Original Investigation
Machine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals

https://doi.org/10.1016/j.jacc.2020.08.017Get rights and content
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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)

Key Words

ASCVD
atherosclerosis
cardiovascular risk scores
machine-learning
subclinical

Abbreviations and Acronyms

ASCVD
atherosclerotic cardiovascular disease
AUC
area under the curve
CACS
coronary artery calcium score
CT
computed tomography
EN
elastic net
GD
general disease
ML
machine learning
ND
no disease
SA
subclinical atherosclerosis
SCORE
Systematic Coronary Risk Evaluation

Cited by (0)

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.

Listen to this manuscript's audio summary by Editor-in-Chief Dr. Valentin Fuster on JACC.org.

Drs. Sánchez-Cabo and Rossello contributed equally to this work.

Drs. Fuster and Lara-Pezzi are co-corresponding authors.