The impact of multi-organ transplant allocation priority on waitlisted kidney transplant candidates.

Kidney-alone transplant (KAT) candidates may be disadvantaged by the allocation priority given to multi-organ transplant (MOT) candidates. This study identified potential KAT candidates not receiving a given kidney offer due to its allocation for MOT. Using the Organ Procurement and Transplant Network (OPTN) database, we identified deceased donors from 2002 to 2017 who had one kidney allocated for MOT and the other kidney allocated for KAT or simultaneous pancreas-kidney transplant (SPK) (n = 7,378). Potential transplant recipient data were used to identify the "next-sequential KAT candidate" who would have received a given kidney offer had it not been allocated to a higher prioritized MOT candidate. In this analysis, next-sequential KAT candidates were younger (p < .001), more likely to be racial/ethnic minorities (p < .001), and more highly sensitized than MOT recipients (p < .001). A total of 2,113 (28.6%) next-sequential KAT candidates subsequently either died or were removed from the waiting list without receiving a transplant. In a multivariable model, despite adjacent position on the kidney match-run, mortality risk was significantly higher for next-sequential KAT candidates compared to KAT/SPK recipients (hazard ratio 1.55, 95% confidence interval 1.44, 1.66). These results highlight implications of MOT allocation prioritization, and potential consequences to KAT candidates prioritized below MOT candidates.

Functional principal component analysis of glomerular filtration rate curves after kidney transplant.

This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.