Non-invasive tests (aspartate aminotransferase-to-platelet ratio index [APRI] and transient elastography [FibroScan]) were recommended in the 2015 WHO guidelines to guide treatment decisions in people with chronic hepatitis B. We updated the systematic review and meta-analysis that informed the 2015 guidelines to inform new cutoffs for non-invasive tests for the diagnosis of significant fibrosis and cirrhosis for the 2024 WHO guidelines for chronic hepatitis B.

We searched PubMed (MEDLINE), Embase, and Science Citation Index Expanded (Web of Science) for studies published in any language between Jan 1, 2014, and Feb 15, 2023. We included all studies that reported cross-sectional data on the staging of fibrosis or cirrhosis with APRI, Fibrosis-4 (FIB-4), and FibroScan compared with liver biopsy as the reference standard in people with chronic hepatitis B. We excluded studies in which the maximum interval between liver biopsy and non-invasive fibrosis test was more than 6 months; that reported on fewer than ten patients with advanced fibrosis or cirrhosis; that were done exclusively in children; and did not report diagnostic accuracy across our prespecified ranges of test cutoffs. The results of this updated search were collated with the meta-analysis that informed the 2015 guidelines. Outcomes of interest were the sensitivity and specificity of non-invasive tests using defined index test cutoffs for detecting significant fibrosis (≥F2), advanced fibrosis (≥F3), and cirrhosis (F4) based on the METAVIR staging system. We performed meta-analyses using a bivariate random-effects model.

Of 19 933 records identified by our search strategy, 195 were eligible for our systematic review and combined with the 69 studies from the previous meta-analysis to total 264. Two studies were at low risk of bias, 31 studies had unclear risk of bias, and 231 studies had a high risk of bias. Of these 264, 211 studies with 61 665 patients were used in the meta-analysis. For the diagnosis of significant fibrosis (≥F2), sensitivity and specificity were 72·9% (95% CI 70·2-75·5) and 64·7% (95% CI 61·0-68·2) for the APRI low cutoff (>0·3 to 0·7), 30·5% (23·7-38·3) and 92·3% (89·3-94·6) for the APRI high cutoff (>1·3 to 1·7), and 75·1% (72·2-77·7) and 79·3% (76·2-82·2) for FibroScan (>6·0 to 8·0 kPa), respectively. For the diagnosis of cirrhosis (F4), sensitivity and specificity were 59·4% (53·2-65·2) and 73·9% (70·1-77·4) for the APRI low cutoff (>0·8 to 1·2), 30·2% (24·2-36·9) and 88·2% (85·4-90·6) for the APRI high cutoff (>1·8 to 2·2), and 82·6% (77·8-86·5) and 89·0% (86·3-91·2) for FibroScan (>11·0 to 14·0 kPa), respectively. Using a hypothetical population of 1000 unselected patients with chronic hepatitis B with a 25% prevalence of significant fibrosis (≥F2), the APRI low cutoff for significant fibrosis (≥F2) would result in 262 (26·2%) false positives but only 68 (6·8%) false negatives. The FibroScan cutoff would result in 158 (15·8%) false positives and 63 (6·3%) false negatives. In a population with a 5% prevalence of cirrhosis (F4), the APRI low cutoff for cirrhosis (F4) would result in 247 (24·7%) false positives and 21 (2·1%) false negatives and the FibroScan cutoff would result in 105 (10·5%) false positives and nine (0·9%) false negatives.

These findings have informed new thresholds of APRI and FibroScan for diagnosis of significant fibrosis and cirrhosis in the 2024 WHO guidelines on chronic hepatitis B, with an APRI score greater than 0·5 or a FibroScan value greater than 7·0 kPa considered to identify most adults with significant fibrosis (≥F2) and an APRI score greater than 1·0 or a FibroScan value greater than 12·5 kPa to identify most adults with cirrhosis (F4). These patients are a priority for antiviral treatment.

WHO.

The global prevalence of non-alcoholic steatohepatitis (NASH) and its associated risk of adverse outcomes, particularly in patients with advanced liver fibrosis, underscores the importance of early and accurate diagnosis.

To develop a machine learning-based diagnostic model for advanced liver fibrosis in NASH patients.

