ObjectiveTo investigate the influencing factors for recompensation in patients with decompensated liver cirrhosis， as well as the impact of recompensation on the prognosis of such patients， and to provide a basis for early identification of high-risk patients in clinical practice. MethodsA retrospective analysis was performed for the clinical data of patients who attended The Third People’s Hospital of Kunming from January 2016 to December 2022 and were diagnosed with decompensated liver cirrhosis due to hepatitis B， hepatitis C， alcoholic hepatitis， and autoimmune hepatitis， and they were divided into recompensation group and persistent decompensation group. To control for confounding factors， whether recompensation occurred was used as the rouping variable，and BMI， alcohol consumption history， HIV infection history， TG， CHOL， LDL， and HDL were used as covariates. The propensity score was calculated， and 1：1 nearest neighbor matching was performed with a caliper value of 0.1. After propensity score matching， the recompensation group and the persistent decompensation group with relatively balanced covariates were obtained. Univariate and multivariate Cox proportional-hazards regression model analyses were used to investigate the influencing factors for recompensation； the “rms” package was used to establish a nomogram； the receiver operating characteristic （ROC） curve was plotted to calculate the area under the ROC curve （AUC）； the Hosmer-Lemeshow test was used to assess the goodness of fit of the model； the “Calibration Curves” package was used to plot calibration curves for model assessment. The Kaplan-Meier method was used to plot survival curves， and the Log-rank test was used for comparison of survival curves. ResultsAmong the 863 patients with decompensated liver cirrhosis， 305 experienced recompensation， resulting in an incidence rate of 35.3%. After PSM， 610 cases were successfully matched， with 305 cases in each group. The univariate and multivariate Cox regression analyses showed that etiology （hepatitis C： hazard ratio［HR］=0.288， P=0.002）； male（HR=0.701， P=0.016）， age（HR=0.988， P=0.047）， hemoglobin （HGB）（HR=1.006， P=0.017）， and CD4 T cell（HR=1.001，P=0.047）， TIPS procedure （HR=1.808，P=0.042） were independent influencing factors for recompensation in patients with decompensated liver cirrhosis. During follow-up， 116 patients died of liver disease-related causes， with 27 patients （8.85%） in the recompensation group and 89 （15.95%） in the persistent decompensation group； 109 patients developed HCC， with 23 patients （7.54%） in the recompensation group and 86 （15.41%） in the persistent decompensation group. The Kaplan-Meier survival curves showed significant separation between the patients with different states of compensation in terms of liver disease-related mortality rate and the incidence rate of HCC， and the Log-rank test showed that there were significant differences between the two groups in liver disease-related mortality rate （χ²=9.023， P=0.003） and the incidence rate of HCC （χ²=10.526， P=0.001）. ConclusionEtiology，sex，age，TIPS，HGB，and CD4 T cell are independent influencing factors for recompensation in patients with decompensated liver cirrhosis. There is a significant difference in the incidence rate of recompensation between decompensated liver cirrhosis patients with different etiologies， and female patients and patients with a younger age，a history of TIPS， a higher HGB level， and a higher CD4 lymphocyte count are more likely to experience recompensation. Recompensation is the key to improving the long-term prognosis of patients and can significantly reduce long-term liver disease-related mortality rate and the incidence rate of HCC.

ObjectiveTo investigate the predictive factors for HBsAg clearance in chronic hepatitis B （CHB） patients with a low level of hepatitis B surface antigen （HBsAg） treated with pegylated interferon α-2b （PEG-IFN-α-2b）， to establish a combined predictive model and a nomogram based on multiple factors， and to provide a reference for formulating individualized treatment regimens and predicting treatment outcome in clinical practice. MethodsA retrospective analysis was performed for 167 CHB patients with HBsAg <1 500 IU/mL who attended The Third People’s Hospital of Kunming from January 2022 to January 2024 and were treated with PEG-IFN-α-2b. According to whether clinical cure was achieved， the patients were divided into HBsAg clearance group and HBsAg non-clearance group. Related data were collected， including general information and serological/biochemical/virological indicators at different time points during treatment. The independent samples t-test was used for comparison of normally distributed continuous data， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data； the chi-square test was used for comparison of categorical data. The multivariate logistic regression analysis was used to identify independent influencing factors. The receiver operating characteristic （ROC） curve was used to assess the value of indicators used alone or in combination in predicting clinical cure， and calibration curves were plotted to assess the risk prediction model. ResultsThe univariate analysis showed that there were significant differences between the two groups in age （t=-6.839， P<0.05）， history of nucleos（t）ide analogue treatment for over 1 year （χ²=59.339， P<0.05）， genotype （χ²=4.610， P<0.05）， nonalcoholic fatty liver disease （χ²=5.319， P<0.05）， hepatitis B virus DNA status before treatment （χ²=60.861， P <0.05）， compensated liver cirrhosis （χ²=10.960， P<0.05）， HBeAg status before treatment （χ²=19.060， P<0.05）， a history of interferon treatment （χ²=8.162， P<0.05）， presence of interferon antibodies after treatment （χ²=12.858， P<0.05）， HBsAg level before treatment （Z=-7.412， P<0.05）， alanine aminotransaminase （ALT） level at baseline （Z=-6.117， P<0.05）， ALT level at 12 weeks of treatment （Z=-7.171， P<0.05）， platelet count （PLT） at 24 weeks of treatment （Z=-3.622， P<0.05）， and thyroid stimulating hormone （TSH） level at 24 weeks of treatment （Z=-2.830， P<0.05）. The multivariate logistic regression analysis showed that age （odds ratio ［OR］=1.230， P=0.007）， history of nucleos（t）ide analogue treatment for over 1 year （OR=0.008， P=0.011）， HBeAg status before treatment （OR=0.003， P=0.012）， HBsAg level before treatment （OR=1.005， P=0.014）， ALT level at baseline （OR=0.949， P=0.014）， ALT level at 12 weeks of treatment （OR=0.969， P=0.016）， PLT at 24 weeks of treatment （OR=0.969， P=0.022）， and TSH level at 24 weeks of treatment （OR=3.608， P=0.045） were independent influencing factors for HBsAg clearance at 48 weeks of treatment in CHB patients with HBsAg <1 500 IU/mL. The Hosmer-Lemeshow goodness-of-fit test yielded χ²=1.398， P=0.994， indicating that the model had good fitting. The Bootstrap method was used to perform internal validation of the nomogram model， and there was a good degree of fitting between the calibration curve and the ideal curve， with a mean absolute error of 0.029. The ROC curve analysis showed that the combination of predictive factors had an area under the ROC curve of 0.982 （95% confidence interval： 0.961 — 0.999）， with a sensitivity of 94.10% and a specificity of 93.10%， suggesting that the nomogram model had a good discriminatory ability. For the CHB patients with HBsAg <1 500 IU/mL and different features， further analysis of HBsAg clearance rate at 48 weeks of treatment showed an HBsAg clearance rate of 69.60% for those with HBsAg ≤67.65 IU/mL before treatment， 58.30% for those with a baseline ALT level of ≥62.50 U/L， 68.30% for those with an ALT level of ≥92.50 U/L at 12 weeks of treatment， 42.40% for those with PLT ≥104×10⁹/L at 24 weeks of treatment， and 48.30% for those with a TSH level of ≤1.38 μIU/mL at 24 weeks of treatment， with significant differences between the two groups （all P<0.001）. ConclusionAge， history of nucleos（t）ide analogue treatment for over 1 year， HBeAg status before treatment， HBsAg level before treatment， baseline ALT level， ALT level at 12 weeks of treatment， PLT level at 24 weeks， and TSH level at 24 weeks of treatment are independent predictive factors. The combined prediction nomogram model constructed in this study has a relatively high value in predicting clinical cure at 48 weeks of PEG-IFN-α-2b treatment in CHB patients with HBsAg<1 500 IU/mL， thereby providing a reference for selecting suitable treatment population and predicting clinical cure.

ObjectiveTo investigate the influencing factors for recompensation in patients with decompensated hepatitis C cirrhosis， and to establish a predictive model. MethodsA total of 217 patients who were diagnosed with decompensated hepatitis C cirrhosis and were admitted to The Third People’s Hospital of Kunming l from January， 2019 to December， 2022 were enrolled， among whom 63 patients who were readmitted within at least 1 year and had no portal hypertension-related complications were enrolled as recompensation group， and 154 patients without recompensation were enrolled as control group. Related clinical data were collected， and univariate and multivariate analyses were performed for the factors that may affect the occurrence of recompensation. The independent-samples t test was used for comparison of normally distributed measurement data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed measurement data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. A binary Logistic regression analysis was used to investigate the influencing factors for recompensation in patients with decompensated hepatitis C cirrhosis， and the receiver operating characteristic （ROC） curve was used to assess the predictive performance of the model. ResultsAmong the 217 patients with decompensated hepatitis C cirrhosis， 63 （29.03%） had recompensation. There were significant differences between the recompensation group and the control group in HIV history （χ²=4.566， P=0.034）， history of partial splenic embolism （χ²=6.687， P=0.014）， Child-Pugh classification （χ²=11.978， P=0.003）， grade of ascites （χ²=14.229， P<0.001）， albumin （t=4.063， P<0.001）， prealbumin （Z=-3.077， P=0.002）， high-density lipoprotein （t=2.854， P=0.011）， high-sensitivity C-reactive protein （Z=-2.447， P=0.014）， prothrombin time （Z=-2.441， P=0.015）， carcinoembryonic antigen （Z=-2.113， P=0.035）， alpha-fetoprotein （AFP） （Z=-2.063， P=0.039）， CA125 （Z=-2.270， P=0.023）， TT3 （Z=-3.304， P<0.001）， TT4 （Z=-2.221， P=0.026）， CD45⁺ （Z=-2.278， P=0.023）， interleukin-5 （Z=-2.845， P=0.004）， tumor necrosis factor-α （Z=-2.176， P=0.030）， and portal vein width （Z=-5.283， P=0.005）. The multivariate analysis showed that history of partial splenic embolism （odds ratio ［OR］=3.064， P=0.049）， HIV history （OR=0.195， P=0.027）， a small amount of ascites （OR=3.390， P=0.017）， AFP （OR=1.003， P=0.004）， and portal vein width （OR=0.600， P<0.001） were independent influencing factors for the occurrence of recompensation in patients with decompensated hepatitis C cirrhosis. The ROC curve analysis showed that HIV history， grade of ascites， history of partial splenic embolism， AFP， portal vein width， and the combined predictive model of these indices had an area under the ROC curve of 0.556， 0.641， 0.560， 0.589， 0.745， and 0.817， respectively. ConclusionFor patients with decompensated hepatitis C cirrhosis， those with a history of partial splenic embolism， a small amount of ascites， and an increase in AFP level are more likely to experience recompensation， while those with a history of HIV and an increase in portal vein width are less likely to experience recompensation.

ObjectiveTo investigate the value of albumin-bilirubin （ALBI）， easy albumin-bilirubin （EZ-ALBI）， and platelet-albumin-bilirubin （PALBI） scores in predicting 2-year survival in patients with HCV-associated hepatocellular carcinoma （HCV-HCC）. MethodsA retrospective analysis was performed for the clinical data of 174 patients with HCV-HCC who were admitted to The Third People’s Hospital of Kunming from January 2020 to January 2022， and the patients were followed up till 2 years after admission. According to the follow-up results， the patients were divided into survival group with 95 patients and death group with 79 patients. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. Univariate and multivariate Cox proportional-hazards regression model analyses were used to investigate the influencing factors for the survival of HCV-HCC patients. The Kaplan-Meier method was used to plot survival curves and analyze the 2-year survival rate of HCV-HCC patients with different EZ-ALBI grades， and the log-rank test was used for comparison between groups. ResultsThere were significant differences between the survival group and the death group in platelet count， aspartate aminotransferase （AST）， total bilirubin， albumin （Alb）， alpha-fetoprotein （AFP）， prealbumin， prothrombin time， international normalized ratio， PALBI score， ALBI score， EZ-ALBI score， Model for End-Stage Liver Disease （MELD） score， HCV genotype， peritoneal effusion， and vascular invasion （all P<0.05）. The univariate Cox regression analysis showed that AST， Alb， AFP， ALBI score， EZ-ALBI score， PALBI score， MELD score， Barcelona Clinic Liver Cancer Staging， and peritoneal effusion were influencing factors for the survival of patients （all P<0.05）， and the multivariate Cox regression analysis showed that EZ-ALBI score （hazard ratio ［HR］=1.850， 95% confidence interval ［CI］： 1.054 — 3.247， P=0.032） and peritoneal effusion （HR=1.993， 95%CI： 1.030 — 3.858， P=0.041） were independent risk factors for the survival of HCV-HCC patients. The survival curve analysis showed that the patients with EZ-ALBI grade 1/2/3 had a 2-year survival rate of 90.9%， 60.2%， and 32.2%， respectively， and there was a significant difference in cumulative survival rate between the patients with different EZ-ALBI grades （χ²=26.294， P<0.001）. ConclusionEZ-ALBI score and the presence or absence of peritoneal effusion can be used as predictors of the survival of HCV-HCC patients.

ObjectiveTo investigate the influencing factors for hepatic hydrothorax （HH） before intervention for primary hepatic carcinoma （PHC）， and to construct and assess the nomogram risk prediction model. MethodsA retrospective analysis was performed for the clinical data of 353 hospitalized patients who attended the Third People’s Hospital of Kunming for the first time from October 2012 to October 2021 and there diagnosed with PHC， and according to the presence or absence of HH， they were divided into HH group with 153 patients and non-HH group with 200 patients. General data and the data of initial clinical testing after admission were collected from all PHC patients. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. After the multicollinearity test was performed for the variables with statistical significance determined by the univariate analysis， the multivariate Logistic regression analysis was used to identify independent influencing factors. The “rms” software package was used to construct a nomogram risk prediction model， and the Hosmer-Lemeshow test and the receiver operating characteristic （ROC） curve were used to assess the risk prediction model； the “Calibration Curves” software package was used to plot the calibration curve， and the “rmda” software package was used to plot the clinical decision curve and the clinical impact curve. ResultsAmong the 353 patients with PHC， there were 153 patients with HH， with a prevalence rate of 43.34%. Child-Pugh class B （odds ratio ［OR］=2.652， 95% confidence interval ［CI］： 1.050 — 6.698， P=0.039）， Child-Pugh class C （OR=7.963， 95%CI： 1.046‍ ‍—‍ ‍60.632， P=0.045）， total protein （OR=0.947， 95%CI： 0.914‍ ‍—‍ ‍0.981， P=0.003）， high-sensitivity C-reactive protein （OR=1.007， 95%CI： 1.001‍ ‍—‍ ‍1.014， P=0.025）， and interleukin-2 （OR=0.801， 95%CI： 0.653‍ ‍—‍ ‍0.981， P=0.032） were independent influencing factors for HH before PHC intervention， and a nomogram risk prediction model was established based on these factors. The Hosmer-Lemeshow test showed that the model had a good degree of fitting （χ²=5.006， P=0.757）， with an area under the ROC curve of 0.752 （95%CI： 0.701‍ ‍—‍ ‍0.803）， a sensitivity of 78.40%， and a specificity of 63.50%. The calibration curve showed that the model had good consistency in predicting HH before PHC intervention， and the clinical decision curve and the clinical impact curve showed that the model had good clinical practicability within a certain threshold range. ConclusionChild-Pugh class， total protein， interleukin-2， and high-sensitivity C-reactive protein are independent influencing factors for developing HH before PHC intervention， and the nomogram model established based on these factors can effectively predict the risk of developing HH.

Tricuspid regurgitation associated with cardiac implantable electronic devices (CIED) constitutes a significant subset of secondary tricuspid regurgitation, characterized by a multifactorial etiology involving pacing lead-mediated mechanical interference and CIED-related systemic factors. The pathogenesis of CIED-related tricuspid regurgitation encompasses direct mechanical trauma or functional disruption of the tricuspid valve apparatus by pacing leads, pacing mode-induced hemodynamic alterations, and clinical risk factors such as permanent atrial fibrillation, apical pacing, and high right ventricular pacing burden. The natural progression and clinical outcomes of CIED-related tricuspid regurgitation parallel those of tricuspid regurgitation stemming from other etiologies. Advanced imaging modalities, including echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging, enable precise diagnosis and longitudinal assessment of CIED-related tricuspid regurgitation. Management strategies emphasize multidisciplinary collaboration as well as integration of preventive approaches-such as refined lead implantation techniques and tailored pacing modalities-with therapeutic interventions ranging from pharmacotherapy to surgical valve repair or replacement. This article reviews the current understanding of CIED-related tricuspid regurgitation to provide a reference for clinical practice and research.
