Revisiting Mac-2-Binding Protein Glycosylation Isomer (M2BPGi) for Diagnosing High-Risk Liver Fibrosis in Chronic Hepatitis B Patients: A Stepwise Diagnostic Analysis

Abstract* Background The level of liver fibrosis is the basis for the treatment of chronic hepatitis B (CHB), and it is necessary to adapt non-invasive liver fibrosis modalities. We aimed to investigate the use of M2BPGi as a single or combined diagnostic modality for liver fibrosis in CHB patients through a stepwise diagnostic analysis. Methods Cross-sectional data were taken from patients between October 2021 and August 2022. Demographic data, blood profile, liver function, and liver stiffness were measured in CHB patients over 18 years old, willing to take part in the research, and had complete data. APRI, FIB-4, and AAR were calculated using the well-known formulas. Serum M2BPGi-levels were converted into a cut-off index (COI). The patients were divided into low-risk (LR) and high-risk fibrosis (HR) groups. A cut-off for each predictor variable to differentiate between the LR and HR groups was determined. The obtained cut-off was assessed for its association with the grouping of liver elastography results. Models to diagnose the liver stiffness measurement (LSM) ≥8 kPa were created and compared through multivariate and ROC analyses. Results The number of patients that met the inclusion and exclusion criteria was 143 (HR = 65, LR = 78). The cut-off for diagnosing LSM ≥8kPa was 0.311, 0.742, 0.635, and 1.434 for APRI, FIB-4, AAR, and M2BPGi, respectively. This cut-off was significantly associated with the results of the HR and LR groupings. A multivariate analysis found that FIB4, AAR, and M2BPGi added significantly to the model. Statistically, the most optimal use of M2BPGi was combined with FIB-4, with an AUC of 0.835. Conclusions The optimal cut-off of M2BPGi for diagnosing high-risk liver fibrosis in this study was 1.434. M2BPGi should be used with FIB-4 as a diagnostic tool for diagnosing liver fibrosis, especially in the absence of a liver biopsy or elastography.


Introduction
Globally, an estimated 296 million people, with 18 million in Southeast Asia, are projected to have a CHB infection by the World Health Organization (WHO).The annual rate of new infections is about 1.5 million.Hepatocellular carcinoma (HCC) and liver fibrosis caused by hepatitis B were responsible for 820,000 deaths in 2019. 1 In determining the severity of fibrosis or inflammation in the liver, a liver biopsy is the primary option, but it is an invasive procedure.The American Association for the Study of Liver Diseases (AASLD) has suggested several non-invasive techniques. 2 Most non-invasive techniques were easily performed and reproduced in the outpatient clinic, have low cost, and have been validated for specific populations were the other advantages. 3c-2-binding protein (M2BP) is a glycoprotein that, when changes are made to its N-glycan residue, forms M2BPGi.M2BPGi is produced by hepatic stellate cells (HSCs) and it induces profibrotic cytokine expression in Kupffer cells (KCs), namely Mac-2.Subsequently, Mac-2 activates HSCs and cause fibrogenesis. 4,5][7][8][9][10][11][12][13][14][15][16][17][18] In several previous studies, M2BPGi helped to diagnose liver fibrosis in a CHB population 6,11 and it could be used as a single predictor variable to diagnose liver fibrosis grade. 12,13his marker could also complement and be used with other modalities. 14,15[21] Adapting non-invasive liver fibrosis modalities to each type of chronic liver disease and each region is necessary due to the heterogeneity of outcomes.A stepwise diagnostic analysis has yet to be conducted to determine whether M2BPGi should be utilized alone or in conjunction with modalities to assess liver fibrosis.Thus, we aimed to investigate the use of M2BPGi as a single or combined diagnostic modality for liver fibrosis in CHB patients through a stepwise diagnostic analysis.

