Predicting Kidney Transplantation Outcomes from Donor and Recipient Characteristics at Time Zero: Development of a Mobile Application for Nephrologists

Abstract

(1) Background: We report on the development of a predictive tool that can estimate kidney transplant survival at time zero. (2) Methods: This was an observational, retrospective study including 5078 transplants. Death-censored graft and patient survivals were calculated. (3) Results: Graft loss was associated with donor age (hazard ratio [HR], 1.021, 95% confidence interval [CI] 1.018–1.024, p < 0.001), uncontrolled donation after circulatory death (DCD) (HR 1.576, 95% CI 1.241–2.047, p < 0.001) and controlled DCD (HR 1.567, 95% CI 1.372–1.812, p < 0.001), panel reactive antibody percentage (HR 1.009, 95% CI 1.007–1.011, p < 0.001), and previous transplants (HR 1.494, 95% CI 1.367–1.634, p < 0.001). Patient survival was associated with recipient age (> 60 years, HR 5.507, 95% CI 4.524–6.704, p < 0.001 vs. < 40 years), donor age (HR 1.019, 95% CI 1.016–1.023, p < 0.001), dialysis vintage (HR 1.0000263, 95% CI 1.000225–1.000301, p < 0.01), and male sex (HR 1.229, 95% CI 1.135–1.332, p < 0.001). The C-statistics for graft and patient survival were 0.666 (95% CI: 0.646, 0.686) and 0.726 (95% CI: 0.710–0.742), respectively. (4) Conclusions: We developed a mobile app to estimate survival at time zero, which can guide decisions for organ allocation.