A short-term predictive model for disease progression in acute-on-chronic liver failure: integrating spectral CT extracellular liver volume and clinical characteristics

Background Acute-on-chronic liver failure (ACLF) is a life-threatening hepatic syndrome. Therefore, this study aimed to develop a comprehensive model combining extracellular liver volume derived from spectral CT (ECVIC-liver) and sarcopenia, for the early prediction of short-term (90-day) disease progression in ACLF. Materials and methods A retrospective cohort of 126 ACLF patients who underwent hepatic spectral CT scans was included. According to the Asia-Pacific Association for the Study of the Liver (APASL) criteria, patients were divided into the progression group (n = 70) and the stable group (n = 56). ECVIC-liver was measured on the equilibrium period (EP) images of spectral CT, and L3-SMI was measured on unenhanced CT images, with sarcopenia assessed. A comprehensive model was developed by combining independent predictors. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Results In the univariate analysis, BMI, WBC, PLT, PTA, L3-SMI, IC-EP, Z-EP, K-140-EP, NIC-EP, ECVIC-liver, and Sarcopenia demonstrated associations with disease progression status at 90 days in ACLF patients. In multivariate logistic regression, white blood cell count (WBC) (OR = 1.19, 95% CI: 1.02-1.40; P = 0.026), ECVIC-liver (OR = 1.27, 95% CI: 1.15-1.40; P < 0.001), sarcopenia (OR = 4.15, 95% CI: 1.43-12.01; P = 0.009), MELD-Na score (OR = 1.06, 95%CI: 1.01-1.13;P = 0.042), and CLIF-SOFA score (OR = 1.37, 95%CI:1.15-1.64; P<0.001) emerged as independent risk factors for ACLF progression. The combined model exhibited superior predictive performance (AUCs = 0.910, sensitivity = 80.4%, specificity = 90.0%, PPV = 0.865, NPV = 0.851) compared to CLIF-SOFA, MELD-Na, MELD and CTP scores(both P < 0.001). Calibration curves and DCA confirmed the high clinical utility of the combined model. Conclusions Patients without sarcopenia and/or with a lower ECVIC-liver have a better prognosis, and the integration of WBC, ECVIC-liver, Sarcopenia, CLIF-SOFA and MELD-Na scores in a composite model offers a concise and effective tool for predicting disease progression in ACLF patients.