Study design and patients
We obtained ethical approval from the Research Ethics Committee of Dr. Hasan Sadikin General Hospital Bandung (LB.02.01/X.6.5/299/2021) in order to protect the rights and welfare of research subjects, and to guarantee the study to be conducted according to ethical, legal, social implications, and other applicable regulations.This was a cross-sectional study; the subjects of this study were patients from the Gastroenterohepatology outpatient clinic, Hasan Sadikin General Hospital, Indonesia, between October 2021 and August 2022.All patients were older than 18 years and were positive for serum hepatitis B surface antigen (HBsAg) for at least six months.The criteria for exclusion were as follows: 1) acute hepatitis; 2) acute exacerbation of chronic hepatitis; 3) hepatitis C; 4) autoimmune liver disease; 5) hepatitis B co-infection with hepatitis C or HIV; 6) co-morbidities (type 2 diabetes mellitus, heart disease, chronic kidney disease, pulmonary tuberculosis, or cancer); 7) patients with a history of alcohol use (>20 grams of alcohol per day); 8) pregnant or breastfeeding woman; 9) body mass index (BMI) >27 kg/m 2 ; 10) hemoglobin <5 g/dL; and 11) pulmonary fibrosis, chronic pancreatitis, liver cancer, or pancreatic cancer.
There are several ways to determine the optimum sample size for a binary logistic regression analysis.First, using the rule of thumb method with N = number of independent variables multiplied by 10-50, the value for our sample size was between 30 and 150.Another method is by including the prevalence correction factor with the formula: N = 10 k/p, where k is the number of independent variables and p is the prevalence correction factor. 22In our study subjects, the prevalence was 45%; thus, the number of efficient samples is 66.67 = 67 patients.Our study was conducted on 143 subjects.

Clinical data and laboratory test
CHB patients who met the inclusion and exclusion criteria received information about the study.After obtaining written (informed) consent, their demographic data were collected.The research subjects underwent supporting examinations of liver elastography and routine laboratory investigations, including measurements of CBC, AST, ALT, PT, INR, and M2BPGi serum levels.All laboratory examinations were carried out in the clinical pathology laboratory of Hasan Sadikin

REVISED Amendments from Version 1
This version clarifies the title to better represent our manuscript's contents.The research results and discussion elaboration were more precise and broader, using confidence interval data and subgroup analysis.Various potential limitations and future research directions from this research were discussed in more depth.The underlying and extended data were updated to support reproducibility, clarity, and understanding.
Any further responses from the reviewers can be found at the end of the article General Hospital.All equipment underwent quality control before use and was used by trained technicians.The formulas used to calculate the non-invasive liver fibrosis scores are as follows 23,24 :

Transient elastography
Transient elastography (TE) is a non-invasive examination to determine the level of liver fibrosis, with results given in the form of an LSM (in kPa).This examination was performed using the FibroScan ® 502 Series F00734 (Echosens, Paris, France) with the M or XL probe.Liver stiffness was expressed as the median value of more than ten valid examinations.The value of LSM can be trusted if the success rate is greater than 60% and the interquartile range (IQR) ratio to the median liver stiffness is below 30%.This examination was performed by a gastroenterohepatologist at Hasan Sadikin General Hospital.FibroScan was cleaned and disinfected regularly to ensure optimal operation.Delegates from Echosens calibrate FibroScan yearly.

Statistical analysis
First, a normality test was conducted to determine the subsequent statistical analysis procedure.The patient characteristic data are presented in Table 1; those with a normal distribution are expressed as the mean and standard deviation, while those that are not normally distributed are presented as the median and minimum-maximum values.The results of the RRID: SCR_012763) was used for the stepwise diagnostic analysis.Each predictor variable was grouped based on their cut-off value.The association between each grouped predictor variable and the HR and LR groupings was assessed using a chi-square analysis.Diagnostic models were created and subjected to stepwise logistic regression.The receiver operating characteristic (ROC) analysis determined each model's accuracy.Then, ROC analysis comparisons were carried out between each model to evaluate whether there was a significant difference in adding predictors.A two-tailed p < 0.05 was considered statistically significant.

Subject characteristics
The total number of CHB patients at Hasan Sadikin General Hospital during the research period was 157.All patients were entered into the Hasan Sadikin Chronic Hepatitis B Registry.After all examinations and data collection, 14 patients were excluded because of incomplete data.The number of patients who met the inclusion and exclusion criteria was 143.The patients' baseline characteristics are summarized in Table 1.Based on the transient elastography results, the patients were split into two groups: high-risk fibrosis (HR) (n = 65) and low-risk fibrosis (LR) (n = 78) groups.The flowchart for the selection of the participants is shown in Figure 1.

