Evidence map›Paper›PMID 41440872›Full record

ReviewJournal of cardiovascular development and disease2025

Coronary Computed Tomography Angiography to Exclude Acute Coronary Syndrome in Low-Risk Chest Pain Patients.

Lauren Ling, Asim Shaikh, Matthew Sibbald

Abstract readReview
In one paragraph

Review in Journal of cardiovascular development and disease, 2025. 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
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.

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

3 authors.

Lauren LingSchulich School of Medicine & Dentistry, Western University, London, ON N6A 5C1, Canada.ORCID 0009-0006-1939-2413
Asim ShaikhMichael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada.ORCID 0000-0001-7008-2919
Matthew SibbaldMichael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada.ORCID 0000-0002-0022-2370

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronary computed tomography angiography (CCTA) is a non-invasive imaging tool used predominantly in suspected chronic coronary artery disease (CAD) patients, due to its high negative predictive value. However, increasing focus has been placed on CCTA to manage and risk stratify acute chest pain patients in emergency departments (ED).

objectiveThis scoping review summarizes the available evidence on the role of CCTA to exclude acute coronary syndrome (ACS) in low-risk acute chest pain patients, focusing on its diagnostic accuracy, safety, and application in the context of high sensitivity cardiac troponin assays (hs-cTn).

methodsArticles published between January 2015 and March 2025 investigating CCTA use in low-risk acute chest pain patients were retrieved from Medline, Embase, Emcare, and Web of Science databases.

results22 articles (13,617 patients) were retrieved. CCTA had strong diagnostic performance, with an excellent negative predictive value (99.8-100%) and sensitivity (94-100%) for ACS diagnosis and prediction of major adverse cardiovascular events. Specificity and positive predictive values were lower and less consistent. When combined with hs-cTn, the diagnostic accuracy of CCTA for ACS was improved significantly. CCTA was associated with low rates of ACS at follow-up (0-3.5%), which were lower than or comparable to the safety outcomes of standard care and stress testing.

Indexed as

acute chest painacute coronary syndromecoronary computed tomography angiographyemergency departmentlow-risk chest painmajor adverse cardiovascular events

Identifiers

PMID41440872
PMCPMC12733454

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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.