Predictive value of targeted proteomics for coronary plaque morphology in patients with suspected coronary artery disease.

ABSTRACT: 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?

SUBMITTER: Bom MJ 

PROVIDER: S-EPMC6355456 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature