsy in patients with chronic hepatitis B infection. 46

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Keywords: Chronic Hepatitis B virus; non-invasive index; liver biopsy; Xi’an 47

Meeting Scoring System 48

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Strengths and limitations of this study 49

▪The AA index had an AUROC curve of 0.822, 0.845, and 0.893 respectively for all 50

patients, patients with ALT <2×ULN, and patients with HBeAg negative for 51

predicting significant fibrosis. 52

▪The AA index exhibited a significantly higher AUROC for the prediction of 53

significant fibrosis compared with some non-invasive Indices. 54

▪According to the cutoff values of 0.007 and 0.127, the presence of significant fibrosis 55

was predicted with high sensitivity (90.5%) and high specificity (88.2%). 56

▪Hepatic fibrosis was evaluated only using the Xi’an stages. 57

58

59

60 61

62

63

64

65

66

67

68

69

70

71

72

73

74

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Introduction 75

Chronic hepatitis B virus infection (CHB) poses serious public health problems, and 76

can progress to liver fibrosis, liver cirrhosis, and hepatocellular carcinoma 1

. The 77

degree of liver fibrosis is an important parameter in the determination of appropriate 78

antiviral treatment and prognosis for patients with CHB 2,3

. 79

The "reference standard" for evaluating the degree of liver fibrosis is liver biopsy 3. 80

However, liver biopsy has some recognized limitations, such as its invasive nature, 81

pain, sampling error, inter-observer variability, non-dynamic evaluation of liver 82

fibrosis, and even a small risk of life-threatening complications 2,3

. Due to these 83

limitations and risks, it is desirable to investigate novel non-invasive methods to 84

evaluate liver fibrosis 4. These methods include biological approaches based on serum 85

biomarkers of fibrosis and physical approaches based on the measurement of liver 86

stiffness using transient elastography 3. In recent years, some non-invasive indices 87

based on routine serum biomarkers have been demonstrated to have high diagnostic 88

accuracy and cost-effectiveness in identifying significant fibrosis and cirrhosis in 89

patients with CHB and/or hepatitis C 5,6

. 90

The aim of this study was to develop a novel predictive index based on routine 91

parameters for predicting significant fibrosis according the Xi’an Meeting Scoring 92

System 7 in patients with CHB. The diagnostic performance of the new index was 93

then compared with that of several indirect non-invasive indices, including AST to 94

ALT ratio (AAR) 8, AST to platelet ratio index (APRI)

9, Forns index

10, platelet count 95

(PLT), age, AST, and INR index (FIB-4) 11

, fibro-quotient (Fibro Q)12

, AST, platelet, 96

GGT, and AFP index (APGA) 13

, Platelet, Age, Phosphatase, AFP, and AST index 97

(PAPAS) 14

, Göteborg University Cirrhosis Index (GUCI) 15

, RDW to platelet ratio 98

(RPR) 16

, and Globulin-platelet model (GP) 17

. 99

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Subjects and Methods 100

Subjects 101

Seven hundred eighty-seven consecutive urban and rural patients with CHB, including 102

476 male and 311 female aged 18–84 years (40.0±11.4 years) seen by the hepatology 103

specialty Department of Infectious Disease, The First Affiliated Hospital, College of 104

Medicine, Zhejiang University (China) between July 2010 and December 2013 were 105

considered for inclusion in the study if they had received liver biopsy and a fasting 106

serum sample collected on their first admission. A diagnosis of CHB infection 107

required a previous history of hepatitis B or hepatitis B surface antigen (HBsAg) 108

positivity for >6 months, and persistently positive HBsAg and/or hepatitis B virus 109

(HBV) DNA 18

. Exclusion criteria included age under 18 years, concurrent infection 110

with hepatitis C virus, hepatitis D virus, hepatitis G virus, and/or human 111

immunodeficiency virus, any autoimmune liver disease, hepatocellular carcinoma, 112

metabolic liver disease, alcoholic liver disease (20 grams per day for females, 30 113

grams per day for males), liver transplantation, and decompensated cirrhosis. Two 114

hundred eighty one patients were excluded because the above reasons. Finally, 506 115

patients (337 male and 169 female, 37.45±9.60years) were enrolled retrospectively. 116

Written consent was obtained prior to liver biopsy, and the trial was approved by the 117

Ethics Committee of the First Affiliated Hospital, College of Medicine, Zhejiang 118

University, China. After receiving a liver biopsy (as described below), the cohort was 119

randomly divided into estimation (n=253) and validation (n=253) cohorts for 120

derivation of the prediction model for significant fibrosis and subsequent validation 121

(Figure 1). 122

Data Collection 123

Patient demographics and laboratory parameters were recorded on the first admission. 124

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These included age, gender, HBsAg and HBeAg status, HBV DNA levels, alanine 125

aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), 126

triglyceride (TG), total cholesterol (Tch), total protein (TP), albumin (ALB), alkaline 127

phosphatase (ALP), gamma glutamyl transpeptidase (GGT), fasting plasma glucose 128

(FPG), alpha fetal protein (AFP), activated partial thromboplastin time (APTT), 129

D-dimers, fibrinogen, prothrombin time (PT), hemoglobin (Hgb), red cell distribution 130

width (RDW), White blood cell (WBC), Red blood cell (RBC), and platelet count 131

(PLT). The upper limit of normal (ULN) of ALT was 40 U/L in men and 35 U/L in 132

women. The real-time fluorescent polymerase chain reaction system (7300; Applied 133

Biosystems, Inc., Carlsbad, CA, USA) was used to detect HBV DNA levels, with a 134

lower limit of detection of 20 IU/ml. The ALT, AST, TBIL, TG, Tch, TP, ALB, ALP, 135

