Abstract Objective This study aimed to construct a scientific, accurate, and readily applicable clinical all-cause mortality prediction mode
Abstract Objective This study aimed to construct a scientific, accurate, and readily applicable clinical all-cause mortality prediction model for patients with metabolic dysfunction-associated steatotic liver disease (MASLD) to enhance the efficiency of disease management and improve patient prognosis. Methods This study was a retrospective cohort study based on the National Health and Nutrition Examination Survey database. The 17,861 participants diagnosed with MASLD were randomly assigned to either a training cohort (n = 12,503) or a validation cohort (n = 5358). Potential predictors were subjected to LASSO regression analysis, and independent risk factors were subsequently identified through multivariate Cox regression analysis. An all-cause mortality prediction model was constructed based on the significant predictors, and a nomogram was generated to illustrate the survival probability of patients at various time points. The model’s performance was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA) curves. Results A multiple Cox regression analysis identified several independent predictors significantly influencing all-cause mortality in patients with MASLD. These included gender, age, smoking status, hypertension, red blood cell count, albumin, glutamyl transpeptidase, glycosylated hemoglobin, and creatinine. The constructed predictive model demonstrated high accuracy in the training and validation cohorts, with AUC values approaching 0.85 at 3, 5, and 10 years, respectively. Calibration and DCA curves were employed to verify the stability and generalizability of the model. Conclusions We successfully constructed and validated an all-cause mortality prediction model for MASLD patients. This model provides a powerful tool for clinical risk assessment and treatment decision-making.
