Literature DB >> 31394020

The decreasing predictive power of MELD in an era of changing etiology of liver disease.

Elizabeth L Godfrey1, Tahir H Malik1, Jennifer C Lai2, Ayse L Mindikoglu1,3, N Thao N Galván1, Ronald T Cotton1, Christine A O'Mahony1, John A Goss1, Abbas Rana1.   

Abstract

The field of liver transplantation has shifted considerably in the MELD era, including changing allocation, immunosuppression, and liver failure etiologies, as well as better supportive therapies. Our aim was to evaluate the predictive accuracy of the MELD score over time. The United Network for Organ Sharing provided de-identified data on 120 156 patients listed for liver transplant from 2002-2016. The ability of the MELD score to predict 90-day mortality was evaluated by a concordance (C-) statistic and corroborated with competing risk analysis. The MELD score's concordance with 90-day mortality has downtrended from 0.80 in 2003 to 0.70 in 2015. While lab MELD scores at listing and transplant climbed in that interval, score at waitlist death remained steady near 35. Listing age increased from 50 to 54 years. HCV-positive status at listing dropped from 33 to 17%. The concordance of MELD and mortality does not differ with age (>60 = 0.73, <60 = 0.74), but is lower in diseases that are increasing most rapidly-alcoholic liver disease and non-alcoholic fatty liver disease-and higher in those that are declining, particularly in HCV-positive patients (HCV positive = 0.77; negative = 0.73). While MELD still predicts mortality, its accuracy has decreased; changing etiology of disease may contribute.
© 2019 The American Society of Transplantation and the American Society of Transplant Surgeons.

Entities:  

Keywords:  United Network for Organ Sharing (UNOS); classification systems: Model for Endstage Liver Disease (MELD); clinical research/practice; liver transplantation/hepatology; waitlist management

Mesh:

Year:  2019        PMID: 31394020     DOI: 10.1111/ajt.15559

Source DB:  PubMed          Journal:  Am J Transplant        ISSN: 1600-6135            Impact factor:   8.086


  10 in total

1.  Declining predictive performance of the MELD: Cause for concern or reflection of changes in clinical practice?

Authors:  Nadim Mahmud; David S Goldberg
Journal:  Am J Transplant       Date:  2019-10-23       Impact factor: 8.086

2.  MELD 3.0: The Model for End-Stage Liver Disease Updated for the Modern Era.

Authors:  W Ray Kim; Ajitha Mannalithara; Julie K Heimbach; Patrick S Kamath; Sumeet K Asrani; Scott W Biggins; Nicholas L Wood; Sommer E Gentry; Allison J Kwong
Journal:  Gastroenterology       Date:  2021-09-03       Impact factor: 22.682

3.  The Evolution of the MELD Score and Its Implications in Liver Transplant Allocation: A Beginner's Guide for Trainees.

Authors:  Hirsh D Trivedi
Journal:  ACG Case Rep J       Date:  2022-05-04

4.  Improving the Model for End-Stage Liver Disease with sodium by incorporating kidney dysfunction types.

Authors:  Giuseppe Cullaro; Elizabeth C Verna; Charles E McCulloch; Jennifer C Lai
Journal:  Hepatology       Date:  2022-03-31       Impact factor: 17.298

5.  Metabolomic biomarkers are associated with mortality in patients with cirrhosis caused by primary biliary cholangitis or primary sclerosing cholangitis.

Authors:  Ayse L Mindikoglu; Cristian Coarfa; Antone R Opekun; Vijay H Shah; Juan P Arab; Konstantinos N Lazaridis; Nagireddy Putluri; Chandrashekar R Ambati; Matthew J Robertson; Sridevi Devaraj; Prasun K Jalal; Abbas Rana; John A Goss; Thomas C Dowling; Matthew R Weir; Stephen L Seliger; Jean-Pierre Raufman; David W Bernard; John M Vierling
Journal:  Future Sci OA       Date:  2019-12-17

6.  Validation of the Model for End-stage Liver Disease sodium (MELD-Na) score in the Eurotransplant region.

Authors:  Ben F J Goudsmit; Hein Putter; Maarten E Tushuizen; Jan de Boer; Serge Vogelaar; I P J Alwayn; Bart van Hoek; Andries E Braat
Journal:  Am J Transplant       Date:  2020-08-04       Impact factor: 8.086

7.  Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.

Authors:  Aixia Guo; Nikhilesh R Mazumder; Daniela P Ladner; Randi E Foraker
Journal:  PLoS One       Date:  2021-08-31       Impact factor: 3.240

8.  Modelling kidney outcomes based on MELD eras - impact of MELD score in renal endpoints after liver transplantation.

Authors:  Paulo Ricardo Gessolo Lins; Roberto Camargo Narciso; Leonardo Rolim Ferraz; Virgilio Gonçalves Pereira; Ben-Hur Ferraz-Neto; Marcio Dias De Almeida; Bento Fortunato Cardoso Dos Santos; Oscar Fernando Pavão Dos Santos; Júlio Cesar Martins Monte; Marcelino Souza Durão Júnior; Marcelo Costa Batista
Journal:  BMC Nephrol       Date:  2022-08-23       Impact factor: 2.585

9.  Refitting the Model for End-Stage Liver Disease for the Eurotransplant Region.

Authors:  Jacques Pirenne; Bart van Hoek; Andries E Braat; Ben F J Goudsmit; Hein Putter; Maarten E Tushuizen; Serge Vogelaar; Ian P J Alwayn
Journal:  Hepatology       Date:  2021-05-09       Impact factor: 17.425

10.  Building a Utility-based Liver Allocation Model in Preparation for Continuous Distribution.

Authors:  Catherine E Kling; James D Perkins; Scott W Biggins; Anji E Wall; Jorge D Reyes
Journal:  Transplant Direct       Date:  2022-01-13
  10 in total

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