Degree of Discordance Between FIB-4 and Transient Elastography: An Application of Current Guidelines on General Population Cohort.

BACKGROUND & AIMS: In the American Gastroenterological Association/American Association for the Study of Liver Diseases (AGA/AASLD) Clinical Care Pathway, Fibrosis-4 index (FIB-4) is used to stratify patients at risk for metabolic dysfunction-associated steatotic liver disease (MASLD) as low-, indeterminate-, or high-risk for developing advanced liver fibrosis. We assessed the performance of FIB-4 in a general population. METHODS: Using the 2017 to 2020 National Health and Nutrition Examination Surveys dataset, we selected subjects ≥18 years who had FibroScan data. We followed AGA/AASLD guidelines to identify subjects with characteristics that place them at risk for MASLD-associated liver fibrosis. Other causes of liver disease were excluded. Our final cohort had 3741 subjects. We then categorized these subjects based on recommended FIB-4 cutoffs. FibroScan liver stiffness measurement (LSM) served as the outcome measurement. RESULTS: Among the 2776 subjects (74.2%) classified as low risk by FIB-4, 277 subjects (10%) were not classified at low risk by LSM, and 75 subjects (2.7%) were classified as high risk by LSM. Among the 86 subjects classified as high risk by FIB-4, 68 subjects (79.1%) were not at high risk by LSM, and 54 subjects (62.8%) were at low risk by LSM. Subjects misclassified by FIB-4 as low risk were older; had a higher body mass index, waist circumference, glycohemoglobin A1c level, alanine transaminase, aspartate transaminase, diastolic blood pressure, controlled attenuation parameter score, white blood cell count, alkaline phosphatase, and fasting glucose level; but had lower high-density lipoprotein, and albumin level (all P < .05). Misclassified subjects were also more likely to have prediabetes/diabetes. CONCLUSION: Using FIB-4 in the AGA/AASLD guidelines to risk-stratify subjects at risk for MASLD-associated fibrosis results in many subjects being misclassified into the low- and high-risk categories. Therefore, it may be worthwhile considering caution in interpretation and/or alternative strategies.

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.

Nonalcoholic fatty liver disease prevalence and severity in Asian Americans from the national health and nutrition examination surveys 2017-2018.

Studies have examined nonalcoholic fatty liver disease (NAFLD) prevalence and severity in Asians; however, this is not well understood in Asian Americans (both East and South Asian Americans) as few studies have analyzed this population. We aimed to describe characteristics, prevalence of NAFLD, and its severity in Asian Americans in the National Health and Nutrition Examination Surveys (NHANES) from 2017 to 2018. Respondents 18 years and older with interview, laboratory testing, and transient elastography data were included. Other causes of liver disease were excluded. Controlled attenuation parameter (CAP) cutoff ≥ 274 dB/m, as published in the literature, defined NAFLD. Sensitivity analysis for CAP cutoffs ≥ 248 and ≥302 dB/m were performed. We found that 450 out of 3639 respondents were Asian Americans, and prevalence using CAP ≥ 274 dB/m was 43.23%. Using sensitivity analysis cutoffs of CAP ≥ 248 dB/m and CAP ≥ 302 dB/m, the prevalence was 57.38% and 28.03%, respectively. Compared with non-Asian Americans with NAFLD, Asian Americans with NAFLD had significantly lower body mass index (BMI) and less prevalent smoking history. Comorbidities, such as prediabetes, diabetes, and hypertension, were not significantly different between Asian and non-Asian Americans with NAFLD. Compared to non-Asian Americans with NAFLD, Asian Americans with NAFLD exhibited higher aminotransferases and triglycerides. Fibrosis assessed by transient elastography was not significantly different between Asian and non-Asian Americans with NAFLD. Despite decreased prevalence of BMI ≥ 30 kg/m2 , Asian Americans experienced similar NAFLD prevalence with increased hepatocellular injury and triglyceridemia compared to non-Asian Americans. Fibrosis stages were similar to non-Asian Americans.