Predicting kidney graft function and failure among kidney transplant recipients

Abstract

Background: Graft loss is a major health concern for kidney transplant (KTx) recipients. It is of clinical interest to develop a prognostic model for both graft function, quantified by estimated glomerular filtration rate (eGFR), and the risk of graft failure. Additionally, the model should be dynamic in the sense that it adapts to accumulating longitudinal information, including time-varying at-risk population, predictor-outcome association, and clinical history. Finally, the model should also properly account for the competing risk by death with a functioning graft. A model with the features above is not yet available in the literature and is the focus of this research.

Methods: We built and internally validated a prediction model on 3,893 patients from the Wisconsin Allograft Recipient Database (WisARD) who had a functioning graft 6 months after kidney transplantation. The landmark analysis approach was used to build a proof-of-concept dynamic prediction model to address the aforementioned methodological issues: the prediction of graft failure, accounted for competing risk of death, as well as the future eGFR value, are updated at each post-transplant time. We used 21 predictors including recipient characteristics, donor characteristics, transplant-related and post-transplant factors, longitudinal eGFR, hospitalization, and rejection history. A sensitivity analysis explored a less conservative variable selection rule that resulted in a more parsimonious model with reduced predictors.

Results: For prediction up to the next 1 to 5 years, the model achieved high accuracy in predicting graft failure, with the AUC between 0.80 and 0.95, and moderately high accuracy in predicting eGFR, with the root mean squared error between 10 and 18 mL/min/1.73m2 and 70%-90% of predicted eGFR falling within 30% of the observed eGFR. The model demonstrated substantial accuracy improvement compared to a conventional prediction model that used only baseline predictors.

Conclusion: The model outperformed conventional prediction model that used only baseline predictors. It is a useful tool for patient counseling and clinical management of KTx and is currently available as a web app.

Keywords: Competing risk; Dynamic prediction; Graft failure; Kidney transplantation; Renal function.

Fig. 1

The time-dependent AUC and BS of LM and SPM in predicting competing risks. Legend: The AUC and BS values vary with prediction time (ranging from 6 to 60 months post-transplant), and for three prediction horizons (1, 3, 5 years). The asterisk (*) indicates that the difference in AUC or BS between LM and SPM is statistically significant as the 95% bootstrap confidence interval does not cover zero. A graft failure; B death

Fig. 2

Comparison of landmark GEE model with static GEE model in predicting the future Egfr. Legend: Predictive accuracy metrics for eGFR include the proportion that the predicted values fall within 30% (P30) and 50% (P50) of observed eGFR values at the prediction horizon, and the root mean squared error (RMSE, mL/min/1.73m²) between the predicted and observed eGFR. The results are displayed by prediction horizon (1, 3, 5 years) and the prediction time ranges from 6 to 60 months post-transplant