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dc.contributor.authorSanchez-Cabo, Fatima 
dc.contributor.authorRossello, Xavier 
dc.contributor.authorFuster, Valentin 
dc.contributor.authorde Benito, Fernando M 
dc.contributor.authorManzano, Jose Pedro
dc.contributor.authorSilla-Castro, Juan Carlos 
dc.contributor.authorFernandez-Alvira, Juan Miguel 
dc.contributor.authorOliva, Belen 
dc.contributor.authorFernandez-Friera, Leticia 
dc.contributor.authorLopez-Melgar, Beatriz 
dc.contributor.authorMendiguren, Jose M
dc.contributor.authorSanz, Javier 
dc.contributor.authorOrdovas, Jose M 
dc.contributor.authorAndres, Vicente 
dc.contributor.authorFernandez-Ortiz, Antonio 
dc.contributor.authorBueno, Héctor 
dc.contributor.authorIbanez, Borja 
dc.contributor.authorGarcia-Ruiz, Jose M 
dc.contributor.authorLara-Pezzi, Enrique 
dc.date.accessioned2020-10-28T09:54:51Z
dc.date.available2020-10-28T09:54:51Z
dc.date.issued2020-10-06
dc.identifier.citationJ Am Coll Cardiol. 2020; 76(14):1674-1685es_ES
dc.identifier.issn0735-1097
dc.identifier.urihttp://hdl.handle.net/20.500.12105/11236
dc.description.abstractClinical practice guidelines recommend assessment of subclinical atherosclerosis using imaging techniques in individuals with intermediate atherosclerotic cardiovascular risk according to standard risk prediction tools. The purpose of this study was to develop a machine-learning model based on routine, quantitative, and easily measured variables to predict the presence and extent of subclinical atherosclerosis (SA) in young, asymptomatic individuals. The risk of having SA estimated by this model could be used to refine risk estimation and optimize the use of imaging for risk assessment. The Elastic Net (EN) model was built to predict SA extent, defined by a combined metric of the coronary artery calcification score and 2-dimensional vascular ultrasound. The performance of the model for the prediction of SA extension and progression was compared with traditional risk scores of cardiovascular disease (CVD). An external independent cohort was used for validation. EN-PESA (Progression of Early Subclinical Atherosclerosis) yielded a c-statistic of 0.88 for the prediction of generalized subclinical atherosclerosis. Moreover, EN-PESA was found to be a predictor of 3-year progression independent of the baseline extension of SA. EN-PESA assigned an intermediate to high cardiovascular risk to 40.1% (n = 1,411) of the PESA individuals, a significantly larger number than atherosclerotic CVD (n = 267) and SCORE (Systematic Coronary Risk Evaluation) (n = 507) risk scores. In total, 86.8% of the individuals with an increased risk based on EN-PESA presented signs of SA at baseline or a significant progression of SA over 3 years. The EN-PESA model uses age, systolic blood pressure, and 10 commonly used blood/urine tests and dietary intake values to identify young, asymptomatic individuals with an increased risk of CVD based on their extension and progression of SA. These individuals are likely to benefit from imaging tests or pharmacological treatment. (Progression of Early Subclinical Atherosclerosis [PESA]; NCT01410318).es_ES
dc.description.sponsorshipThe PESA study is cofunded equally by the Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain, and Banco Santander, Madrid, Spain. The study also receives funding from the Instituto de Salud Carlos III (PI15/02019) and the European Regional Development Fund “Una manera de hacer Europa.” The CNIC is supported by the Ministerio de Ciencia, Innovacion y Universidades and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). Dr. Bueno has received research funding from the Instituto de Salud Carlos III, Spain (PIE16/00021 and PI17/01799), AstraZeneca, Bristol-Myers Squibb and Novartis; has received consulting fees from AstraZeneca, Bayer, Bristol-Myers Squibb-Pfizer, and Novartis; and has received speaker fees or support for attending scientific meetings from Amgen, AstraZeneca, Bayer, Bristol-Myers Squibb-Pfizer, Novartis, and MEDSCAPE-the heart.org.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleMachine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals.es_ES
dc.typeArtículoes_ES
dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.identifier.pubmedID33004133es_ES
dc.format.volume76es_ES
dc.format.number14es_ES
dc.format.page1674-1685es_ES
dc.identifier.doi10.1016/j.jacc.2020.08.017es_ES
dc.contributor.funderCentro Nacional de Investigaciones Cardiovasculares (CNIC)es_ES
dc.contributor.funderInstituto de Salud Carlos III - ISCIIIes_ES
dc.contributor.funderEuropean Regional Development Fund (ERDF/FEDER)es_ES
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es_ES
dc.contributor.funderFundación ProCNICes_ES
dc.contributor.funderBanco Santanderes_ES
dc.contributor.funderFundación AstraZenecaes_ES
dc.contributor.funderBristol-Myers Squibbes_ES
dc.contributor.funderNovartis Farmaceutica S.A.es_ES
dc.description.peerreviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.jacc.2020.08.017es_ES
dc.identifier.journalJournal of the American College of Cardiologyes_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Laboratorio Traslacional para la Imagen y Terapia Cardiovasculares_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Imagen Cardiovascular y Estudios Poblacionaleses_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Investigación Cardiovascular Traslacional Multidisciplinariaes_ES
dc.repisalud.orgCNICCNIC::Unidades técnicas::Bioinformáticaes_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Regulación Molecular de la Insuficiencia Cardiacaes_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Fisiopatología Cardiovascular Molecular y Genéticaes_ES
dc.repisalud.institucionCNICes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/SEV-2015-0505es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PI15/02019es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PIE16/00021es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PI17/01799es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
This item is licensed under a: Attribution-NonCommercial-NoDerivatives 4.0 Internacional