Tricuspid Valve Insufficiency/diagnosis* ; Humans ; Defibrillators, Implantable/adverse effects* ; Pacemaker, Artificial/adverse effects*

Objective:This study aimed to retrospectively analyze the incidence and influencing factors of tricuspid regurgitation (TR) in the short term after pacemaker lead implantation at the Second Affiliated Hospital of Zhejiang University School of Medicine, so as to provide evidence for understanding pacemaker lead-related TR.Methods:Consecutive patients who underwent single- or dual-chamber permanent pacemaker implantation between June 2019 and December 2023 in the Department of Cardiology were enrolled. General clinical data and relevant parameters were collected. Changes in TR severity before and shortly after the procedure were assessed using echocardiography. TR progression was defined as an increase by one grade or more, and TR improvement as a decrease by one grade or more. Logistic regression analysis was employed to identify factors associated with TR progression.Results:A total of 219 patients were included (128 males, 91 females), with a mean age of 69.7 ± 11.2 years. The median follow-up time was 99 (26, 199) days. TR remained unchanged in 114 patients (52.1%), improved in 46 (21.0%)—including 36 (16.4%) with one-grade reduction, 9 (4.1%) with two-grade reduction, and 1 (0.5%) with three-grade reduction—and progressed in 59 patients (26.9%). Among those with progression, 51 (23.3%) had mild-to-moderate TR worsening by one grade, and 8 (3.7%) had moderate or worse TR worsening by at least two grades. Notably, one case involved lead perforation of the leaflet and two cases had lead impingement. Compared with the non-progression group ( n = 114), pacemaker indication (AV block vs. sick sinus syndrome), baseline left atrial diameter, pulmonary artery systolic pressure (PASP), and the severity of mitral regurgitation (MR) and TR were significantly associated with TR progression or improvement (all P < 0.05). Ordinal logistic regression analysis identified preoperative TR severity [ OR=10.57 (3.77–29.68), P < 0.001] and pacemaker indication [ OR=0.452 (0.222–0.918), P = 0.028] as independent predictors of postoperative TR progression. Patients with AV block were more likely to receive left bundle branch pacing ( P < 0.001), which may contribute to their lower risk of TR. Conclusions:Short-term progression of TR after pacemaker implantation is relatively common, although severe TR remains rare. Preoperative TR severity and pacemaker indication are independent predictors of short-term TR progression. The use of physiological pacing modalities may help reduce the incidence of TR following pacemaker lead implantation.

Objective To analyze the influencing factors of chronic liver failure in patients with primary hepatic carcinoma(PHC)before intervention,and to establish and evaluate a nomogram risk prediction model.Methods A retrospective analysis was conducted by collecting general data and clinical test data within 24 hours of admission for PHC patients.Univariate analysis and Lasso regression were used for variable selection,followed by multivariate logistic regression analysis to identify independent influencing factors for CLF before PHC intervention,leading to the establishment of a nomogram risk prediction model.The model was evaluated using the Hosmer-Lemeshow test,receiver operating characteristic(ROC)curve,calibration curve,clinical decision curve,and clinical impact curve.Result A total of 353 cases of PHC patients were collected,including 153 cases in the liver failure group and 200 cases in the non-liver failure group,with a prevalence rate of 43.3％.Variables selected by Lasso regression included gastrointestinal bleeding,prothrombin time(PT),albumin(ALB),total bilirubin(TBIL),and gamma glutamyl transferase(GGT).Multivariate logistic regression analysis showed that gastrointestinal bleeding(OR=13.549,95％CI:2.899～63.322,P=0.001),PT(OR=1.599,95％CI:1.282～1.995,P＜0.001),TBIL(OR=1.016,95％CI:1.006～1.025,P=0.002),and GGT(OR=1.002,95％CI:1.000～1.003,P=0.028)were independent risk factors for chronic liver failure prior to PHC intervention,leading to the establishment of a nomogram risk prediction model.The Hosmer Lemeshow test showed that the model had a good fit(x2=6.152,P＞0.05);the area under ROC was 0.902(0.869-0.934),with a sensitivity of 80.4％and a specificity of 87.5％.The calibration curve indicated that the model predicts chronic liver failure prior to PHC intervention with good consistency.Clinical decision curve analysis and clinical impact curve analysis showed that the model has good clinical utility within a certain threshold range.Conclusion Gastrointestinal bleeding,PT ≥16.05s,TBIL≥37.80 mmol/L,and GGT≥ 99.00 U/L are independent risk factors for the occurrence of chronic liver failure before PHC intervention.The established nomogram risk prediction model has certain clinical application value in predicting the risk of chronic liver failure before PHC intervention.

