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Background: Risk stratification is crucial to improve tailored therapy in patients with suspected coronary artery disease (CAD). This study investigated the ability of targeted proteomics to predict presence of high-risk plaque or absence of coronary atherosclerosis in patients with suspected CAD, defined by coronary computed tomography angiography (CCTA). Methods: Patients with suspected CAD (n = 203) underwent CCTA. Plasma levels of 358 proteins were used to generate machine learning models for the presence of CCTA-defined high-risk plaques or complete absence of coronary atherosclerosis. Performance was tested against a clinical model containing generally available clinical characteristics and conventional biomarkers. Findings: A total of 196 patients with analyzable protein levels (n = 332) was included for analysis. A subset of 35 proteins was identified predicting the presence of high-risk plaques. The developed machine learning model had fair diagnostic performance with an area under the curve (AUC) of 0·79 ± 0·01, outperforming prediction with generally available clinical characteristics (AUC = 0·65 ± 0·04, p

Objectives. Quantitative flow ratio (QFR) computes fractional flow reserve (FFR) based on invasive coronary angiography (ICA). Residual QFR estimates post‐percutaneous coronary intervention (PCI) FFR. This study sought to assess the relationship of residual QFR with post-PCI FFR. Methods. Residual QFR analysis, using pre-PCI ICA, was attempted in 159 vessels with post-PCI FFR. QFR lesion location was matched with the PCI location to simulate the performed intervention and allow computation of residual QFR. A post-PCI FFR

Objective 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 (65 had more PV and CP than patients 65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p