A total of 749 patients who underwent liver biopsy at Beijing Ditan Hospital, Capital Medical University, between January 2010 and January 2020 were included. Patients were randomly divided into training (n = 522) and validation (n = 224) cohorts. Five machine learning models were applied to predict advanced liver fibrosis, with feature selection based on Shapley Additive Explanations (SHAP). The diagnostic performance of these models was compared to traditional scores such as the aspartate aminotransferase to platelet ratio index (APRI) and fibrosis index based on the 4 factors (FIB-4), using metrics including the area under the receiver operating characteristic curve (AUROC), decision curve analysis (DCA), and calibration curves.

The Extreme Gradient Boosting (XGBoost) model outperformed all other machine learning models, achieving an AUROC of 0.934 (95%CI: 0.914-0.955) in the training cohort and 0.917 (95%CI: 0.880-0.953) in the validation cohort (P < 0.001). Incorporating liver stiffness measurement into the model further improved its performance, with an AUROC of 0.977 (95%CI: 0.966-0.980) in the training cohort and 0.970 (95%CI: 0.950-0.990) in the validation cohort, significantly surpassing APRI and FIB-4 scores (P < 0.001). The XGBoost model also demonstrated superior clinical utility, as evidenced by DCA and calibration curve analysis in both cohorts.

The XGBoost model provides a highly accurate, non-invasive diagnosis of advanced liver fibrosis in NASH patients, outperforming traditional methods. An online tool based on this model has been developed to assist clinicians in evaluating the risk of advanced liver fibrosis.

Chronic hepatitis B (CHB) infection represents a significant global public health issue, often leading to hepatitis B virus (HBV)-related liver cirrhosis (HBV-LC) with poor prognoses. Early identification of HBV-LC risk is essential for timely intervention. This study develops and compares nine machine learning (ML) models to predict HBV-LC risk in CHB patients using routine clinical and laboratory data. A retrospective analysis was conducted involving 777 CHB patients, with 50.45% (392/777) progressing to HBV-LC. Admission data consisted of 52 clinical and laboratory variables, with missing values addressed using multiple imputation. Feature selection utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression and the Boruta algorithm, identifying 24 key variables. The evaluated ML models included XGBoost, logistic regression (LR), LightGBM, random forest (RF), AdaBoost, Gaussian naive Bayes (GNB), multilayer perceptron (MLP), support vector machine (SVM), and k-nearest neighbors (KNN). The data set was partitioned into an 80% training set (n = 621) and a 20% independent testing set (n = 156). Cross-validation (CV) facilitated hyperparameter tuning and internal validation of the optimal model. Performance metrics included the area under the receiver operating characteristic curve (AUC), Brier score, accuracy, sensitivity, specificity, and F1 score. The RF model demonstrated superior performance, with AUCs of 0.992 (training) and 0.907 (validation), while the reconstructed model achieved AUCs of 0.944 (training) and 0.945 (validation), maintaining an AUC of 0.863 in the testing set. Calibration curves confirmed a strong alignment between observed and predicted probabilities. Decision curve analysis indicated that the RF model provided the highest net benefit across threshold probabilities. The SHAP algorithm identified RPR, PLT, HBV DNA, ALT, and TBA as critical predictors. This interpretable ML model enhances early HBV-LC prediction and supports clinical decision-making in resource-limited settings.

Blood-based biomarkers have been proposed as an alternative to liver biopsy for noninvasive liver disease assessment in chronic liver disease. Our aims for this systematic review were to evaluate the diagnostic utility of selected blood-based tests either alone, or in combination, for identifying significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4), as compared to biopsy in chronic liver disease.

We included a comprehensive search of databases including Ovid MEDLINE(R), EMBASE, Cochrane Database, and Scopus through to April 2022. Two independent reviewers selected 286 studies with 103,162 patients. The most frequently identified studies included the simple aspartate aminotransferase-to-platelet ratio index and fibrosis (FIB)-4 markers (with low-to-moderate risk of bias) in HBV and HCV, HIV-HCV/HBV coinfection, and NAFLD. Positive (LR+) and negative (LR-) likelihood ratios across direct and indirect biomarker tests for HCV and HBV for F2-4, F3-4, or F4 were 1.66-6.25 and 0.23-0.80, 1.89-5.24 and 0.12-0.64, and 1.32-7.15 and 0.15-0.86, respectively; LR+ and LR- for NAFLD F2-4, F3-4, and F4 were 2.65-3.37 and 0.37-0.39, 2.25-6.76 and 0.07-0.87, and 3.90 and 0.15, respectively. Overall, the proportional odds ratio indicated FIB-4 <1.45 was better than aspartate aminotransferase-to-platelet ratio index <0.5 for F2-4. FIB-4 >3.25 was also better than aspartate aminotransferase-to-platelet ratio index >1.5 for F3-4 and F4. There was limited data for combined tests.