Bivariate analysis for high-risk fibrosis
Each predictor underwent an ROC analysis to obtain the optimal cut-off point.It was found that the best cutoff values for M2BPGi, APRI, FIB-4, and AAR were 1.434, 0.311, 0.742, and 0.635, respectively.The results of the bivariate analysis between the predictor variables for the HR and LR groups are shown in Table 2. Based on the liver elastography results, M2BPGi, APRI, FIB-4, and AAR were significantly associated with the HR and LR groupings.
Stepwise multivariate analysis for high-risk fibrosis Through a Spearman analysis, APRI with FIB-4 had the strongest correlation, with a correlation coefficient of 0.78.
Based on the correlation coefficient data, it was decided to exclude APRI from the diagnostic model to avoid violating the multicollinearity rule.The models used different combinations of M2BPGi, FIB-4, and AAR.Model 1 consisted of M2BPGi only, followed by the addition of other predictors one by one to produce Model 2, which consisted of M2BPGi and AAR; Model 3, which consisted of M2BPGi and FIB-4; and Model 4, which consisted of M2BPGi, FIB-4, and AAR.All variables were analyzed to assess the association.The results of the multivariate analysis are shown in Table 3.All models were statistically significant (p-value < 0.001) compared to the intercept-only model and had a Nagelkerke R 2 fit at 0.31, 0.36, 0.46, and 0.49 for Models 1, 2, 3, and 4, respectively.

Model's accuracy for diagnosing high-risk fibrosis
The ROC analysis's area under the curve (AUC) evaluates each model's accuracy (Table 4).The model utilizing M2BPGi with the best accuracy was in combination with FIB-4 and AAR.The abilities of each model were compared, and the results are shown in Table 5.

