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In Journal of the American College of Cardiology ; h5-index 167.0

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).

Sánchez-Cabo Fátima, Rossello Xavier, Fuster Valentín, Benito Fernando, Manzano Jose Pedro, Silla Juan Carlos, Fernández-Alvira Juan Miguel, Oliva Belén, Fernández-Friera Leticia, López-Melgar Beatriz, Mendiguren José María, Sanz Javier, Ordovás Jose María, Andrés Vicente, Fernández-Ortiz Antonio, Bueno Héctor, Ibáñez Borja, García-Ruiz José Manuel, Lara-Pezzi Enrique


ASCVD, atherosclerosis, cardiovascular risk scores, machine-learning, subclinical