Blood-based biomarkers are associated with small-to-moderate change in pretest probability for diagnosing F2-4, F3-4, and F4 in viral hepatitis, HIV-HCV coinfection, and NAFLD, with limited comparative or combination studies for other chronic liver diseases.

The global rise of chronic hepatitis B (CHB) superimposed on hepatic steatosis (HS) warrants noninvasive, precise tools for assessing fibrosis progression. This study leveraged machine learning (ML) to develop diagnostic models for advanced fibrosis and cirrhosis in this patient population.

Treatment-naive CHB patients with concurrent HS who underwent liver biopsy in 10 medical centers were enrolled as a training cohort and an independent external validation cohort (NCT05766449). Six ML models were implemented to predict advanced fibrosis and cirrhosis. The final models, derived from SHAP (Shapley Additive exPlanations), were compared with Fibrosis-4 Index, nonalcoholic fatty liver disease Fibrosis Score, and aspartate aminotransferase-to-platelet ratio index using the area under receiver-operating characteristic curve (AUROC) and decision curve analysis (DCA).

Of 1,198 eligible patients, the random forest model achieved AUROCs of 0.778 (95% confidence interval [CI], 0.749-0.807) for diagnosing advanced fibrosis (random forest advanced fibrosis model) and 0.777 (95% CI, 0.748-0.806) for diagnosing cirrhosis (random forest cirrhosis model) in the training cohort, and maintained high AUROCs in the validation cohort. In the training cohort, the random forest advanced fibrosis model obtained an AUROC of 0.825 (95% CI, 0.787-0.862) in patients with hepatitis B virus DNA ≥105 IU/mL, and the random forest cirrhosis model had an AUROC of 0.828 (95% CI, 0.774-0.883) in female patients. The 2 models outperformed Fibrosis-4 Index, nonalcoholic fatty liver disease Fibrosis Score, and aspartate aminotransferase-to-platelet ratio index in the training cohort, and also performed well in the validation cohort.

The random forest models provide reliable, noninvasive tools for identifying advanced fibrosis and cirrhosis in CHB patients with concurrent HS, offering a significant advancement in the comanagement of the 2 diseases.

gov, Number: NCT05766449.

Background: There is a strong correlation between systemic inflammation intensity and clinical presentation, disease progression, and survival during liver cirrhosis decompensation. This study aimed to evaluate the prognostic performance of blood-based biomarkers as meta-inflammation markers, including NLR, PLR, LMR, INPR, MPR, ALBI, FIB4, and APRI, in predicting hospital mortality in patients with acute decompensation of alcohol-related liver cirrhosis. Methods: Data from 411 patients with their first onset of acute decompensation were analyzed, forming two groups: deceased and survived during hospitalization. Generalized partial least squares regression analysis was applied to explore the effects of surrogate indicators on mortality rates, using mortality rate as the dependent variable. Root Mean Square Error, Akaike's, and Bayesian information criteria determined that four components accounted for most of the variance. Results: Variables with significant negative contributions to the outcome prediction (ranked by standardized regression coefficients) were encephalopathy grade, total bilirubin, Child-Turcotte-Pugh score, MELD, NLR, MPV, FIB4, INR, PLR, and ALT. Coefficient sizes ranged from -0.63 to -0.09, with p-values from 0 to 0.018. Conclusions: NLR, PLR, and FIB4 significantly contribute to hospital mortality prediction in patients with acute decompensation of alcohol-related liver cirrhosis. Conversely, some variables used to predict liver disease severity, including INPR, APRI, LMR, and ALBI score, did not significantly contribute to hospital mortality prediction in this patient population.

