AIMS: To validate the performance of the dual-cutoffs (8/12 kPa) and the proposed algorithm to identify patients with cACLD in three well-characterised Asian nonalcoholic fatty liver disease (NAFLD) cohorts.

METHODS: We included 830 patients with biopsy-proven NAFLD. Liver stiffness was measured using transient elastography (FibroScan).

RESULTS: cACLD was found in 21.8% of patients. Compared with the original Baveno VI elastography criteria (10/15 kPa), the new cutoffs showed a comparable specificity and a higher sensitivity for identifying cACLD. We developed a simplified risk model incorporating age, liver stiffness value, and platelet count, which outperformed liver stiffness measurement alone in two Chinese cohorts (P = 0.001), and was further validated in a Malaysian cohort (P = 0.04). Overall, the "two-step" screening of cACLD improved classification rates from 73.5% by the original dual-cutoffs to 86.7%. Notably, usage of our simplified risk model resulted in significantly lower false-negative rate than the refined screening approach by Papatheodoridi et al (27.1% vs 41.4%; P = 0.01).

AIMS: We developed and validated MAFLD fibrosis score (MFS) for identifying advanced fibrosis (≥F3) among MAFLD patients.

METHODS: This cross-sectional, multicentre study consecutively recruited MAFLD patients receiving tertiary care (Malaysia as training cohort [n = 276] and Hong Kong and Wenzhou as validation cohort [n = 431]). Patients completed liver biopsy, vibration-controlled transient elastography (VCTE), and clinical and laboratory assessment within 1 week. We used machine learning to select 'highly important' predictors of advanced fibrosis, followed by backward stepwise regression to construct MFS formula.