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Liver transplantation and waitlist mortality for HCC and non-HCC candidates following the 2015 HCC exception policy change.

Tanveen IshaqueAllan B MassieMary Grace BowringChristine E HaugenJessica M RuckSamantha E HalpernMadeleine M WaldramMacey L LevanJacqueline M Garonzik WangAndrew M CameronBenjamin PhilosopheShane OttmannAnne F RositchDorry L Segev
Published in: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons (2018)
Historically, exception points for hepatocellular carcinoma (HCC) led to higher transplant rates and lower waitlist mortality for HCC candidates compared to non-HCC candidates. As of October 2015, HCC candidates must wait 6 months after initial application to obtain exception points; the impact of this policy remains unstudied. Using 2013-2017 SRTR data, we identified 39  350 adult, first-time, active waitlist candidates and compared deceased donor liver transplant (DDLT) rates and waitlist mortality/dropout for HCC versus non-HCC candidates before (October 8, 2013-October 7, 2015, prepolicy) and after (October 8, 2015-October 7, 2017, postpolicy) the policy change using Cox and competing risks regression, respectively. Compared to non-HCC candidates with the same calculated MELD, HCC candidates had a 3.6-fold higher rate of DDLT prepolicy (aHR = 3.49 3.69 3.89 ) and a 2.2-fold higher rate of DDLT postpolicy (aHR = 2.09 2.21 2.34 ). Compared to non-HCC candidates with the same allocation priority, HCC candidates had a 37% lower risk of waitlist mortality/dropout prepolicy (asHR = 0.54 0.63 0.73 ) and a comparable risk of mortality/dropout postpolicy (asHR = 0.81 0.95 1.11 ). Following the policy change, the DDLT advantage for HCC candidates remained, albeit dramatically attenuated, without any substantial increase in waitlist mortality/dropout. In the context of sickest-first liver allocation, the revised policy seems to have established allocation equity for HCC and non-HCC candidates.
Keyphrases
  • healthcare
  • public health
  • cardiovascular events
  • mental health
  • cardiovascular disease
  • machine learning
  • risk assessment
  • coronary artery disease
  • deep learning
  • electronic health record
  • climate change