A data-driven analysis of patient selection for xenotransplant human clinical trials.

The demand for transplant organs far exceeds the available supply. In the United States alone, more than 90,000 patients are currently on the kidney transplant waitlist, yet only about one third of them will ever receive a transplant. Xenotransplantation, organ transplants from gene edited pigs, offers a potential solution to this shortage. Successful investigational transplants of pig kidneys into brain-dead recipients and expanded access cases involving living human recipients have resulted in the green-lighting of the first human clinical trials. Using the benchmark of 2-year survival of non-human primates in pre-clinical studies, we developed a tool that can identify individual wait-listed patients predicted to have a shorter life expectancy than with a xenotransplant, utilizing Random Survival Forest, DeepSurv and Cox Proportional-Hazards models. We found that it is hard to identify patients that reach clinical equipoise unless the expected xenograft survival exceeds two years, with the Random Survival Forest model identifying less than 5% of such patients. Few patients would benefit based on survival alone and potential beneficiaries are spread across more than 200 transplant centers. Several incentives could allow more patients to reach equipoise. At the same benchmark of 2-year xenograft survival, keeping patients inactive on the waitlist while they have a functioning xeno-kidney increases the percentage achieving equipoise by up to 1.7% across cohorts. Granting patients with failed xenografts the same priority as prior living donors increases this by up to 17.9%, while assigning them the highest priority raises it by up to 28.5%. We are able, however, to identify phenotypes that have a high mortality and low transplant rates in the current allocation system that could serve as acceptable candidates; while not achieving equipoise, they would enjoy the benefits of being dialysis free.