Evidence map›Paper›PMID 26369642›Full record

ArticleThe international journal of cardiovascular imaging2015

Computed tomography segmental calcium score (SCS) to predict stenosis severity of calcified coronary lesions.

Francesca Pugliese, M G M Hunink, Willem B Meijboom, Katarzyna Gruszczynsnka, Marco Rengo, Lu Zou, Ian Baron, Marcel L Dijkshoorn, Gabriel P Krestin, Pim J de Feyter

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In one paragraph

Article in The international journal of cardiovascular imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Cardiovascular imaging 2015 in the International Journal of Cardiovascular Imaging.The international journal of cardiovascular imaging · 2016
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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

10 authors.

Francesca PuglieseCentre for Advanced Cardiovascular Imaging, NIHR Cardiovascular Biomedical Research Unit at Barts, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. f.pugliese@qmul.ac.uk.
M G M HuninkDepartment of Radiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.
Willem B MeijboomDepartment of Cardiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.
Katarzyna GruszczynsnkaMedical University of Silesia, Katowice, Poland.
Marco RengoUniversity of Rome La Sapienza, Polo Pontino, Italy.
Lu ZouExperimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Ian BaronMedical University of Silesia, Katowice, Poland.
Marcel L DijkshoornDepartment of Radiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.
Gabriel P KrestinDepartment of Radiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.
Pim J de FeyterDepartment of Radiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To estimate the probability of ≥ 50% coronary stenoses based on computed tomography (CT) segmental calcium score (SCS) and clinical factors. The Institutional Review Board approved the study. A training sample of 201 patients underwent CT calcium scoring and conventional coronary angiography (CCA). All patients consented to undergo CT before CCA after being informed of the additional radiation dose. SCS and calcification morphology were assessed in individual coronary segments. We explored the predictive value of patient's symptoms, clinical history, SCS and calcification morphology. We developed a prediction model in the training sample based on these variables then tested it in an independent test sample. The odds ratio (OR) for ≥ 50% coronary stenosis was 1.8-fold greater (p = 0.006) in patients with typical chest pain, twofold (p = 0.014) greater in patients with acute coronary syndromes, twofold greater (p < 0.001) in patients with prior myocardial infarction. Spotty calcifications had an OR for ≥ 50% stenosis 2.3-fold (p < 0.001) greater than the absence of calcifications, wide calcifications 2.7-fold (p < 0.001) greater, diffuse calcifications 4.6-fold (p < 0.001) greater. In middle segments, each unit of SCS had an OR 1.2-fold (p < 0.001) greater than in distal segments; in proximal segments the OR was 1.1-fold greater (p = 0.021). The ROC curve area of the prediction model was 0.795 (0.95 confidence interval 0.602-0.843). Validation in a test sample of 201 independent patients showed consistent diagnostic performance. In conjunction with calcification morphology, anatomical location, patient's symptoms and clinical history, SCS can be helpful to estimate the probability of ≥ 50% coronary stenosis.

Indexed as

Decision Support TechniquesMultidetector Computed TomographyAdultAgedAged, 80 and overAlgorithmsArea Under CurveChi-Square DistributionCoronary AngiographyCoronary Artery DiseaseCoronary StenosisCoronary VesselsFemaleHumansLogistic ModelsMaleAtherosclerosisComputed tomographyCoronary arteriesCoronary calcificationCoronary computed tomography angiography

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Registered trials

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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.