Machine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals

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.
The 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. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Matthew Budoff, MD, served as Guest Associate Editor for this paper. P.K. Shah, MD, served as Guest Editor-in-Chief for this paper. The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors' institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC author instructions page.