Exception Policy Change Increased the Simultaneous Kidney-liver Transplant Probability of Polycystic Disease in the Centers With High Median MELD at Transplantation

Transplantation. 2024 Mar 29. doi: 10.1097/TP.0000000000004950. Online ahead of print.

Abstract

Background: In 2019, Organ Procurement and Transplantation Network/United Network for Organ Sharing changed the exception policy for liver allocation to the median model for end-stage liver disease at transplantation (MMaT). This study evaluated the effects of this change on-waitlist outcomes of simultaneous liver-kidney transplantation (SLKT) for patients with polycystic liver-kidney disease (PLKD).

Methods: Using the Organ Procurement and Transplantation Network/United Network for Organ Sharing registry, 317 patients with PLKD listed for SLKT between January 2016 and December 2021 were evaluated. Waitlist outcomes were compared between prepolicy (Era 1) and postpolicy (Era 2) eras.

Results: One-year transplant probability was significantly higher in Era 2 than in Era 1 (55.7% versus 37.9%; P = 0.001), and the positive effect on transplant probability of Era 2 was significant after risk adjustment (adjusted hazard ratio, 1.76; 95% confidence interval, 1.22-2.54; P = 0.002 [ref. Era 1]), whereas waitlist mortality was comparable. Transplant centers were separated into the high and low MMaT groups with a score of 29 (median MMaT) and transplant probability in each group between eras was compared. In the high MMaT transplant centers, the 1-y transplant probability was significantly higher in Era 2 (27.5% versus 52.4%; P = 0.003). The positive effect remained significant in the high MMaT center group (adjusted hazard ratio, 2.79; 95% confidence interval, 1.43-5.46; P = 0.003 [ref. Era 1]) but not in the low MMaT center group. Although there was a difference between center groups in Era 1 (P = 0.006), it became comparable in Era 2 (P = 0.54).

Conclusions: The new policy increased 1-y SLKT probability in patients with PKLD and successfully reduced the disparities based on center location.