• Parsia A Vagefi, Sandy Feng, Jennifer L Dodge, James F Markmann, and John P Roberts.
• Division of Transplant Surgery, Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA. Electronic address: pvagefi@mgh.harvard.edu.
• J. Am. Coll. Surg. 2014 Sep 1; 219 (3): 496-504.

BackgroundGeographic disparity in access to liver transplantation (LT) exists. This study sought to examine Model for End-Stage Liver Disease-era multiply listed (ML) LT candidate (ie, candidates who list at 2 or more LT centers to receive a liver transplant).Study DesignData on adult, primary, non-status 1 LT candidates (n = 59,557) listed from January 1, 2005 to December 31, 2011 were extracted from the United Network for Organ Sharing's Standard Transplant Analysis and Research files. Comparisons of ML vs singly listed LT candidates were performed, with additional analysis performed at the donor service area (DSA) and regional level, as well as assessment of the donor population used.ResultsThere were 1,358 (2.3%) ML candidates during the 7-year study period. Multiply listed candidates compared with singly listed candidates were more often male, white, blood type O, nondiabetic, college educated, and privately insured. The odds of pursuing ML increased considerably as time on the waitlist increased. Of the ML candidates, 918 (67.6%) went on to receive a liver transplant (ML-LT), 767 (83.6%) at the secondary listing DSA, which was a median of 588 miles (range 229 to 1095 miles) from the primary listing DSA. When compared with the primary listing DSA, the secondary listing DSA had significantly lower match Model for End-Stage Liver Disease scores, as well as shorter wait times. Regional analysis demonstrated significantly higher odds for pursuing ML from LT candidates located within regions 1, 5, and 9.ConclusionsA small and distinctive cohort of LT candidates pursue ML, indicating willingness and means to travel to receive a liver transplant. Efforts toward equalizing LT access across regional disparities are warranted, and can help obviate the need for ML.Copyright © 2014 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

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