GGT, and FPG levels were measured on a Hitachi 7600 automatic biochemical 136

analyzer (Hitachi Ltd., Tokyo, Japan) using Roche Diagnostics GmbH reagents 137

(Roche Diagnostics, Mannheim, Germany). HBsAg, HBeAg, and AFP levels were 138

measured on an Architect Ci8200 automated immunoassay analyzer (Abbott 139

Laboratories, Abbott Park, IL, USA) using Abbott reagents. APTT, PT, and Fbg were 140

measured by a coagulation method using a Sysmex CA7000 system (Sysmex, Kobe, 141

Japan) and Siemens reagents (Siemens, Marburg, Germany) 19,20

. WBC, RBC, and 142

PLT count was assessed using a Sysmex XE-2100 automated hematology analyzer 143

(Sysmex Corp, Kobe, Japan) using Sysmex reagents. 144

Liver biopsy 145

Liver biopsy enables the reliable diagnosis of hepatic lesions, and is an important aid 146

to treatment and prognosis. For patients with HBV, liver biopsy is used for grading, 147

staging, exclusion of comorbidities, evaluation of the degree of fibrosis and/or 148

inflammation and is an important factor in the choice of an antiviral treatment strategy 149

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21. The indications for liver biopsy in the current study were: viral hepatitis, 150

autoimmune hepatitis, cholestatic liver diseases, storage diseases and metabolic 151

diseases, acute liver failure, liver transplantation, tumor, and hepatopathy of unknown 152

cause 21

. All patients underwent percutaneous liver biopsy guided by ultrasonography. 153

Liver biopsy was performed using 18G BioPince biopsy needles (InterV-MDTech, 154

Gainesville, Florida). A minimum of 1.5 cm of liver tissue with at least 5 portal tracts 155

was required for appropriate diagnosis. The specimens were fixed, paraffin-embedded, 156

and stained with hematoxylin and eosin (HE). The histologic staging of liver fibrosis 157

(S0 to S4) of liver biopsy specimens were analyzed according to the Xi’an Meeting 158

Scoring System 7 by a single pathologist who was unaware of patient characteristics. 159

Hepatic fibrosis was assessed using the Xi’an Meeting Scoring System as follows: S0, 160

no fibrosis; S1, fibrosis confined to portal tracts, periportal spaces, and perisinusoidal 161

spaces, or fibrous scar in the hepatic lobule; hepatic lobular structure integrity; S2, 162

bridging fibrosis, mainly caused by bridging necrosis; most of hepatic lobular 163

structure integrity; S3, a lot of fibrous septa are separated and/or involve the hepatic 164

lobule with distortion of the lobular structure, but without obvious cirrhosis; possible 165

with portal hypertension and esophageal varices; S4, early cirrhosis, liver parenchyma 166

is damaged extensively, with diffuse fiber hyperplasia, liver cells are in various 167

degrees of regeneration, and false flocculus is formed 7. S0 and S1 were considered to 168

indicate no fibrosis, while S2, S3, and S4 were considered to indicate significant liver 169

fibrosis. 170

Published Non-invasive Indices for Predicting Significant Liver Fibrosis 171

Some published non-invasive indices for significant fibrosis were calculated for each 172

patient based on previously described formulas, which have been summarized in 173

Table 1. The indices included AAR 8, APRI

9, Forns index

10, FIB-4

11, FibroQ

12, 174

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APGA index 13

, PAPAS index 14

, GUCI 15

, RPR 16

, and GP 17

. 175

Statistical Analysis 176

Statistical analysis was performed using SPSS16.0 (SPSS Inc., IL, USA) and 177

MedCalc v.9.3 software (MedCalc, Mariakerke, Belgium). Data are presented as 178

mean ± SD or median (range), and categorical data are presented as percentages. For 179

continuous variables, the differences between two groups were assessed with an 180

independent samples t-test or the Mann-Whitney U test, as appropriate. Categorical 181

variables were analyzed using the chi-square test. Spearman's rank correlation test 182

was used for correlation analysis. Multivariate forward stepwise regression analysis 183

was used to assess the association between the clinical/ laboratory parameters and the 184

Xi’an Meeting Scoring System fibrosis stages and to develop an index for predicting 185

significant fibrosis. A predictive index was constructed by modeling the values of 186

independent variables and their coefficient of regression. The receiver operating 187

characteristic (ROC) curve was used to assess the diagnostic performance of the novel 188

index. Differences between the diagnostic performance of the novel index and other 189

non-invasive indices were compared by using ROC curves, and the area under the 190

ROC curves (AUROC). Statistical significance was defined at two sides as P<0.05. 191

192

Results 193

Baseline Characteristics of the Patients 194

The enrolled CHB patients were divided into two cohorts: the estimation cohort and 195

the validation cohort. The demographic, laboratory and histological characteristics of 196

the estimation cohort, validation cohort, and entire cohort are shown in Table 2. There 197

were no significant differences in demographic and laboratory parameters between the 198

estimation and validation cohorts, except that the ALT levels were significantly 199

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10

higher in estimation cohort than those in the validation cohort. 200

Development of a Novel Index for Predicting Significant Fibrosis 201

The relevant variables of the estimation cohort based on the Xi’an Meeting Scoring 202

System (S0-1 and S2-4) are shown in Table 3. The RDW, GGT, ALP, PT, APTT, and 203

AFP levels were significantly higher in S2-4 group than those in the S0-1 group, 204

while the PLT, TP, ALB, TG, and Tch levels were significantly lower. Other 205

demographic and laboratory parameters were not significantly different between the 206

S2-4 and the S0-1 groups. Among these variables, AFP (P=0.039) and APTT 207

(P<0.001) were identified as independent predictors for significant fibrosis based on 208

multivariate forward stepwise logistic regression analysis. The relationship between 209

the Xi’an Meeting Scoring System stages and AFP and APTT separately are 210

displayed in Figure 2. It was clear from Figure 2 that as the fibrosis progressed, the 211

