METHODS: This is a retrospective analysis of the United Network for Organ Sharing registry data of LT recipients from January 1, 2000, to December 31, 2021. Outcomes analysis was performed using Cox proportional model for all-cause mortality and graft failure. Confounding was reduced by coarsened exact matching causal inference analysis.
RESULTS: Of 66 960 donors identified, 7178 (10.7%) had diabetes. Trend analysis revealed a longitudinal increase in the prevalence of donor diabetes ( P
AIMS: To determine the prevalence of alcohol abstinence, factors associated with alcohol abstinence and the impact of abstinence on morbidity and overall survival in people with alcohol-associated cirrhosis.
METHODS: We searched Medline and Embase from inception to 15 April 2023 for prospective and retrospective cohort studies describing alcohol abstinence in people with known alcohol-associated cirrhosis. Meta-analysis of proportions for pooled estimates was performed. The method of inverse variance, employing a random-effects model, was used to pool the hazard ratio (HR) comparing outcomes of abstinent against non-abstinent individuals with alcohol-associated cirrhosis.
RESULTS: We included 19 studies involving 18,833 people with alcohol-associated cirrhosis. The prevalence of alcohol abstinence was 53.8% (CI: 44.6%-62.7%). Over a mean follow-up duration of 48.6 months, individuals who continued to consume alcohol had significantly lower overall survival compared to those who were abstinent (HR: 0.611, 95% CI: 0.506-0.738). These findings remained consistent in sensitivity/subgroup analysis for the presence of decompensation, study design and studies that assessed abstinence throughout follow-up. Alcohol abstinence was associated with a significantly lower risk of hepatic decompensation (HR: 0.612, 95% CI: 0.473-0.792).
CONCLUSIONS: Alcohol abstinence is associated with substantial improvement in overall survival in alcohol-associated cirrhosis. However, only half of the individuals with known alcohol-associated cirrhosis are abstinent.
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 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.