1. Predictive value of targeted proteomics for coronary plaque morphology in patients with suspected coronary artery diseaseResearch in context
- Author
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Michiel J. Bom, Evgeni Levin, Roel S. Driessen, Ibrahim Danad, Cornelis C. Van Kuijk, Albert C. van Rossum, Jagat Narula, James K. Min, Jonathon A. Leipsic, João P. Belo Pereira, Charles A. Taylor, Max Nieuwdorp, Pieter G. Raijmakers, Wolfgang Koenig, Albert K. Groen, Erik S.G. Stroes, and Paul Knaapen
- Subjects
Medicine ,Medicine (General) ,R5-920 - 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
- Published
- 2019
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