Comparison of seven liver allocation models with respect to lives saved among patients on the liver transplant waiting list

Authors

  • Laurence S. Magder,

    1.  Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland, Baltimore, MD, USA
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  • Arie Regev,

    1.  Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
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  • Ayse L. Mindikoglu

    1.  Division of Gastroenterology and Hepatology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
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  • Conflicts of interest
    The authors have no conflicts of interest.

Laurence Magder PhD, MPH, Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland, 660 W. Redwood Street, Baltimore, MD 21201-1596, USA. Tel.: 410 706 3253; fax: 410 706 8548; e-mail: lmagder@epi.umaryland.edu

Summary

The patients with end-stage liver disease (ESLD) on the liver transplant waiting list are prioritized for transplant based on the model for end-stage liver disease (MELD) score. We developed and used an innovative approach to compare MELD to six proposed alternatives with respect to waiting list mortality. Our analysis was based on United Network for Organ Sharing data of patients with ESLD on the waiting list between January 2006 and June 2009. We compared six allocation models to MELD. Two models were based on reweighting the variables used by MELD: an “updated” MELD, and ReFit MELD. Four models also included serum sodium: MESO, MeldNa, UKELD, and ReFit MELDNa. We estimated that UKELD and the updated MELD would result in significantly fewer lives saved. There were no significant differences between the other models. Our new approach can supplement standard methods to provide insight into the relative performance of liver allocation models in reducing waiting list mortality. Our analysis suggests that UKELD and the updated MELD score would not be optimal for reducing waiting list mortality in the United States.

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