Evidence map›Paper›PMID 32779078›Full record

ReviewThe international journal of cardiovascular imaging2020

Physiology and coronary artery disease: emerging insights from computed tomography imaging based computational modeling.

Parastou Eslami, Vikas Thondapu, Julia Karady, Eline M J Hartman, Zexi Jin, Mazen Albaghdadi, Michael Lu, Jolanda J Wentzel, Udo Hoffmann

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The international journal of cardiovascular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Diagnostics of atherosclerosis: Overview of the existing methods.Frontiers in cardiovascular medicine · 2023
    Review
  6. Introduction topical issue on CT plaque burden.The international journal of cardiovascular imaging · 2020
    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

9 authors.

Parastou EslamiDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. peslami1@mgh.harvard.edu.ORCID http://orcid.org/0000-0001-9092-2620
Vikas ThondapuDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Julia KaradyDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Eline M J HartmanDepartment of Cardiology, Biomedical Engineering, Erasmus MC, Rotterdam, The Netherlands.
Zexi JinDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Mazen AlbaghdadiDepartment of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Michael LuDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Jolanda J WentzelDepartment of Cardiology, Biomedical Engineering, Erasmus MC, Rotterdam, The Netherlands.
Udo HoffmannDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Funding

Cardiac MR and CT ResearchT32HL076136 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI NEILAN, TOMAS G · 2004 to 2019
$4.9M
Cardiovascular Imaging in Ischemic Heart DiseaseK24HL113128 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI HOFFMANN, UDO · 2012 to 2020
$1.1M
NHLBI NIH HHS K24 HL113128NHLBI NIH HHS T32 HL076136NHLBI NIH HHS T32HL076136
6 · The paper itself

Abstract

Improvements in spatial and temporal resolution now permit robust high quality characterization of presence, morphology and composition of coronary atherosclerosis in computed tomography (CT). These characteristics include high risk features such as large plaque volume, low CT attenuation, napkin-ring sign, spotty calcification and positive remodeling. Because of the high image quality, principles of patient-specific computational fluid dynamics modeling of blood flow through the coronary arteries can now be applied to CT and allow the calculation of local lesion-specific hemodynamics such as endothelial shear stress, fractional flow reserve and axial plaque stress. This review examines recent advances in coronary CT image-based computational modeling and discusses the opportunity to identify lesions at risk for rupture much earlier than today through the combination of anatomic and hemodynamic information.

Indexed as

Computed Tomography AngiographyCoronary AngiographyModels, CardiovascularPatient-Specific ModelingAnimalsClinical Decision-MakingCoronary Artery DiseaseCoronary CirculationCoronary VesselsHemodynamicsHumansHydrodynamicsPredictive Value of TestsPrognosisComputational fluid dynamicsCoronary computed tomography angiographyEndothelial shear stressPathophysiologyPatient-specific modelingPlaque burden

Identifiers

PMID32779078
PMCPMC8323761

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.