Validation of the new 2021 EASL algorithm for the noninvasive diagnosis of advanced fibrosis in NAFLD.

BACKGROUND AND AIMS: The European Association for the Study of the Liver (EASL) has recently proposed an algorithm for the diagnosis of advanced liver fibrosis. We aimed to evaluate the diagnostic accuracy of this algorithm in nonalcoholic fatty liver disease (NAFLD). APPROACH AND RESULTS: One thousand fifty-one patients with NAFLD, liver biopsy, and four noninvasive tests (NITs; Fibrosis-4 [FIB4], vibration controlled transient elastography [VCTE], FibroMeter, Fibrotest) were included. The enhanced liver fibrosis (ELF) score was available in 396 patients. A cohort of 230 patients from primary care/diabetes clinics had FIB4, VCTE, and ELF. Compared with the performance of single NITs, agreement between two NITs (FIB4 and VCTE, VCTE and patented serum tests) increased specificity and positive predictive value by 20%, thus justifying the sequential use proposed in the EASL algorithm. The FIB4/VCTE/FibroMeter and FIB4/VCTE/Fibrotest algorithms performed similarly, providing 85% diagnostic accuracy and a liver biopsy requirement rate of only 10%. The FIB4/VCTE/ELF algorithm performed similarly in the subgroup where ELF was available. Simulations of algorithm accuracies at different prevalence showed that positive predictive values rapidly increased, reaching a plateau above 75% starting at 15% prevalence. Negative predictive values remained higher than 90% up to 25% prevalence. The rate of liver biopsy requirement remained stable, increasing by only 5% between low and high prevalence settings. When the EASL algorithm was applied in the primary care/diabetes clinic cohort, liver biopsy requirement was only 3%, and the agreement among the three steps provided 75% positive predictive value. CONCLUSIONS: Our study validates the algorithm proposed by the EASL in its latest 2021 guidelines for the diagnosis of advanced fibrosis in the setting of NAFLD.

Point-of-Care Noninvasive Prediction of Liver-Related Events in Patients With Nonalcoholic Fatty Liver Disease.

BACKGROUND & AIMS: Individual risk prediction of liver-related events (LRE) is needed for clinical assessment of nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) patients. We aimed to provide point-of-care validated liver stiffness measurement (LSM)-based risk prediction models for the development of LRE in patients with NAFLD, focusing on selecting patients for clinical trials at risk of clinical events. METHODS: Two large multicenter cohorts were evaluated, 2638 NAFLD patients covering all LSM values as the derivation cohort and 679 more advanced patients as the validation cohort. We used Cox regression to develop and validate risk prediction models based on LSM alone, and the ANTICIPATE and ANTICIPATE-NASH models for clinically significant portal hypertension. The main outcome of the study was the rate of LRE in the first 3 years after initial assessment. RESULTS: The 3 predictive models had similar performance in the derivation cohort with a very high discriminative value (c-statistic, 0.87-0.91). In the validation cohort, the LSM-LRE alone model had a significant inferior discrimination (c-statistic, 0.75) compared with the other 2 models, whereas the ANTICIPATE-NASH-LRE model (0.81) was significantly better than the ANTICIPATE-LRE model (0.79). In addition, the ANTICIPATE-NASH-LRE model presented very good calibration in the validation cohort (integrated calibration index, 0.016), and was better than the ANTICIPATE-LRE model. CONCLUSIONS: The ANTICIPATE-LRE models, and especially the ANTICIPATE-NASH-LRE model, could be valuable validated clinical tools to individually assess the risk of LRE at 3 years in patients with NAFLD/NASH.

Impact of Type 2 Diabetes on the Accuracy of Noninvasive Tests of Liver Fibrosis With Resulting Clinical Implications.

BACKGROUND & AIMS: Noninvasive tests (NITs) of liver fibrosis have been suggested to be less accurate in type 2 diabetes mellitus (T2DM). We aimed to compare the accuracy of 6 NITs between patients with or without T2DM, explain any differences, and adapt diagnostic algorithms for clinical practice accordingly. METHODS: We included 1051 patients with nonalcoholic fatty liver disease with liver biopsy, blood fibrosis tests (Nonalcoholic Fatty Liver Disease Fibrosis Score, FIB4, Fibrotest, FibroMeter), vibration-controlled transient elastography (VCTE), and the combinatory elasto-blood test FibroMeterVCTE. The study endpoint was advanced fibrosis on liver biopsy. RESULTS: NIT areas under the receiver operating characteristic curve were significantly lower in patients with T2DM, mostly because of a decrease in specificity. For FIB4, this decrease in specificity was only related to the higher age of patients with T2DM enrolled. For Fibrotest, FibroMeter, and FibroMeterVCTE, the decrease in specificity was related to age but also to higher alpha2-macroglobulin level, which is known to increase in T2DM. Sensitivity was unaffected by T2DM, but it masked a doubled raw number of false negatives because of the 2-fold higher prevalence of advanced fibrosis in that setting. The sequential algorithm FIB4-vibration-controlled transient elastography had 90.3% accuracy in patients without T2DM vs 79.0% in those with (P < .001). Algorithms using first-line specialized tests maintained a low rate of false negatives and false positives in T2DM. CONCLUSIONS: The decrease in NIT accuracy observed in T2DM is partly biased by the different characteristics of the groups studied, but also linked to T2DM itself through modification of the levels of some NIT biomarkers. Specialized tests should be used first-line to diagnose advanced liver fibrosis in T2DM.