Understanding how transplant data are collected is crucial to understanding how the data can be used. The collection and use of Organ Procurement and Transplantation Network/Scientific Registry of Transplant Recipients (OPTN/SRTR) data continues to evolve, leading to improvements in data quality, timeliness and scope while reducing the data collection burden. Additional ascertainment of outcomes completes and validates existing data, although caveats remain for researchers. We also consider analytical issues related to cohort choice, timing of data submission, and transplant center variations in follow-up data. All of these points should be carefully considered when choosing cohorts and data sources for analysis.The second part of the article describes some of the statistical methods for outcome analysis employed by the SRTR. Issues of cohort and follow-up period selection lead into a discussion of outcome definitions, event ascertainment, censoring and covariate adjustment. We describe methods for computing unadjusted mortality rates and survival probabilities, and estimating covariate effects through regression modeling. The article concludes with a description of simulated allocation modeling, developed by the SRTR for comparing outcomes of proposed changes to national organ allocation policies.
The demand for donated organs greatly exceeds supply and many candidates die awaiting transplantation. Policies for allocating deceased donor organs may address equity of access and medical efficacy, but typically must be implemented with incomplete information. Simulation-based analysis can inform the policy process by predicting the likely effects of alternative policies on a wide variety of outcomes of interest. This paper describes a family of simulations developed by the US Scientific Registry of Transplant Recipients and initial experience in the application of one member of this family, the Liver Simulated Allocation Model (LSAM).
Turndowns of offers of deceased donor kidneys for transplantation can contribute to inefficiencies in the organ distribution system and inequality in access to donated organs. Match run data were obtained for 4967 'good' kidneys placed and transplanted in 2005 after fewer than 50 offers. These kidneys were not recovered from donation after cardiac death or expanded criteria donors, or from donors with a history of substance abuse. On average, these good kidneys were not accepted until after seven offers to candidates and after offers to 2.4 programs. Models for the likelihood of acceptance found several donor and candidate characteristics to be significantly related to acceptance rates (p < 0.05). After accounting for these variables, there remained 2-to 3-fold differences among transplant programs in acceptance rates. These models could be used to identify kidney transplant centers with exceptional acceptance practices. Several strategies might be employed to increase acceptance rates for good organs.
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