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Non-invasive Risk Stratification for NAFLD Among Living Liver Donor Candidates: A Proposed Algorithm.

Nilay DanisSharon R WeeksAhyoung KimAzarakhsh BaghdadiMaryam GhadimiIhab R KamelBehnam SaberiTinsay WoretaJacqueline Garonzik-WangBenjamin PhilosopheAhmet GurakarRohit L Loomba
Published in: Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society (2021)
To reduce waitlist mortality, living donor liver transplantation (LDLT) has increased over the past decade in US, however, not at a rate sufficient to completely mitigate organ shortage. As a result, there are ongoing efforts to expand the living liver donor pool. Simultaneously, the prevalence of Non-alcoholic Fatty Liver Disease (NAFLD) in the general population has increased, which has significant implications on the pool of potential living liver donors. As such, a clinical assessment algorithm that exhaustively evaluates for NAFLD and fibrosis is critical to the safe expansion of LDLT. An ideal algorithm would employ safe and non-invasive methods, relying on liver biopsy only when necessary. While exclusion of NAFLD and fibrosis by non-invasive means is widely studied within the general population, there are no well-accepted guidelines for evaluation of living donors using these modalities. Here we review the current literature regarding non-invasive NALFD and fibrosis evaluation and propose a potential algorithm to apply these modalities for the selection of living liver donors.
Keyphrases
  • machine learning
  • deep learning
  • systematic review
  • risk factors
  • cardiovascular disease
  • cardiovascular events
  • liver fibrosis
  • type diabetes
  • fine needle aspiration
  • clinical evaluation