Evidence map›Paper›PMID 28198820›Full record

ArticleScientific reports2017

Predicting short-term survival after liver transplantation on eight score systems: a national report from China Liver Transplant Registry.

Qi Ling, Haojiang Dai, Runzhou Zhuang, Tian Shen, Weilin Wang, Xiao Xu, Shusen Zheng

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
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  4. Novel treatment of acute and acute-on-chronic liver failure: Interleukin-22.Liver international : official journal of the International Association for the Study of the Liver · 2025
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Qi LingDivision of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Collaborative innovation center for diagnosis and treatment of infectious diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Haojiang DaiDivision of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Collaborative innovation center for diagnosis and treatment of infectious diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Runzhou ZhuangDivision of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Collaborative innovation center for diagnosis and treatment of infectious diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Tian ShenDivision of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Collaborative innovation center for diagnosis and treatment of infectious diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Weilin WangDivision of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Collaborative innovation center for diagnosis and treatment of infectious diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Xiao XuDivision of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Collaborative innovation center for diagnosis and treatment of infectious diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Shusen ZhengDivision of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Collaborative innovation center for diagnosis and treatment of infectious diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Zhejiang University · CNFirst Affiliated Hospital Zhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

RegistriesAdultArea Under CurveChinaFemaleHumansLiver CirrhosisLiver TransplantationMaleModels, TheoreticalProbabilityPrognosisRisk FactorsROC CurveSurvival AnalysisTime Factors

Identifiers

PMID28198820
PMCPMC5304182
OpenAlexW2588244195

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.