Discussion
Non-invasive methods for assessing liver fibrosis are currently being developed.][7][8][9][10][11][12][13][14][15][16][17][18] A stepwise diagnostic analysis has not yet been conducted to determine the value of M2BPGi in assessing liver fibrosis.Here, we aimed to investigate the use of M2BPGi as a single or combined diagnostic modality for liver fibrosis in CHB patients through a stepwise diagnostic analysis.
Currently, a liver biopsy is the gold standard for assessing liver inflammation and fibrosis.However, biopsy is an invasive procedure and has several risks, such as bleeding, hematoma, and mild discomfort to severe pain; hence, it is not suitable for routine use. 26There are various non-invasive methods to assess liver fibrosis.Liver elastography is the primary alternative for assessing liver fibrosis.To determine liver stiffness in this study, we used the FibroScan ® tool and determined the fibrosis class based on the EASL recommendations in FibroScan ® .In the outside liver clinic settings, the results of LSM are divided into ≥8 kPa for high-risk fibrosis and <8 kPa for low-risk. 3Operator skills and experience, the selection of appropriate probes, and special conditions such as obesity are challenges in applying the liver elastography method.There was no significant difference in gender proportion between the HR and LR groupings.However, the HR group had an increment in age compared with the LR group.
The AUC of M2BPGi, APRI, FIB-4, and AAR were 0.721 (95%CI 0.631-0.811),0.757 (95%CI 0.678-0.836),0.822 (95%CI 0.753-0.891),0.618 (95%CI 0.526-0.71),respectively in our samples.The ROC coordinate points obtained a new cut-off for M2BPGi, APRI, FIB-4, and AAR (Table 2).This optimal cut-off is not too different from those of Zou's study 15 for diagnosing METAVIR grade ≥F2 with an APRI cut-off of 0.51, a FIB-4 cut-off of 0.92, and an AAR cut-off of 0.55.In a study to distinguish LSM ≥7 kPa in Vietnam, cut-offs of 0.5 and 1.8 were obtained for APRI and FIB-4, respectively. 12In patients with liver elastography results ≥9 kPa in Egypt, the cut-offs were at 0.256, 0.74, and 0.8 for APRI, FIB-4, and AAR, respectively. 27For predicting Knodell histologic activity index (HAI) ≥F2 results, APRI and FIB-4 had the best cut-offs at 0.9 and 0.35, respectively. 11The APRI cut-offs found to date vary widely.In diagnosing LSM ≥8 kPa, the value appeared lower in this study (0.311) compared to other studies, regardless of the dependent variable groupings. 11,12,15From the various reviews that have been done, the possibility of this result could occur because the baseline data on platelet count in our population was considered to be the highest, with an average of 237.2 (Â10 9 /L).
A high platelet count causes the distribution of APRI values to be lower, lowering the further obtained cut-off.Zou, 15 Jekarl, 11 and Bui 12 had median platelet counts of 155, 185, and 206, respectively, resulting in a decreasing trend of APRI cut-offs of 1.06, 0.9, and 0.569, respectively.
The research regarding the ability of non-invasive liver fibrosis modalities can be broadly divided into two areas, using liver biopsy or TE as the comparison.We are among those who used TE results as the gold standard.While TE was rarely used, Bui et al. found an M2BPGi cut-off of 0.79 for diagnosing LSM ≥7 kPa. 12The cut-off of M2BPGi that we obtained to diagnose LSM ≥8 kPa was 1.434.M2BPGi levels were higher in women in this study (p-value = 0.022).Our cut-off is quite close to the previous cut-off for diagnosing significant liver fibrosis using biopsy as the gold standard by Yeh et al. 14 and Ishii et al. 6 at 1.345 and 1.4, respectively.Our M2BPGi cut-off is greater than 1, which we suspect is due to aging.There was a significant difference between the ages of the HR and LR groups in our study, with a weak correlation between age and M2BPGi levels (r = 0.314).
In several previous studies, the cut-off for M2BPGi was around COI 1. 6,10,11,13,15 Cheng et al. found that aging increases M2BPGi levels in healthy patients. 18This finding may explain why our cut-off results were more than one.However, the effects of aging on M2BPGi levels require further research.Based on the cut-off found, the four predictor variables were divided into categorical data; all predictors were associated with the categorical classification of liver elastography with a cut-off of 8 kPa (Table 2).
The highest bivariate correlation analysis results were found between APRI and FIB-4; this was based on the fact that both indices consist of AST and platelet counts as the primary variables.This robust correlation made APRI excluded to produce an unbiased multivariate analysis.APRI was also excluded because its association with HR and LR grouping was weaker than FIB-4 (Table 2).Four models were developed involving M2BPGi, FIB-4, and AAR to assess the performance of M2BPGi on its own.M2BPGi, FIB-4, and AAR (Model 4) were able to predict the HR group.Patients with any result equal to or more than the M2BPGi, FIB-4, and AAR cut-offs will result in a probability of 6.476, 6.873, and 2.685, respectively, for classification into the HR group.If used alone, each COI M2BPGi value ≥1.434 will produce a probability of 11.562 (Table 3).
Model 4 had the best diagnostic ability with an AUC of 0.852 (Table 4).The use of M2BPGi as a single modality (Model 1) in diagnosing high-risk liver fibrosis was quite good, with an AUC of 0.733.In diagnosing liver biopsy at ≥F2, Yeh et al. 14 and Zou et al. 15 obtained an AUC for M2BPGi of 0.78 and 0.753, respectively.Bui et al. found an AUC of 0.77 for diagnosing LSM ≥7 kPa. 12Ichikawa et al., in determining F≥2 based on the revised Inuyama classification, found that M2BPGi had an AUC of 0.713. 10In the group of patients with treatment-naïve CHB to diagnose portal fibrosis without septal involvement (F≥2), an AUC of 0.77 was obtained by Ishii et al. 6 There were significant differences in the diagnostic abilities of the models (p = 0.0019); a post hoc analysis was performed to determine whether the addition of a modality was statistically significant (Table 5).In Model 2, M2BPGi was coupled with AAR, which increased the diagnostic capability compared to the M2BPGi-only model by around 4.9%.Model 3, which consisted of M2BPGi and FIB-4, was statistically the best model, with an AUC of 0.835.The addition of FIB-4 increased the AUC to 10.2%.Adding AAR to Model 3 to form Model 4 increased the diagnostic capability by 1.7% but this was not statistically significant.Therefore, Model 3 was the most efficient diagnostic model.M2BPGi can be used efficiently, and its application should be combined with FIB-4 to diagnose high-risk liver fibrosis.
In some earlier studies, the use of M2BPGi combined with other variables was proposed, as was performed by Yeh et al. 14 and Zou et al. 15 Yeh et al. supported using M2BPGi in models involving age and platelet counts to increase the specificity in the prediction of advanced fibrosis. 14Zou et al. suggested measuring M2BPGi levels as a complementary method for liver biopsies and elastography. 15However, both studies showed that the AUC value of M2BPGi was always superior to other scoring methods. 14,15Bui et al. found that M2BPGi and APRI had the same AUC value (0.77) as a single indicator.However, in combining M2BPGi with other modalities, they only formed a single model to predict significant fibrosis using M2BPGi and APRI.By adding APRI to M2BPGi, the accuracy in detecting LSM ≥7 kPa was increased.Based on the high coefficient correlation between M2BPGi and liver elastography results, the paper stated that M2BPGi could be used as an alternative liver fibrosis test in CHB patients, especially in settings with limited resources. 12Mak et al. performed an ROC analysis and created two predictive models for F3/F4 biopsy results.M2BPGi always produced statistically significant correlation in both models, while APRI, FIB-4, and AAR did not.They stated that M2BPGi was a potential marker for easily diagnosing F3/F4 without the need for a liver biopsy. 13is study of serum M2BPGi levels in CHB patients aimed to aid its diagnostic application outside of liver clinic settings.The use of M2BPGi levels as part of a non-invasive method for diagnosing liver fibrosis outcomes based on liver elastography values was compared with several scoring methods.In our study, M2BPGi showed good diagnostic performance when used alone.However, our stepwise diagnostic analysis found that M2BPGi had a better result in diagnosing liver fibrosis when combined with FIB-4.The findings of COI M2BPGi ≥1.434 and FIB-4 ≥0.742 had an AUC of 85.2% in diagnosing high-risk liver fibrosis conditions.If those data are obtained simultaneously, further liver fibrosis investigations should be done.
Our study was conducted cross-sectionally; the causal relationships were actually limited to associations.Potential bias has been avoided.To avoid selection bias, research participants comprehensively excluded various conditions affecting the purity of research samples and the examination results (TE and M2BPGi).We tried to include all patients initially because of the basis for establishing the Hepatitis B registry, but a majority reduced the number due to the completeness of the data.All data was obtained from objective sources (letters) to avoid information bias.The laboratory analyst did not know the patient participation status in the research.The patient's diagnosis was also unknown to the TE operator.
Since this is the first comprehensive statistical analysis performed on M2BPGi utilization, future studies should examine the use of serum M2BPGi levels by applying the stepwise diagnostic analysis method.In addition, validating research using model findings from our research can be carried out in other chronic liver diseases.In conclusion, after considering all the statistical comparisons and the stepwise diagnostic analysis, we believe that M2BPGi should be used with FIB-4 as a diagnostic tool for liver fibrosis, especially in the absence of liver biopsies or elastography.

