Evidence map›Paper›PMID 39470055›Full record

ArticleJournal of the American Heart Association2024

Characterizing Nonculprit Lesions and Perivascular Adipose Tissue of Patients Following Acute Myocardial Infarction Using Coronary Computed Tomography Angiography: A Comparative Study.

Xiaomeng Wang, Ching H Sia, Philip D Adamson, Charlotte E Greer, Weimin Huang, Hwee K Lee, Shuang Leng, Yan T Loong, Nur A S Raffiee, Swee Y Tan and 11 more

Abstract readComparative Study
In one paragraph

Article in Journal of the American Heart Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

21 authors.

Xiaomeng WangCardiovascular and Metabolic Disorders Programme Duke-National University of Singapore Singapore.ORCID 0000-0002-1733-4109
Ching H SiaNational University Heart Center Singapore Singapore.ORCID 0000-0002-2764-2869
Philip D AdamsonDepartment of Medicine University of Otago Christchurch New Zealand.ORCID 0000-0002-6177-956X
Charlotte E GreerDepartment of Medicine University of Otago Christchurch New Zealand.ORCID 0000-0001-5755-5355
Weimin HuangInstitute for Infocomm Research, Agency for Science, Technology and Research (A*Star) Singapore Singapore.
Hwee K LeeBioinformatics Institute Agency for Science, Technology and Research (A*Star) Singapore Singapore.ORCID 0000-0003-1932-5377
Shuang LengNational Heart Center Singapore Singapore.
Yan T LoongNational Heart Center Singapore Singapore.
Nur A S RaffieeNational Heart Center Singapore Singapore.
Swee Y TanCardiovascular and Metabolic Disorders Programme Duke-National University of Singapore Singapore.
Sock H TanYong Loo Lin School of Medicine National University of Singapore Singapore.ORCID 0000-0001-9774-2083
Lynette L S TeoDepartment of Diagnostic Imaging National University Hospital Singapore Singapore.ORCID 0000-0002-2817-9458
Sung L WongNational Heart Center Singapore Singapore.ORCID 0000-0002-9595-8275
Xiaoxun YangYong Loo Lin School of Medicine National University of Singapore Singapore.ORCID 0000-0003-4544-2040
Min S YewDepartment of Cardiology Tan Tock Seng Hospital Singapore Singapore.ORCID 0000-0002-1491-5864
Thon H YongDepartment of Cardiology Changi General Hospital Singapore Singapore.ORCID 0000-0001-5838-5351
Liang ZhongCardiovascular and Metabolic Disorders Programme Duke-National University of Singapore Singapore.ORCID 0000-0003-3608-4173
Leslee J ShawIcahn School of Medicine at Mount Sinai New York City NY USA.ORCID 0000-0003-1268-1491
Mark Y Y ChanNational University Heart Center Singapore Singapore.ORCID 0000-0003-0389-7450
Derek J HausenloyCardiovascular and Metabolic Disorders Programme Duke-National University of Singapore Singapore.
Lohendran BaskaranNational Heart Center Singapore Singapore.ORCID 0000-0003-0565-7151

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe comparison of coronary computed tomography angiography plaques and perivascular adipose tissue (PVAT) between patients with acute myocardial infarction (AMI) posttreatment and patients with stable coronary artery disease is poorly understood. Our objective was to evaluate the differences in coronary computed tomography angiography-quantified plaque and PVAT characteristics in patients post-AMI and identify signs of residual inflammation. METHODS AND

resultsWe analyzed 205 patients (age, 59.77±9.24 years; 92.20% men) with AMI ≤1 month and matched them with 205 patients with stable coronary artery disease (age, 60.52±10.04 years; 90.24% men) based on age, sex, and cardiovascular risk factors. Coronary computed tomography angiography scans were assessed for nonculprit plaque and vessel characteristics, plaque volumes by composition, high-risk plaques, and PVAT mean attenuation. Both patient groups exhibited similar noncalcified plaque volumes (383.35±313.23 versus 378.63±426.25 mm

conclusionsPatients post-AMI displayed heightened noncalcified plaque components, largely due to fibrofatty and necrotic core content, more high-risk plaques, and increased PVAT mean attenuation on a per-patient level, highlighting the necessity for refined risk assessment in patients with AMI after treatment.

Indexed as

Adipose TissueComputed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseMyocardial InfarctionPlaque, AtheroscleroticAgedCoronary VesselsFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective Studiesacute myocardial infarctioncoronary atheromacoronary computed tomography angiographyhigh‐risk plaquesresidual inflammatory risk

Identifiers

PMID39470055
PMCPMC11935734

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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.