Direct-Acting Antiviral Therapy for Hepatitis C Virus in Patients with BCLC Stage B/C Hepatocellular Carcinoma

Validating the Baveno Elastography Criteria of Advanced Liver Fibrosis in Two-Dimensional Shear Wave Elastography: A Prospective Pathology-Based Study

INTRODUCTION

The Baveno criteria for assessing advanced liver fibrosis were mainly determined by transient elastography (TE), and its pathology-based validation studies in two-dimensional shear wave elastography (2D-SWE) remain limited. We aimed to validate the Baveno criteria through use of 2D-SWE.

METHOD

Consecutive patients who underwent liver biopsies for various benign liver diseases were prospectively recruited. Liver stiffness measurement (LSM) was simultaneously evaluated by TE and 2D-SWE. The optimal cutoff value to predict advanced liver fibrosis was determined by the Youden Index, and the diagnostic performance was estimated using area under the receiver operating characteristic (AUROC) analysis.

RESULTS

A total of 101 patients were enrolled having a median age of 55.0 (IQR: 46.0-63.5) years, with 53 (52.48%) of them being male. Using <9 and >14 kPa as the optimal dual cutoffs, the AUROC values in TE and 2D-SWE were 0.92 (95% CI: 0.83-0.97) and 0.93 (95% CI: 0.84-0.98), respectively (p = 0.61). The sensitivity and specificity of LSM by TE/2D-SWE achieved rates of 94.44%/94.44% and 86.00%/88.00%, respectively. However, using the Baveno criteria, the AUROC values in TE and 2D-SWE could remain achieving 0.91 (95% CI: 0.82-0.97) and 0.93 (95% CI: 0.84-0.98), respectively (p = 0.36). The sensitivity and specificity in TE/2D-SWE were 88.24%/88.24% and 86.79%/90.57%, respectively.

CONCLUSION

This study establishes the compatibility of the Baveno dual cutoff criteria with 2D-SWE, positioning it as an easily used criteria in clinical practice and research.

Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program

BACKGROUND/AIMS

Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy.

METHODS

We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment.

RESULTS

The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset.

CONCLUSION

Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.