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Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

2021-11-16Artigo científico Acesso aberto
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dc.contributor.authorJonas, Rebecca
dc.contributor.authorEarls, James
dc.contributor.authorMarques, Hugo
dc.contributor.authorPinto Marques, Hugo
dc.contributor.authorChang, Hyuk Jae
dc.contributor.authorChoi, Jung Hyun
dc.contributor.authorDoh, Joon Hyung
dc.contributor.authorHer, Ae Young
dc.contributor.authorKoo, Bon Kwon
dc.contributor.authorNam, Chang Wook
dc.contributor.authorPark, Hyung Bok
dc.contributor.authorShin, Sanghoon
dc.contributor.authorCole, Jason
dc.contributor.authorGimelli, Alessia
dc.contributor.authorKhan, Muhammad Akram
dc.contributor.authorLu, Bin
dc.contributor.authorGao, Yang
dc.contributor.authorNabi, Faisal
dc.contributor.authorNakazato, Ryo
dc.contributor.authorSchoepf, U. Joseph
dc.contributor.authorDriessen, Roel S.
dc.contributor.authorBom, Michiel J.
dc.contributor.authorThompson, Randall C.
dc.contributor.authorJang, James J.
dc.contributor.authorRidner, Michael
dc.contributor.authorRowan, Chris
dc.contributor.authorAvelar, Erick
dc.contributor.authorGénéreux, Philippe
dc.contributor.authorKnaapen, Paul
dc.contributor.authorDe Waard, Guus A.
dc.contributor.authorPontone, Gianluca
dc.contributor.authorAndreini, Daniele
dc.contributor.authorAl-Mallah, Mouaz H.
dc.contributor.authorJennings, Robert
dc.contributor.authorCrabtree, Tami R.
dc.contributor.authorVillines, Todd C.
dc.contributor.authorMin, James K.
dc.contributor.authorChoi, Andrew D.
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - pólo NMS
dc.contributor.pblBMJ Publishing Group
dc.date.accessioned2022-02-24T23:22:05Z
dc.date.available2022-02-24T23:22:05Z
dc.date.issued2021-11-16
dc.descriptionFunding Information: ADC is supported by a grant from the GW Heart and Vascular Institute.
dc.description.abstractObjective The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). Methods This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (<50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age <65 and ≥65 years. Results The cohort was 64.4±10.2 years and 29% women. Overall, patients >65 had more PV and CP than patients <65. On a lesion level, patients >65 had more CP than younger patients in both obstructive (29.2 mm 3 vs 48.2 mm 3; p<0.04) and non-obstructive lesions (22.1 mm 3 vs 49.4 mm 3; p<0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p<0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. Conclusion AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent707924
dc.identifier.doi10.1136/openhrt-2021-001832
dc.identifier.issn2398-595X
dc.identifier.otherPURE: 35418905
dc.identifier.otherPURE UUID: b6e59eda-35db-4ed2-bca9-d10e0c3196fc
dc.identifier.otherScopus: 85119967334
dc.identifier.otherPubMed: 34785589
dc.identifier.otherORCID: /0000-0003-3540-0488/work/108814272
dc.identifier.otherWOS: 000719934500001
dc.identifier.urihttp://hdl.handle.net/10362/133561
dc.identifier.urlhttps://www.scopus.com/pages/publications/85119967334
dc.language.isoeng
dc.peerreviewedyes
dc.subjectatherosclerosis
dc.subjectcarotid artery diseases
dc.subjectcomputed tomography angiography
dc.subjectcoronary angiography
dc.subjectdiagnostic imaging
dc.subjectCardiology and Cardiovascular Medicine
dc.titleRelationship of age, atherosclerosis and angiographic stenosis using artificial intelligenceen
dc.typejournal article
degois.publication.issue2
degois.publication.titleOpen Heart
degois.publication.volume8
dspace.entity.typePublication
person.familyNamePinto Marques
person.givenNameHugo
person.identifier.orcid0000-0003-3540-0488
person.identifier.scopus-author-id24537571800
rcaap.rightsopenAccess
relation.isAuthorOfPublicationa3ed0804-c7c1-437a-b08c-931f4de4b3b4
relation.isAuthorOfPublication.latestForDiscoverya3ed0804-c7c1-437a-b08c-931f4de4b3b4

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