Abstract
Abstract Background and Aims Access to individualized, evidence-based prognostics is needed to facilitate shared decision making about the clinical implications of whether to opt for kidney transplantation or long-term dialysis. We aim to implement a data-driven support tool (Nierkeuze.nl) that incorporates basic patient characteristics and treatment options. Method Consecutive patients (N = 14.275) above 18 years of age with end-stage-renal-disease (ESRD) who were eligible candidates for their first kidney transplantation and started renal replacement therapy between 2000 and 2019 were included from the national registries RENINE and NOTR. SF-12 quality of life data was obtained from RENINE and a local transplant database. Several algorithms were developed to predict waiting time, short- and long-term patient survival, graft survival, and quality of life according to factors that are known at ESRD. Probabilities of 3- and 5-year mortality were calculated using two-stage distinct Cox regression: 1) waitlist-mortality according to median time on the waiting list, 2) mortality after transplantation in remaining follow-up. Product and complement rules defined the final probabilities. No mortality on the waiting list was assumed for living donor transplants. Missing data were imputed. Model performance was evaluated by internally validated C-statistics and calibration plots. Results We included 4.889 deceased donor transplants, of which 990 were within the Eurotransplant Senior allocation Program (ESP), and 6.251 living donor transplants. Discrimination varied for patient survival models (C-stats: 0.68 deceased-ETKAS-TX; 0.58 for deceased-ESP-TX; 0.74 for living-TX; 0.64 for waitlisted-candidates). Survival models achieved good calibration by visual inspection across the range of predicted probabilities. Figure 1 illustrates a clear 5-year survival benefit of transplantation (living and deceased donor groups) compared with wait-listed candidates for a 65-year-old male with glomerulonephritis. Other developed algorithms of the support tool will be presented with an overview of the first experiences in the kidney failure clinic. Conclusion Nierkeuze.nl depicts the influence of anticipated waiting time, patient characteristics, kidney allocation program, and donor type on survival outcome of the different treatment options. Incorporating this individualized quantitative outcome information may provide valuable support for an informed choice by patients and potential living donors.
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