Identification of a personalized eGFR threshold improves the prediction of kidney failure risk after transplantation.

BACKGROUND: Many factors cause kidney transplant graft failure. To identify at-risk patients and tailor treatment, failure risks must be accurately predicted. We are trying to predict the temporal progression of graft function (as treated with estimated glomerular filtration rate) using only pre- and post-transplantation data to develop a model that could be used clinically to help patient management. METHODS: We develop a method that integrates a dynamic model of glomerular filtration rate with machine learning and explicitly accounts for parameter uncertainty. The algorithm is trained on a cohort of 892 kidney transplant recipients and validated in an independent cohort of 847 recipients. The model uses routinely collected pre-transplant variables together with early post-operative graft function as inputs. RESULTS: Here we show that the model predicts the temporal evolution of graft function and identifies a threshold in estimated glomerular filtration rate that enables classification of high-risk patients. Clinical events following kidney transplantation are predicted using only a limited number of inputs, achieving a graft outcome prediction accuracy of 0.88 ± 0.04 (F1-score: 0.81 ± 0.06) for the first postoperative year. An accuracy of 0.85 ± 0.05 (F1-score: 0.79 ± 0.07) is also achieved for clinical events in the second postoperative year, thereby strengthening the reliability of the prediction methodology over time. CONCLUSIONS: This study demonstrates that combining a dynamic filtration model with machine learning provides a robust basis for individualized prediction of graft outcomes after kidney transplantation and has the potential to be developed into a clinical decision-support tool for early identification of high-risk patients.