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Metabolic dysfunction-associated steatotic liver disease (MASLD) is the latest term for steatotic liver disease associated with metabolic syndrome. MASLD is the most common cause of chronic liver disease and is the leading cause of liver-related morbidity and mortality. It is important that all stakeholders be involved in tackling the public health threat of obesity and obesity-related diseases, including MASLD. A simple and clear assessment and referral pathway using non-invasive tests is essential to ensure that patients with severe MASLD are identified and referred to specialist care, while patients with less severe disease remain in primary care, where they are best managed. While lifestyle intervention is the cornerstone of the management of patients with MASLD, cardiovascular disease risk must be properly assessed and managed because cardiovascular disease is the leading cause of mortality. No pharmacological agent has been approved for the treatment of MASLD, but novel antihyperglycemic drugs appear to have benefit. Medications used for the treatment of diabetes and other metabolic conditions may need to be adjusted as liver disease progresses to cirrhosis, especially decompensated cirrhosis. Based on non-invasive tests, the concepts of compensated advanced chronic liver disease and clinically significant portal hypertension provide a practical approach to stratifying patients according to the risk of liverrelated complications and can help manage such patients. Finally, prevention and management of sarcopenia should be considered in the management of patients with MASLD. [ABSTRACT FROM AUTHOR]

Summary: Background: The precise estimation of cases with significant fibrosis (SF) is an unmet goal in non‐alcoholic fatty liver disease (NAFLD/MASLD). Aims: We evaluated the performance of machine learning (ML) and non‐patented scores for ruling out SF among NAFLD/MASLD patients. Methods: Twenty‐one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically‐proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological‐SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1‐score as model‐selection criteria). Results: Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological‐SF were included in the study. Patients with SFvs.no‐SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p < 0.001, each). ML models showed 7%–12% better discrimination than FIB‐4 to detect SF. Optimised random forest (RF) yielded best NPV/F1 in overall set (0.947/0.754), test set (0.798/0.588) and validation set (0.852/0.559), as compared to FIB4 in overall set (0.744/0.499), test set (0.722/0.456), and validation set (0.806/0.507). Compared to FIB‐4, RF could pick 10 times more patients with SF, reduce unnecessary referrals by 28%, and prevent missed referrals by 78%. Age, AST, ALT fasting plasma glucose, and platelet count were top features determining the SF. Sequential use of SAFE < 140 and FIB4 < 1.2 (when SAFE > 140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set). Conclusions: ML with clinical, anthropometric data and simple blood investigations perform better than FIB‐4 for ruling out SF in biopsy‐proven Asian NAFLD/MASLD patients. [ABSTRACT FROM AUTHOR]