Using ethics, epidemiology and high-quality data to optimize the allocation of livers for transplantation

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7. Project Summary/Abstract The demand for livers suitable for a liver transplant (LT) in the US outnumbers the supply. In 2017, although 7,500 patients received a LT, more than 2,500 were removed from the waitlist after dying or becoming “too sick to transplant.” These numbers don't account for the fact that 5 out of 6 patients with end-stage liver disease (ESLD) meeting LT criteria are never waitlisted. Consequently, patients inevitably die on a waiting list, or die never having been waitlisted. An optimal transplant system should allocate organs by realizing three values: 1) equality—fair access to transplant for all; 2) priority to the worst off, and 3) utility—maximizing the `expected net amount of overall good' for the population of patients. By using the Model for End Stage Liver Disease (MELD) score to prioritize patients, the current system focuses predominantly on urgency, favoring the currently sickest as the `worst off.' This minimizes waitlist mortality. However, this does not consider fair access (equality) for different patient subgroups (i.e., traditionally disadvantaged groups), or utility (e.g., maximizing survival or net benefit of LT). As a result, many LTs are allocated to patients with limited life expectancy after LT, while others on the waitlist with equally strong ethical claims to LT die. Improving LT allocation requires new data and an innovative paradigm that seeks to optimize use of a scarce resource that can extend life by >15 years. This requires more than simply a revised Model for End-Stage Liver Disease (MELD) score (the score used for waitlist prioritization). First, to estimate survival without a LT, robust longitudinal data are needed that extend beyond waitlist registry data, given that median waitlist time is <180 days and >50% patients receive a LT within one year of listing. Second, long-term post-LT risk models must be developed, to account for factors that are highly discriminatory for long-term survival (e.g., age, acute vs chronic kidney failure). Third, allocation policies for patients with hepatocellular carcinoma (HCC) must consider the relative long-term benefit of LT vs. other HCC treatments given the unintended consequences of prioritizing HCC patients over those with decompensated cirrhosis. This work will be fundamental to the future design of an improved allocation system that drives major increases in survival for patients with ESLD. We will leverage powerful and detailed data from the Veterans Health Administration, the largest US provider of liver care, and United Network for Organ Sharing (UNOS), to address these aims: 1) to develop time-updated, long-term pre- LT survival models accounting for acute and chronic kidney disease; 2) to develop a time-updated, long-term pre-LT survival model using tumor, laboratory data, and clinical variables for patients with HCC; 3) to develop a long-term post-LT risk model incorporating demographic, clinical, and laboratory variables; 4) to build allocation simulations that quantify the magnitude of difference in life-years gained under several different allocation schemes across a range of time horizons, with a focus on NIH-designated health disparity populations.