Evidence map›Paper›PMID 35037968›Full record

ArticleEuropean radiology2022

Additive value of epicardial adipose tissue quantification to coronary CT angiography-derived plaque characterization and CT fractional flow reserve for the prediction of lesion-specific ischemia.

Verena Brandt, Josua Decker, U Joseph Schoepf, Akos Varga-Szemes, Tilman Emrich, Gilberto Aquino, Richard R Bayer, Landin Carson, Allison Sullivan, Lauren Ellis and 4 more

Abstract read
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Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  10. Machine learning to predict hemodynamically significant CAD based on traditional risk factors, coronary artery calcium and epicardial fat volume.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023
    Article
  11. Article
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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

14 authors.

Verena BrandtDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Josua DeckerDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
U Joseph SchoepfDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA. schoepf@musc.edu.ORCID http://orcid.org/0000-0002-6164-5641
Akos Varga-SzemesDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Tilman EmrichDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Gilberto AquinoDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Richard R BayerDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Landin CarsonDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Allison SullivanDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Lauren EllisDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Philipp L von Knebel DoeberitzDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Ullrich EbersbergerDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Raffi BekeredjianDepartment of Cardiology and Angiology, Robert-Bosch-Hospital, Stuttgart, Germany.
Christian TescheDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesEpicardial adipose tissue (EAT) from coronary CT angiography (CCTA) is strongly associated with coronary artery disease (CAD). We investigated the additive value of EAT volume to coronary plaque quantification and CT-derived fractional flow reserve (CT-FFR) to predict lesion-specific ischemia.

methodsPatients (n = 128, 60.6 ± 10.5 years, 61% male) with suspected CAD who had undergone invasive coronary angiography (ICA) and CCTA were retrospectively analyzed. EAT volume and plaque measures were derived from CCTA using a semi-automatic software approach, while CT-FFR was calculated using a machine learning algorithm. The predictive value and discriminatory power of EAT volume, plaque measures, and CT-FFR to identify ischemic CAD were assessed using invasive FFR as the reference standard.

resultsFifty-five of 152 lesions showed ischemic CAD by invasive FFR. EAT volume, CCTA ≥ 50% stenosis, and CT-FFR were significantly different in lesions with and without hemodynamic significance (all p < 0.05). Multivariate analysis revealed predictive value for lesion-specific ischemia of these parameters: EAT volume (OR 2.93, p = 0.021), CCTA ≥ 50% (OR 4.56, p = 0.002), and CT-FFR (OR 6.74, p < 0.001). ROC analysis demonstrated incremental discriminatory value with the addition of EAT volume to plaque measures alone (AUC 0.84 vs. 0.62, p < 0.05). CT-FFR (AUC 0.89) showed slightly superior performance over EAT volume with plaque measures (AUC 0.84), however without significant difference (p > 0.05).

conclusionsEAT volume is significantly associated with ischemic CAD. The combination of EAT volume with plaque quantification demonstrates a predictive value for lesion-specific ischemia similar to that of CT-FFR. Thus, EAT may aid in the identification of hemodynamically significant coronary stenosis. KEY POINTS: • CT-derived EAT volume quantification demonstrates high discriminatory power to identify lesion-specific ischemia. • EAT volume shows incremental diagnostic performance over CCTA-derived plaque measures in detecting lesion-specific ischemia. • A combination of plaque measures with EAT volume provides a similar discriminatory value for detecting lesion-specific ischemia compared to CT-FFR.

Indexed as

Coronary Artery DiseaseCoronary StenosisFractional Flow Reserve, MyocardialPlaque, AtheroscleroticAdipose TissueComputed Tomography AngiographyCoronary AngiographyFemaleHumansIschemiaMalePredictive Value of TestsRetrospective StudiesTomography, X-Ray ComputedAngiographyComputed tomographyCoronary artery diseaseEpicardial adipose tissue

Identifiers

PMID35037968

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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.