METHODS: A review of the literature identified studies containing histology verified CAP data (M probe, vibration controlled transient elastography with FibroScan®) for grading of steatosis (S0-S3). Receiver operating characteristic analysis after correcting for center effects was used as well as mixed models to test the impact of covariates on CAP. The primary outcome was establishing CAP cut-offs for distinguishing steatosis grades.

RESULTS: Data from 19/21 eligible papers were provided, comprising 3830/3968 (97%) of patients. Considering data overlap and exclusion criteria, 2735 patients were included in the final analysis (37% hepatitis B, 36% hepatitis C, 20% NAFLD/NASH, 7% other). Steatosis distribution was 51%/27%/16%/6% for S0/S1/S2/S3. CAP values in dB/m (95% CI) were influenced by several covariates with an estimated shift of 10 (4.5-17) for NAFLD/NASH patients, 10 (3.5-16) for diabetics and 4.4 (3.8-5.0) per BMI unit. Areas under the curves were 0.823 (0.809-0.837) and 0.865 (0.850-0.880) respectively. Optimal cut-offs were 248 (237-261) and 268 (257-284) for those above S0 and S1 respectively.

CONCLUSIONS: CAP provides a standardized non-invasive measure of hepatic steatosis. Prevalence, etiology, diabetes, and BMI deserve consideration when interpreting CAP. Longitudinal data are needed to demonstrate how CAP relates to clinical outcomes.

AIM: To determine how to use CAP in interpreting liver stiffness measurements.

METHODS: This is a secondary analysis of data from an individual patient data meta-analysis on CAP. The main exclusion criteria for the current analysis were unknown aetiology, unreliable elastography measurement and data already used for the same research question. Aetiology-specific liver stiffness measurement cut-offs were determined and used to estimate positive and negative predictive values (PPV/NPV) with logistic regression as functions of CAP.

RESULTS: Two thousand and fifty eight patients fulfilled the inclusion criteria (37% women, 18% NAFLD/NASH, 42% HBV, 40% HCV, 51% significant fibrosis ≥ F2). Youden optimised cut-offs were only sufficient for ruling out cirrhosis (NPV of 98%). With sensitivity and specificity-optimised cut-offs, NPV for ruling out significant fibrosis was moderate (70%) and could be improved slightly through consideration of CAP. PPV for significant fibrosis and cirrhosis were 68% and 55% respectively, despite specificity-optimised cut-offs for cirrhosis.

METHODS AND RESULTS: This was an individual patient data meta-analysis of 1780 patients with biopsy-proven NAFLD and T2D. The index tests of interest were FIB-4, NAFLD Fibrosis Score (NFS), aspartate aminotransferase-to-platelet ratio index, liver stiffness measurement (LSM) by vibration-controlled transient elastography, and AGILE 3+. The target conditions were advanced fibrosis, NASH, and fibrotic NASH(NASH plus F2-F4 fibrosis). The diagnostic performance of noninvasive tests. individually or in sequential combination, was assessed by area under the receiver operating characteristic curve and by decision curve analysis. Comparison with 2278 NAFLD patients without T2D was also made. In NAFLD with T2D LSM and AGILE 3+ outperformed, both NFS and FIB-4 for advanced fibrosis (area under the receiver operating characteristic curve:LSM 0.82, AGILE 3+ 0.82, NFS 0.72, FIB-4 0.75, aspartate aminotransferase-to-platelet ratio index 0.68; p < 0.001 of LSM-based versus simple serum tests), with an uncertainty area of 12%-20%. The combination of serum-based with LSM-based tests for advanced fibrosis led to a reduction of 40%-60% in necessary LSM tests. Decision curve analysis showed that all scores had a modest net benefit for ruling out advanced fibrosis at the risk threshold of 5%-10% of missing advanced fibrosis. LSM and AGILE 3+ outperformed both NFS and FIB-4 for fibrotic NASH (area under the receiver operating characteristic curve:LSM 0.79, AGILE 3+ 0.77, NFS 0.71, FIB-4 0.71; p < 0.001 of LSM-based versus simple serum tests). All noninvasive scores were suboptimal for diagnosing NASH.

DESIGN: Individual patient data meta-analysis of studies evaluating LSM-VCTE against liver histology was conducted. FIB-4 and NFS were computed where possible. Sensitivity, specificity and area under the receiver operating curve (AUROC) were calculated. Biomarkers were assessed individually and in sequential combinations.

RESULTS: Data were included from 37 primary studies (n=5735; 45% women; median age: 54 years; median body mass index: 30 kg/m2; 33% had type 2 diabetes; 30% had advanced fibrosis). AUROCs of individual LSM-VCTE, FIB-4 and NFS for advanced fibrosis were 0.85, 0.76 and 0.73. Sequential combination of FIB-4 cut-offs (<1.3; ≥2.67) followed by LSM-VCTE cut-offs (<8.0; ≥10.0 kPa) to rule-in or rule-out advanced fibrosis had sensitivity and specificity (95% CI) of 66% (63-68) and 86% (84-87) with 33% needing a biopsy to establish a final diagnosis. FIB-4 cut-offs (<1.3; ≥3.48) followed by LSM cut-offs (<8.0; ≥20.0 kPa) to rule out advanced fibrosis or rule in cirrhosis had a sensitivity of 38% (37-39) and specificity of 90% (89-91) with 19% needing biopsy.