Background: Several risk models to predict outcome after liver transplantation (LT) have been developed in the last decade. This study compares the predictive performance of 7 risk models.
Methods: Data on 62 294 deceased donor LTs performed in recipients ≥18 years old between January 2005 and December 2015 in the United Network for Organ Sharing region were used for this study. The balance of risk, donor risk index (DRI), Eurotransplant-DRI, donor-to-recipient model (DRM), simplified recipient risk index, Survival Outcomes Following Liver Transplantation (SOFT), and donor Model for End-stage Liver Disease scores were calculated, and calibration and discrimination were evaluated for patient, overall graft, and death-censored graft survival. Calibration was evaluated by outcome of high-risk transplantations (>80th percentile of the respective risk score) and discrimination by concordance index (c-index).
Results: Patient survival at 3 months was best predicted by the SOFT (c-index: 0.68) and Balance of Risk score (c-index: 0.64), while the DRM and SOFT score had the highest predictive capacity at 60 months (c-index: 0.59). Overall, graft survival was best predicted by the SOFT score at 3-month follow-up (c-index: 0.65) and by the SOFT and DRM at 60-month follow-up (c-index: 0.58). Death-censored graft survival at 60-month follow-up is best predicted by the DRI (c-index: 0.59) and Eurotransplant-DRI (c-index: 0.58). For patient and overall graft survival, high-risk transplantations were best defined by the DRM. For death-censored graft survival, this was best defined by the DRI.
Conclusions: This study shows that models dominated by recipient factors have the best performance for short-term patient survival. Models that also include sufficient donor factors have better performance for long-term graft survival. Death-censored graft survival is best predicted by models that predominantly included donor factors.