Objective Post-renal transplantation anemia(PTA)occurs frequently in kidney transplant recipients,significantly impacting their quality of life and graft loss. Currently,effective methods to predictthe risk of persistent PTA early post-transplantation are lacking. This study aimed to develop a nomogram prediction model for early persistent PTA specifically tailored to kidney transplant recipients. Methods Using the electronic medical record system of Southern Hospital of Southern Medical University,patient data from January 1,2020 to December 31,2022 were obtained,and 245 subjects were ultimately selected as the research subjects. Among these,85% were randomly selected as the training set for model development,and the remaining 15% constituted the testing set. Using the Least Absolute Shrinkage and Selection Operator(Lasso)regression model,variables potentially affecting early persistent PTA were screened to identify predictive factors.A logistic regression analysis was employed to establish the prediction model. Model performance was assessed using Receiver operating characteristic(ROC)curves,area under the curve(AUC),Calibration plots,and decision Curve Analysis(DCA). Results Identified predictive factors after screening included recipient's preoperative body mass index,preoperative serum albumin level,preoperative hemoglobin level,preoperative mean corpuscular volume,perioperative use of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers,exogenous iron supplementation,and exogenous erythropoietin supplementation. The model demonstrated good discriminativeability with an AUC of 0.87 for the training set and 0.75 for the testing set,indicating robust predictive performance. Calibration and DCA further confirmed the accuracy and clinical utility of the model. Conclusion This nomogram prediction model utilizes early recipient information,including demographic characteristics,laboratory data,and medication regimens,to accurately predict individualized risk of early persistent PTA in kidney transplant recipients. This provides a basis for early clinical intervention,potentially improving patient prognosis and quality of life.