METHODS: Individual data were collected from 14 registry centers on patients with biopsy-proven non-alcoholic fatty liver disease (NAFLD), and in all patients, circulating CK-18 M30 levels were measured. Individuals with a NAFLD activity score (NAS) ≥5 with a score of ≥1 for each of steatosis, ballooning, and lobular inflammation were diagnosed as having definite NASH; individuals with a NAS ≤2 and no fibrosis were diagnosed as having non-alcoholic fatty liver (NAFL).

AIM: To study factors associated with nonalcoholic steatohepatitis (NASH) and advanced fibrosis, and medical treatment of biopsy-proven nonalcoholic fatty liver disease (NAFLD) patients.

METHODS: Retrospective study of biopsy-proven NAFLD patients from centres in the GO ASIA Workgroup. Independent factors associated with NASH and with advanced fibrosis on binary logistic regression analyses in a training cohort were used for the development of their corresponding risk score, which were validated in a validation cohort.

RESULTS: We included 1008 patients from nine centres across eight countries (NASH 62.9%, advanced fibrosis 17.2%). Independent predictors of NASH were body mass index ≥30 kg/m2 , diabetes mellitus, dyslipidaemia, alanine aminotransferase ≥88 U/L and aspartate aminotransferase ≥38 U/L, constituting the Asia Pacific NASH risk score. A high score has a positive predictive value of 80%-83% for NASH. Independent predictors of advanced fibrosis were age ≥55 years, diabetes mellitus and platelet count <150 × 109 /L, constituting the Asia-Pacific NAFLD advanced fibrosis risk score. A low score has a negative predictive value of 95%-96% for advanced fibrosis. Only 1.7% of patients were referred for structured lifestyle program, 4.2% were on vitamin E, and 2.4% were on pioglitazone.

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.

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.