This study aimed to construct a non-invasive diagnostic nomogram based on high-frequency ultrasound and magnetic resonance imaging results for early liver cirrhosis patients with chronic hepatitis B (CHB) which cannot be detected by conventional non-invasive examination methods but can only be diagnosed through invasive liver puncture for pathological examination. 72 patients with CHB were enrolled in this prospective study, and divided into S4 stage of liver cirrhosis and S0-S3 stage of non-liver cirrhosis according to pathological findings. Binary logistic regression analysis was performed to identify independent predictors, and a diagnostic nomogram was constructed for CHB-related early cirrhosis. It was validated and calibrated by bootstrap self-extraction. Binary logistic regression analysis showed that age (OR 1.14, 95% CI (1.04-1.27)), right hepatic vein diameter (OR 0.43, 95% CI 0.23-0.82), presence or absence of nodules (OR 31.98, 95% CI 3.84-266.08), and hepatic parenchymal echogenicity grading (OR 12.82, 95% CI 2.12-77.51) were identified as independent predictive indicators. The nomogram based on the 4 factors above showed good performance, with a sensitivity and specificity of 90.70% and 89.66%, respectively. The area under the curve (AUC) of the prediction model was 0.96, and the predictive model showed better predictive performance than APRI score (AUC 0.57), FIB-4 score (AUC 0.64), INPR score (AUC 0.63), and LSM score (AUC 0.67). The calibration curve of the prediction model fit well with the ideal curve, and the decision curve analysis showed that the net benefit of the model was significant. The nomogram in this study can detect liver cirrhosis in most CHB patients without liver biopsy, providing a direct, fast, and accurate practical diagnostic tool for clinical doctors.

Spontaneous intracerebral hemorrhage (ICH) poses a public health issue due to its elevated mortality rates. The International Normalized Ratio-platelet index (INR-Plt index) has recently been recognized as a predictive factor for liver disease progression. The potential of applying the INR-Plt index in forecasting ICH prognosis presents an intriguing subject. This study endeavors to examine the correlation between the INR-Plt index and hospital outcomes in patients with spontaneous supratentorial ICH.

A retrospective examination of 283 adult ICH patients was undertaken. The INR-Plt index was computed using the formula: [INR/platelet counts (1000/μL)] × 100. The clinical outcomes evaluated consisted of mortality rates and the Modified Rankin Scale (mRS) at discharge. An unfavorable outcome was defined as an mRS score from 4 to 6.

The study found a significant correlation between the INR-Plt index and hospital mortality (odds ratio: 4.31, 95% CI: 1.07-17.31, P = 0.04). There was a 43% rise in mortality risk for every 0.1 unit increase in the INR-Plt index. Kaplan-Meier survival curves illustrated a considerably lower survival rate at discharge for patients with an INR-Plt index >0.8 (log-rank test: P = 0.047). Regarding unfavorable outcomes, the INR-Plt index was not a significant factor according to logistic regression analyses.

The INR-Plt index is a predictor of hospital mortality in patients with spontaneous supratentorial ICH. A higher INR-Plt index value is associated with an increased risk of mortality, underlining the potential usefulness of this composite index in guiding clinical decision-making and enabling risk stratification.

Rebleeding following endoscopic treatment in patients with cirrhosis is a serious life-threatening complication. In the present study, a novel, reliable and non-invasive score for prediction of rebleeding following endoscopic therapy for esophagogastric variceal bleeding (EGVB) was developed. The present retrospective study recruited cirrhotic patients with EGVB (n=596) who underwent endoscopic therapy. Patients hospitalized from January 2015 to January 2020 were grouped into a training (n=437) cohort to develop the new score and those hospitalized from February 2020 to February 2022 were grouped into a validation (n=159) cohort to validate the score. The international normalized ratio (INR) and albumin-bilirubin (ALBI) grade were used to develop the INR-ALBI (IALBI) score to predict risk of rebleeding. In the training cohort, the prognostic performance of the IALBI score and other ALBI-associated scores (modified ALBI, platelet-ALBI and ALBI-fibrosis-4) at 1, 3 and 12 months was assessed using receiver operating characteristic (ROC) curve and Kaplan-Meier analysis. At each time point, most areas under the ROC curve of IALBI were higher than those of other ALBI-associated scores, particularly for prediction of early rebleeding. At 1 month, the rebleeding rates of patients with IALBI grade 2 and 3 were ~10.0- and 19.5-times higher than those of patients with grade 1, respectively. The negative predictive value (NPV) of IALBI for the training and validation cohort at 1 month was 100.0 and 97.8%, respectively. For viral and non-viral patients in the training cohort, IALBI showed good predictive ability and NPV for early rebleeding. The IALBI grading system successfully assessed rebleeding, particularly early rebleeding, in cirrhotic patients with EGVB following endoscopic therapy IALBI grade 1, predicted low risk of rebleeding and may not require endoscopic treatment again in the short-term.

