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Center use of technical variant grafts varies widely and impacts pediatric liver transplant waitlist and recipient outcomes in the United States.

George Vincent MazariegosEmily R PeritoJames E SquiresKyle A SoltysAdam D GriesemerSarah A TaylorEric Pahl
Published in: Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society (2023)
To assess the impact of technical variant grafts (TVG) (including living donor [LD] and deceased donor split/partial grafts) on waitlist (WL) and transplant outcomes for pediatric liver transplant (LT) candidates, we performed a retrospective analysis of OPTN data on first-time LT or liver-kidney pediatric candidates listed at centers that performed >10 LT during the study period, 2004-2020. Center variance was plotted for LT volume, TVG usage, and survival. A composite center metric of TVG usage and WL mortality was developed to demonstrate existing variation and potential for improvement. 64 centers performed 7842 LT; 657 children died on the WL. Proportions of WL mortality by center ranged from 0-31% and TVG usage from 0-76%. Higher TVG usage, from deceased or LD, independently or in combination, significantly correlated with lower WL mortality. In multivariable analyses, death from listing was significantly lower with increased center TVG usage (HR 0.611, CI [0.40-0.92]) and LT volume (HR 0.995, CI [0.99-1.0]). Recipients of living donor transplants (HR 0.637, CI [0.51-0.79]) had significantly increased survival from transplant compared with other graft types, and recipients of deceased donor technical variant grafts (HR 1.066, CI [0.93-1.22]) had statistically similar outcomes compared to whole graft recipients. Increased TVG utilization may decrease WL mortality in the U.S. Policy and training to increase TVG usage, availability and expertise is critical.
Keyphrases
  • kidney transplantation
  • cardiovascular events
  • risk factors
  • healthcare
  • public health
  • mental health
  • machine learning
  • electronic health record