Background/Aims: Benefits of the Mediterranean diet (MD) in reducing hepatic steatosis among individuals with metabolic dysfunction–associated steatotic liver disease (MASLD) have been well established. This study aims to evaluate the relationship between MD adherence and liver fibrosis among the MASLD population. Methods: This cross‐sectional study analyzed data from the National Health and Nutrition Examination Survey 2017 to March 2020. Individuals with MASLD were identified based on the vibration‐controlled transient elastography–defined steatosis. Participants' MD adherence was evaluated using the alternate Mediterranean diet (aMED) score, with higher scores indicating greater adherence. Risk of significant liver fibrosis was compared between low, moderate, and high aMED groups. Results: Of 2672 MASLD participants, 27.2%, 42.3%, and 30.6% were categorized into low, moderate, and high aMED groups, respectively. After adjusting for the number of cardiometabolic risk factors (CMRFs), physical activity, and other covariates, participants in the high aMED group were associated with a lower risk of significant liver fibrosis compared with those in the low aMED group (aMED 5–9 vs. aMED 0–2: OR = 0.662, 95% confidence interval [CI]: 0.660–0.663; p for trend < 0.0001). In the sensitivity analyses, the protective association of higher MD adherence against the risk of significant liver fibrosis weakened with higher cardiometabolic burden (aMED 5–9: aOR 0.582, 95% CI: 0.580–0.584 [4–5 CMRFs] vs. aOR 0.383, 95% CI: 0.380–0.386 [1 CMRF]). Conclusions: In MASLD patients, higher MD adherence was associated with lower risk of significant liver fibrosis. Our findings support the recommendation of the MD as a crucial lifestyle intervention to lower the risk of liver fibrosis in this population.

Here we aimed to assess the mortality risk and distribution of deaths from different complications and etiologies for non-alcoholic liver cirrhosis (NALC) adult inpatients and compare them with that of the general hospitalized adult population. Hospitalized patients with a primary diagnosis of NALC and aged between 30 and 80 years of age from 1999 to 2010 were identified using a population-based administrative claims database in Taiwan. They were matched with a general, non-NALC population of hospitalized patients. Causes of death considered were variceal hemorrhage, ascites, hepatic encephalopathy, spontaneous bacterial peritonitis, hepatocellular carcinoma, jaundice, and hepatorenal syndrome. A total of 109,128 NALC inpatients were included and then matched with 109,128 inpatients without NALC. Overall mortality rates were 21.2 (95% CI: 21.0–21.4) and 6.27 (95% CI: 6.17–6.37) per 100 person-years, respectively. Among complications that caused death in NALC patients, variceal hemorrhage was the most common (23.7%, 11.9 per 100 person-years), followed by ascites (20.9%, 10.4 per 100 person-years) and encephalopathy (18.4%, 9.21 per 100 person-years). Among all etiologies, mortality rates were highest for NALC patients with HBV infection (43.7%, 21.8 per 100 person-years), followed by HBV-HCV coinfection (41.8%, 20.9 per 100 person-years), HCV infection (41.2%, 20.6 per 100 person-years), and NAFLD (35.9%, 17.9 per 100 person-years). In this study, we demonstrated that mortality risks in NALC patients may differ with their etiology and their subsequent complications. Patients’ care plans, thus, should be formulated accordingly.

Background/Objectives: Fatality of cirrhotic patients greatly increases when they progress to the decompensated state. Only a few studies to date have applied machine learning (ML) methods to predict decompensation in cirrhosis patients. In the present study, we attempted to apply self-developed ML models for validating their capability of predicting different complications in hepatitis B virus (HBV)-related cirrhosis patients. Methods: Data were extracted from electronic health records of 50,047 patients who were tested and diagnosed with HBV in a tertiary hospital. Four different algorithms (Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF)) were utilized, and a total of 32 ML models were trained and tested to predict variceal bleeding, ascites, jaundice, and multiple complications (≥2 complications) in HBV-related cirrhosis patients. The use of two antiviral drugs were considered: entecavir (ETV) and lamivudine (LAM). Performance of the models was assessed using area under receiver operating characteristic curve (AUROC) and accuracy score. Results: SVM and RF classifications produced the best overall predictions for decompensation in HBV-related cirrhosis patients, with AUROCs ranging from 0.85 to 0.93 and accuracy scores between 0.77 to 0.88 for ascites, jaundice, and multiple complications. The SVM and LR algorithms generated the best performance in differentiating ascites among ETV users, with AUROC of 0.93 and 0.92 and accuracy of 0.88 and 0.86, respectively. Antiviral treatment (type, length of use, adherence), and other routinely collected clinical information may serve as informative markers in differentiating decompensated cirrhosis. Conclusions: ML-based prediction of decompensation using electronic health records may assist clinicians in decision making. Findings of this study also underline the impact of antiviral therapy as a key predictor for decompensation.