DOI: 10.1161/circ.148.suppl_1.14595 ISSN: 0009-7322

Abstract 14595: Leveraging Deep Learning to Elucidate Subclinical Aortic Stenosis Risk

Shinwan Kany, Joel Rämö, Cody Hou, Sean J Jurgens, Victor Nauffal, Jonathan Cunningham, Emily Lau, Atul Butte, Jennifer E Ho, Jeffrey Olgin, Sammy Elmariah, Mark E Lindsay, Patrick T Ellinor, James Pirruccello
  • Physiology (medical)
  • Cardiology and Cardiovascular Medicine

Background and Aims: Aortic stenosis (AS) is associated with a high global burden of morbidity. Current guidelines do not fully define normal aortic valve (AV) function or mild AS. We aimed to study normal variation in AV function and address current gaps in knowledge.

Methods: We developed a deep learning model to study velocity-encoded magnetic resonance imaging (MRI) in 47,223 UK Biobank participants. We calculated aortic valve area (AVA), peak velocity (PV), and mean gradient (MG).

Results: In healthy individuals, we found an annual decrement of 0.03 cm2 in AVA. Normal variation in AV function was predictive of future AV surgery (HR 2.4 per SD increase in PV, P2.6E-110), heart failure (HR 1.23 per SD, P=2.4E-07), and coronary disease (HR 1.23 per SD, P=2.0E-14). Risk was still elevated when excluding participants with moderate-to-severe AS. We determined thresholds for abnormal AV function as being >95% for all age groups in 31,909 healthy people. This established the boundary between normal variation and mild AS as any of: PV >1.65m/s, MG >4.9mmHg, and AVA <2.1cm2 (men) or <1.7cms2 (women). A total of 3,288 participants (7.0%) met the definition of mild AS which was associated with high risk for AV surgery (HR 57.2, P=4.4E-29). Over a mean 3.1±2.0 years of follow-up, the incidence of AV surgery was 0.02% in those with normal AV function and nearly 20% among participants with MG ≥10mmHg. Greater levels of ApoB, triglycerides, Lp(a), and inflammatory markers were associated with higher AV gradients.

Conclusion: In this study, we report the largest analysis of AV function and cardiovascular disease in the general population with MRI using deep learning. We propose thresholds to define the lower bounds of mild AS and demonstrate a step change in cardiovascular risk between normal AV function and mild AS. Atherogenic and inflammatory biomarkers were associated with increased AV gradients. These findings may inform earlier surveillance and efforts to prevent complications of AS.

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