Evidence map›Paper›PMID 30759782›Full record

ArticleJournal of clinical medicine2019

NT-ProBNP Predicts Total Mortality, Emergency Department Visits, Hospitalization, Intensive-Care Unit Admission, and Cardiovascular Events in Hemodialysis Patients.

Yi-Hsin Chen, Yun-Ching Fu, Ming-Ju Wu

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. 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

3 authors.

Yi-Hsin ChenInstitute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan. nephp06@gmail.com.
Yun-Ching FuInstitute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan. yunchingfu@gmail.com.
Ming-Ju WuInstitute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan. wmj530@gmail.com.ORCID 0000-0002-8585-4392

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

N-terminal pro b-type natriuretic peptide (NT-proBNP) was considered a prognostic factor for mortality in hemodialysis patients in previous studies. However, NT-proBNP has not been fully explored in terms of predicting other clinical outcomes in hemodialysis patients. This study aimed to investigate if NT-proBNP could predict emergency department (ED) visits, hospitalization, admission to intensive-care unit (ICU), and cardiovascular incidents in hemodialysis patients. Serum NT-proBNP and other indicators were collected in 232 hemodialysis patients. Patients were followed up for three years or until mortality. Outcomes included mortality, number of ED visits, hospitalizations, admissions to ICU, and cardiovascular events. NT-proBNP was found to predict recurrent ER visits, hospitalization, admission to ICU, cardiovascular events, and mortality, after adjusting for covariates. Time-dependent area under the curve (AUC) was used to evaluate the NT-proBNP predicting ability. Using time-dependent AUC, NT-proBNP has good predictive ability for mortality, ED visit, hospitalization, ICU admission, and cardiovascular events with the best predictive ability occurring at approximately 1 year, and 5th, 62nd, 63rd, and 63rd days respectively. AUC values for predicting mortality, hospitalization, and ICU admission decreased significantly after one year. NT-proBNP can be applied in predicting ED visits but is only suitable for the short-term. NT-proBNP may be used for predicting mortality in the long term.

Indexed as

area under the curve (AUC)clinical outcomeshemodialysisNT-proBNPtime-varying

Identifiers

PMID30759782
PMCPMC6406702

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.