The aim of this study was to compare the correlation of gamma-glutamyl transpeptidase-to-platelet ratio (GPR), aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis index-4 (FIB-4), and liver stiffness measurement (LSM) in the diagnosis of liver fibrosis, and perform a diagnostic value of GPR for predicting fibrosis in CHB patients with NAFLD.

A retrospective study was conducted on CHB patients concurrent with NAFLD between September 2019 and December 2020. They were divided into control group (LSM ≤ 9.7 kpa) and fibrosis group (LSM ≥ 9.8 kpa). Demographic data were collected; ALT, AST, and PLT were also detected. LSM was measured by transient elastography (TE). The GPR, APRI, and FIB-4 were calculated. The correlation between GPR, APRI, FIB-4, and LSM was compared. The accuracy of predicting liver fibrosis using GPR, APRI, and FIB-4 was assessed.

Eighty-five CHB patients with NAFLD were enrolled. Multivariate analysis showed that age (p = 0.005), GGT (p = 0.001), and PLT (p = 0.013) were the independent risk factors for LSM. The GPR (p = 0.008), APRI (p = 0.001), and FIB-4 (p = 0.001) values in fibrosis group were higher than control group. Pearson linear correlation was used to analyze the correlations between LSM and GPR, APRI, and FIB-4. LSM was correlated with GPR, APRI, and FIB-4. The AUCs of GPR, APRI, and FIB4 were 0.805, 0.766, and 0.826 in assessing liver fibrosis, respectively. No significant differences in the areas of GPR were comparable to that of APRI and FIB-4.

GPR has a good correlation with LSM in assessing liver fibrosis and can be used as a noninvasive index for the assessment of liver fibrosis in patients with concomitant CHB and NAFLD.

Chronic hepatitis B (CHB) as the major inducement of hepatocellular carcinoma and cirrhosis, imposes a heavy health burden upon patients. This research aims to investigate the diagnostic value of serum chitinase 3-like 1 (CHI3L1) in hepatitis B e antigen (HBeAg)-negative CHB liver fibrosis (LF) and to analyze the risk factors. We selected 78 patients with HBeAg-negative CHB admitted to our hospital between October 2018 and October 2019, and grouped them (F_0,1 group, n=38; F_2-4 group, n=40) based on their stages evaluated by the METAVIR scoring system. Cubital venous blood was collected from patients in both groups to quantify the content of CHI3L1 after serum extraction. The correlation of CHI3L1 in CHB with LF diagnostic markers fibrosis 4 (FIB-4) and γ-glutamyltranspeptidase (GGT) to platelet (PLT) ratio (GPR) as well as LF staging was analyzed. The diagnostic value of serum CHI3L1 in HBeAg-negative CHB fibrosis staging was analyzed by receiver operating characteristic (ROC) curve, and the multivariate analysis of the risk factors for FB in HBeAg-negative CHB patients was performed using the Logistic regression model. This study found that serum CHI3L1 was positively correlated not only with LF markers (FIB-4, GPR), but also with LF staging. Serum CHI3L1 had high diagnostic efficiency for LF staging, with the sensitivity and specificity of 80.00% and 71.05%, respectively. In addition, CHI3L1, FIB-4, and GPR were identified to be the risk factors for LF in HBeAg-negative CHB. In conclusion, serum CHI3L1 can be used as a diagnostic marker and risk factor for LF in patients with HBeAg-negative CHB.

At present, there is a lack of cheap, effective and convenient detection methods for hepatitis B-related liver fibrosis, especially in the developing area.

To evaluate the non-invasive methods for the significant and advanced fibrosis stage in chronic hepatitis B virus (HBV) patients in basic hospitals and to assess their diagnostic utility.

The study included 436 consecutive naive HBV individuals who had their livers biopsied. They were examined in one week using aspartate aminotransferase-to-aspartate aminotransferase ratio (AAR), age-platelet index (API), aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 (FIB-4), Forns, gamma-glutamyl transpeptidase-to-platelet ratio (GPR), S-index and transient elastography (TE). Scheuer scoring system was used to determine the histologic fibrosis grades (S0-S4). The diagnostic effectiveness was assessed using AUROCs and the DeLong test, both of which were based on statistical comparisons.

For both substantial (≧S2) and advanced (≧S3) fibrosis phases, TE had good diagnostic performance in determining the hepatic fibrosis. Similar diagnostic performance was shown with Forns and S-index when it came to detecting fibrosis stages lower than S3. One model's diagnostic value was not significantly improved by combining serum models. Correlation coefficients between clinical features and fibrosis phases were greatest for Forns (r = 0.397), S-index (r = 0.382) and TE (r = 0.535) when compared to other variables.

