Evidence map›Paper›PMID 26817414›Full record

ArticleJournal of cardiovascular computed tomography

Total coronary atherosclerotic plaque burden assessment by CT angiography for detecting obstructive coronary artery disease associated with myocardial perfusion abnormalities.

Satoru Kishi, Tiago A Magalhães, Rodrigo J Cerci, Matthew B Matheson, Andrea Vavere, Yutaka Tanami, Pieter H Kitslaar, Richard T George, Jeffrey Brinker, Julie M Miller and 10 more

Open access · greenAbstract readComparative StudyMulticenter Study
In one paragraph

Article in Journal of cardiovascular computed tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.3field-weighted citation impact, top 13% of its field
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

16 citing papers in PubMed, 31 citations in OpenAlex.

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  9. From CT to artificial intelligence for complex assessment of plaque-associated risk.The international journal of cardiovascular imaging · 2020
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  13. Coronary computed tomography angiography: a method coming of age.Clinical research in cardiology : official journal of the German Cardiac Society · 2018
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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 at 11 institutions in 5 countries.

Satoru KishiDepartment of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Tiago A MagalhãesDepartment of Medicine, Division of Cardiology, Catholic University of Paraná (PUC-PR), Brazil.
Rodrigo J CerciDepartment of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Matthew B MathesonJohns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
Andrea VavereDepartment of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Yutaka TanamiDepartment of Radiology, Keio University, Tokyo, Japan.
Pieter H KitslaarDivision of Image Processing, Department of Radiology, Leiden University Medical Center / Medis Medical Imaging Systems, Leiden, The Netherlands.
Richard T GeorgeDepartment of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jeffrey BrinkerDepartment of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Julie M MillerDepartment of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Melvin E ClouseBeth Israel Deaconess Medical Center, Harvard University, Boston, MA, USA.
Pedro A LemosHeart Institute (InCor), University of Sao Paulo Medical School, São Paulo, Brazil.
Hiroyuki NiinumaDivision of Cardiology, St. Luke's International Hospital, Tokyo, Japan.
Johan H C ReiberDivision of Image Processing, Department of Radiology, Leiden University Medical Center / Medis Medical Imaging Systems, Leiden, The Netherlands.
Carlos E RochitteHeart Institute (InCor), University of Sao Paulo Medical School, São Paulo, Brazil.
Frank J RybickiThe Ottawa Hospital Research Institute and the Department of Radiology, The University of Ottawa Faculty of Medicine, Ottawa, Canada.
Marcelo F Di CarliDepartment of Radiology, Brigham and Women's Hospital, Harvard University, Boston, MA, USA.
Christopher CoxJohns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
Joao A C LimaDepartment of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Armin Arbab-ZadehDepartment of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: azadeh1@jhmi.edu.
Johns Hopkins University · USJohns Hopkins Medicine · USUniversidade de São Paulo · BRBeth Israel Deaconess Medical Center · USCentre for Medical Systems Biology · NLHarvard University · USKeio University · JPLeiden University Medical Center · NLOttawa Hospital · CAPontifícia Universidade Católica do Paraná · BRSt. Luke's International Hospital · JP

Funding

Quantification and Characterization of Coronary Atherosclerotic Plaque by CTK23HL098368 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI ZADEH, ARMIN A. · 2011 to 2015
$699k
NHLBI NIH HHS K23 HL098368
6 · The paper itself

Abstract

backgroundTotal atherosclerotic plaque burden assessment by CT angiography (CTA) is a promising tool for diagnosis and prognosis of coronary artery disease (CAD) but its validation is restricted to small clinical studies. We tested the feasibility of semi-automatically derived coronary atheroma burden assessment for identifying patients with hemodynamically significant CAD in a large cohort of patients with heterogenous characteristics.

methodsThis study focused on the CTA component of the CORE320 study population. A semi-automated contour detection algorithm quantified total coronary atheroma volume defined as the difference between vessel and lumen volume. Percent atheroma volume (PAV = [total atheroma volume/total vessel volume] × 100) was the primary metric for assessment (n = 374). The area under the receiver operating characteristic curve (AUC) determined the diagnostic accuracy for identifying patients with hemodynamically significant CAD defined as ≥50% stenosis by quantitative coronary angiography and associated myocardial perfusion abnormality by SPECT.

resultsOf 374 patients, 139 (37%) had hemodynamically significant CAD. The AUC for PAV was 0.78 (95% confidence interval [CI] 0.73-0.83) compared with 0.84 [0.79-0.88] by standard expert CTA interpretation (p = 0.02). Accuracy for both CTA (0.91 [0.87, 0.96]) and PAV (0.86 [0.81-0.91]) increased after excluding patients with history of CAD (p < 0.01 for both). Bland-Altman analysis revealed good agreement between two observers (bias of 280.2 mm(3) [161.8, 398.7]).

conclusionsA semi-automatically derived index of total coronary atheroma volume yields good accuracy for identifying patients with hemodynamically significant CAD, though marginally inferior to CTA expert reading. These results convey promise for rapid, reliable evaluation of clinically relevant CAD.

Indexed as

Computed Tomography AngiographyCoronary CirculationMultidetector Computed TomographyAgedAlgorithmsArea Under CurveAutomationCoronary AngiographyCoronary Artery DiseaseCoronary StenosisCoronary VesselsFeasibility StudiesFemaleHemodynamicsHumansMaleAtherosclerotic plaqueCoronary artery diseaseCoronary heart diseaseCT angiographyMyocardial perfusion

Identifiers

PMID26817414
PMCPMC4788549
OpenAlexW2261388433

What OpenQuestion 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 OpenQuestion graph.