Fátima Sánchez-Cabo1, Xavier Rossello2, Valentín Fuster3, Fernando Benito4, Jose Pedro Manzano4, Juan Carlos Silla4, Juan Miguel Fernández-Alvira4, Belén Oliva4, Leticia Fernández-Friera5, Beatriz López-Melgar6, José María Mendiguren7, Javier Sanz8, Jose María Ordovás9, Vicente Andrés10, Antonio Fernández-Ortiz11, Héctor Bueno12, Borja Ibáñez13, José Manuel García-Ruiz14, Enrique Lara-Pezzi15. 1. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain. Electronic address: https://twitter.com/fsanchezcabo. 2. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; CIBER de enfermedades CardioVasculares (CIBERCV), Spain; Hospital Universitari Son Espases & Health Research Institute of the Balearic Islands (IdISBa), Mallorca, Spain. Electronic address: https://twitter.com/RosselloXavier. 3. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; The Zena and Michael A. Wiener Cardiovascular Institute/Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, New York. Electronic address: vfuster@cnic.es. 4. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain. 5. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; CIBER de enfermedades CardioVasculares (CIBERCV), Spain; HM Hospitales-Centro Integral de Enfermedades Cardiovasculares HM CIEC, Madrid, Spain. 6. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; HM Hospitales-Centro Integral de Enfermedades Cardiovasculares HM CIEC, Madrid, Spain. 7. Banco de Santander, Madrid, Spain. 8. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; The Zena and Michael A. Wiener Cardiovascular Institute/Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, New York. 9. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; IMDEA Food Institute, CEI UAM + CSIC, Madrid, Spain; U.S. Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, Massachusetts. 10. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; CIBER de enfermedades CardioVasculares (CIBERCV), Spain. 11. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; CIBER de enfermedades CardioVasculares (CIBERCV), Spain; Hospital Clínico San Carlos, Madrid, Spain. 12. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; Hospital Universitario 12 de Octubre, Madrid, Spain. 13. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; CIBER de enfermedades CardioVasculares (CIBERCV), Spain; IIS-Fundación Jiménez Díaz Hospital, Madrid, Spain. 14. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; CIBER de enfermedades CardioVasculares (CIBERCV), Spain; Hospital Universitario Central de Oviedo, Asturias, Spain. 15. Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; CIBER de enfermedades CardioVasculares (CIBERCV), Spain. Electronic address: elara@cnic.es.
Abstract
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. (Progression of Early Subclinical Atherosclerosis [PESA]; NCT01410318).
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. (Progression of Early Subclinical Atherosclerosis [PESA]; NCT01410318).
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