Evidence map›Paper›PMID 32180833›Full record

ReviewEuropean cardiology2020

Latest Advances in Cardiac CT.

Thomas D Heseltine, Scott W Murray, Balazs Ruzsics, Michael Fisher

Abstract readReview
In one paragraph

Review in European cardiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Why Current Detection of Vascular Calcification Falls Short and How to Improve on It.TH open : companion journal to thrombosis and haemostasis · 2024
    Review
  4. Article
  5. Role of Computed Tomography in Cardiac Electrophysiology.Radiologic clinics of North America · 2024
    Review
  6. Article
  7. Review
  8. Review
  9. Recent technologies in cardiac imaging.Frontiers in medical technology · 2022
    Review
  10. Review
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

4 authors.

Thomas D HeseltineRoyal Liverpool University Hospital, Liverpool, UK.
Scott W MurrayRoyal Liverpool University Hospital, Liverpool, UK.
Balazs RuzsicsRoyal Liverpool University Hospital, Liverpool, UK.
Michael FisherLiverpool Centre for Cardiovascular Science, Liverpool, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent rapid technological advancements in cardiac CT have improved image quality and reduced radiation exposure to patients. Furthermore, key insights from large cohort trials have helped delineate cardiovascular disease risk as a function of overall coronary plaque burden and the morphological appearance of individual plaques. The advent of CT-derived fractional flow reserve promises to establish an anatomical and functional test within one modality. Recent data examining the short-term impact of CT-derived fractional flow reserve on downstream care and clinical outcomes have been published. In addition, machine learning is a concept that is being increasingly applied to diagnostic medicine. Over the coming decade, machine learning will begin to be integrated into cardiac CT, and will potentially make a tangible difference to how this modality evolves. The authors have performed an extensive literature review and comprehensive analysis of the recent advances in cardiac CT. They review how recent advances currently impact on clinical care and potential future directions for this imaging modality.

Indexed as

atherosclerosisCardiac CTcardiovascular disease riskcoronary artery calcium scorecoronary artery diseaseCT coronary angiographyfractional flow reserve CTmachine learning

Identifiers

PMID32180833
PMCPMC7066830

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.