Objective To identify and analyze risk factors associated with acute renal failure (ARF) after lung transplantation (LTx) and develop a predictive model. Methods Patients for this study were obtained from the United Network for Organ Sharing (UNOS) database who underwent unilateral or bilateral lung transplantation between 2015 and 2022. Preoperative and postoperative clinical characteristics of the patients were analyzed. A combined approach using random forest and Lasso regression was employed to identify key preoperative factors associated with the incidence of ARF following lung transplantation. Random forest was used to assess the importance of each feature variable, while Lasso regression further filtered the variables contributing most significantly to the model. The predictive performance of the constructed model was evaluated in both training and validation sets, with ROC curves and AUC values used to verify and compare model effectiveness. ResultsA total of 15 110 LTx patients were included in the study, comprising 6 041 males and 9 069 females, with a median age of 64 years. Findings indicated that preoperative lung diagnosis, estimated glomerular filtration rate (eGFR), mechanical ventilation, ICU admission prior to transplantation, extracorporeal membrane oxygenation (ECMO) support, infection within two weeks before transplantation, Karnofsky Performance Status (KPS) score, donor age, waitlist duration, double-lung transplantation, and ischemia time showed statistically significant differences between groups (P<0.05). Model evaluation results demonstrated that the constructed predictive model achieved high accuracy in both the training and validation sets, with favorable AUC values, confirming its validity and reliability. ConclusionThis study discusses common risk factors for ARF following lung transplantation and introduces an effective predictive model with potential clinical application.