Association between metabolic syndrome and 90-day mortality in acute-on-chronic liver failure patients based on propensity score matching and prognostic model construction

BACKGROUND: This study aimed to evaluate the association between metabolic syndrome (MetS) and clinical outcomes in patients hospitalized for acute-on-chronic liver failure (ACLF), and to establish a prognostic prediction model incorporating MetS. METHODS: A retrospective cohort study was conducted involving 303 ACLF patients admitted to a tertiary hospital between May 2023 and May 2025. Patients were categorized into two groups based on their MetS status (MetS group vs. non-MetS group). The primary outcome was 90-day all-cause mortality. Propensity score matching (PSM) was employed to balance baseline characteristics. The association was assessed using multivariable Cox regression and logistic regression analyses after adjusting for confounders. Based on the results of the multivariable analysis, a nomogram model for predicting 90-day mortality was constructed. The model's discriminative ability, calibration, and clinical utility were evaluated in both the training and testing sets using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: After PSM, 206 patients (103 in the MetS group and 103 in the non-MetS group) were included in the final analysis. The 90-day mortality rate was significantly higher in the MetS group than in the non-MetS group (49.51% vs. 27.18%, p < 0.001). Multivariate Cox regression analysis showed that MetS, age, alanine aminotransferase (ALT), artificial liver support system (ALSS), transfusion times (TT), international normalized ratio (INR), C-reactive protein (CRP) and hepatocellular carcinoma (HCC) were independent predictors of 90-day death risk in patients with ACLF. The nomogram prediction model constructed based on these variables demonstrated excellent discriminative ability in both the training set (area under the curve, AUC = 0.877) and the testing set (AUC = 0.820). The calibration curve showed a high consistency between the predicted probabilities and the actual observations. Decision curve analysis confirmed the model's favorable net clinical benefit. CONCLUSION: MetS is an independent predictor of poor short-term prognosis in patients with ACLF, significantly increasing the risk of mortality. This study established a nomogram prediction model that integrates MetS, which can accurately assess patients' short-term mortality risk and may assist clinicians in early risk stratification.