Few equations have been developed to predict endstage renal disease (ESRD) after deceased donor liver transplant. This retrospective observational cohort study analyzed all adult deceased donor liver transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database, 1995-2010. The prediction equation for ESRD was developed using candidate predictor variables available in SRTR after implementation of the allocation policy based on the model for end-stage liver disease. ESRD was defined as initiation of maintenance dialysis therapy, kidney transplant or registration on the kidney transplant waiting list. We used Cox proportional hazard models to develop separate equations for assessing risk of ESRD by 6 months posttransplant and between 6 months and 5 years posttransplant. Variables in the 6-month equation included recipient age, history of diabetes, history of dialysis before liver transplant, history of malignancy, body mass index, serum creatinine and liver donor risk index. Variables in the 6-month to 5-year equation included recipient race, history of diabetes, hepatitis C status, serum albumin, serum bilirubin and serum creatinine. The prediction equations have good calibration and discrimination (C statistics 0.74-0.78). We have produced risk prediction equations that can be used to aid in understanding the risk of ESRD after liver transplant.

Because of differences in case-mix across states, state-level case-mix-adjusted end-stage renal disease (ESRD) incident rates are reported in each United States Renal Data System Annual Data Report to make the across-state comparisons valid. The adjusted rates were estimated by the direct adjustment method, a widely used method for adjusted event rate calculation, based on observed category-specific ESRD incident rates in each state (called the observation-based method). However, when some adjusting categories in a state are small, the adjusted rate and the standard error for this state as estimated by this method may be inaccurate. This report proposes a model-based method that can overcome the disadvantages of the observation-based method and can be extended to continuous adjusting variables. National ESRD incident data and national population data from 1990 to 1999 were used. State-level adjusted ESRD incident rates were estimated by both the observation- and the model-based methods. For the model-based method, a Poisson regression model was used to estimate category-specific ESRD incident rates. For large-population states, both observation- and model-based methods produced similar estimates for adjusted ESRD incident rates. For small-population states, however, the observation-based method produced year-to-year estimates of adjusted ESRD incident rates that varied considerably and also had very large standard errors. In contrast, the model-based method produced stable estimates. The model-based method can overcome the disadvantages of the observation-based method for estimating state-level adjusted ESRD incident rates, especially for small states.

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