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

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          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 < 0·05). Conversely, a different subset of 34 proteins was predictive for the absence of CAD (AUC = 0·85 ± 0·05), again outperforming prediction with generally available characteristics (AUC = 0·70 ± 0·04, p < 0·05).

          Interpretation

          Using machine learning models, trained on targeted proteomics, we defined two complementary protein signatures: one for identification of patients with high-risk plaques and one for identification of patients with absence of CAD. Both biomarker subsets were superior to generally available clinical characteristics and conventional biomarkers in predicting presence of high-risk plaque or absence of coronary atherosclerosis. These promising findings warrant external validation of the value of targeted proteomics to identify cardiovascular risk in outcome studies.

          Fund

          This study was supported by an unrestricted research grant from HeartFlow Inc. and partly supported by a European Research Area Network on Cardiovascular Diseases (ERA-CVD) grant (ERA CVD JTC2017, OPERATION). Funders had no influence on trial design, data evaluation, and interpretation.

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          Most cited references 26

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              Prognostic value of cardiac computed tomography angiography: a systematic review and meta-analysis.

              The purpose of this study was to systematically review and perform a meta-analysis of the ability of cardiac computed tomography angiography (CCTA) to predict future cardiovascular events and death. The diagnostic accuracy of CCTA is well reported. The prognostic value of CCTA has been described in several studies, but many were underpowered. Pooling outcomes increases the power to predict rare events. We searched multiple databases for longitudinal studies of CCTA with at least 3 months follow-up of symptomatic patients with suspected coronary artery disease (CAD) reporting major adverse cardiovascular events (MACE), consisting of death, myocardial infarction (MI), and revascularization. Annualized event rates were pooled using a bivariate mixed-effects binomial regression model to calculate summary likelihood ratios and receiver-operating characteristic curves. Eighteen studies evaluated 9,592 patients with a median follow-up of 20 months. The pooled annualized event rate for obstructive (any vessel with >50% luminal stenosis) versus normal CCTA was 8.8% versus 0.17% per year for MACE (p < 0.05) and 3.2% versus 0.15% for death or MI (p < 0.05). The pooled negative likelihood ratio for MACE after normal CCTA findings was 0.008 (95% confidence interval [CI]: 0.0004 to 0.17, p < 0.001), the positive likelihood ratio was 1.70 (95% CI: 1.42 to 2.02, p < 0.001), sensitivity was 0.99 (95% CI: 0.93 to 1.00, p < 0.001), and specificity was 0.41 (95% CI: 0.31 to 0.52, p < 0.001). Stratifying by no CAD, nonobstructive CAD (worst stenosis <50%), or obstructive CAD, there were incrementally increasing adverse events. Adverse cardiovascular events among patients with normal findings on CCTA are rare. There are incrementally increasing future MACE with increasing CAD by CCTA. Copyright © 2011 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                EBioMedicine
                EBioMedicine
                EBioMedicine
                Elsevier
                2352-3964
                23 December 2018
                January 2019
                23 December 2018
                : 39
                : 109-117
                Affiliations
                [a ]Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
                [b ]HorAIzon BV, Rotterdam, the Netherlands
                [c ]Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
                [d ]Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
                [e ]Icahn School of Medicine, Mount Sinai Hospital, NY, New York, United States
                [f ]Dalio Institute for Cardiovascular Imaging, Weill-Cornell Medical College, NY, New York, United States
                [g ]Department of Medicine and Radiology, University of British Columbia, Vancouver, Canada
                [h ]Department of Bioengineering, Stanford University, Stanford, CA, United States
                [i ]Wallenberg Laboratory, University of Gothenberg, Gothenberg, Sweden
                [j ]Department of Internal Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
                [k ]Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
                [l ]DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Munich, Germany
                Author notes
                [* ]Corresponding author. p.knaapen@ 123456vumc.nl
                [1]

                MJB and EL contributed equally to the manuscript.

                Article
                S2352-3964(18)30610-8
                10.1016/j.ebiom.2018.12.033
                6355456
                30587458
                © 2018 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                Categories
                Research paper

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