Prognostic value of coronary plaque composition in symptomatic patients with obstructive coronary artery disease.

Objectives: To determine whether plaque composition analysis defined by cardiac CT can provide incremental prognostic value above coronary artery disease (CAD) burden markers in symptomatic patients with obstructive CAD.

Materials And Methods: Between 2009 and 2019, a multicentric registry included all consecutive symptomatic patients with obstructive CAD (at least one ≥ 50% stenosis on CCTA) and was followed for major adverse cardiovascular (MACE) defined by cardiovascular death or nonfatal myocardial infarction. Each coronary segment was scored visually for both the degree of stenosis and composition of plaque, which were classified as non-calcified, mixed, or calcified.

A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.

Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.

Prognostic Value of Cardiac MRI Late Gadolinium Enhancement Granularity in Participants with Ischemic Cardiomyopathy.

Background Ischemic late gadolinium enhancement (LGE) assessed with cardiac MRI is a well-established prognosticator in ischemic cardiomyopathy. However, the prognostic value of additional LGE parameters, such as extent, transmurality, location, and associated midwall LGE, remains unclear. Purpose To assess the prognostic value of ischemic LGE features to predict all-cause mortality in ischemic cardiomyopathy.