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Predicting short-term survival after liver transplantation on eight score systems: a national report from China Liver Transplant Registry
To compare the performance of eight score systems (MELD, uMELD, MELD-Na. iMELD, UKELD, MELD-AS, CTP, and mCTP) in predicting the post-transplant mortality, we analyzed the data of 6,014 adult cirrhotic patients who underwent liver transplantation between January 2003 and December 2010 from the China...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5304182/ https://www.ncbi.nlm.nih.gov/pubmed/28198820 http://dx.doi.org/10.1038/srep42253 |
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author | Ling, Qi Dai, Haojiang Zhuang, Runzhou Shen, Tian Wang, Weilin Xu, Xiao Zheng, Shusen |
author_facet | Ling, Qi Dai, Haojiang Zhuang, Runzhou Shen, Tian Wang, Weilin Xu, Xiao Zheng, Shusen |
author_sort | Ling, Qi |
collection | PubMed |
description | To compare the performance of eight score systems (MELD, uMELD, MELD-Na. iMELD, UKELD, MELD-AS, CTP, and mCTP) in predicting the post-transplant mortality, we analyzed the data of 6,014 adult cirrhotic patients who underwent liver transplantation between January 2003 and December 2010 from the China Liver Transplant Registry database. In hepatitis B virus (HBV) group, MELD, uMELD and MELD-AS showed good predictive accuracies at 3-month mortality after liver transplantation; by comparison with other five models, MELD presented the best ability in predicting 3-month, 6-month and 1-year mortality, showing a significantly better predictive ability than UKELD and iMELD. In hepatitis C virus and Alcohol groups, the predictive ability did not differ significantly between MELD and other models. Patient survivals in different MELD categories were of statistically significant difference. Among patients with MELD score >35, a new prognostic model based on serum creatinine, need for hemodialysis and moderate ascites could identify the sickest one. In conclusion, MELD is superior to other score systems in predicting short-term post-transplant survival in patients with HBV-related liver disease. Among patients with MELD score >35, a new prognostic model can identify the sickest patients who should be excluded from waiting list to prevent wasteful transplantation. |
format | Online Article Text |
id | pubmed-5304182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53041822017-03-14 Predicting short-term survival after liver transplantation on eight score systems: a national report from China Liver Transplant Registry Ling, Qi Dai, Haojiang Zhuang, Runzhou Shen, Tian Wang, Weilin Xu, Xiao Zheng, Shusen Sci Rep Article To compare the performance of eight score systems (MELD, uMELD, MELD-Na. iMELD, UKELD, MELD-AS, CTP, and mCTP) in predicting the post-transplant mortality, we analyzed the data of 6,014 adult cirrhotic patients who underwent liver transplantation between January 2003 and December 2010 from the China Liver Transplant Registry database. In hepatitis B virus (HBV) group, MELD, uMELD and MELD-AS showed good predictive accuracies at 3-month mortality after liver transplantation; by comparison with other five models, MELD presented the best ability in predicting 3-month, 6-month and 1-year mortality, showing a significantly better predictive ability than UKELD and iMELD. In hepatitis C virus and Alcohol groups, the predictive ability did not differ significantly between MELD and other models. Patient survivals in different MELD categories were of statistically significant difference. Among patients with MELD score >35, a new prognostic model based on serum creatinine, need for hemodialysis and moderate ascites could identify the sickest one. In conclusion, MELD is superior to other score systems in predicting short-term post-transplant survival in patients with HBV-related liver disease. Among patients with MELD score >35, a new prognostic model can identify the sickest patients who should be excluded from waiting list to prevent wasteful transplantation. Nature Publishing Group 2017-02-13 /pmc/articles/PMC5304182/ /pubmed/28198820 http://dx.doi.org/10.1038/srep42253 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Ling, Qi Dai, Haojiang Zhuang, Runzhou Shen, Tian Wang, Weilin Xu, Xiao Zheng, Shusen Predicting short-term survival after liver transplantation on eight score systems: a national report from China Liver Transplant Registry |
title | Predicting short-term survival after liver transplantation on eight score systems: a national report from China Liver Transplant Registry |
title_full | Predicting short-term survival after liver transplantation on eight score systems: a national report from China Liver Transplant Registry |
title_fullStr | Predicting short-term survival after liver transplantation on eight score systems: a national report from China Liver Transplant Registry |
title_full_unstemmed | Predicting short-term survival after liver transplantation on eight score systems: a national report from China Liver Transplant Registry |
title_short | Predicting short-term survival after liver transplantation on eight score systems: a national report from China Liver Transplant Registry |
title_sort | predicting short-term survival after liver transplantation on eight score systems: a national report from china liver transplant registry |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5304182/ https://www.ncbi.nlm.nih.gov/pubmed/28198820 http://dx.doi.org/10.1038/srep42253 |
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