Performance of risk prediction models for post-liver transplant patient and graft survival over time

Given liver transplantation organ scarcity, selection of recipients and donors to maximize post-transplant benefit is paramount. Several scores predict post-transplant outcomes by isolating elements of donor and recipient risk, including the donor risk index, Balance of Risk, pre-allocation score to predict survival outcomes following liver transplantation/survival outcomes following liver transplantation (SOFT), improved donor-to-recipient allocation score for deceased donors only/improved donor-to-recipient allocation score for both deceased and living donors (ID2EAL-D/-DR), and survival benefit (SB) models. No studies have examined the performance of these models over time, which is critical in an ever-evolving transplant landscape. This was a retrospective cohort study of liver transplantation events in the UNOS database from 2002 to 2021. We used Cox regression to evaluate model discrimination (Harrell's C) and calibration (testing of calibration curves) for post-transplant patient and graft survival at specified post-transplant timepoints. Sub-analyses were performed in the modern transplant era (post-2014) and for key donor-recipient characteristics. A total of 112,357 transplants were included. The SB and SOFT scores had the highest discrimination for short-term patient and graft survival, including in the modern transplant era, where only the SB model had good discrimination (C ≥ 0.60) for all patient and graft outcome timepoints. However, these models had evidence of poor calibration at 3- and 5-year patient survival timepoints. The ID2EAL-DR score had lower discrimination but adequate calibration at all patient survival timepoints. In stratified analyses, SB and SOFT scores performed better in younger (< 40 y) and higher Model for End-Stage Liver Disease (≥ 25) patients. All prediction scores had declining discrimination over time, and scores relying on donor factors alone had poor performance. Although the SB and SOFT scores had the best overall performance, all models demonstrated declining performance over time. This underscores the importance of periodically updating and/or developing new prediction models to reflect the evolving transplant field. Scores relying on donor factors alone do not meaningfully inform post-transplant risk.