Acute-on-chronic liver failure (ACLF) is a severe condition characterized by a systemic inflammatory response and associated with high mortality. Currently, there is no reliable prediction model for long-term prognosis in ACLF. This study aimed to develop and validate a prognostic model incorporating inflammation indexes to predict the long-term outcome of patients with hepatitis B virus-related ACLF (HBV-ACLF). A retrospective analysis of clinical data from HBV-ACLF patients (n = 986) treated at the Third Affiliated Hospital of Sun Yat-sen University between January 2014 and December 2018 was conducted. Patients were randomly divided into training (n = 690) and validation (n = 296) cohorts. The Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses were used to identify independent risk factors for long-term mortality. The following variables were identified as independent predictors of long-term mortality: age, cirrhosis, hepatic encephalopathy, total bilirubin (TBIL), international normalized ratio (INR), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-platelet ratio (NPR). A novel nomogram was established by assigning weights to each variable. The C-index of the nomogram was 0.777 (95% confidence interval [CI]: 0.752-0.802). In the training set, the area under the curve (AUC) for predicting mortality at 1, 3, and 12 months was 0.841 (95% CI: 0.807-0.875), 0.827 (95% CI: 0.796-0.859), and 0.829 (95% CI: 0.798-0.859), respectively. The nomogram demonstrated superior predictive performance for 12-month survival compared to the model for end-stage liver disease (MELD) score (0.767, 95% CI: 0.730-0.804, p < 0.001) and the clinical overt sepsis in acute liver failure clinical practice Guidelines-ACLF II score (0.807, 95% CI: 0.774-0.840, p = 0.028). Finally, calibration curves and decision curve analysis (DCA) confirmed the clinical utility of the nomogram. The novel inflammation-based scoring system, incorporating MLR and NPR, effectively predicts long-term mortality in HBV-ACLF patients.
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