Rationale
Machine learning may be useful to characterize cardiovascular risk, predict outcomes and identify biomarkers in population studies.
Objective
To test the ability of random survival forests (RF), a machine learning technique, to predict six cardiovascular outcomes in comparison to standard cardiovascular risk scores.
Methods and Results
We included participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Baseline measurements were used to predict cardiovascular outcomes over 12 years of follow-up. MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of CV disease. All 6814 participants from MESA, aged 45 to 84 years, from 4 ethnicities, and 6 centers across USA were included. 735 variables from imaging and non-invasive tests, questionnaires and biomarker panels were obtained. We used the RF technique to identify the top 20 predictors of each outcome. Imaging, electrocardiography and serum biomarkers featured heavily on the top-20 lists as opposed to traditional CV risk factors. Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary artery calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function, and cardiac troponin-T were among the top predictors for incident heart failure. Creatinine, age and ankle brachial index were among the top predictors of atrial fibrillation. Tissue necrosis factor-α and interleukin-2 soluble receptors, and N-terminal pro-Brain Natriuretic Peptide levels were important across all outcomes. The RF technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 10–25%).
Conclusions
Machine learning in conjunction with deep phenotyping improve prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. These methods may lead to greater insights regarding subclinical disease markers without apriori assumptions of causality.
Clinical Trial Registration
Multi-Ethnic Study of Atherosclerosis (MESA) http://mesa-nhlbi.org/.
ClinicalTrials.gov Identifier
NCT00005487
Simultaneously independent control of phase, amplitude, and polarization is pivotal yet challenging for manipulating electromagnetic waves by transmissive metasurfaces. Huygens' metasurface affords a high‐efficiency recipe primarily by engineering phase‐only meta‐atoms, restricting itself from realizing unprecedentedly complex functions of the transmission beam. Here, a 3D chirality‐assisted metasurface concept relying on integrated magnetoelectric meta‐atoms is proposed. It empowers the completely decoupled and arbitrary control of phase and amplitude at large incident angles and arbitrary polarizations. This strategy thus facilitates very sophisticated beam manipulations at close‐to‐unity cross‐polarized efficiency via trilayer integrated resonators with mutual twist. The prescribed phase coverage can be determined by geometrical footprints of the unit cell, while the global azimuthal twist unlocks the capability of tuning amplitudes without affecting the phase. The concept and significance of it are validated to implement several proof‐of‐prototype demanding functionalities by thin metasurfaces of λo/12, which generate self‐accelerating diffraction‐free Airy beams, lateral and axial dual focusing, and even specific multiplexed beam shaping, respectively. This finding opens up an alternative way in very fine control of light with minimalist complexity and advanced performance. It can stimulate novel and high‐performance versatile photonic metadevices, thanks to the fully independent control of phase, amplitude, and polarization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.