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Validation of Serum Test for Advanced Liver Fibrosis in Patients With Nonalcoholic Steatohepatitis.
Clinical Gastroenterology and Hepatology 2018 November 16
BACKGROUND & AIMS: We analyzed markers of fibrosis in serum samples from patients with nonalcoholic fatty liver disease (NAFLD), assessed by liver biopsy. We used serum levels of markers to develop an algorithm to discriminate patients with advanced fibrosis from those with mild or moderate fibrosis and validated its performance in 2 independent cohorts of patients with NAFLD.
METHODS: We performed a retrospective analysis of serum samples from 396 patients with NAFLD and different stages of fibrosis (F0-F4), collected from 2007 through 2017 on the day of liver biopsy (training cohort 1). We measured serum concentrations of alpha-2 macroglobulin (A2M), hyaluronic acid (HA), and TIMP metallopeptidase inhibitor 1 (TIMP1), and used measurements to develop an algorithm that could discriminate patients with NAFLD with advanced fibrosis (F3-F4; 24.1% of cohort) from those with mild or moderate fibrosis (F0-F2; 79.5% of cohort). We validated the algorithm using serum samples collected from a separate 396 patients from the same time period and location (validation cohort 1), as well as 244 patients with NAFLD evaluated at a separate location, from 2011 through 2017, within a median of 11 days of liver biopsy (cohort 2).
RESULTS: The algorithm identified patients with advanced fibrosis vs mild or moderate fibrosis in training cohort 1 with an area under the receiver operating characteristic (AUROC) curve of 0.867 (95% CI, 0.827-0.907), 84.8% sensitivity (95% CI, 75.5%-91.0%), and 72.3% specificity (95% CI, 66.9%-77.3%), at a cutoff score of 17. The AUROC for the combined validation cohorts 1 and 2 (n=640) was 0.856 (95% CI, 0.820-0.892), identifying patients with 79.7% sensitivity (95% CI, 71.9%-86.2%) and 75.7% specificity (95% CI, 71.8%-79.4%) at the predetermined cutoff score of 17. The algorithm had negative predictive values that ranged from 92.5% to 94.7% in the validation cohorts; it correctly classified 90.0% of F0 samples, 75.0% of F1 samples, 77.4% of F3 samples, and 94.4% of F4 samples.
CONCLUSION: We developed an algorithm that identifies patients with advanced fibrosis from those with mild to moderate fibrosis in patients with NAFLD with an AUROC value of approximately 0.86, based on levels of serum biomarkers. We validated the findings in 2 separate sets of patients with biopsy-proven NAFLD. The algorithm can be used non-invasively to determine risk of advanced fibrosis in patients with NAFLD.
METHODS: We performed a retrospective analysis of serum samples from 396 patients with NAFLD and different stages of fibrosis (F0-F4), collected from 2007 through 2017 on the day of liver biopsy (training cohort 1). We measured serum concentrations of alpha-2 macroglobulin (A2M), hyaluronic acid (HA), and TIMP metallopeptidase inhibitor 1 (TIMP1), and used measurements to develop an algorithm that could discriminate patients with NAFLD with advanced fibrosis (F3-F4; 24.1% of cohort) from those with mild or moderate fibrosis (F0-F2; 79.5% of cohort). We validated the algorithm using serum samples collected from a separate 396 patients from the same time period and location (validation cohort 1), as well as 244 patients with NAFLD evaluated at a separate location, from 2011 through 2017, within a median of 11 days of liver biopsy (cohort 2).
RESULTS: The algorithm identified patients with advanced fibrosis vs mild or moderate fibrosis in training cohort 1 with an area under the receiver operating characteristic (AUROC) curve of 0.867 (95% CI, 0.827-0.907), 84.8% sensitivity (95% CI, 75.5%-91.0%), and 72.3% specificity (95% CI, 66.9%-77.3%), at a cutoff score of 17. The AUROC for the combined validation cohorts 1 and 2 (n=640) was 0.856 (95% CI, 0.820-0.892), identifying patients with 79.7% sensitivity (95% CI, 71.9%-86.2%) and 75.7% specificity (95% CI, 71.8%-79.4%) at the predetermined cutoff score of 17. The algorithm had negative predictive values that ranged from 92.5% to 94.7% in the validation cohorts; it correctly classified 90.0% of F0 samples, 75.0% of F1 samples, 77.4% of F3 samples, and 94.4% of F4 samples.
CONCLUSION: We developed an algorithm that identifies patients with advanced fibrosis from those with mild to moderate fibrosis in patients with NAFLD with an AUROC value of approximately 0.86, based on levels of serum biomarkers. We validated the findings in 2 separate sets of patients with biopsy-proven NAFLD. The algorithm can be used non-invasively to determine risk of advanced fibrosis in patients with NAFLD.
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