In response to recommendations from a recent consensus conference and from the Committee of Presidents of Statistical Societies, the Scientific Registry of Transplant Recipients explored the use of Bayesian hierarchical, mixed-effects models in assessing transplant program performance in the United States. Identification of underperforming centers based on 1-year patient and graft survival using a Bayesian approach was compared with current observed-to-expected methods. Fewer small-volume programs (<10 transplants per 2.5-year period) were identified as underperforming with the Bayesian method than with the current method, and more mid-volume programs (10-249 transplants per 2.5-year period) were identified. Simulation studies identified optimal Bayesian-based flagging thresholds that maximize true positives while holding false positive flagging rates to approximately 5% regardless of program volume. Compared against previous program surveillance actions from the Organ Procurement and Transplantation Network Membership and Professional Standards Committee, the Bayesian method would have reduced the number of false positive program identifications by 50% for kidney, 35% for liver, 43% for heart and 57% for lung programs, while preserving true positives for, respectively, 96%, 71%, 58% and 83% of programs identified by the current method. We conclude that Bayesian methods to identify underperformance improve identification of programs that need review while minimizing false flags.
Keywords: Graft survival; quality assurance; solid organ transplantation.
© Copyright 2014 The American Society of Transplantation and the American Society of Transplant Surgeons.