Accuracy of a sequential algorithm based on FIB-4 and ELF to identify high-risk advanced liver fibrosis at the primary care level.

Abstract

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease, and liver fibrosis is the strongest predictor of morbimortality. We aimed to assess the performance of a sequential algorithm encompassing the Fibrosis 4 (FIB-4) and Enhanced Liver Fibrosis (ELF) scores for identifying patients at risk of advanced fibrosis. This cross-sectional study included one hospital-based cohort with biopsy-proven NAFLD (n = 140) and two primary care cohorts from different clinical settings: Type 2 Diabetes (T2D) follow-up (n = 141) and chronic liver disease (CLD) initial study (n = 138). Logistic regression analysis was performed to assess liver fibrosis diagnosis models based on FIB-4 and ELF biomarkers. The sequential algorithm retrieved the following accuracy parameters in predicting stages F3-4 in the biopsy-confirmed cohort: sensitivity (85%), specificity (73%), negative predictive value (79%) and positive predictive value (81%). In both T2D and CLD cohorts, a total of 28% of patients were classified as stages F3-4. Furthermore, of all F3-4 classified patients in the T2D cohort, 80% had a diagnosis of liver disease and 44% were referred to secondary care. Likewise, of all F3-4 classified patients in the CLD cohort, 71% had a diagnosis of liver disease and 44% were referred to secondary care. These results suggest the potential utility of this algorithm as a liver fibrosis stratifying tool in primary care, where updating referral protocols to detect high-risk F3-4 is needed. FIB-4 and ELF sequential measurement is an efficient strategy to prioritize patients with high risk of F3-4 in populations with metabolic risk factors.