A novel noninvasive formula for predicting cirrhosis in patients with chronic hepatitis C

Evaluation of soluble suppression of tumorigenicity 2 (sST2) as serum marker for liver fibrosis

BACKGROUND & AIMS

With the increase in patients at risk of advanced liver disease due to the obesity epidemic, there will be a need for simple screening tools for advanced liver fibrosis. Soluble suppression of tumorigenicity 2 (sST2) is a serum biomarker for fibrotic processes. The aim of this study was to evaluate sST2 as marker for liver fibrosis in patients successfully treated for chronic hepatitis C.

METHODS

424 patients from the Swiss Hepatitis C Cohort Study were screened for inclusion in this post-hoc cohort study. Inclusion criteria were sustained virological response (SVR), available elastography (VCTE) and serum samples for biomarker analysis before and after treatment. For the validation of sST2, values were compared to VCTE, FIB-4 and APRI using Spearman's correlation and AUROC analyses.

RESULTS

Data of 164 subjects were finally analyzed. Median sST2 values slightly increased with VCTE-derived fibrosis stages and remained stable after reaching SVR within the respective fibrosis stage, suggesting that sST2 is not influenced by liver inflammation. However, correlation of sST2 pre- and post-treatment with VCTE was fair (Spearman's rho = 0.39 and rho = 0.36). The area under the curve (AUROC) for sST2 in detecting VCTE-defined F4 fibrosis (vs. F0-F3) before therapy was 0.74 (95%CI 0.65-0.83), and 0.67(95%CI 0.56-0.78) for the discrimination of F3/F4 fibrosis vs. F0-F2. Adding sST2 to either APRI or FIB-4, respectively, increased diagnostic performance of both tests.

CONCLUSIONS

sST2 can potentially identify patients with advanced fibrosis as a single serum marker and in combination with APRI and FIB-4.

A novel formula used for predicting hepatocellular carcinoma after the achievement of sustained virologic response by direct-acting antivirals in patients with chronic hepatitis C

Although eliminating HCV can prevent hepatocellular carcinoma (HCC), some patients develop HCC even after obtaining sustained virologic response (SVR). Previously, we developed a new formula to predict advanced liver fibrosis. This study aimed to clarify the usefulness of this formula for predicting HCC after achieving SVR. Among 351 consecutive patients who had been treated with direct-acting antivirals, 299 were included in this study. New formula scores were used as a marker for predicting liver fibrosis and as a predictive model for HCC incidence. The participants were 172 men and 127 women with a median age of 68 years. The median new formula score was -1.291. The cumulative HCC incidence rates were 4.3%, 9.7%, and 12.5% at 1, 3, and 5 years, respectively. The cumulative incidence of HCC was significantly higher in patients with a history of HCC than in those without treatment history of HCC (P = 2.52×10-26). Multivariate analysis revealed that male (HR = 6.584, 95% CI = 1.291-33.573, P = 0.023) and new formula score (HR = 1.741, 95% CI = 1.041-2.911, P = 0.035) were independent factors associated with the development of HCC in patients without a treatment history of HCC. The optimal cutoff value for predicting the development of HCC was -0.214. The cumulative incidence rates of HCC in patients with new formula scores ≥-0.214 were 5.4%, 15.3%, and 15.3% at 1, 3, and 5 years, respectively, whereas the incidence rates of HCC in patients with new formula scores <-0.214 were 0.0%, 0.6%, and 4.8%, respectively (P = 2.12×10-4). In conclusion, this study demonstrated the usefulness of new formula scores as a predictor of HCC after achieving SVR, especially in patients without past treatment history of treatment for HCC.

Non-Invasive Measurement of Hepatic Fibrosis by Transient Elastography: A Narrative Review

Transient elastography by FibroScan® (Echosens, Paris, France) is a non-invasive method that can provide a reliable measurement of liver fibrosis through the evaluation of liver stiffness. Despite its limitations and risks, liver biopsy has thus far been the only procedure able to provide data to quantify fibrosis. Scientific evidence and clinical practice have made it possible to use FibroScan® in the diagnostic work-up of several liver diseases to monitor patients' long-term treatment response and for complication prevention. For these reasons, this procedure is widely used in clinical practice and is still being investigated for further applications. The aim of this narrative review is to provide a comprehensive overview of the main applications of transient elastography in the current clinical practice.

Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity

(1) Background: The identification of patients at risk for hepatitis B and C viral infection is a challenge for the clinicians and public health specialists. The aim of this study was to evaluate and compare the predictive performances of four machine learning-based models for the prediction of HBV and HCV status. (2) Methods: This prospective cohort screening study evaluated adults from the North-Eastern and South-Eastern regions of Romania between January 2022 and November 2022 who underwent viral hepatitis screening in their family physician's offices. The patients' clinical characteristics were extracted from a structured survey and were included in four machine learning-based models: support vector machine (SVM), random forest (RF), naïve Bayes (NB), and K nearest neighbors (KNN), and their predictive performance was assessed. (3) Results: All evaluated models performed better when used to predict HCV status. The highest predictive performance was achieved by KNN algorithm (accuracy: 98.1%), followed by SVM and RF with equal accuracies (97.6%) and NB (95.7%). The predictive performance of these models was modest for HBV status, with accuracies ranging from 78.2% to 97.6%. (4) Conclusions: The machine learning-based models could be useful tools for HCV infection prediction and for the risk stratification process of adult patients who undergo a viral hepatitis screening program.

Dynamics of type IV collagen 7S fragment on eradication of HCV with direct antiviral agents: Prognostic and metabolomic impacts

BACKGROUND

Liver fibrosis is one of the cardinal clinical features of chronic hepatitis C (CHC). However, the mechanisms underlying the evolution and reversion of liver fibrosis after hepatitis C virus (HCV) eradication and their relationship with clinical outcomes and metabolic alterations are not fully elucidated. Whether any non-invasive fibrosis marker can predict prognosis is unknown.

METHODS

Between October 2014 and September 2019, 418 patients with CHC or compensated cirrhosis with HCV were prospectively recruited in this observational study. 326 patients that were successfully eradicated with interferon-free direct antiviral agents (IFN-free DAAs) were analyzed. Peri-treatment dynamics of serum levels of type IV collagen 7S fragment (4COL7S), a fibrosis marker, and subsequent clinical outcomes, including hepatic decompensation, newly emerged hepatocellular carcinoma (HCC), and all-cause mortality were analyzed.

RESULTS

Ten (3.1%) patients died during the observation period. 4COL7S-defined fibrosis progression (n = 97, 29.8%) at SVR was significantly correlated with worse all-cause mortality post-SVR (P = 0.0062) but not with the probability of newly emerged HCC (P = 0.24). Prognostic tendency was more prominent in patients with advanced fibrosis (P< 0.0001). 4COL7S-defined fibrosis progression at SVR and a baseline platelet count less than 10×104/μL were significantly predicted all-cause mortality (P = 0.0051). In exploratory analyses, a decreased 4COL7S at the end of treatment was correlated with a matrix-degrading phenotype that showed higher serum metalloproteinase to tissue inhibitors of metalloproteinase-1 ratios and characteristic metabolic fingerprints such as increased butyrate, some medium-chain fatty acids, anabolic amino acids, and decreased uremia toxins.

CONCLUSIONS

Peri-treatment dynamics of serum 4COL7S, a non-invasive fibrosis marker, predict prognosis. Non-invasive fibrosis markers may be useful biomarkers for risk stratification post-SVR.