Evidence map›Paper›PMID 39853336›Full record

ArticleEuropean radiology2025

Prognostic value of coronary plaque composition in symptomatic patients with obstructive coronary artery disease.

Emmanuel Gall, Théo Pezel, Solenn Toupin, Thomas Hovasse, Thierry Unterseeh, Bernard Chevalier, Francesca Sanguineti, Stéphane Champagne, Antoinette Neylon, Hakim Benamer and 10 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2025. 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. Review
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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

20 authors.

Emmanuel GallUniversité Paris-Cité, Department of Cardiology, University Hospital of Lariboisiere, (Assistance Publique des Hôpitaux de Paris, AP-HP), 75010, Paris, France.
Théo PezelUniversité Paris-Cité, Department of Cardiology, University Hospital of Lariboisiere, (Assistance Publique des Hôpitaux de Paris, AP-HP), 75010, Paris, France.
Solenn ToupinMIRACL.ai laboratory, Multimodality Imaging for Research and Artificial Intelligence Core Laboratory, University Hospital of Lariboisiere (AP-HP), 75010, Paris, France.
Thomas HovasseHôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Santé, 91300, Massy, France.
Thierry UnterseehHôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Santé, 91300, Massy, France.
Bernard ChevalierHôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Santé, 91300, Massy, France.
Francesca SanguinetiHôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Santé, 91300, Massy, France.
Stéphane ChampagneHôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Santé, 91300, Massy, France.
Antoinette NeylonHôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Santé, 91300, Massy, France.
Hakim BenamerHôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Santé, 91300, Massy, France.
Mariama AkodadHôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Santé, 91300, Massy, France.
Trecy GonçalvesUniversité Paris-Cité, Department of Cardiology, University Hospital of Lariboisiere, (Assistance Publique des Hôpitaux de Paris, AP-HP), 75010, Paris, France.
Antoine LequiparUniversité Paris-Cité, Department of Cardiology, University Hospital of Lariboisiere, (Assistance Publique des Hôpitaux de Paris, AP-HP), 75010, Paris, France.
Jean Guillaume DillingerUniversité Paris-Cité, Department of Cardiology, University Hospital of Lariboisiere, (Assistance Publique des Hôpitaux de Paris, AP-HP), 75010, Paris, France.
Patrick HenryUniversité Paris-Cité, Department of Cardiology, University Hospital of Lariboisiere, (Assistance Publique des Hôpitaux de Paris, AP-HP), 75010, Paris, France.
Tania Ah-SinghUniversité Paris-Cité, Service de Radiologie, Hôpital Lariboisière-APHP, 75010, Paris, France.
Lounis HamziUniversité Paris-Cité, Service de Radiologie, Hôpital Lariboisière-APHP, 75010, Paris, France.
Valérie BoussonUniversité Paris-Cité, Service de Radiologie, Hôpital Lariboisière-APHP, 75010, Paris, France.
Philippe GarotHôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Santé, 91300, Massy, France.
Jérôme GarotHôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Santé, 91300, Massy, France. jgarot@angio-icps.com.ORCID http://orcid.org/0000-0003-3714-1256

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo determine whether plaque composition analysis defined by cardiac CT can provide incremental prognostic value above coronary artery disease (CAD) burden markers in symptomatic patients with obstructive CAD. MATERIALS AND

methodsBetween 2009 and 2019, a multicentric registry included all consecutive symptomatic patients with obstructive CAD (at least one ≥ 50% stenosis on CCTA) and was followed for major adverse cardiovascular (MACE) defined by cardiovascular death or nonfatal myocardial infarction. Each coronary segment was scored visually for both the degree of stenosis and composition of plaque, which were classified as non-calcified, mixed, or calcified. To assess the prognostic value of each CCTA findings, different multivariable Cox regression models were used: model 1: clinical (traditional risk factors); model 2: model 1 + CAD burden (number of proximal segments with stenosis ≥ 50% + number of vessels with obstructive CAD); and model 3: model 2 + plaques feature (number of segments with non-calcified plaque).

resultsOf 2312 patients (mean age 70 ± 12 years, 46% men), 319 experienced a MACE (13.8%) (median follow-up: 6.7 years (5.9-9.1)). The number of proximal segments with ≥ 50% stenosis, the number of vessels with obstructive CAD, and the number of segments with non-calcified plaque were all independently associated with MACEs (all p < 0.001). The addition of plaque composition analysis (model 3) showed the best improvement in model discrimination and reclassification (C-statistic improvement = 0.03; net reclassification improvement = 28.6%; integrative discrimination index = 5.4%, all p < 0.001).

conclusionsIn this population, the analysis of coronary plaque composition had an incremental prognostic value to predict MACEs above a model combining traditional risk factors and CAD burden. KEY POINTS: Question Several coronary computed tomography angiography (CCTA) studies have shown the potential interest of plaque composition analysis, which can be further evaluated. Findings In symptomatic patients with obstructive coronary artery disease (CAD), plaque composition analysis had an incremental prognostic value above a model combining traditional risk factors and CAD burden. Clinical relevance In symptomatic patients with obstructive CAD, plaque composition analysis using CCTA has a strong incremental prognostic value above a model combining traditional risk factors and CAD burden, thus translating into a more accurate long-term major adverse cardiovascular event prediction.

Indexed as

Computed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseCoronary StenosisPlaque, AtheroscleroticAgedFemaleHumansMaleMiddle AgedPrognosisRegistriesRisk FactorsCoronary computed tomography angiographyMajor adverse cardiac eventsPlaque compositionPrognostic value

Identifiers

PMID39853336

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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.