RESUMO
BACKGROUND AND AIMS: The new steatotic liver disease (SLD) nomenclature introduced metabolic and alcohol-associated liver disease (MetALD), describing the intersection of metabolic dysfunction-associated steatotic liver disease and alcohol-associated liver disease. Waitlisting and liver transplantation for MetALD are not well defined. We aimed to develop and validate an algorithm for identifying SLD phenotypes and assessing trends in waitlisting and transplant outcomes. APPROACH AND RESULTS: We conducted a retrospective cohort study using the United Network for Organ Sharing registry, supplemented with detailed single-center data. We developed 5 candidate algorithms for SLD classification and calculated their diagnostic performance. Trends in waitlist registrations and transplants were estimated, and competing risk analyses and Cox regression models were conducted to assess waitlist removal and posttransplant outcomes among SLD phenotypes. The best-performing algorithm demonstrated substantial agreement (weighted kappa, 0.62) for SLD phenotypes, with acceptable sensitivity (73%) for MetALD. Between 2002 and 2022, waitlist registrations and transplants for MetALD increased 2.9-fold and 3.3-fold, respectively. Since 2013, there has been a significant increase in the absolute number of waitlist registrations (122 per year; 95% CI, 111-133) and transplants (107 per year; 95% CI, 94-120) for MetALD. Patients with MetALD experienced higher waitlist removal (adjusted subdistribution hazard ratio, 1.10; 95% CI, 1.03-1.17), all-cause mortality (adjusted hazard ratio, 1.13; 95% CI, 1.03-1.23), and graft failure (adjusted hazard ratio, 1.12; 95% CI, 1.03-1.21) than those with alcohol-associated liver disease. CONCLUSIONS: We developed and validated an algorithm for identifying SLD phenotypes in UNOS. MetALD is the third leading etiology among those waitlisted and underwent transplantation, exhibiting worse pretransplantation and posttransplantation outcomes compared to alcohol-associated liver disease. Identifying and addressing factors determining poor outcomes is crucial in this patient population.