This investigation showed that Forns and S-index may be helpful strategies for detecting advanced fibrosis in HBV patients admitted to community hospitals.

Even though an approved vaccine for hepatitis B virus (HBV) is available and widely used, over 257 million individuals worldwide are living with chronic hepatitis B (CHB) who require monitoring of treatment response, viral activity, and disease progression to reduce their risk of HBV-related liver disease. There is currently a lack of predictive markers to guide clinical management and to allow treatment cessation with reduced risk of viral reactivation. Novel HBV biomarkers are in development in an effort to improve the management of people living with CHB, to predict disease outcomes of CHB, and further understand the natural history of HBV. This review focuses on novel HBV biomarkers and their use in the clinical setting, including the description of and methodology for quantification of serum HBV RNA, hepatitis B core-related antigen (HBcrAg), quantitative hepatitis B surface antigen (qHBsAg), including ultrasensitive HBsAg detection, quantitative anti-hepatitis B core antigen (qAHBc), and detection of HBV nucleic acid-related antigen (HBV-NRAg). The utility of these biomarkers in treatment-naïve and treated CHB patients in several clinical situations is further discussed. Novel HBV biomarkers have been observed to provide critical clinical information and show promise for improving patient management and our understanding of the natural history of HBV.

We aimed to formulate a novel predictive nomogram to discriminate liver fibrosis stage in patients with chronic liver disease.

Nomograms were established based on the results of multivariate analysis. The predictive accuracy of the nomograms was assessed by ROC analysis and calibration. Decision curve analysis (DCA) was used to determine the clinical benefit of the nomograms.

INR, platelets, and N-terminal propeptide type III collagen (PIIINP) were independent predictors for advanced liver fibrosis (≥ S3) and cirrhosis (S4) in patients with chronic liver disease in the training cohort. In the training set, the areas under the ROCs (AUROCs) of nomogram S3S4, APRI, FIB-4, and GPR for stage ≥ S3 were 0.83, 0.71, 0.68, and 0.74, respectively; the AUROCs of nomogram S4, APRI, FIB-4, and GPR for stage S4 were 0.88, 0.74, 0.78, and 0.79, respectively. The calibrations showed optimal agreement between the prediction by the established nomograms and actual observation. In the validation set, the AUROCs of nomogram S3S4, APRI, FIB-4, and GPR for stage ≥ S3 were 0.86, 0.79, 0.78, and 0.81, respectively; the AUROCs of nomogram S4, APRI, FIB-4, and GPR for stage S4 were 0.88, 0.77, 0.81, and 0.83, respectively. Furthermore, the decision curve analysis suggested that the nomograms represent better clinical benefits in both independent cohorts than APRI, FIB-4, and GPR.

The constructed nomograms could be a superior tool for discriminating advanced fibrosis and cirrhosis in chronic liver disease.

Blood parameter ratios, including neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and monocyte to lymphocyte ratio (MLR), have been reported that they are correlated to the progression of liver disease. This study is aimed at evaluating the predictive value of PLR, NLR, and MLR for liver inflammation and fibrosis in patients with chronic hepatitis B (CHB).

We recruited 457 patients with CHB who underwent a liver biopsy and routine laboratory tests. Liver histology was assessed according to the Scheuer scoring system. The predictive accuracy for liver inflammation and fibrosis was assessed by receiver operating characteristics (ROC) analysis.

PLR and NLR presented significantly reverse correlation to liver inflammation and fibrosis. However, these correlations were not observed for MLR and liver histology. The AUROCs of PLR for assessing G2-3 and G3 were 0.676 and 0.705 with cutoffs 74.27 and 68.75, respectively. The AUROCs of NLR in predicting inflammatory scores G2-3 and G3 were 0.616 and 0.569 with cutoffs 1.36 and 1.85, respectively. The AUROCs of PLR for evaluating fibrosis stages S3-4 and S4 were 0.723 and 0.757 with cutoffs 79.67 and 74.27, respectively. The AUROCs of NLR for evaluating fibrosis stages S3-4 and S4 were 0.590 with cutoff 1.14.

Although PLR has similar predictive power of progressive liver fibrosis compared with APRI, FIB-4, and GPR in CHB patients, it has the advantage of less cost and easy application with the potential to be widely used in clinical practice.