A Novel Diagnostic Nomogram for Noninvasive Evaluating Liver Fibrosis in Patients with Chronic Hepatitis B Virus Infection

A novel web-based online nomogram to predict advanced liver fibrosis in patients with autoimmune hepatitis-primary biliary cholangitis overlap syndrome

Background

Patients with autoimmune hepatitis-primary biliary cholangitis (AIH-PBC) overlap syndrome have a worse prognosis compared to AIH or PBC alone and accurately predicting the severity and dynamically monitoring the progression of disease are therefore essential. We aimed to develop a nomogram-based model to predict advanced liver fibrosis in patients with AIH-PBC overlap syndrome.

Methods

A total of 121 patients with AIH-PBC overlap syndrome were retrospectively included and randomly assigned to a development set and a validation set. Backward stepwise regression's best model with the lowest AIC was employed to create a nomogram. Diagnose accuracy was evaluated using the area under the receiver operator characteristic curve (AUROC), calibration analysis, and decision curve analysis (DCA) and was compared with aspartate aminotransferase-to-platelet ratio (APRI) and fibrosis index based on four factors-4 (FIB-4) score.

Results

The median age of patients was 53.0 years (IQR: 46.0-63.0), and female patients accounted for 95.0 %. Platelets, globulin, total bilirubin, and prothrombin time were associated with advanced fibrosis (≥S3) and used to construct an AIH-PBC overlap syndrome fibrosis (APOSF)-nomogram (available online at https://ndth-zzy.shinyapps.io/APOSF-nomogram/). The AUROCs of APOSF-nomogram were 0.845 (95 % CI: 0.754-0.936) and 0.843 (95 % CI: 0.705-0.982) in development set and validation set respectively, which was significantly better than APRI and FIB-4. Calibration revealed that the estimated risk fits well with biopsy-proven observation. DCA outperformed APRI and FIB4 in terms of net benefit, demonstrating clinical utility.

Conclusion

This novel non-invasive web-based online APOSF-nomogram provided a convenient tool for identifying advanced fibrosis in patients with AIH-PBC overlap syndrome. Further prospective, multicenter studies with large sample size are necessary to validate the applicability of APOSF-nomogram.

Establishment and validation of a diagnostic nomogram for significant histopathologic changes of hepatic injury in HBV-infected patients

Background

Significant histopathologic changes of hepatic injury (SHCHI) play a decisive role in evaluating the condition and initiating antiviral in hepatitis B virus (HBV)-infected patients, especially those with normal or mildly elevated alanine transaminase levels. Considering that non-invasive methods were established through experience with chronic hepatitis C, the aim of this study was to establish and verify a nomogram based on hepatitis B for diagnosing SHCHI.

Methods

Three hundred eighty-four patients who fulfilled requirements for participation were randomly assigned to training cohort (n=270) and validation cohort (n=114) according to 7:3. The selection criteria for clinical factors were based on the previous research papers. SHCHI was subgrouped as followed: grade ≥ G2 inflammation and/or stage ≥ S2 fibrosis. The predictive accuracy and discriminative ability of nomogram were determined by a concordance index (C-index), calibration curve and the area under the receiver-operating characteristic curve (AUROC). We also compared diagnostic value of nomogram with model for AST-to-PLT ratio index (APRI) score and model for Fibrosis-4 (FIB-4) score.

Results

Two hundred and two patients (74.44%) and 87 patients (76.32%) were diagnosed as SHCHI, in the training and validation cohort. Logistic regression analysis illustrated that hepatitis B e antigen (HBeAg), aspartate aminotransferase (AST), γ-glutamyl transferase (GGT), and prothrombin time (PT) all independently served as risk factors for SHCHI (P<0.05) and were thus utilized to create the nomogram. The nomogram had well-fitted calibration curves and attained excellent concordance indices of 0.80 and 0.75. The sensitivity of nomogram in the diagnosis of SHCHI was 79.7%, the specificity was 68.1%. The area under the curve {AUC; 0.80 [95% confidence interval (CI): 0.74-0.86]} for diagnosing SHCHI by the nomogram was greater in comparison to that of APRI [0.78 (95% CI: 0.71-0.84)], and FIB-4 [0.76 (95% CI: 0.69-0.82)]. Patients with nomogram scores less than 119 were considered to have a lower risk of SHCHI.

Conclusions

The constructed nomogram is suitable to serve as a SHCHI screening tool in chronic HBV-infected patients. But the dependability of the nomogram will necessitate further confirmation in a prospective study and further external validation is needed.

Retrospective Evaluation of Non-Invasive Assessment Based on Routine Laboratory Markers for Assessing Advanced Liver Fibrosis in Chronic Hepatitis B Patients

Background

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

Aim

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.

Methods

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.

Results

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.

Conclusion

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

A Generic Nomogram Predicting the Stage of Liver Fibrosis Based on Serum Biochemical Indicators Among Chronic Hepatitis B Patients

Introduction: Liver fibrosis staging is of great importance for reducing unnecessary injuries and prompting treatment in chronic viral hepatitis B patients. Liver biopsy is not suitable to act a screening method although it is a gold standard because of various shortcomings. This study aimed to establish a predictive nomogram as a convenient tool to effectively identify potential patients with different stages of liver fibrosis for patients with chronic hepatitis B.

Methods: A nomogram for multinomial model was developed in a training set to calculate the probability for each stage of fibrosis and tested in a validation set. Fibrosis stages were subgrouped as followed: severe fibrosis/cirrhosis (F3–F4), moderate fibrosis (F2), and nil-mild fibrosis (F0–F1). The indicators were demographic characteristics and biochemical indicators of patients. Continuous indicators were divided into several groups according to the optimal candidate value generated by the decision tree.

Results: This study recruited 964 HBV patients undergoing percutaneous liver biopsy. The multinomial model with 10 indicators was transformed into the final nomogram. The calibration plot showed a good agreement between nomogram-predicted and observed probability of different fibrosis stages. Areas under the receiver operating characteristics (AUROCs) for severe fibrosis/cirrhosis were 0.809 for training set and 0.879 for validation set. For moderate fibrosis, the AUROCs were 0.75 and 0.781. For nil-mild fibrosis, the AUROCs were 0.792 and 0.843. All the results above showed great predictive performance in predicting the stage of fibrosis by our nomogram.

Conclusion: Our model demonstrated good discrimination and extensibility in internal and external validation. The proposed nomogram in this study resulted in great reliability and it can be widely used as a convenient and efficient way.