AFP and APTT levels increased. The Spearman correlation coefficient for AFP and 212

APTT, and the Xi’an Meeting Scoring System stages (Xi’an stages) were 0.305 213

(P=0.001) and 0.289 (P<0.001), respectively. A novel index (denoted the AA index) 214

for predicting significant fibrosis was constructed and expressed by a formula 215

consisting of AFP and APTT: 216

Log index = - 9.164 + 0.114 × AFP + 0.236 × APTT. 217

The chi-square Hosmer Lemershore test was 4.215 (P=0.837), and the Spearman 218

correlation coefficient for the new index and Xi’an stages was 0.416 (P<0.001). 219

Diagnostic Performance of the AA Index 220

The diagnostic performance of the AA index for predicting significant fibrosis was 221

assessed using the ROC curve. It was found that the AA index had an AUROC curve 222

of 0.822 (standard error (SE) =0.055; 95% CI, 0.714–0.930; P<0.001) for predicting 223

significant fibrosis in the estimation cohort. 224

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The AA index was compared with ten other published non-invasive indices, as shown 225

in Table 1. The AA, FIB-4, APRI, Forns, Fibro Q, APGA, GUCI, RPR, and GP 226

indices were all correlated with significant fibrosis (r=0.465, 0.247, 0.229, 0.253, 227

0.182, 0.367, 0.261, 0.272, and 0.253, respectively [all P<0.05]) in the estimation 228

cohort, except AAR (r=0.039, P=0.566) and PAPAS (r=0.183, P=0.063). The 229

AUROC of the AA and other indices for predicting significant fibrosis in all patients, 230

patients with ALT levels lower than the 2-fold ULN (ALT< 2×ULN), and patients 231

with hepatitis B e antigen (HBeAg) negativity in the estimation cohort are shown in 232

Table 4. The ROC curves for AA and these ten indices for all patients, patients with 233

ALT< 2×ULN, and HBeAg negative patients are shown in Figures 3a, 3b and 3c, 234

respectively. The AA index exhibited a significantly higher AUROC for the 235

prediction of significant fibrosis compared to AAR (P=0.003) and PAPAS (P=0.033). 236

No significant differences were observed between the AUROCs of FIB-4 (P=0.141), 237

Forns (P=0.123), APGA (P=0.444), GP (P=0.101), APRI (P=0.177), Fibro Q 238

(P=0.078), GUCI (P=0.262), and RPR (P=0.262) indices and AA index in the 239

prediction of significant fibrosis. 240

Definition Cut-off Values 241

We selected low (0.007) and high (0.127) cutoff values that achieved an excess of 242

90% for both sensitivity and specificity in the diagnosis of significant fibrosis in the 243

estimation cohort. The sensitivity, specificity, positive predictive value (PPV), 244

negative predictive value (NPV), positive likelihood ratio (+LR), and negative 245

likelihood ratio (-LR) of AA are shown in Table 5. 246

Validation Cohort 247

Applying the new AA index to the validation cohort, the AUROC for predicting 248

significant fibrosis was 0.773 (SE=0.053; 95% CI, 0.669–0.877; P<0.001). The 249

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AUROC in the total cohort was 0.795 (SE=0.038; 95% CI, 0.720–0.871; P<0.001). 250

There were no significant differences in the AUCs between the estimation and 251

validation cohorts (Z=0.642, P=0.521). The AA, FIB-4, APRI, Forns, Fibro Q, APGA, 252

PAPAS, GUCI, RPR, and GP indices all correlated well with significant fibrosis 253

(r=0.358, 0.247, 0.271, 0.285, 0.168, 0.323, 0.211, 0.291, 0.274, and 0.256, 254

respectively [all P<0.05]) in validation cohort, except AAR (r=-0.023, P=0.736). The 255

AUROCs of AAR, FIB-4, APRI, Forns, Fibro Q, APGA, PAPAS, GUCI, RPR, and 256

GP indices were 0.412, 0.670, 0.707, 0.708, 0.592, 0.745, 0.672, 0.722, 0.689, and 257

0.716, respectively. The AA index exhibited a significantly higher AUC in the 258

prediction of significant fibrosis compared to AAR (P<0.001) and Fibro Q (P=0.008). 259

No significant difference was observed between the AUROCs of FIB-4 (P=0.079), 260

Forns (P=0.154), APGA (P=0.366), GP (P=0.209), APRI (P=0.181), PAPAS 261

(P=0.113), GUCI (P=0.249), and RPR (P=0.132) indices and the AA index in the 262

prediction of significant fibrosis. The AUROCs of AA, AAR, FIB-4, APRI, Forns, 263

Fibro Q, APGA, PAPAS, GUCI, RPR, and GP indices were 0.792, 0.187, 0.448, 264

0.556, 0.606, 0.390, 0.683,0.564, 0.633, 0.579, and 0.595, respectively, for HBeAg 265

negative patients, and were 0.762, 0.407, 0.665, 0.696, 0.677, 0.618, 0.698, 0.570, 266

0.692, 0.693, and 0.698, respectively, for patients with ALT< 2×ULN. According to 267

the cutoff values of 0.007 and 0.127, the presence of significant fibrosis was predicted 268

with high sensitivity (90.5%) and high specificity (88.2%) in the validation cohort 269

(Table 5). 270

271

Discussion 272

Many studies have indicated that non-invasive indices containing simple serum 273

markers are valuable in the evaluation of liver fibrosis in patients with chronic liver 274

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diseases 8-17

. Most of the non-invasive methods were developed in patients with 275

chronic hepatitis C (CHC) virus infections 9-12,15

. In recent years, these indices have 276

been used to evaluate CHB patients 13,14,16,17

. In CHB, evaluation of liver fibrosis is 277

crucial since HBV cannot be eradicated completely from the patient by treatment for 278

the persistence of covalently closed circular DNA in the nucleus of infected 279

hepatocytes 22,23

. European Association for the Study of the Liver (EASL) guidelines 280

have stated that non-invasive evaluation of fibrosis would be of interest in CHB 24

