Abstract
Background
Several models have been proposed to predict kidney graft failure in adult recipients but none in younger recipients. Our objective was to propose a dynamic prediction model for graft failure in young kidney transplant recipients.
Methods
We included 793 kidney transplant recipients waitlisted before the age of 18 years who received a first kidney transplantation before the age of 21 years in France in 2002–13 and survived >90 days with a functioning graft. We used a Cox model including baseline predictors only (sex, age at transplant, primary kidney disease, dialysis duration, donor type and age, human leucocyte antigen matching, cytomegalovirus serostatus, cold ischaemia time and delayed graft function) and two joint models also accounting for post-transplant estimated glomerular filtration rate (eGFR) trajectory. Predictive performances were evaluated using a cross-validated area under the curve (AUC) and R2 curves.
Results
When predicting the risk of graft failure from any time within the first 7 years after paediatric kidney transplantation, the predictions for the following 3 or 5 years were accurate and much better with the joint models than with the Cox model (AUC ranged from 0.83 to 0.91 for the joint models versus 0.56 to 0.64 for the Cox model).
Conclusion
Accounting for post-transplant eGFR trajectory strongly increased the accuracy of graft failure prediction in young kidney transplant recipients.
Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study
