ABSTRACT
Background.
Kidney transplantation faces organ shortages, underscoring the need for early risk stratification of graft loss. We developed and validated a 12-month prediction model that treats death as a competing event.
Methods.
We conducted a retrospective cohort study of 2030 adult kidney transplant recipients (2008–2023) from Colombia’s largest transplant network. Models included a random survival forest for competing risks (RSF-CR) and Fine–Gray (FG) regression. Internal validation used stratified cross-validation. Model performance was evaluated via discrimination (C-index), calibration and clinical utility (decision curve analysis).
Results.
Key predictors included donor type, stroke cause of death (deceased donor), recipient age, donor creatinine, panel reactive antibodies (PRA) class I >20, expanded criteria donor, donor age, years on dialysis, PRA class II >20, donor hypertension, donor–recipient compatibility and retransplantation. The RSF-CR model outperformed the FG, achieving a C-index of 0.87 (versus 0.72) and high sensitivity (88%). It accurately identified low-risk candidates (negative predictive value 98%) and showed a positive net benefit.
Conclusion.
We developed and validated a predictive model for first-year graft loss in kidney transplant recipients using a machine learning for competing risks model. The model showed strong discriminative ability and moderate calibration. Further temporal validation in our population and external validation in other clinical contexts is required to ensure its applicability.
Keywords: competing risk, graft failure, kidney transplantation, machine learning, prediction model