. 281

Although these methods cannot replace liver biopsy in chronic liver diseases, they 282

narrow the group who really need biopsy and provide an evaluation of liver damage 283

without biopsy 24

. Many clinicians have already used these tests for patients with 284

CHB in the same way as for CHC 21

. 285

The AA index was based on two routine serum parameters: AFP and APTT. The 286

Spearman correlation found AFP and APTT were significantly correlated with the 287

Xi’an stages. The addition of other variables in our study did not further improve the 288

accuracy of the index. AFP has been shown in previous studies to be associated with 289

significant fibrosis in CHB 13,14,17,25,26

. AFP is related to hepatic impairment and 290

chronic fibrosis and can aid in the differential diagnosis of hepatic diseases 25

. APTT 291

measures the intrinsic pathway of coagulation, and the APTT values are in accordance 292

with the fact that the degree of impairment of clotting factors is related to the severity 293

of liver damage 27

. There were some non-routine parameters used in some indices for 294

predicting fibrosis, including hyaluronic acid, a-2 macroglobulin, haptoglobin, 295

apolipoprotein A1, and Golgi protein 73, however, use of these parameters in 296

predictive models might hinder widespread use of these indices 22,25,28,29

. 297

Liver biopsy also has its limitations and the AUROC cannot be compared without 298

particular care that of other studies when the prevalence of the different stage of 299

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fibrosis are not the same. The AUROC in evaluating non-invasive indices of fibrosis 300

never reached a perfect value of 1.0, and barely reached 0.90 30,31

. Indices such as 301

FIB-4, GUCI, APRI, FibroQ, and Forns were developed based on patients with CHC 302

9-12,15, while APGA, PAPAS, RPR, and GP indices were developed based on patients 303

with CHB 13,14,16,17

. Even with using the same indices, a different study population 304

will lead to different results; only the APGA, RPR, and GP indices were based on a 305

Chinese population. The AUROC of FIB-4, GUCI, APRI, Fibro Q, and Forns indices 306

for CHB patients in our estimation cohort and in our validation cohort were all lower 307

than those of previous studies with CHC patients 9-12,15

. A study by Erdogan et al. 32

308

also evaluated the AUROC of FIB-4, GUCI, APRI, FibroQ, and Forns indices in 309

CHB patients and found that the AUROC was lower than those in CHC patients 9-12,15

, 310

which is similar to the current results. The pathogenesis of liver fibrosis in CHB is 311

different from that of CHC 33-36

. First, the total amount of liver fibrosis reflected by 312

the fibrosis area is significantly lower in CHB than in CHC 34

. Second, hepatitis B 313

patients tend to progress to cirrhosis with larger nodules (macronodular cirrhosis) than 314

hepatitis C patients 34

. Third, bridging necrosis is the main pathogenic predictors of 315

fibrosis progression in CHB. However, CHC has a more progressive natural history 316

with persistent inflammation associated with liver fibrosis and cirrhosis 33

. Fourth, 317

hepatic stellate cells (HSCs) have a key role in the development of fibrosis in chronic 318

liver disease, and activated HSCs synthesize and secrete chemokines, extracellular 319

matrix proteins, and other factors, which all contribute to remodel liver fibrosis 35,36

. 320

HBV Dane particles and X and C protein may induce HSC proliferation through the 321

platelet-derived growth factor-B/PDGF receptor-β signal pathway 36

. However, HCV 322

core protein may directly activate hepatic fibrogenesis a by a toll-like receptor 323

2-dependent manner 35

. Fifth, the liver biopsies in these studies were assessed for 324

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fibrosis stages according to different staging systems 9-12,14-16

, except that APGA was 325

based on liver stiffness measurements13

. The Scheuer 10

, Ishak 9,14,15

, and METAVIR 326

11,12,16 staging systems are often used in clinical pathology, but the Xi’an stages

7 are 327

widely used in China. These staging systems all have advantages and disadvantages 37

. 328

A comparison of the four staging systems for chronic hepatitis fibrosis stages are 329

listed in Table 6; the selection of staging system depends on the comfort of the 330

pathologist and the needs of the involved clinicians 37,38

. Compared with FIB-4, GUCI, 331

APRI, FibroQ, and Forns, which were developed based on patients with CHC by 332

ROC analyses 9-12,15

, the AA index is more suitable for patients with CHB, FIB-4, 333

GUCI, APRI, FibroQ; Forns is more suitable for patients with CHC. The APGA, 334

PAPAS, RPR, and GP indices were developed based on patients with CHB 13,14,16,17

. 335

However, the APGA was based on liver stiffness measurements, and not on liver 336

biopsy, and the study population of the PAPAS index was not Chinese. 337

Defects in the design of diagnostic studies include problems with the study population 338

and bias. Despite the fact that ROC analysis is widely used in diagnostic test 339

evaluations, a proper design with a broad study population and avoidance of bias are 340

required to obtain valid and reliable conclusions in the assessment of diagnostic test 341

evaluations 39

. A broad study population is required to evaluate the accuracy and 342

specificity. Bias can manifest in many different ways, including different diagnosis 343

procedures and a non-blind design. Bias can lead to a false low or high 344

sensitivity/specificity and, therefore, a false low or high AUC 39

. In the current study, 345

we included varying degrees of fibrosis (S0-S4). Although the 346

prevalence of severe fibrosis (S4) was low, hepatic fibrosis was assessed using the 347

Xi’an staging system, which led to a limitation in the comparison of AUCs. Low 348

(0.007, absence of fibrosis) and high (0.127, presence of fibrosis) cutoff values that 349

