A scientific registry of transplant recipients Bayesian method for identifying underperforming transplant programs

N. Salkowski, J. J. Snyder, D. A. Zaun, T. Leighton, E. B. Edwards, A. K. Israni, B. L. Kasiske

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

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. This study identifies optimal thresholds for flagging underperforming transplant programs using Bayesian methodology, compares the Bayesian approach to results of the current flagging system, and assesses the Bayesian approach using a historical group of flagged programs to suggest that the Bayesian methods improve identification of programs that need review while minimizing false flags. Also see the viewpoint on page 1271 and the editorial by Schold and Axelrod on page 1231.

Original languageEnglish (US)
Pages (from-to)1310-1317
Number of pages8
JournalAmerican Journal of Transplantation
Volume14
Issue number6
DOIs
StatePublished - Jun 2014
Externally publishedYes

Keywords

  • Graft survival
  • quality assurance
  • solid organ transplantation

ASJC Scopus subject areas

  • Immunology and Allergy
  • Transplantation
  • Pharmacology (medical)

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