Osteoporosis is a prevalent skeletal condition characterized by reduced bone mass and strength, leading to increased fragility. Buqi-Tongluo (BQTL) decoction, a traditional Chinese medicine (TCM) prescription, has yet to be fully evaluated for its potential in treating bone diseases such as osteoporosis. To investigate the mechanism by which BQTL decoction inhibits osteoclast differentiation in vitro and validate these findings through in vivo experiments. We employed MTS assays to assess the potential proliferative or toxic effects of BQTL on bone marrow macrophages (BMMs) at various concentrations. TRAcP experiments were conducted to examine BQTL's impact on osteoclast differentiation. RT-PCR and Western blot analyses were utilized to evaluate the relative expression levels of osteoclast-specific genes and proteins under BQTL stimulation. Finally, in vivo experiments were performed using an osteoporosis model to further validate the in vitro findings. This study revealed that BQTL suppressed receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis and osteoclast resorption activity in vitro in a dose-dependent manner without observable cytotoxicity. The inhibitory effects of BQTL on osteoclast formation and function were attributed to the downregulation of NFATc1 and c-fos activity, primarily through attenuation of the MAPK, NF-κB, and Calcineurin signaling pathways. BQTL's inhibitory capacity was further examined in vivo using an ovariectomized (OVX) rat model, demonstrating a strong protective effect against bone loss. BQTL may serve as an effective therapeutic TCM for the treatment of postmenopausal osteoporosis and the alleviation of bone loss induced by estrogen deficiency and related conditions.
Animals ; NFATC Transcription Factors/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Ovariectomy ; Osteoclasts/metabolism* ; Female ; Osteogenesis/drug effects* ; Rats, Sprague-Dawley ; Rats ; NF-kappa B/genetics* ; Osteoporosis/genetics* ; Signal Transduction/drug effects* ; Bone Resorption/genetics* ; Cell Differentiation/drug effects* ; Humans ; RANK Ligand/metabolism* ; Mitogen-Activated Protein Kinases/genetics* ; Transcription Factors

ObjectiveTo investigate the value of noninvasive imaging detection （FibroScan）， two serological models of gamma-glutamyl transpeptidase-to-platelet ratio （GPR） score and S index， and two inflammatory factors of interleukin-6 （IL-6） and tumor necrosis factor-α （TNF-α） in predicting liver fibrosis in patients with HBeAg-positive chronic hepatitis B （CHB）， as well as the consistency of liver biopsy in pathological staging， and to provide early warning for early intervention of CHB. MethodsA retrospective analysis was performed for 131 HBeAg-positive CHB patients who underwent liver biopsy in The Third People’s Hospital of Kunming from January 2019 to December 2023. The results of liver biopsy were collected from all patients， and related examinations were performed before liver biopsy， including total bilirubin， alanine aminotransferase， platelet count， gamma-glutamyl transpeptidase， albumin， IL-6， TNF-α， liver stiffness measurement （LSM）， and abdominal ultrasound. An analysis of variance was used for comparison of normally distributed continuous data between groups， and the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. A Kappa analysis was used to investigate the consistency between LSM noninvasive histological staging and pathological staging based on liver biopsy， and the Spearman analysis was used to investigate the correlation between each variable and FibroScan in the diagnosis of liver fibrosis stage. The Logistic regression analysis was used to construct joint predictive factors. The receiver operating characteristic （ROC） curve was used to evaluate the value of each indicator alone and the joint predictive model in the diagnosis of liver fibrosis， and the Delong test was used for comparison of the area under the ROC curve （AUC）. ResultsIn the consistency check， inflammation degree based on liver biopsy had a Kappa value of 0.807 （P<0.001）， and liver fibrosis degree based on liver biopsy had a Kappa value of 0.827 （P<0.001）， suggesting that FibroScan noninvasive histological staging and liver biopsy showed good consistency in assessing inflammation degree and liver fibrosis stage. Age was positively correlated with LSM， GPR score， S index， IL-6， and TNF-α （all P<0.05）， and GPR score， S index， IL-6， and TNF-α were positively correlated with LSM （all P<0.05）. GPR score， S index， IL-6， and TNF-α were all independent risk factors for diagnosing significant liver fibrosis （≥S2） and progressive liver fibrosis （≥S3） （all P<0.05）. As for each indicator alone， GPR score had the highest value in the diagnosis of significant liver fibrosis （≥S2）， followed by S index， IL-6， and TNF-α， while S index had the highest value in the diagnosis of progressive liver fibrosis （≥S3）， followed by GPR score， TNF-α， and IL-6. The joint model had a higher predictive value than each indicator alone （all P<0.05）. ConclusionThere is a good consistency between FibroScan noninvasive histological staging and pathological staging based on liver biopsy. GPR score， S index， IL-6， and TNF-α are independent risk factors for evaluating different degree of liver fibrosis in CHB， and the combined prediction model established by them can better diagnose liver fibrosis.