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achieved high sensitivity and specificity for the diagnosis of significant fibrosis were 350

selected. A clinician may choose not to perform a liver biopsy with AA index < 0.007 351

or > 0.127, avoiding biopsy associated risks and costs, and may choose clinical 352

follow-up instead. International guidelines of Chronic Hepatitis B suggest that CHB 353

patients with ALT > 2×ULN should be treated. However, recent reports have 354

suggested that CHB patients with persistently normal ALT levels may experience 355

severe histologic liver damage 29,40

. HBeAg negative CHB patients are usually 356

asymptomatic for the first 30-40 years 41

. The characteristics, therapy and prognosis 357

of HBeAg negative CHB are different from positive ones, spontaneous recovery of 358

HBeAg negative CHB is rare, and the long-term prognosis is poor with rapid 359

evolution to cirrhosis and Hepatocellular Carcinoma 41,42

. Therefore, the degree of 360

hepatic fibrosis can guide treatment decisions and monitor progress in patients with 361

ALT < 2×ULN and that are HBeAg negative 43,44

Only a few studies have addressed 362

ALT < 2×ULN and HBeAg negative CHB patients with hepatic fibrosis 43,44

. Our 363

study found that the AUCs of the AA index in our study were higher than other 364

indices, and may be useful to predict significant fibrosis in CHB patients with ALT < 365

2×ULN who are HBeAg negative. 366

The present study has several limitations. First, hepatic fibrosis was evaluated only 367

using the Xi’an stages by a single pathologist, and APPT and AFP are not available in 368

some developing countries. Second, some liver tissue had only 5 portal tracts in the 369

liver biopsy. As this is less than the recommended 11 portal tracts 45

, this is a clear 370

limitation of the current study. Third, the AA index was developed for patients with 371

CHB, and the prevalence of severe fibrosis in the Chinese CHB population was low; 372

thus, the AA index may not be applicable to patients with CHC or other causes of 373

hepatic fibrosis and other ethnic populations. Fourth, some non-routine parameters 374

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used in some indices for predicting fibrosis, including hyaluronic acid, a-2 375

macroglobulin, haptoglobin, apolipoprotein A1, and Golgi protein 73, were not 376

available for use in the current study 22,25,28,29

. Fibrotest (Fibrosure in the USA) is a 377

non-invasive index for predicting significant fibrosis in patients with CHC or CHB. 378

The laboratory parameters for calculating Fibrotest include α2-macroglobulin, 379

apolipoprotein A1, haptoglobin, γ-GT, and total bilirubin obtained on the same day as 380

liver biopsy. However, α2-macroglobulin and haptoglobin were not routinely 381

available in our hospital. Therefore, it was not possible to perform Fibrotest, and 382

compare the AUROC of the AA index for predicting significant fibrosis with that of 383

Fibrotest. The AUROC of the Fibrotest index for predicting significant fibrosis was 384

reported by Leroy et al. 22

to be 0.77 (95%CI:0.71-0.83), which was smaller than that 385

of our study. In contrast, Kim et al. 46

found the AUROC was 0.903 (0.838–0.968), 386

which was larger than that of our study. Morever, FibroScan can calculate liver 387

elasticity using a low frequency elastic wave transmitted through the liver, and has 388

been considered the most accurate non-invasive model to assess liver fibrosis among 389

patients with chronic liver diseases due to various etiologies 47

. While Fibroscan 390

certainly has value as a non-invasive measure of liver fibrosis, the instrument was not 391

available in our institution. Therefore, we were unable to compare results. Therefore, 392

we were unable to compare our index with these indices. Fifth, although the AA index 393

can be accurately used to predict significant fibrosis, it cannot truly replace 394

histological fibrosis staging. The AA index is likely to be most useful as a supplement 395

to liver biopsy. Sixth, larger sample sizes as well as multi-center and multi-ethnic 396

studies will be necessary to validate the clinical application of the AA index. These 397

non-invasive indices were used to diagnose significant fibrosis (F2) and/or cirrhosis 398

(F4) using various AUROC and cutoff values. However, we only studied a binary 399

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comparison of no fibrosis (S0, S1) versus some fibrosis (S2, S3, S4). If only a small 400

number of patients with advanced fibrosis were included in the studies, the accuracy 401

of the non-invasive indices for higher stages of fibrosis (F3 and F4) can influence the 402

validity of the serum markers investigated 5,15,16

. A small number of patients with 403

advanced fibrosis is a limitation of the current study. In conclusion, we found that an 404

index (AA) containing AFP and APTT can accurately predict significant fibrosis in 405

CHB patients with ALT< 2×ULN that are HBeAg negative. The AA index is more 406

accurate than the AAR, APRI, Forns, FIB-4, FibroQ, APGA, PAPAS, GUCI, RPR, 407

and GP indices. The parameters used in the AA index are widely available, and can be 408

used as non-invasive index to predict significant HBV-related fibrosis. Use of the AA 409

index may decrease the need of liver biopsy in patients with CHB. 410

Acknowledgments 411

We thank Medjaden Bioscience, Limited for helping in proofreading and editing the 412

manuscript. 413

414

Footnotes 415

Contributorship statement YZ and LF contributed to the study conception/design. 416

LF, KS, JZ, and GF contributed to the data collection. YZ and LF conducted the data 417

analysis, critically revised the article and reviewed the draft of the article. 418

Funding This work was supported by grants from the Department of Education 419

Foundation of Zhejiang Province, China (Nos. Y201017380 and Y201330146). The 420

funders had no role in study design, data collection and analysis, decision to publish, 421

or preparation of the manuscript. 422

Competing interests: None declared. 423

Patient consent Obtained. 424

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Ethics approval This study was approved by the ethics committee of the First 425