Data availability
Figshare: CHB for stepwise M2BPGi -Rev 1, https://doi.org/10.6084/m9.figshare.26195954.v1. 28is project contains the following underlying data: Your detailed explanations and revisions to the manuscript have significantly enhanced its clarity and quality.The additional data and insights have strengthened the overall argument and contributed to a better understanding of the study's implications.
After reviewing the revised manuscript, we are pleased to conclude that it meets the necessary standards for getting approved for indexing.

Best regards, Chyntia Olivia Maurine Jasirwan
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: gastroenterology and hepatology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Version 1
Reviewer

Chyntia Olivia Maurine Jasirwan
Universitas Indonesia and Dr. Cipto Mangunkusumo Hospital, Jakarta, Indonesia The manuscript investigates the use of Mac-2-binding protein glycosylation isomer (M2BPGi) as a diagnostic marker for liver fibrosis in chronic hepatitis B (CHB) patients.The study aims to determine the optimal cut-off values for M2BPGi and other non-invasive fibrosis markers (APRI, FIB-4, and AAR) and assess the diagnostic performance of these markers, both individually and in combination through a stepwise diagnostic analysis.
This manuscript clearly presents objectives, methods, results, and conclusions, includes a comprehensive literature review that situates the study within the context of existing research, and accurately and appropriately cites relevant studies.

