To investigate the correlation between acute kidney injury (AKI) and 1-year mortality in patients with granulomatosis with polyangiitis (GPA). Clinical data for GPA patients were extracted from the MIMIC-IV (version 3.0) database. Logistic and Cox regression analyses, Kaplan–Meier (KM) survival analysis, and mediation effect analysis were used to assess the association between AKI, renal function indicators, and 1-year mortality in GPA patients. Predictive models were constructed using machine learning algorithms, and tree-based feature selection was applied to evaluate the contributions of AKI and renal function indicators to mortality prediction. A total of 127 GPA patients were included in the analysis. Multivariate logistic regression identified AKI (OR > 1, P < 0.05) as a significant predictor of 1-year mortality. Similarly, multivariate Cox regression analysis revealed AKI (HR > 1, P < 0.05) as an independent risk factor for 1-year mortality. KM survival analysis demonstrated that GPA patients with AKI had significantly lower survival rates than those without AKI (P < 0.0001). Additionally, renal function indicators modestly mediated the relationship between AKI and 1-year mortality in GPA patients. The machine learning analysis indicated that the random forest algorithm performed the best, with an area under the curve of 0.894. Feature selection using tree model analysis highlighted both AKI and renal function indicators as significant contributors to mortality prediction in GPA patients. Our study suggested AKI was an independent risk factor for increased 1-year mortality in GPA patients. Additionally, renal function indicators partially mediated the relationship between AKI and 1-year mortality in these patients.
