Evidence map›Paper›PMID 31876748›Full record

ArticleMedicine2019

Plasma D-dimer as a novel biomarker for predicting poor outcomes in HBV-related decompensated cirrhosis.

Jing Zhou, WeiLin Mao, LiangJun Shen, HongGuang Huang

Abstract read
In one paragraph

Article in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Assessing Biomarkers in Viral Infection.Advances in experimental medicine and biology · 2023
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
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

4 authors.

Jing ZhouDepartment of Brain Surgery, Shengzhou People's Hospital, Shengzhou Branch of the First Affiliated Hospital of Zhejiang University, Shengzhou.
WeiLin MaoDepartment of Clinical Laboratory, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang.
LiangJun ShenDepartment of Brain Surgery, Shengzhou People's Hospital, Shengzhou Branch of the First Affiliated Hospital of Zhejiang University, Shengzhou.
HongGuang HuangDepartment of Neurosurgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aim was to evaluate whether plasma D-dimer levels could serve as a novel prognostic biomarker for 1-month mortality in patients with HBV-related decompensated cirrhosis (HBV-DeCi).This was a retrospective study that enrolled 132 HBV-DeCi patients. Univariate and multivariate regression models were used to identify risk factors for mortality. The area under the receiver operating characteristic curve was calculated to estimate and compare the predictive values of different prognostic markers.In the present study, the plasma D-dimer levels were higher in the nonsurviving group than in the surviving group. Additionally, the D-dimer level was positively correlated with the model for end-stage liver disease (MELD) score. The results of multivariate analysis showed that both the MELD score and D-dimer level are independent predictors of 1-month mortality in HBV-DeCi patients (both P < .01).Plasma D-dimer can be considered a new additional prognostic marker for 1-month mortality in HBV-DeCi patients.

Indexed as

AdultAgedBiomarkersFemaleFibrin Fibrinogen Degradation ProductsHepatitis B, ChronicHumansLiver CirrhosisMaleMiddle AgedPredictive Value of TestsPrognosisRetrospective StudiesBiomarkersFibrin Fibrinogen Degradation Productsfibrin fragment D

Identifiers

PMID31876748
PMCPMC6946568

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Registered trials

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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.