Risk Reclassification With Coronary Computed Tomography Angiography-Visualized Nonobstructive Coronary Artery Disease According to 2018 American College of Cardiology/American Heart Association Cholesterol Guidelines (from the Coronary Computed Tomography Angiography Evaluation for Clinical Outcomes : An International Multicenter Registry [CONFIRM])

Han, Donghee; Beecy, Ashley; Anchouche, Khalil; Gransar, Heidi; Dunham, Patricia; Lee, Ji Hyun; Achenbach, Stephan; Al-Mallah, Mouaz; Andreini, Daniele; Berman, Daniel S.; Bax, Jeroen J.; Budoff, Matthew J.; Cademartiri, Filippo; Callister, Tracy Q.; Chang, Hyuk Jae; Chinnaiyan, Kavitha M.; Chow, Benjamin J.W.; Cury, Ricardo C.; Delago, Augustin; Feuchtner, Gudrun; Hadamitzky, Martin; Hausleiter, Jöerg; Kaufmann, Philipp A.; Kim, Yong Jin; Leipsic, Jonathon; Maffei, Erica; Marques, Hugo; Gonçalves, Pedro de Araújo; Raff, Gilbert; Rubinshtein, Ronen; Villines, Todd C.; Lu, Yao; Peña, Jessica M.; Shaw, Leslee J.; Min, James K.; Lin, Fay Y.

doi:10.1016/j.amjcard.2019.07.045

Cited by 13 publications

(7 citation statements)

References 28 publications

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“… 14 Noninvasive coronary imaging, such as coronary calcium scans or coronary CT angiography scans, may help to personalize risk assessment and shared decision-making regarding the intensity needed for the preventive strategy. 25 , 26 , 27 …”

Section: Discussionmentioning

confidence: 99%

Association of Cardiovascular Disease Risk Factor Burden With Progression of Coronary Atherosclerosis Assessed by Serial Coronary Computed Tomographic Angiography

et al. 2020

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Key Points Question Is the risk factor burden of cardiovascular disease, as assessed by atherosclerotic cardiovascular disease risk score, associated with coronary plaque progression and the development of adverse plaque characteristics? Findings In this cohort study of 1005 adult patients from an international multicenter registry who underwent serial coronary computed tomographic angiography, the progression of coronary atherosclerotic plaque volume and the development of adverse plaque characteristics was greater in patients with a high atherosclerotic cardiovascular disease risk score. Meaning The study findings suggest that the overall cardiovascular disease risk burden is associated with the progression of coronary atherosclerosis; the progression of fibrofatty plaque and low-attenuation plaque and the development of adverse plaque characteristics appear to be accelerated in patients with a high risk of atherosclerotic cardiovascular disease.

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Section: Discussionmentioning

confidence: 99%

Association of Cardiovascular Disease Risk Factor Burden With Progression of Coronary Atherosclerosis Assessed by Serial Coronary Computed Tomographic Angiography

et al. 2020

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“…The CCTA is a noninvasive imaging method that has emerged in recent years, which accurately and clearly shows the coronary arteries and ventricular wall. The current literatures report that CCTA plays an important role in the diagnosis of coronary atherosclerotic disease (11)(12)(13). Myocardial infarction due to coronary plaque shedding is an important cause of adverse cardiovascular events.…”

Section: Discussionmentioning

confidence: 99%

The association of coronary non-calcified plaque loading based on coronary computed tomography angiogram and adverse cardiovascular events in patients with unstable coronary heart disease-a retrospective cohort study

Yi¹,

Huang²,

Li³

et al. 2022

J Thorac Dis

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Background: Coronary computed tomography angiogram (CCTA) has the characteristics of noninvasive, high resolution, and can accurately determine the characteristics of tubular wall plaques. The non-calcified plaque loading of the coronary arteries is unstable and prone to shedding, leading to adverse cardiovascular events. However, few studies focused on the predictive value of non-calcified plaque loading for adverse cardiovascular events in patients with unstable coronary heart disease (CHD). The present study was conducted to investigate the association of coronary non-calcified plaque loading based on CCTA and adverse cardiovascular events in patients with unstable CHD.Methods: A total of 206 patients with unstable CHD were collected and followed up for 1 year. The patients were divided into an observation group (n=56) and a control group (n=150) according to whether adverse cardiovascular events occurred or not. We analyzed the predictive value of coronary artery noncalcified plaque loading for adverse cardiovascular events in unstable CHD using receiver operating characteristic and multivariate logistics regression analysis.Results: Compared with the control group, the non-calcified plaque volume in the observation group was increased (160.10±44.02 vs. 128.06±42.22 mm 3 , P=0.000); non-calcified plaque loading increased (26.93%±7.98% vs. 21.46%±7.62%, P=0.000); carotid intima-media thickness increased (1.49±0.17 vs. 1.40%±0.18 mm, P=0.001); and left ventricular ejection fraction (LVEF) was significantly reduced (53.28%±7.39% vs. 58.02%±7.91%, P=0.000). Non-calcified plaque volume and non-calcified plaque loading have certain diagnostic value for recurrence of adverse cardiovascular events within 1 year (P<0.05). A noncalcified plaque volume >145.58 mm 3 is a risk factor for recurrence of adverse cardiovascular events (P<0.05).Conclusions: Increased non-calcified plaque volume in patients with unstable CHD is associated with the development of adverse cardiovascular events in patients with unstable CHD.

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“…The ML derived AUC (0.771) was significantly higher in CT than conventional scoring parametric systems (0.685-0.701) for anticipating major cardiovascular events, with a notable difference ( P < 0.001). Han et al [ 49 ] assessed an ML-derived predictive capacity for all-cause mortality in 86155 patients. Notably, the AUC (0.82) noted to be higher than Framingham risk score and other traditional metrics ( P < 0.05).…”

Section: Big Data Utilization For Prediction Of Outcomes In Ctmentioning

confidence: 99%

Artificial intelligence and machine learning in cardiovascular computed tomography

Seetharam

Bhat

Orris

et al. 2021

WJC

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Computed tomography (CT) is emerging as a prominent diagnostic modality in the field of cardiovascular imaging. Artificial intelligence (AI) is making significant strides in the field of information technology, the commercial industry, and health care. Machine learning (ML), a branch of AI, can optimize the performance of CT and augment the assessment of coronary artery disease. These ML platforms can automate multiple tasks, perform calculations, and integrate information from a variety of data sources. In this review article, we explore the ML in CT imaging.

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Cited by 13 publications

References 28 publications

Association of Cardiovascular Disease Risk Factor Burden With Progression of Coronary Atherosclerosis Assessed by Serial Coronary Computed Tomographic Angiography

Association of Cardiovascular Disease Risk Factor Burden With Progression of Coronary Atherosclerosis Assessed by Serial Coronary Computed Tomographic Angiography

The association of coronary non-calcified plaque loading based on coronary computed tomography angiogram and adverse cardiovascular events in patients with unstable coronary heart disease-a retrospective cohort study

Artificial intelligence and machine learning in cardiovascular computed tomography

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