Evidence map›Paper›PMID 32016113›Full record

ReviewBioMed research international2020

The Biomarkers for Acute Myocardial Infarction and Heart Failure.

Xi-Ying Wang, Fen Zhang, Chi Zhang, Liang-Rong Zheng, Jian Yang

Abstract readReview
In one paragraph

Review in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.

0numbers the graph read from it
0cells of the map it votes in
74citing 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

74 citing papers in PubMed.

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14 more citing papers are in PubMed but not listed here.

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

5 authors.

Xi-Ying WangDepartment of Cardiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Fen ZhangDepartment of Cardiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Chi ZhangDepartment of Cardiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Liang-Rong ZhengDepartment of Cardiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.ORCID https://orcid.org/0000-0002-3998-1999
Jian YangDepartment of Cardiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.ORCID https://orcid.org/0000-0002-4163-2309

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of a large number of cardiovascular biomarkers, meant to complement the use of the electrocardiogram, echocardiography cardiac imaging, and clinical symptom assessment, has become a routine in clinical diagnosis, differential diagnosis, risk stratification, and prognosis and guides the management of patients with suspected cardiovascular diseases. There is a broad consensus that cardiac troponin and natriuretic peptides are the preferred biomarkers in clinical practice for the diagnosis of the acute coronary syndrome and heart failure, respectively, while the roles and possible clinical applications of several other potential biomarkers are still under study. This review mainly focuses on the recent studies of the roles and clinical applications of troponin and natriuretic peptides, which seem to be the best-validated markers in distinguishing and predicting the future cardiac events of patients with suspected cardiovascular diseases. Additionally, the review briefly discusses some of the large number of potential markers that may play a more prominent role in the future.

Indexed as

Acute Coronary SyndromeBiomarkersDiagnosis, DifferentialElectrocardiographyHeart FailureHumansMyocardial InfarctionNatriuretic PeptidesPrognosisRisk AssessmentTroponinBiomarkersNatriuretic PeptidesTroponin

Identifiers

PMID32016113
PMCPMC6988690

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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.