Future cardiovascular events prediction from invasive coronary angiography: A graph representation learning perspective

Future cardiovascular events prediction from invasive coronary angiography: A graph representation learning perspective | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Future cardiovascular events prediction from invasive coronary angiography: A graph representation learning perspective Xiaowu Sun, Theofilos Belmpas, Ortal Senouf, Emmanuel Abbé, Pascal Frossard, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4344029/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Improving risk stratification for coronary artery disease, the leading cause of death worldwide, continues to present a daily challenge in clinical practice, highlighting the urgent need for innovative approaches to early prediction of future cardiovascular events. In this work, we propose AngioGraphCAD, a deep learning based framework that employs graph neural networks to leverage geometry features and a masked attention to fuse geometry features from multiple coronary stenoses for future events prediction at both lesion and patient level from invasive coronary angiography. AngioGraphCAD is evaluated across two clinical cohorts at the lesion level and one datatset at the patient level, achieving superior performance compared to clinical measures. This is the first study that highlights the importance of geometry information in advancing future events prediction from invasive coronary angiography. Given the significance of the clinical question and the innovative nature of the proposed methodology, this work could pave the way for the development of an AI framework fueled by patient-specific data in cardiology, potentially revolutionizing personalized decision-making in managing coronary artery diseases for individual patients. Physical sciences/Engineering/Biomedical engineering Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases/Vascular diseases/Coronary artery disease and stable angina Physical sciences/Mathematics and computing/Computer science Graph neural networks masked attention invasive coronary angiography coronary geometry clinical events prediction Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplementary.pdf Supplementary experiment results Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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