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Temporal trends in utilization and outcomes of steatotic donor livers in the United States.

Kyle R JacksonJennifer D MotterChristine E HaugenCourtenay M HolscherJane J LongAllan B MassieBenjamin PhilosopheAndrew M CameronJacqueline Garonzik-WangDorry L Segev
Published in: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons (2019)
Steatotic donor livers (SDLs) (macrosteatosis ≥30%) represent a possible donor pool expansion, but are frequently discarded due to a historical association with mortality and graft loss. However, changes in recipient/donor demographics, allocation policy, and clinical protocols might have altered utilization and outcomes of SDLs. We used Scientific Registry of Transplant Recipients data from 2005 to 2017 and adjusted multilevel regression to quantify temporal trends in discard rates (logistic) and posttransplant outcomes (Cox) of SDLs, accounting for Organ Procurement Organization-level variation. Of 4346 recovered SDLs, 58.0% were discarded in 2005, versus only 43.1% in 2017 (P < .001). SDLs were always substantially more likely discarded versus non-SDLs, although this difference decreased over time (adjusted odds ratio in 2005-2007:13.15 15.2817.74 ; 2008-2011:11.77 13.4115.29 ; 2012-2014:9.87 11.3713.10 ; 2015-2017:7.79 8.8910.15 , P < .001 for all). Conversely, posttransplant outcomes of recipients of SDLs improved over time: recipients of SDLs from 2012 to 2017 had 46% lower risk of mortality (adjusted hazard ratio [aHR]: 0.43 0.540.68 , P < .001) and 47% lower risk of graft loss (aHR: 0.42 0.530.67 , P < .001) compared to 2005 to 2011. In fact, in 2012 to 2017, recipients of SDLs had equivalent mortality (aHR: 0.90 1.041.21 , P = .6) and graft loss (aHR: 0.90 1.041.20 , P = .6) to recipients of non-SDLs. Increasing utilization of SDLs might be a reasonable strategy to expand the donor pool.
Keyphrases
  • cardiovascular events
  • healthcare
  • public health
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  • coronary artery disease
  • machine learning
  • artificial intelligence
  • big data
  • skeletal muscle
  • data analysis