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Background & Aims: aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B‐related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction‐associated steatotic liver disease (MASLD; formerly NAFLD)‐related fibrosis diagnosis and establish a machine‐learning (ML) model to improve the diagnostic performance. Methods: A total of 946 biopsy‐proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125). Results: The AUROCs of aMAP versus fibrosis‐4 index (FIB‐4) and aspartate aminotransferase‐platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut‐off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB‐4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM‐plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM‐plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM‐plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively). Conclusions: The aMAP score is capable of diagnosing MASLD‐related fibrosis. The LSM‐plus model could accurately identify MASLD‐related cirrhosis and advanced fibrosis. [ABSTRACT FROM AUTHOR]

Background the incidence of acute-on-chronic liver disease (AoCLD) is increasing. Objective to investigate the clinical features and risk factors of AoCLD and construct an effective prognostic nomogram model for older patients with AoCLD. Methods data from 3,970 patients included in the CATCH-LIFE study were used, including 2,600 and 1,370 patients in the training and validation sets, respectively. Multivariate Cox regression analyses were performed to identify predictive risk factors in older individuals, and an easy-to-use nomogram was established. Performance was assessed using area under the curve, calibration plots and decision curve analysis (DCA). Results of the 3,949 patients with AoCLD, 809 were older with a higher proportion of autoimmune-related abnormalities, hepatitis C viral infection and schistosomiasis. In the older patient group, the incidence of cirrhosis, hepatic encephalopathy (HE), infection, ascites and gastrointestinal bleeding; neutrophil-to-lymphocyte ratio (NLR), aspartate-to-alanine transaminase ratio (AST/ALT), creatinine and blood urea nitrogen levels were higher, whereas incidence of acute-on-chronic liver failure, white blood cell, platelet and haemoglobin levels; albumin, total bilirubin (TB), AST and ALT levels; international normalised ratio (INR), estimated glomerular filtration rate and blood potassium levels were lower than in the younger group. The final nomogram was developed based on the multivariate Cox analysis in training cohort using six risk factors: ascites, HE grades, NLR, TB, INR and AST/ALT. Liver transplantation-free mortality predictions were comparable between the training and validation sets. DCA showed higher net benefit for the nomograph than the treat-all or treat-none strategies, with wider threshold probabilities ranges. Conclusions our analysis will assist clinical predictions and prognoses in older patients with AoCLD. [ABSTRACT FROM AUTHOR]