Prognostic Value of EASIX (ln-EASIX) in Hospitalized Patients with Cirrhosis

Prognostic Value of ln-EASIX in Cirrhosis

Abstract

Objective: To investigate the prognostic value of the natural logarithm of the Endothelial Activation and Stress Index (ln-EASIX) for mortality and readmission outcomes in hospitalized patients with cirrhosis, and to compare its discriminative performance with the Model for End-Stage Liver Disease–Sodium (MELD-Na) score.
Methods: This retrospective cohort study included 244 hospitalized patients with cirrhosis. Because the EASIX values demonstrated a right-skewed distribution, logarithmic transformation was applied before the analysis. Patients were categorized into low- and high-ln-EASIX groups using the receiver operating characteristic (ROC) curve -derived optimal threshold determined by the Youden index for 30-day mortality in the analytic dataset. Baseline demographic, clinical, and laboratory characteristics were compared between the groups. Mortality and readmission were assessed, and receiver operating characteristic analysis was performed to evaluate the discriminative ability of the selected endpoints. Readmission analyses were considered secondary endpoints.
Results: Thirty-day mortality was higher in the high ln-EASIX group than in the low ln-EASIX group (11.6% vs. 1.6%, p = 0.004). ln-EASIX demonstrated moderate discrimination for 30-day mortality area under the curve (AUC) 0.725, 95% confidence interval (CI) 0.613–0.837, whereas MELD-Na showed numerically higher discrimination (AUC 0.782, 95% CI 0.692–0.872). In a MELD-Na-adjusted logistic regression model, ln-EASIX was not independently associated with 30-day mortality (adjusted OR 1.72, 95% CI 0.82–3.62, p = 0.153).
CONCLUSION: In hospitalized patients with cirrhosis, a higher ln-EASIX was associated with a more advanced disease profile and worse early outcomes. ln-EASIX may serve as a simple adjunctive marker for early bedside risk stratification; however, the findings should be interpreted cautiously until validated in larger studies with explicit cutoff definitions, multivariable modeling, and formal model-comparison analyses.