Donation after circulatory death (DCD) liver transplantation (LT) may imply a risk for decreased graft survival, caused by posttransplantation complications such as primary nonfunction or ischemic‐type biliary lesions. However, similar survival rates for DCD and donation after brain death (DBD) LT have been reported. The objective of this study is to determine the longterm outcome of DCD LT in the Eurotransplant region corrected for the Eurotransplant donor risk index (ET‐DRI). Transplants performed in Belgium and the Netherlands (January 1, 2003 to December 31, 2007) in adult recipients were included. Graft failure was defined as either the date of recipient death or retransplantation whichever occurred first (death‐uncensored graft survival). Mean follow‐up was 7.2 years. In total, 126 DCD and 1264 DBD LTs were performed. Kaplan‐Meier survival analyses showed different graft survival for DBD and DCD at 1 year (77.7% versus 74.8%, respectively; P = 0.71), 5 years (65.6% versus 54.4%, respectively; P = 0.02), and 10 years (47.3% versus 44.2%, respectively; P = 0.55; log‐rank P = 0.038). Although there was an overall significant difference, the survival curves almost reach each other after 10 years, which is most likely caused by other risk factors being less in DCD livers. Patient survival was not significantly different (P = 0.59). Multivariate Cox regression analysis showed a hazard ratio of 1.7 (P < 0.001) for DCD (corrected for ET‐DRI and recipient factors). First warm ischemia time (WIT), which is the time from the end of circulation until aortic cold perfusion, over 25 minutes was associated with a lower graft survival in univariate analysis of all DCD transplants (P = 0.002). In conclusion, DCD LT has an increased risk for diminished graft survival compared to DBD. There was no significant difference in patient survival. DCD allografts with a first WIT > 25 minutes have an increased risk for a decrease in graft survival. Liver Transplantation 22 1107–1114 2016 AASLD

Objective In 2008, the preprocurement pancreas suitability score (P-PASS) was introduced within Eurotransplant to predict suitability of pancreas donors. A P-PASS of 17 or higher would have lower graft survival compared with pancreatic grafts from donors with a P-PASS lower than 17. In 2010, a continuous model, the pancreas donor risk index (PDRI), was designed. Before using this model in the European donor population, it has to be validated in the European setting. Methods In this study, P-PASS and PDRI were validated using the results of all pancreas transplants performed at our center. The P-PASS and PDRI were compared as both continuous and dichotomous values. The original cutoff point of 17 divided P-PASS groups. Median PDRI (1.24) divided PDRI groups. Results In total, 349 pancreas transplantations were performed. The P-PASS of 17 or higher was not associated with graft survival (P = 0.448). The PDRI of 1.24 or higher was associated with reduced graft survival in univariate analysis (P = 0.007) and multivariate analysis (P = 0.002). The PDRI concordance index was 0.69. Conclusions The P-PASS has no predictive value for pancreas graft survival and should not be used in clinical decision making. The PDRI is a significant predictor of pancreas graft survival but should be used carefully, because good results can be achieved with grafts from high-PDRI donors.

Recently the Eurotransplant donor risk index (ET‐DRI) was published, a model based on data from the Eurotransplant database that can be used for risk indication of liver donors within the Eurotransplant region. Because outcome after liver transplantation (LT) depends both on donor and recipient risk factors, a combined donor‐recipient model (DRM) would give a more complete picture of the overall risk involved. All liver transplants in adult recipients from January 1, 2008 to December 31, 2010 in the Eurotransplant region were included. Risk factors in donors and recipients for failure‐free (retransplant free) survival were analyzed in univariate and multivariate analyses. A simplified recipient risk index (sRRI) was constructed using all available recipient factors. A total of 4466 liver transplants were analyzed. Median donor risk index and ET‐DRI were 1.78 and 1.91, respectively. The ET‐DRI was validated in this new cohort (P < 0.001; concordance index [c‐index], 0.59). After construction of a simplified recipient risk index of significant recipient factors, Cox regression analysis showed that the combination ET‐DRI and sRRI into a new DRM gave the highest predictive value (P < 0.001; c‐index, 0.62). The combined model of ET‐DRI and sRRI gave a significant prediction of outcome after orthotopic LT in the Eurotransplant region, better than the ET‐DRI alone. This DRM has potential in comparing data in the literature and correcting for sickness/physical condition of transplant recipients. It is a first step toward benchmarking of graft survival in the Eurotransplant region. Liver Transpl 21:1486‐1493, 2015. © 2015 AASLD.

Background Outcome after liver transplantation (LT) is determined by donor, transplant and recipient risk factors. These factors may have different impact on either patient or graft survival (outcome type). In the literature, there is wide variation in the use of outcome types and points in time (short term or long term). Objective of this study is to analyze the predictive capacity of risk factors and risk models in LT and how they vary over time and per outcome type. Methods All LTs performed in the Netherlands from January 1, 2002, to December 31, 2011, were analyzed with multivariate analyses at 3-month, 1-year, and 5-year for patient and (non-)death-censored graft survival. The predictive capacity of the investigated risk models was compared with concordance indices. Results Recipient age, model for end-stage liver disease sodium, ventilatory support, diabetes mellitus, hepatocellular carcinoma, previous malignancy, hepatitis C virus antibody, hepatitis B virus antibody, perfusion fluid, and Eurotransplant donor risk index (ET-DRI) had significant impact on outcome (graft or patient survival) at 1 or multiple points in time. Significant factors at 3-month patient survival (recipient age, model for end-stage liver disease sodium, ventilatory support) were used to compose a concept model. This model, had a higher c-index than the balance-of-risk score, DRI, ET-DRI, donor-recipient model and simplified recipient risk index for long-term patient and non–death-censored graft survival. Conclusions In this study, the effects of recipient risk factors and models on different outcome types and time points were shown. Short-term patient survival mainly depends on recipient risk factors, long-term graft survival on donor risk factors and is more difficult to predict. Next to the concept model, the donor-recipient model has a higher predictive capacity to other risk models for (long-term) patient and non–death-censored graft survival. The DRI and ET-DRI best predicted death-censored graft survival. Knowledge about risk factors and models is critical when using these for waitlist management and/or help in organ allocation and decision-making.

Apart from donor and recipient risk factors, the effect of center‐related factors has significant impact on graft survival after liver transplantation (LT). To investigate this effect in Eurotransplant, a retrospective database analysis was performed, including all LT's in adult recipients (≥18 years) in the Eurotransplant region from 1.1.2007 until 31.12.2013. Additionally, a survey was sent out to all transplant centers requesting information on surgeons’ experience and exposure. In total, 10 265 LT's were included (median follow‐up 3.3 years), performed in 39 transplant centers. Funnel plots showed significant differences in graft survival between the transplant centers. After correction for donor and recipient risk, with the Eurotransplant donor risk index (ET‐DRI) and the simplified recipient risk index (sRRI) and random effects, these differences diminished. Mean historical volume (in the preceding 5 years) was a significant (P < 0.001), nonlinear marker for graft survival in the multivariate analysis. This study demonstrates that funnel plots can be used for benchmarking purposes in LT. Case‐mix correction can be performed with the use of the ET‐DRI and sRRI. The center effect encompasses the entire complex process of preoperative workup, operation to follow‐up.