Evidence map›Paper›PMID 37025691›Full record

ArticleFrontiers in cardiovascular medicine2023

Establishment of a nomogram model for acute chest pain triage in the chest pain center.

Na Yan, Ling Wei, Zhiwei Li, Yu Song

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 93% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 2 institutions in 1 country.

Na YanDepartment of Emergency, TEDA International Cardiovascular Hospital, Clinical School of Cardiovascular Disease, Tianjin Medical University, Tianjin, China.
Ling WeiDepartment of Emergency, TEDA International Cardiovascular Hospital, Clinical School of Cardiovascular Disease, Tianjin Medical University, Tianjin, China.
Zhiwei LiDepartment of Pathophysiology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Yu SongDepartment of Emergency, TEDA International Cardiovascular Hospital, Clinical School of Cardiovascular Disease, Tianjin Medical University, Tianjin, China.
TEDA International Cardiovascular Hospital · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Acute myocardial infarction (AMI) is the leading life-threatening disease in the emergency department (ED), so rapid chest pain triage is important. This study aimed to establish a clinical prediction model for the risk stratification of acute chest pain patients based on the Point-of-care (POC) cardiac troponin (cTn) level and other clinical variables. Methods: We conducted a Results: We analyzed data from 5,397 patients that were included in this research. The median turnaround time (TAT) of POC cTnI was 16 min. The model was constructed with 6 variables: ECG ischemia, POC cTnI level, hypotension, chest pain symptom, Killip class, and sex. The area under the ROC curve (AUC) in the training and validation cohorts was 0.924 and 0.894, respectively. The diagnostic performance was superior to the GRACE score (AUC: 0.737). Conclusion: A practical predictive model was created and could be used for rapid and effective triage of acute chest pain patients in the CPC.

Indexed as

acute chest pain triageacute coronary syndromeacute myocardial infarctionpoint-of-care (POC)troponin

Identifiers

PMID37025691
PMCPMC10070711
OpenAlexW4328116275

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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.