Increased accuracy in identifying NAFLD with advanced fibrosis and cirrhosis: independent validation of the Agile 3+ and 4 scores.

BACKGROUND AND AIMS: We explored 2 novel scores, Agile 3+ and 4, to identify advanced fibrosis (≥F3) and cirrhosis (F4), respectively, in NAFLD and compared their diagnostic performances to liver stiffness measurement (LSM) by vibration-controlled transient elastography and fibrosis-4 index (FIB-4) (for Agile 3+). APPROACH AND RESULTS: This multicenter study included 548 NAFLD patients with laboratory testing, liver biopsy, and vibration-controlled transient elastography within 6 months. Agile 3+ and 4 were applied and compared with FIB-4 or LSM alone. Goodness of fit was evaluated using a calibration plot and discrimination using area under the receiver operating curve. Area under the receiver operating curves was compared using the Delong test. Dual cutoff approaches were applied to rule out and rule in ≥F3 and F4. Median (interquartile range) age was 58 (15) years. Median body mass index was 33.3 (8.5) kg/m2. Fifty-three percent had type 2 diabetes, 20% had F3, and 26% had F4. Agile 3+ demonstrated an area under the receiver operating curve of 0.85 (0.81; 0.88) similar to that of LSM [0.83 (0.79; 0.86), p=0.142] but significantly higher than that of FIB-4 [0.77 (0.73; 0.81), p<0.0001). Agile 4's area under the receiver operating curve [0.85 (0.81; 0.88)] was similar to that of LSM [0.85 (0.81; 0.88), p=0.065). However, the percentage of patients with indeterminate results was significantly lower with Agile scores compared with FIB-4 and LSM (Agile 3+: 14% vs. FIB-4: 31% vs. LSM: 13%, p<0.001; Agile 4: 23% vs. LSM: 38%, p<0.001). CONCLUSIONS: Agile 3+ and 4 are novel vibration-controlled transient elastography-based noninvasive scores that increase accuracy in the identification of advanced fibrosis and cirrhosis respectively and are ideal for clinical use due to a lower percentage of indeterminant outputs compared with FIB-4 or LSM alone.

Machine learning models are superior to noninvasive tests in identifying clinically significant stages of NAFLD and NAFLD-related cirrhosis.

BACKGROUND AND AIMS: We assessed the performance of machine learning (ML) models in identifying clinically significant NAFLD-associated liver fibrosis and cirrhosis. APPROACH AND RESULTS: We implemented ML models including logistic regression (LR), random forest (RF), and artificial neural network to predict histological stages of fibrosis using 17 demographic/clinical features in 1370 patients with NAFLD who underwent liver biopsy, FibroScan, and labs within a 6-month period at multiple U.S. centers. Histological stages of fibrosis (≥F2, ≥F3, and F4) were predicted using ML, FibroScan liver stiffness measurements, and Fibrosis-4 index (FIB-4). NASH with significant fibrosis (NAS ≥ 4 + ≥F2) was assessed using ML, FibroScan-AST (FAST) score, FIB-4, and NAFLD fibrosis score (NFS). We used 80% of the cohort to train and 20% to test the ML models. For ≥F2, ≥F3, F4, and NASH + NAS ≥ 4 + ≥F2, all ML models, especially RF, had primarily higher accuracy and AUC compared with FibroScan, FIB-4, FAST, and NFS. AUC for RF versus FibroScan and FIB-4 for ≥F2, ≥F3, and F4 were (0.86 vs. 0.81, 0.78), (0.89 vs. 0.83, 0.82), and (0.89 vs. 0.86, 0.85), respectively. AUC for RF versus FAST, FIB-4, and NFS for NASH + NAS ≥ 4 + ≥F2 were (0.80 vs. 0.77, 0.66, 0.63). For NASH + NAS ≥ 4 + ≥F2, all ML models had lower/similar percentages within the indeterminate zone compared with FIB-4 and NFS. Overall, ML models performed better in sensitivity, specificity, positive predictive value, and negative predictive value compared with traditional noninvasive tests. CONCLUSIONS: ML models performed better overall than FibroScan, FIB-4, FAST, and NFS. ML could be an effective tool for identifying clinically significant liver fibrosis and cirrhosis in patients with NAFLD.

Fatty liver in hepatitis C patients post-sustained virological response with direct-acting antivirals.

AIM: To determine steatosis and fibrosis prevalence in hepatitis C patients after a sustained virological response achieved with direct-acting antivirals. METHODS: Transient elastography with controlled attenuation parameter (CAP) was used to assess hepatic steatosis post-sustained virological response (SVR); the CAP technology was not available in the United States at study initiation. Liver stiffness/fibrosis was measured before and 47 wk after treatment completion. Patients with genotype 3 and patients with cirrhosis were excluded. RESULTS: One hundred and one patients were included in the study. Post-SVR there were decreases from baseline in alanine aminotransferase (ALT) (63.1 to 17.8 U/L), aspartate aminotransferase (51.8 to 21.5 U/L) and fibrosis score (7.4 to 6.1 kPa) (P < 0.05). Post-SVR, 48 patients (47.5%) had steatosis on CAP; of these, 6.25% had advanced fibrosis. Patients with steatosis had higher body mass index (29.0 vs 26.1 kg/m2), glucose (107.8 vs 96.6 mg/dL), ALT (20.4 vs 15.3 mg/dL), CAP score (296.3 vs 212.4 dB/m) and fibrosis score (7.0 vs 5.3 kPa); P < 0.05. Interestingly, compared to baseline, both patients with and without steatosis had change in fibrosis score post-SVR (7.7 kPa vs 7.0 kPa and 7.0 kPa vs 5.3 kPa); alternatively, (P < 0.05) and therefore patients with steatosis continued to have clinically significant stiffness (≥ 7 kPa). CONCLUSION: Fatty liver is very common in hepatitis C virus (HCV) patients post-SVR. These patients continue to have elevated mean fibrosis score (≥ 7 kPa) compared to those without fatty liver; some have advanced fibrosis. Long term follow up is needed to assess steatosis and fibrosis in HCV patients post-SVR.