Evidence map›Paper›PMID 27226154›Full record

ReviewNature reviews. Cardiology2016

Imaging of coronary atherosclerosis - evolution towards new treatment strategies.

Marc R Dweck, Mhairi K Doris, Manish Motwani, Philip D Adamson, Piotr Slomka, Damini Dey, Zahi A Fayad, David E Newby, Daniel Berman

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Cardiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 2 pooled it
–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

31 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
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  7. Review
  8. Review
  9. Article
  10. Let's embrace optical imaging: a growing branch on the clinical molecular imaging tree.European journal of nuclear medicine and molecular imaging · 2021
    Article
  11. Review
  12. Hiding beyond plain sight: Textural analysis of positron emission tomography to identify high-risk plaques in carotid atherosclerosis.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021
    Article
  13. Training and validation of a deep learning architecture for the automatic analysis of coronary angiography.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2021
    Article
  14. Article
  15. Review
  16. 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

9 authors.

Marc R DweckBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
Mhairi K DorisBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
Manish MotwaniCedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, California 90048, USA.
Philip D AdamsonBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
Piotr SlomkaCedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, California 90048, USA.
Damini DeyCedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, California 90048, USA.
Zahi A FayadTranslational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, New York 10029, USA.
David E NewbyBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
Daniel BermanCedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, California 90048, USA.

Funding

British Heart Foundation FS/13/77/30488British Heart Foundation FS/14/78/31020Medical Research Council G0701127
6 · The paper itself

Abstract

Coronary atherosclerosis and the precipitation of acute myocardial infarction are highly complex processes, which makes accurate risk prediction challenging. Rapid developments in invasive and noninvasive imaging technologies now provide us with detailed, exquisite images of the coronary vasculature that allow direct investigation of a wide range of these processes. These modalities include sophisticated assessments of luminal stenoses and myocardial perfusion, complemented by novel measures of the atherosclerotic plaque burden, adverse plaque characteristics, and disease activity. Together, they can provide comprehensive, individualized assessments of coronary atherosclerosis as it occurs in patients. Not only can this information provide important pathological insights, but it can also potentially be used to guide personalized treatment decisions. In this Review, we describe the latest advances in both established and emerging imaging techniques, focusing on the strengths and weakness of each approach. Moreover, we discuss how these technological advances might be translated from attractive images into novel imaging strategies and definite improvements in clinical risk prediction and patient outcomes. This process will not be easy, and the many potential barriers and difficulties are also reviewed.

Indexed as

Cardiac Imaging TechniquesPlaque, AtheroscleroticClinical Decision-MakingCoronary Artery DiseaseCoronary StenosisCoronary VesselsHumansPredictive Value of TestsPrognosisReproducibility of ResultsSeverity of Illness Index

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.