2026-06-26T12:55:38Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/112362024-11-29T00:08:57Zcom_20.500.12105_19604com_20.500.12105_2051com_20.500.12105_15322col_20.500.12105_19605col_20.500.12105_16967col_20.500.12105_16977col_20.500.12105_19607
Repisalud author Sanchez-Cabo, Fatima author Rossello, Xavier author Fuster, Valentin author de Benito, Fernando M author Manzano, Jose Pedro author Silla-Castro, Juan Carlos author Fernandez-Alvira, Juan Miguel author Oliva, Belen author Fernandez-Friera, Leticia author Lopez-Melgar, Beatriz author Mendiguren, Jose M author Sanz, Javier author Ordovas, Jose M author Andres, Vicente author Fernandez-Ortiz, Antonio author Bueno, Hector author Ibáñez, Borja author Garcia-Ruiz, Jose M author Lara-Pezzi, Enrique funder Centro Nacional de Investigaciones Cardiovasculares Carlos III (España) funder Instituto de Salud Carlos III funder Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF) funder Ministerio de Ciencia, Innovación y Universidades (España) funder Fundación ProCNIC funder Banco Santander funder Fundación AstraZeneca funder Bristol-Myers Squibb funder Novartis 2020-10-28T09:54:51Z 2020-10-28T09:54:51Z 2020-10-06 J Am Coll Cardiol. 2020; 76(14):1674-1685 10.1016/j.jacc.2020.08.017 0735-1097 Journal of the American College of Cardiology http://hdl.handle.net/20.500.13003/17234 33004133 L2007891822 2-s2.0-85091231710 http://hdl.handle.net/20.500.12105/11236 579523600008 BACKGROUND: Clinical practice guidelines recommend assessment of subclinical atherosclerosis using imaging techniques in individuals with intermediate atherosclerotic cardiovascular risk according to standard risk prediction tools. OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. eng ASCVD Atherosclerosis Cardiovascular risk scores Machine-learning Subclinical Machine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals research article 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 URL https://repisalud.isciii.es/bitstreams/3719461e-95ea-4e06-803c-fbee4850a44c/download File MD5 4edf6a4da152851176b9cbaf6a44322b 665302 application/pdf MachineLearningImprovesCardiovascular_2020.pdf URL https://repisalud.isciii.es/bitstreams/b3342333-0233-441e-ad20-12b7dbec1245/download File MD5 29cb264660446d31fc35831fc575e264 50298 text/plain MachineLearningImprovesCardiovascular_2020.pdf.txt