Affiliated Hospital of Zhejiang University School of Medicine, China. 426

Data sharing statement No additional unpublished data are available. 427

428

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46. Kim BK, Kim SU, Kim HS, et al. Prospective validation of FibroTest in 550

comparison with liver stiffness for predicting liver fibrosis in Asian subjects with 551

chronic hepatitis B. PLoS One 2012;7(4):e35825. 552

47. Poynard T, Ngo Y, Munteanu M, Thabut D, Ratziu V. Noninvasive Markers of 553

Hepatic Fibrosis in Chronic Hepatitis B. Curr Hepat Rep 2011;10(2):87-97. 554

555

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Figure Legends 556

Figure 1. The flow diagram of the study. 557

558

Figure 2. Box plots displaying the values of alpha fetal protein and activated partial 559

thromboplastin time based on Xi’an Meeting Scoring System. 560

561

The top and bottom of each box represents the 25th and 75th percentile interval. The 562

line through the box in the median and the error bars are the 5th and 95th percentile 563

intervals. AFP, alpha fetal protein; APTT, activated partial thromboplastin time. 564

565

566

Figure 3. Receiver operating characteristic curves for prediction of significant fibrosis 567

in the estimation cohort using the new index in comparison with several other 568

calculated indices. 569

570

a: all patients, b: patients with ALT< 2×ULN, c: patients HBeAg negative. AA, the 571

new index consisted of alpha fetal protein and activated partial thromboplastin time; 572

ROC, receiver operating characteristic; ALT, alanine aminotransferase; ULN, upper 573

limit of normal; HBeAg, hepatitis B e antigen. 574

575

576

577

578

579

580

581

582

583

584

585

586

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587

588

589

590

Table 1. Summary of Non-invasive Indices for Predicting Significant Liver Fibrosis 591

Index Formula

AAR [8] AST/ALT

FIB-4 [11] (Age×AST)/ (PLT (109/L)×ALT

1/2]

Forns index[10] 7.811–3.131×LN(PLT)+0.781×LN(GGT)+3.467×LN(age)-0.014×Tch

APRI [9] [(AST/ULN)/PLT (109/L)] ×100

Fibro Q [12] (10×age×AST×PT INR)/(PLT×ALT)

APGA [13] Log(index)=1.44+0.1490×log(GGT)+0.3308×log(AST)-0.5846×log(PL

T)+0.1148×log (AFP+1)

PAPAS [14] Log(index+1)=0.0255+0.0031×age+0.1483×log(ALP)-0.004×log(AST)

+0.0908×log(AFP+1)-0.028×log (PLT)

GUCI [15] [(AST/ULN) ×prothrombin-INR] ×100/ PLT

RPR [16] RDW/PLT

GP [17] GLOB×100/PLT

ALT, alanine aminotransferase; AST, aspartate aminotransferase; PLT, platelet count; GGT, 592

gamma glutamyl transpeptidase; Tch, total cholesterol; INR, the international normalized ratio; 593

AFP, alpha fetal protein; ALP, alkaline phosphatase; ULN, upper limit of normal; RDW, red cell 594

distribution width; GLOB, globulin. Units of AST, ALT, GGT, and ALP: U/L; Units of age, Tch, 595

AFP, GLOB, PLT, and RDW: years, mmol/L, ng/mL, g/dL, 109/L, and %, respectively. 596

597

598

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Table 2. Baseline Characteristics of the Patients in the Estimation and Validation 599

Cohorts 600

Variable All patients

(n=506)

Estimation cohort

(n=253)

Validation cohort

(n=253)

P value

Age (years) 37.45±9.6 37.51±10.0 37.39±9.1 0.886

Male, n (%) 337 (66.6) 166 (65.6) 171 (68.0) 0.637#

BMI (kg/m2) 22.5±3.51 22.5±3.60 22.5±3.49 0.957

WBC (109/L) 5.51±1.60 5.47±1.61 5.55±1.49 0.558

RBC (1012

/L) 4.81±0.51 4.80±0.52 4.81±0.50 0.817

Hgb (g/L) 148±18 147±19 148±17 0.501

RDW (%) 13.0±1.0 13.1±1.2 13.0±0.8 0.092

PLT (109/L) 178±53 181±55 175±51 0.191

TP (g/L) 72.2±6.7 72.5±6.7 71.8±6.7 0.268

ALB (g/L) 45.4±5.2 45.2±5.2 45.6±5.1 0.482

TBIL (µmol/L) 13 (3-436) 13 (4-436) 14 (3-280) 0.748*

AST (U/L) 30 (14-479) 31 (14-479) 30 (14-358) 0.478*

ALT (U/L) 40 (8-631) 45 (8-569) 38 (10-631) 0.019*

GGT (U/L) 25 (6-586) 25 (7-586) 25 (6-456) 0.653*

ALP (U/L) 68 (22-292) 68 (29-184) 69 (22-292) 0.871*

FPG (mmol/L) 4.72±1.41 4.64±0.90 4.80±1.80 0.268

TG (mmol/L) 0.97 (0.38-9.41) 0.99 (0.39-9.41) 0.94 (0.38-7.67) 0.780*

Tch (mmol/L) 4.47±1.04 4.46±1.03 4.47±1.05 0.917

PT (s) 11.8±1.6 11.8±2.1 11.8±0.9 0.752

APTT (s) 28.2±4.8 28.2±5.4 28.2±4.2 0.952

Fbg (g/L) 2.31±0.61 2.31±0.64 2.31±0.57 0.918

AFP (ng/mL) 3.4 (0.8-644.3) 3.4 (0.8-644.3) 3.4 (1.1-259.8) 0.811*

HBeAg status, n (%) 285 (56.3) 142 (56.1) 143 (56.5) 0.929#

HBV DNA detectable (%) 351 (69.4) 182 (71.9) 169 (66.8) 0.210#

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anti-HBV therapy (%) 47 (9.3) 23 (9.1) 24 (9.5) 0.878#

