Cited 19 times in
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
DC Field | Value | Language |
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dc.contributor.author | 장혁재 | - |
dc.date.accessioned | 2020-09-28T11:43:38Z | - |
dc.date.available | 2020-09-28T11:43:38Z | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 1934-5925 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/179270 | - |
dc.description.abstract | Aims: 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.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Elsevier | - |
dc.relation.isPartOf | JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Case-Control Studies | - |
dc.subject.MESH | Computed Tomography Angiography* | - |
dc.subject.MESH | Coronary Angiography* | - |
dc.subject.MESH | Coronary Artery Disease / diagnostic imaging* | - |
dc.subject.MESH | Coronary Artery Disease / epidemiology | - |
dc.subject.MESH | Coronary Stenosis / diagnostic imaging* | - |
dc.subject.MESH | Coronary Stenosis / epidemiology | - |
dc.subject.MESH | Diabetes Mellitus* / diagnosis | - |
dc.subject.MESH | Diabetes Mellitus* / epidemiology | - |
dc.subject.MESH | Disease Progression | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Multidetector Computed Tomography* | - |
dc.subject.MESH | Predictive Value of Tests | - |
dc.subject.MESH | Prognosis | - |
dc.subject.MESH | Propensity Score | - |
dc.subject.MESH | Registries | - |
dc.subject.MESH | Risk Assessment | - |
dc.subject.MESH | Risk Factors | - |
dc.subject.MESH | Severity of Illness Index | - |
dc.title | 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 | - |
dc.title.alternative | 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 | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Inge J van den Hoogen | - |
dc.contributor.googleauthor | Alexander R van Rosendael | - |
dc.contributor.googleauthor | Fay Y Lin | - |
dc.contributor.googleauthor | Yao Lu | - |
dc.contributor.googleauthor | Aukelien C Dimitriu-Leen | - |
dc.contributor.googleauthor | Jeff M Smit | - |
dc.contributor.googleauthor | Arthur J H A Scholte | - |
dc.contributor.googleauthor | Stephan Achenbach | - |
dc.contributor.googleauthor | Mouaz H Al-Mallah | - |
dc.contributor.googleauthor | Daniele Andreini | - |
dc.contributor.googleauthor | Daniel S Berman | - |
dc.contributor.googleauthor | Matthew J Budoff | - |
dc.contributor.googleauthor | Filippo Cademartiri | - |
dc.contributor.googleauthor | Tracy Q Callister | - |
dc.contributor.googleauthor | Hyuk-Jae Chang | - |
dc.contributor.googleauthor | Kavitha Chinnaiyan | - |
dc.contributor.googleauthor | Benjamin J W Chow | - |
dc.contributor.googleauthor | Ricardo C Cury | - |
dc.contributor.googleauthor | Augustin DeLago | - |
dc.contributor.googleauthor | Gudrun Feuchtner | - |
dc.contributor.googleauthor | Martin Hadamitzky | - |
dc.contributor.googleauthor | Joerg Hausleiter | - |
dc.contributor.googleauthor | Philipp A Kaufmann | - |
dc.contributor.googleauthor | Yong-Jin Kim | - |
dc.contributor.googleauthor | Jonathon A Leipsic | - |
dc.contributor.googleauthor | Erica Maffei | - |
dc.contributor.googleauthor | Hugo Marques | - |
dc.contributor.googleauthor | Pedro de Araújo Gonçalves | - |
dc.contributor.googleauthor | Gianluca Pontone | - |
dc.contributor.googleauthor | Gilbert L Raff | - |
dc.contributor.googleauthor | Ronen Rubinshtein | - |
dc.contributor.googleauthor | Todd C Villines | - |
dc.contributor.googleauthor | Heidi Gransar | - |
dc.contributor.googleauthor | Erica C Jones | - |
dc.contributor.googleauthor | Jessica M Peña | - |
dc.contributor.googleauthor | Leslee J Shaw | - |
dc.contributor.googleauthor | James K Min | - |
dc.contributor.googleauthor | Jeroen J Bax | - |
dc.identifier.doi | 10.1016/j.jcct.2019.11.015 | - |
dc.contributor.localId | A03490 | - |
dc.relation.journalcode | J01291 | - |
dc.identifier.eissn | 1876-861X | - |
dc.identifier.pmid | 31836415 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S1934592519300280 | - |
dc.subject.keyword | Atherosclerosis | - |
dc.subject.keyword | Computed tomography (CT) | - |
dc.subject.keyword | Diabetes mellitus | - |
dc.subject.keyword | Prognostic application | - |
dc.subject.keyword | Risk stratification | - |
dc.contributor.alternativeName | Chang, Hyuck Jae | - |
dc.contributor.affiliatedAuthor | 장혁재 | - |
dc.citation.volume | 14 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 251 | - |
dc.citation.endPage | 257 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY, Vol.14(3) : 251-257, 2020-05 | - |
dc.identifier.rimsid | 67391 | - |
dc.type.rims | ART | - |
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