Mohammad Shadab Siddiqui1, Goro Yamada2, Raj Vuppalanchi3, Mark Van Natta2, Rohit Loomba4, Cynthia Guy5, Danielle Brandman6, James Tonascia2, Naga Chalasani3, Brent Neuschwander-Tetri7, Arun J Sanyal8. 1. Virginia Commonwealth University, Richmond, Virginia. Electronic address: mohammad.siddiqui@vcuhealth.org. 2. Johns Hopkins University, Baltimore, Maryland. 3. Indiana University, Indianapolis, Indiana. 4. University of California at San Diego, San Diego, California. 5. Duke University, Durham, North Carolina. 6. University of California at San Francisco, San Francisco, California. 7. Saint Louis University, St Louis, Missouri. 8. Virginia Commonwealth University, Richmond, Virginia.
Abstract
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.
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.
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