Fibrosis stage, n (%) 0.933#

S0 251 (49.6) 123 (48.6) 128 (50.6)

S1 167 (33.0) 84 (33.2) 83 (32.8)

S2 48 (9.5) 26 (10.2) 22 (8.7)

S3 22 (4.3) 10 (4.0) 12 (4.7)

S4 18 (3.6) 10 (4.0) 8 (3.2)

WBC, white blood cell; RBC, red blood cell; Hgb, hemoglobin; RDW, red cell distribution width; 601

PLT, platelet count; TP, total protein; ALB, albumin; TBIL, total bilirubin; AST, aspartate 602

aminotransferase; ALT, alanine aminotransferase; GGT, gamma glutamyl transpeptidase; ALP, 603

alkaline phosphatase; FPG, fasting plasma glucose; TG, triglyceride; Tch, total cholesterol; PT, 604

prothrombin time; APTT, activated partial thromboplastin time; AFP, alpha fetal protein. P values 605

are comparisons between the estimation cohort and validation cohort using independent samples 606

t-test, except: #: using Chi-squared test, *: using Mann-Whitney U test. 607

608

609

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Table 3. Variables Associated with Significant Fibrosis in the Estimation Cohort 610

Variable S0-1 (n=207) S2-4 (n=46) P value

Age (years) 37.2±9.8 38.9±10.8 0.288

Male, n (%) 132 (63.8) 34 (73.9) 0.190#

BMI (kg/m2) 22.6±3.26 22.2±4.28 0.316

WBC (109/L) 5.46±1.49 5.53±2.10 0.773

RBC (1012

/L) 4.81±0.50 4.76±0.59 0.536

Hgb (g/L) 148±19 144±19 0.238

RDW (%) 13.0±1.1 13.5±1.5 0.021

PLT (109/L) 189±53 150±55 <0.001

TP (g/L) 73.1±6.3 70.0±7.7 0.006

ALB (g/L) 45.9±4.8 42.5±6.2 <0.001

TBIL (µmol/L) 13 (4-69) 14 (4-436) 0.857*

AST (U/L) 30 (14-429) 31 (16-479) 0.458*

ALT (U/L) 45 (8-379) 40 (8-569) 0.759*

GGT (U/L) 24 (7-175) 37 (10-586) <0.001*

ALP (U/L) 68 (29-169) 75 (36-184) 0.044*

FPG (mmol/L) 4.60±0.52 4.83±1.77 0.159

TG (mmol/L) 1.03 (0.42-9.41) 0.88 (0.39-2.88) 0.032*

Tch (mmol/L) 4.62±0.95 3.79±1.10 <0.001

PT (s) 11.5±0.8 12.9±4.4 <0.001

APTT (s) 27.4±4.3 31.7±8.1 <0.001

Fbg (g/L) 2.34±0.58 2.17±0.82 0.103

AFP (ng/mL) 3.2 (1.0-20.7) 4.8 (0.8-644.3) 0.034*

HBeAg status, n (%) 117 (56.5) 25 (54.3) 0.788#

WBC, white blood cell; RBC, red blood cell; Hgb, hemoglobin; RDW, red cell distribution width; 611

PLT, platelet count; TP, total protein; ALB, albumin; TBIL, total bilirubin; AST, aspartate 612

aminotransferase; ALT, alanine aminotransferase; GGT, gamma glutamyl transpeptidase; ALP, 613

alkaline phosphatase; FPG, fasting plasma glucose; TG, triglyceride; Tch, total cholesterol; PT, 614

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prothrombin time; APTT, activated partial thromboplastin time; AFP, alpha fetal protein. P values 615

are of comparisons between S0-1 and S2-4 using independent samples t-test, except: #: using 616

Chi-squared test, *: using Mann-Whitney U test. 617

618

619

Table 4. Area under curve of AA and Other Non-invasive Indices in the Estimation Cohort 620

Index All patients Patients with ALT< 2×ULN Patients with HBeAg negative

AUC (95% CI) SE AUC (95% CI) SE AUC (95% CI) SE

AA 0.822 (0.714-0.930)* 0.055 0.845

(0.718-0.971)*

0.064 0.893 (0.779-0.999) * 0.058

AAR 0.554 (0.411-0.697) 0.073 0.566 (0.406-0.727) 0.082 0.638 (0.447-0.829) 0.098

FIB-4 0.691 (0.555-0.827)* 0.070 0.643 (0.470-0.815) 0.088 0.614 (0.398-0.829) 0.110

Forns 0.687 (0.554-0.820)* 0.068 0.690

(0.534-0.847)*

0.080 0.636 (0.417-0.856) 0.112

APRI 0.707 (0.579-0.834)* 0.065 0.690

(0.546-0.835)*

0.074 0.685 (0.484-0.886) 0.103

Fibro Q 0.661 (0.517-0.804)* 0.073 0.667

(0.494-0.839)*

0.088 0.675 (0.469-0.882) 0.105

APGA 0.758 (0.635-0.881)* 0.063 0.748

(0.605-0.892)*

0.073 0.695 (0.502-0.887) 0.098

PAPAS 0.630 (0.491-0.769) 0.071 0.621 (0.462-0.781) 0.081 0.565 (0.344-0.786) 0.113

GUCI 0.729 (0.607-0.851)* 0.062 0.719

(0.583-0.855)*

0.069 0.714 (0.521-0.908) * 0.099

RPR 0.723 (0.589-0.858)* 0.069 0.703

(0.531-0.875)*

0.088 0.682 (0.466-0.897) 0.110

GP 0.671 (0.526-0.817)* 0.074 0.710

(0.546-0.874)*

0.084 0.685 (0.476-0.894) 0.107

AUC: area under curve, 95% CI: 95% confidence interval, SE: standard error, * : P < 0.05. 621

622

623

624

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625

Table 5 Diagnostic accuracy of AA index in the estimation and validation cohorts 626

cutoff S0-1 S2-4 Sensitivity (%) Specificity (%) PPV (%) NPV (%) +LR -LR

Estimation cohort (N=253) 207 46

Low cutoff 91.3(51.8-99.7) 50.0(37.8-62.2) 28.8(6.0-62.2) 96.3(86.4-99.5) 1.83(1.3-2.5) 0.17(0.03-1.2)

< 0.007 103 4

≥ 0.007 104 42

High cutoff 65.2 (29.9-92.5) 90.0(80.5-95.5) 60.0(9.4-90.6) 92.1%(82.7-96.9) 6.52(2.3-15.7) 0.39(0.1-1.3)

< 0.127 186 16

≥ 0.127 20 30

Validation cohort (N=253) 211 42

Low cutoff 90.5(51.6-97.7) 42.2(31.2-54.0) 23.8(6.5-45.5) 95.7(78.1-99.9) 1.57(0.6-9.5) 0.23(0.04-1.8)

< 0.007 89 4

≥ 0.007 122 38

High cutoff 66.7(34.9-94.1) 88.2(79.2-94.3) 52.8(0.8-90.6) 93.0(79.6-97.6) 5.65(0.5-18.2) 0.38(0.05-2.0)

< 0.127 186 14

≥ 0.127 25 28 PPV: positive predictive value, NPV: negative predictive value, +LR: positive likelihood ratio, -LR: negative likelihood ratio 627

628

629

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630

Table 6. Four stage systems for chronic hepatitis fibrosis stages 631

Fibrosis

stage

Scheuer stages Ishak stages Metavir stages Xi’an stages

0 No fibrosis No fibrosis No fibrosis No fibrosis

1 Enlarged, fibrotic

portal tracts

Portal fibrosis,

with or without

short fibrous septa

Portal fibrosis

without septa

fibrosis confined to

portal tracts, periportal

spaces, and

perisinusoidal spaces

2 Periportal or

portal-portal

septa, but intact

architecture

Fibrous septa portal fibrosis

with rare septa

bridging fibrosis, with

fibrous septa

3 Fibrosis with

architectural

distortion, but no

obvious cirrhosis

Transition to

cirrhosis

portal fibrosis

with many

septa

a lot of fibrous septa

separate without

obvious cirrhosis

4 Probable or

definite cirrhosis

Probable or

definite cirrhosis

cirrhosis early cirrhosis

632

633

634

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57x30mm (300 x 300 DPI)

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27x11mm (300 x 300 DPI)

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STARD checklist for reporting of studies of diagnostic accuracy

(version January 2003)

Section and Topic Item

#

On page #

TITLE/ABSTRACT/

KEYWORDS

1 Identify the article as a study of diagnostic accuracy (recommend MeSH

heading 'sensitivity and specificity').

1-2

INTRODUCTION 2 State the research questions or study aims, such as estimating diagnostic

accuracy or comparing accuracy between tests or across participant

groups.

4

METHODS

Participants 3 The study population: The inclusion and exclusion criteria, setting and

locations where data were collected.

5

4 Participant recruitment: Was recruitment based on presenting symptoms,

results from previous tests, or the fact that the participants had received

the index tests or the reference standard?

5

5 Participant sampling: Was the study population a consecutive series of

participants defined by the selection criteria in item 3 and 4? If not,

specify how participants were further selected.

5

6 Data collection: Was data collection planned before the index test and

reference standard were performed (prospective study) or after

(retrospective study)?

5-6

Test methods 7 The reference standard and its rationale. 6-7

8 Technical specifications of material and methods involved including how

and when measurements were taken, and/or cite references for index

tests and reference standard.

9 Definition of and rationale for the units, cut-offs and/or categories of the

results of the index tests and the reference standard.

5-8

10 The number, training and expertise of the persons executing and reading

the index tests and the reference standard.

5

11 Whether or not the readers of the index tests and reference standard

were blind (masked) to the results of the other test and describe any

other clinical information available to the readers.

7

Statistical methods 12 Methods for calculating or comparing measures of diagnostic accuracy,

and the statistical methods used to quantify uncertainty (e.g. 95%

confidence intervals).

8

13 Methods for calculating test reproducibility, if done. no

RESULTS

Participants 14 When study was performed, including beginning and end dates of

recruitment.

8-9,table 2

15 Clinical and demographic characteristics of the study population (at least

information on age, gender, spectrum of presenting symptoms).

table 2

16 The number of participants satisfying the criteria for inclusion who did or

did not undergo the index tests and/or the reference standard; describe

why participants failed to undergo either test (a flow diagram is strongly

recommended).

55

Test results 17 Time-interval between the index tests and the reference standard, and

any treatment administered in between.

8-9,table 2

18 Distribution of severity of disease (define criteria) in those with the target

condition; other diagnoses in participants without the target condition.

9

19 A cross tabulation of the results of the index tests (including

indeterminate and missing results) by the results of the reference

standard; for continuous results, the distribution of the test results by the

results of the reference standard.

9-10, table

2

20 Any adverse events from performing the index tests or the reference

standard.

no

Estimates 21 Estimates of diagnostic accuracy and measures of statistical uncertainty

(e.g. 95% confidence intervals).

9-11

22 How indeterminate results, missing data and outliers of the index tests

were handled.

no

23 Estimates of variability of diagnostic accuracy between subgroups of

participants, readers or centers, if done.

10

24 Estimates of test reproducibility, if done. no

DISCUSSION 25 Discuss the clinical applicability of the study findings. 12-17

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BMJ Open...STARD checklist for reporting of studies of diagnostic accuracy (version January 2003) Section and Topic Item # On page # TITLE/ABSTRACT/ KEYWORDS 2 standard.

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