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Maximizing donors' gifts: A comparison of actual and expected solid organ yield among VCA donors.

Gabriel R VeceAmanda RobinsonJohn RosendaleWida CherikhChristopher CurranChristopher WholleyDarren DiBatistaDavid K KlassenJennifer L Wainright
Published in: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons (2020)
Vascularized Composite Allograft (VCA) transplantation provides life-changing transplants, but VCA adds complexity to the donation process and timing, possibly impeding solid organ donation. Expanding upon descriptive analyses, this study examines risk-adjusted predictions versus the observed number of organs donated by VCA donors. Our cohort included VCA donors in the United States during January 1, 2008-December 31, 2017 (n = 51), using OPTN Deceased Donor Registration Form data and the Scientific Registry of Transplant Recipients (SRTR) donor yield models to calculate observed-to-expected (O:E) yield ratios. Almost all VCA donors' livers (48/51; 94.1%) and kidneys (92/102; 90.2%) were transplanted, with fewer hearts (28/51; 54.9%), lungs (46/102; 45.1%), pancreata (15/51; 29.4%), and intestines (3/51; 5.9%) transplanted. O:E ratios for overall organ yield were slightly greater than expected for VCA donors (1.10; 95% CI: 1.02-1.17). Liver (1.17: 1.08-1.27) and lung yields (1.38: 1.07-1.68) were both greater than expected, while kidney, heart, and pancreas yields were similar to expected. Across VCA types, bilateral upper limb and abdominal wall donors had better-than-expected yields while uterus, face, and unilateral upper limb donors all had similar-to-expected yields. Solid organ yield among VCA donors was as good or better than predicted, suggesting that VCA donation does not compromise recovery and transplantation of lifesaving organs.
Keyphrases
  • kidney transplantation
  • upper limb
  • heart failure
  • atrial fibrillation
  • stem cells
  • bone marrow
  • big data
  • mesenchymal stem cells
  • electronic health record
  • deep learning