A machine learning-based nomogram for predicting graft survival in allograft kidney transplant recipients: a 20-year follow-up study.

Background: Kidney transplantation is the optimal form of renal replacement therapy, but the long-term survival rate of kidney graft has not improved significantly. Currently, no well-validated model exists for predicting long-term kidney graft survival over an extended observation period. Methods: Recipients undergoing allograft kidney transplantation at the Organ Transplantation Center of the First Affiliated Hospital of Kunming Medical University from 1 August 2003 to 31 July 2023 were selected as study subjects. A nomogram model was constructed based on least absolute selection and shrinkage operator (LASSO) regression, random survival forest, and Cox regression analysis. Model performance was assessed by the C-index, area under the curve of the time-dependent receiver operating characteristic curve, and calibration curve. Decision curve analysis (DCA) was utilized to estimate the net clinical benefit. Results: The machine learning-based nomogram included cardiovascular disease in recipients, delayed graft function in recipients, serum phosphorus in recipients, age of donors, serum creatinine in donors, and donation after cardiac death for kidney donation. It demonstrated excellent discrimination with a consistency index of 0.827. The calibration curves demonstrated that the model calibrated well. The DCA indicated a good clinical applicability of the model. Conclusion: This study constructed a nomogram for predicting the 20-year survival rate of kidney graft after allograft kidney transplantation using six factors, which may help clinicians assess kidney transplant recipients individually and intervene.