Suggestions :
-Abstract: Briefly mention the statistical methods used in the analysis.
-Introduction: Add a few sentences highlighting the specific advantages of non-invasive methods over liver biopsies.
-Discussion of Limitations: Provide a more detailed discussion of the study's limitations, such as the cross-sectional design, potential biases, and generalizability of the findings.
-Future Directions: Suggest specific areas for future research, such as validating the findings in larger, diverse populations.
-Analytical Code: Provide the exact scripts or commands used in SPSS and STATA for the analyses.This can be included as supplementary files.
Quality Control Measures: Please mention any quality control measures or calibration procedures for the laboratory tests and liver elastography.
-Supplementary Material: Include supplementary material detailing the step-by-step process of data processing and analysis.Confidence Intervals: Include confidence intervals for key statistics such as AUC values and cutoff points to provide a sense of the estimates' precision.
-Validation: Discuss the potential for external validation of the findings in other populations or settings.
-Subgroup Analysis: Perform and discuss subgroup analyses (e.g., based on age and gender) to provide additional insights.
-Processed Data: Include processed data and the analytical code used for statistical analyses.
-Detailed Data Files: Ensure data files are well-documented with clear labels and descriptions of each variable.
-Reproducibility Checklist: Provide a checklist or protocol outlining all steps from data collection to final analysis.
-Limitations: Expand on the limitations section to provide a balanced perspective.
-Future Research Directions: Include specific suggestions for future research based on the study's findings.
-Confidence Intervals and Effect Sizes: Include these in the results to provide a clearer sense of the precision and reliability of the estimates.

Conclusion
The manuscript is scientifically sound, with a clear presentation, appropriate study design, and robust statistical analysis.Minor enhancements, such as providing detailed analytical code, discussing limitations more explicitly, and suggesting future research directions, will further strengthen the manuscript.Addressing these points will ensure the article meets high standards of scientific rigor and transparency.

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility?Partly Are the conclusions drawn adequately supported by the results?revised our manuscript to improve the quality and meet the high standards of F1000 and the reviewers without conflict of interest.Kemal Fariz Kalista 1 Universitas Indonesia, Depok, West Java, Indonesia 2 Internal Medicine, Cipto Mangunkusumo Hospital, Jakarta, Jakarta, Indonesia I think this is very good work.This study comparing M2BPGi with elastography and many scoring system to assess liver fibrosis like APRI, FIB-4 and AAR, so i think this is a strength of this study, because not many studies comparing M2BPGi with multiple scoring system like this study.I consider to add "patient with chronic hepatitis B" in study title, because population in this study only include patient with hepatitis B. In discussion, I also consider to elaborate further regarding why APRI cut off for diagnosing liver fibrosis (0.311) is lower than many previous studies.It would be appreciate if author more explain why APRI was excluded from the model, because I think APRI is quite easy way to do in daily practice.Regarding study title "stepwise diagnosis analysis" I would suggest the author to provide how to apply study result to real life practice, eg: when considering patient for further fibrosis assessment with elastography or liver biopsy or if the M2BPGi result < 1.4 and FIB-4 > 0.7 what author suggestion in this kind of situation.Minor revision maybe it would be better if in table 2, in variable APRI, FIB-4, AAR and M2BPGi row, add note which one is lower or higher than cut off level.I think this study is approved with reservation, need to add further revision.

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate?Yes Are all the source data underlying the results available to ensure full reproducibility?

Figure 1 .
Figure 1.Flowchart for selection of study subjects.
Report 27 June 2024 https://doi.org/10.5256/f1000research.161310.r286137© 2024 Jasirwan C.This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reviewer Report 14
June 2024 https://doi.org/10.5256/f1000research.161310.r273641© 2024 Kalista K.This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The p-value is the result of the binary logistic regression analysis.M2BPGi: Mac-2-binding protein glycosylation isomer; APRI: AST-toplatelet ratio index; FIB4: fibrosis index based on 4 factors; AAR: AST-to-ALT ratio.

Table 4 .
ROC analysis of the models for diagnosing high-risk fibrosis (LSM ≥8 kPa) amongst 143 patients.

Table 5 .
ROC comparison of the models for diagnosing high-risk fibrosis (LSM ≥8 kPa) amongst 143 patients.