Predictive Capacity of Risk Models in Liver Transplantation.

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.