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Response to: “Towards optimally replacing the current version of MELD”

  • Jin Ge
    Affiliations
    Division of Gastroenterology and Hepatology, Department of Medicine, University of California – San Francisco, San Francisco, CA, USA
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  • W. Ray Kim
    Correspondence
    Corresponding author. Address: 430 Broadway Street, Floor 3, Redwood City, CA 94063-3132, USA; Tel.: +1-650-723-5135, fax: +1-650-723-5488.
    Affiliations
    Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
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  • Jennifer C. Lai
    Affiliations
    Division of Gastroenterology and Hepatology, Department of Medicine, University of California – San Francisco, San Francisco, CA, USA
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  • Allison J. Kwong
    Affiliations
    Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
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Published:November 16, 2022DOI:https://doi.org/10.1016/j.jhep.2022.11.008

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      • “Beyond MELD” – Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation
        Journal of HepatologyVol. 76Issue 6
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          In this review article, we discuss the model for end-stage liver disease (MELD) score and its dual purpose in general and transplant hepatology. As the landscape of liver disease and transplantation has evolved considerably since the advent of the MELD score, we summarise emerging concepts, methodologies, and technologies that may improve mortality prognostication in the future. Finally, we explore how these novel concepts and technologies may be incorporated into clinical practice.
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      • Towards optimally replacing the current version of MELD
        Journal of Hepatology
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          I was delighted to read Ge et al.’s review (2022)1 published in the Journal of Hepatology that included a summary of the leading liver allocation models and computational methodologies, including model for end-stage liver disease (MELD)-Plus, a model which was developed as a collaboration between Massachusetts General Hospital and IBM Research in 2017.2 The authors also highlighted MELD 3.0, which was proposed to replace the current version of MELD, the MELD-Na score.3 The methodology of developing MELD 3.0 involved evaluating and using additional variables that were not included in the MELD-Na model.
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      On building a better mousetrap
      To the Editor:
      We thank Dr. Kartoun for his interest in our manuscript with regard to leading liver transplant allocation models that have demonstrated enhancements beyond the existing model for end-stage liver disease (MELD)-Na model.
      • Kartoun U.
      Towards optimally replacing the current version of MELD.
      ,
      • Ge J.
      • Kim W.R.
      • Lai J.C.
      • Kwong A.J.
      Beyond MELD" - emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation.
      In agreement with the general sentiment of Dr. Kartoun’s letter, we believe that there is room for improvement of MELD and other prognostic models.
      If we may reiterate the main points made in our manuscript, there are at least three applications for prognostic models in chronic liver disease – (1) for liver transplant allocation, (2) prognostication for patient management and (3) for continuous monitoring of patient status for care optimization.
      • Ge J.
      • Kim W.R.
      • Lai J.C.
      • Kwong A.J.
      Beyond MELD" - emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation.
      Clearly, MELD was developed for the first purpose, but has been used frequently for other purposes as well, because of familiarity, convenience, and direct applicability in potential transplant candidates, as well as wide acceptance of its predictive accuracy. We also outline ways in which models may be improved for patient management purposes, using large amounts of data in electronic records and modern modeling techniques.
      With regards to the specific question about MELD 3.0, a wide array of variables was extracted as potential predictors of waitlist survival, including demographics, clinical status, and laboratory values (including components of the MELD and Child-Pugh scores and additional variables). Consistent with principles outlined in the development of the original MELD score, variable selection for MELD 3.0 was conducted so that the included variables were: (1) measurable in an objective fashion, (2) broadly generalizable, (3) devoid of unnecessary volatility without biological significance, and (4) reportable to the Organ Procurement and Transplant Network (OPTN) without causing an undue burden. For instance, potentially subjective variables, such ascites and encephalopathy, and those with ambiguous policy implications, such as age, were excluded a priori from the development of MELD 3.0.
      • Kim W.R.
      • Mannalithara A.
      • Heimbach J.K.
      • Kamath P.S.
      • Asrani S.K.
      • Biggins S.W.
      • et al.
      MELD 3.0: the model for end-stage liver disease updated for the modern era.
      While additional variables such as lactate, blood urea nitrogen, white blood cells, and total cholesterol may provide additional prognostic insight to MELD-based models,
      • Kartoun U.
      • Corey K.E.
      • Simon T.G.
      • Zheng H.
      • Aggarwal R.
      • Ng K.
      • et al.
      The MELD-Plus: a generalizable prediction risk score in cirrhosis.
      • Asrani S.K.
      • Jennings L.W.
      • Kim W.R.
      • Kamath P.S.
      • Levitsky J.
      • Nadim M.K.
      • et al.
      MELD-GRAIL-Na: glomerular filtration rate and mortality on liver-transplant waiting list.
      • Mahmud N.
      • Asrani S.K.
      • Kaplan D.E.
      • Ogola G.O.
      • Taddei T.H.
      • Kamath P.S.
      • et al.
      The predictive role of model for end-stage liver disease-lactate and lactate clearance for in-hospital mortality among a national cirrhosis cohort.
      they do not fulfill all of the principles outlined above.
      We thank Dr. Kartoun for highlighting the strengths of the MELD-Plus model. It has been shown to be more discriminating compared to the MELD-Na in the Mass General Brigham and IBM Explorys database,
      • Kartoun U.
      • Corey K.E.
      • Simon T.G.
      • Zheng H.
      • Aggarwal R.
      • Ng K.
      • et al.
      The MELD-Plus: a generalizable prediction risk score in cirrhosis.
      which suggest that it may have a role in patient management scenarios. On the other hand, for the reasons highlighted above it is not quite suitable for allocation of organs for transplant. This discussion, however, remains helpful in highlighting the various purposes of prognostic models and just because one model is optimal for one purpose, it may not necessarily be the best for other purposes.
      We would like to take this opportunity to update the reader that MELD 3.0, developed based on the OPTN data and tested on the liver simulated allocation model,
      • Thompson D.
      • Waisanen L.
      • Wolfe R.
      • Merion R.M.
      • McCullough K.
      • Rodgers A.
      Simulating the allocation of organs for transplantation.
      has been adopted by the OPTN Board to replace the current MELD-Na to determine the organ allocation priorities in the US. Congruent with the direction of this dialogue, we continue to look for opportunities to improve policy and clinical tools to optimize patient outcomes.

      Financial support

      The authors were supported by KL2TR001870 (National Center for Advancing Translational Sciences, Ge), the AASLD Anna S. Lok Advanced/Transplant Hepatology Award (AASLD Foundation, Ge), P30DK026743 (UCSF Liver Center Grant, Ge and Lai), R01DK127224 (National Institute of Diabetes and Digestive and Kidney Diseases, Kim), R01AG059183 (National Institute on Aging, Lai), the Clinical, Translational, and Outcomes Research Award (AASLD Foundation, Kwong), and K23AA029197 (National Institute on Alcohol Abuse and Alcoholism, Kwong). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agencies. The funding agencies played no role in the analysis of the data or the preparation of this manuscript.

      Conflict of interest

      The authors of this manuscript have no conflicts of interest to disclose as described by the Journal of Hepatology.
      Please refer to the accompanying ICMJE disclosure forms for further details.

      Supplementary data

      The following are the supplementary data to this article:

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