Dataset Information

Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B.

ABSTRACT:

Background and aims

Noninvasive models have been developed for fibrosis assessment in patients with chronic hepatitis B. However, the sensitivity, specificity and diagnostic accuracy in evaluating liver fibrosis of these methods have not been validated and compared in the same group of patients. The aim of this study was to verify the diagnostic performance and reproducibility of ten reported noninvasive models in a large cohort of Asian CHB patients.

Methods

The diagnostic performance of ten noninvasive models (HALF index, FibroScan, S index, Zeng model, Youyi model, Hui model, APAG, APRI, FIB-4 and FibroTest) was assessed against the liver histology by ROC curve analysis in CHB patients. The reproducibility of the ten models were evaluated by recalculating the diagnostic values at the given cut-off values defined by the original studies.

Results

Six models (HALF index, FibroScan, Zeng model, Youyi model, S index and FibroTest) had AUROCs higher than 0.70 in predicting any fibrosis stage and 2 of them had best diagnostic performance with AUROCs to predict F?2, F?3 and F4 being 0.83, 0.89 and 0.89 for HALF index, 0.82, 0.87 and 0.87 for FibroScan, respectively. Four models (HALF index, FibroScan, Zeng model and Youyi model) showed good diagnostic values at given cut-offs.

Conclusions

HALF index, FibroScan, Zeng model, Youyi model, S index and FibroTest show a good diagnostic performance and all of them, except S index and FibroTest, have good reproducibility for evaluating liver fibrosis in CHB patients.

Registration number

ChiCTR-DCS-07000039.

SUBMITTER: Cheng J

PROVIDER: S-EPMC4692502 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B.

Cheng Jieyao J Hou Jinlin J Ding Huiguo H Chen Guofeng G Xie Qing Q Wang Yuming Y Zeng Minde M Ou Xiaojuan X Ma Hong H Jia Jidong J

PloS one 20151228 12

<h4>Background and aims</h4>Noninvasive models have been developed for fibrosis assessment in patients with chronic hepatitis B. However, the sensitivity, specificity and diagnostic accuracy in evaluating liver fibrosis of these methods have not been validated and compared in the same group of patients. The aim of this study was to verify the diagnostic performance and reproducibility of ten reported noninvasive models in a large cohort of Asian CHB patients.<h4>Methods</h4>The diagnostic perfor ...[more]

PMID: 26709706

Similar Datasets

Project description:BACKGROUND & AIMS:Noninvasive methods are needed to determine disease stage in patients with nonalcoholic fatty liver disease (NAFLD). We evaluated the diagnostic performance of several widely available fibrosis models for the assessment of hepatic fibrosis in patients with NAFLD. METHODS:We performed a retrospective analysis of data from individuals enrolled in the NIDDK NASH Clinical Research Network, from 2004 through 2018. Using biopsy as the reference standard, we determined the diagnostic performance of the aspartate aminotransferase (AST):platelet ratio (APRI), FIB-4, ratio of AST:alanine aminotransferase (ALT) and the NAFLD fibrosis score (NFS) in a cross-sectional study of 1904 subjects. The ability of these models to detect changes in fibrosis stage was assessed in a longitudinal data set of 292 subjects with 2 biopsies and accompanying laboratory data. Outcomes were detection of fibrosis of any stage (stages 0-4), detection of moderate fibrosis (stages 0-1 vs 2-4), and detection of advanced fibrosis (stages 0-2 vs 3-4). Diagnostic performance was evaluated using the C-statistic, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) analyses. RESULTS:In the cross-sectional study, FIB-4 and NFS outperformed other non-invasive models for detecting advanced fibrosis; the C-statistics were 0.80 for FIB-4 and 0.78 for NFS. In the longitudinal study, 216 patients had non-advanced fibrosis at baseline and 35 patients progressed to advanced fibrosis after median follow up of 2.6 years. After we adjusted for fibrosis stage and model score at initial biopsy, change in APRI, FIB-4, and NFS were significantly associated with change in fibrosis. A unit change in APRI, FIB-4, or NFS was associated with changes in fibrosis stage of 0.33 (95% CI, 0.20-0.45; P < .001), 0.26 (95% CI, 0.15-0.37; P < .001), and 0.19 (95% CI, 0.07-0.31; P = .002), respectively. The cross-validated C-statistic for detecting progression to advanced fibrosis for APRI was 0.82 (95% CI, 0.74-0.89), for FIB-4 was 0.81 (95% CI, 0.73-0.81), and for NFS was 0.80 (95% CI, 0.71-0.88). CONCLUSIONS:In a combined analysis of data from 2 large studies, we found that FIB-4, APRI, and NFS can detect advanced fibrosis and fibrosis progression in patients with NAFLD.

Project description:BACKGROUND: Chronic infection with hepatitis C virus (HCV) is a risk factor for liver diseases such as fibrosis, cirrhosis and hepatocellular carcinoma. HCV genetic heterogeneity was hypothesized to be associated with severity of liver disease. However, no reliable viral markers predicting disease severity have been identified. Here, we report the utility of sequences from 3 HCV 1b genomic regions, Core, NS3 and NS5b, to identify viral genetic markers associated with fast and slow rate of fibrosis progression (RFP) among patients with and without liver transplantation (n = 42). METHODS: A correlation-based feature selection (CFS) method was used to detect and identify RFP-relevant viral markers. Machine-learning techniques, linear projection (LP) and Bayesian Networks (BN), were used to assess and identify associations between the HCV sequences and RFP. RESULTS: Both clustering of HCV sequences in LP graphs using physicochemical properties of nucleotides and BN analysis using polymorphic sites showed similarities among HCV variants sampled from patients with a similar RFP, while distinct HCV genetic properties were found associated with fast or slow RFP. Several RFP-relevant HCV sites were identified. Computational models parameterized using the identified sites accurately associated HCV strains with RFP in 70/30 split cross-validation (90-95% accuracy) and in validation tests (85-90% accuracy). Validation tests of the models constructed for patients with or without liver transplantation suggest that the RFP-relevant genetic markers identified in the HCV Core, NS3 and NS5b genomic regions may be useful for the prediction of RFP regardless of transplant status of patients. CONCLUSIONS: The apparent strong genetic association to RFP suggests that HCV genetic heterogeneity has a quantifiable effect on severity of liver disease, thus presenting opportunity for developing genetic assays for measuring virulence of HCV strains in clinical and public health settings.

Dataset Information

Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B.

Background and aims

Methods

Results

Conclusions

Registration number

Publications

Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets