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Coronary atherosclerosis scoring with semiquantitative CCTA risk scores for prediction of major adverse cardiac events: Propensity score-based analysis of diabetic and non-diabetic patients

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dc.contributor.author장혁재-
dc.date.accessioned2020-09-28T11:43:38Z-
dc.date.available2020-09-28T11:43:38Z-
dc.date.issued2020-05-
dc.identifier.issn1934-5925-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/179270-
dc.description.abstractAims: We aimed to compare semiquantitative coronary computed tomography angiography (CCTA) risk scores - which score presence, extent, composition, stenosis and/or location of coronary artery disease (CAD) - and their prognostic value between patients with and without diabetes mellitus (DM). Risk scores derived from general chest-pain populations are often challenging to apply in DM patients, because of numerous confounders. Methods: Out of a combined cohort from the Leiden University Medical Center and the CONFIRM registry with 5-year follow-up data, we performed a secondary analysis in diabetic patients with suspected CAD who were clinically referred for CCTA. A total of 732 DM patients was 1:1 propensity-matched with 732 non-DM patients by age, sex and cardiovascular risk factors. A subset of 7 semiquantitative CCTA risk scores was compared between groups: 1) any stenosis ≥50%, 2) any stenosis ≥70%, 3) stenosis-severity component of the coronary artery disease-reporting and data system (CAD-RADS), 4) segment involvement score (SIS), 5) segment stenosis score (SSS), 6) CT-adapted Leaman score (CT-LeSc), and 7) Leiden CCTA risk score. Cox-regression analysis was performed to assess the association between the scores and the primary endpoint of all-cause death and non-fatal myocardial infarction. Also, area under the receiver-operating characteristics curves were compared to evaluate discriminatory ability. Results: A total of 1,464 DM and non-DM patients (mean age 58 ± 12 years, 40% women) underwent CCTA and 155 (11%) events were documented after median follow-up of 5.1 years. In DM patients, the 7 semiquantitative CCTA risk scores were significantly more prevalent or higher as compared to non-DM patients (p ≤ 0.022). All scores were independently associated with the primary endpoint in both patients with and without DM (p ≤ 0.020), with non-significant interaction between the scores and diabetes (interaction p ≥ 0.109). Discriminatory ability of the Leiden CCTA risk score in DM patients was significantly better than any stenosis ≥50% and ≥70% (p = 0.003 and p = 0.007, respectively), but comparable to the CAD-RADS, SIS, SSS and CT-LeSc that also focus on the extent of CAD (p ≥ 0.265). Conclusion: Coronary atherosclerosis scoring with semiquantitative CCTA risk scores incorporating the total extent of CAD discriminate major adverse cardiac events well, and might be useful for risk stratification of patients with DM beyond the binary evaluation of obstructive stenosis alone.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherElsevier-
dc.relation.isPartOfJOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAged-
dc.subject.MESHCase-Control Studies-
dc.subject.MESHComputed Tomography Angiography*-
dc.subject.MESHCoronary Angiography*-
dc.subject.MESHCoronary Artery Disease / diagnostic imaging*-
dc.subject.MESHCoronary Artery Disease / epidemiology-
dc.subject.MESHCoronary Stenosis / diagnostic imaging*-
dc.subject.MESHCoronary Stenosis / epidemiology-
dc.subject.MESHDiabetes Mellitus* / diagnosis-
dc.subject.MESHDiabetes Mellitus* / epidemiology-
dc.subject.MESHDisease Progression-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHMultidetector Computed Tomography*-
dc.subject.MESHPredictive Value of Tests-
dc.subject.MESHPrognosis-
dc.subject.MESHPropensity Score-
dc.subject.MESHRegistries-
dc.subject.MESHRisk Assessment-
dc.subject.MESHRisk Factors-
dc.subject.MESHSeverity of Illness Index-
dc.titleCoronary atherosclerosis scoring with semiquantitative CCTA risk scores for prediction of major adverse cardiac events: Propensity score-based analysis of diabetic and non-diabetic patients-
dc.title.alternativeCoronary atherosclerosis scoring with semiquantitative CCTA risk scores for prediction of major adverse cardiac events: Propensity score-based analysis of diabetic and non-diabetic patients-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorInge J van den Hoogen-
dc.contributor.googleauthorAlexander R van Rosendael-
dc.contributor.googleauthorFay Y Lin-
dc.contributor.googleauthorYao Lu-
dc.contributor.googleauthorAukelien C Dimitriu-Leen-
dc.contributor.googleauthorJeff M Smit-
dc.contributor.googleauthorArthur J H A Scholte-
dc.contributor.googleauthorStephan Achenbach-
dc.contributor.googleauthorMouaz H Al-Mallah-
dc.contributor.googleauthorDaniele Andreini-
dc.contributor.googleauthorDaniel S Berman-
dc.contributor.googleauthorMatthew J Budoff-
dc.contributor.googleauthorFilippo Cademartiri-
dc.contributor.googleauthorTracy Q Callister-
dc.contributor.googleauthorHyuk-Jae Chang-
dc.contributor.googleauthorKavitha Chinnaiyan-
dc.contributor.googleauthorBenjamin J W Chow-
dc.contributor.googleauthorRicardo C Cury-
dc.contributor.googleauthorAugustin DeLago-
dc.contributor.googleauthorGudrun Feuchtner-
dc.contributor.googleauthorMartin Hadamitzky-
dc.contributor.googleauthorJoerg Hausleiter-
dc.contributor.googleauthorPhilipp A Kaufmann-
dc.contributor.googleauthorYong-Jin Kim-
dc.contributor.googleauthorJonathon A Leipsic-
dc.contributor.googleauthorErica Maffei-
dc.contributor.googleauthorHugo Marques-
dc.contributor.googleauthorPedro de Araújo Gonçalves-
dc.contributor.googleauthorGianluca Pontone-
dc.contributor.googleauthorGilbert L Raff-
dc.contributor.googleauthorRonen Rubinshtein-
dc.contributor.googleauthorTodd C Villines-
dc.contributor.googleauthorHeidi Gransar-
dc.contributor.googleauthorErica C Jones-
dc.contributor.googleauthorJessica M Peña-
dc.contributor.googleauthorLeslee J Shaw-
dc.contributor.googleauthorJames K Min-
dc.contributor.googleauthorJeroen J Bax-
dc.identifier.doi10.1016/j.jcct.2019.11.015-
dc.contributor.localIdA03490-
dc.relation.journalcodeJ01291-
dc.identifier.eissn1876-861X-
dc.identifier.pmid31836415-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1934592519300280-
dc.subject.keywordAtherosclerosis-
dc.subject.keywordComputed tomography (CT)-
dc.subject.keywordDiabetes mellitus-
dc.subject.keywordPrognostic application-
dc.subject.keywordRisk stratification-
dc.contributor.alternativeNameChang, Hyuck Jae-
dc.contributor.affiliatedAuthor장혁재-
dc.citation.volume14-
dc.citation.number3-
dc.citation.startPage251-
dc.citation.endPage257-
dc.identifier.bibliographicCitationJOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY, Vol.14(3) : 251-257, 2020-05-
dc.identifier.rimsid67391-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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