| Title: | New Threshold for Defining Mild Aortic Stenosis Derived From Velocity-Encoded MRI in 60,000 Individuals |
| Journal: | Journal of the American College of Cardiology |
| Published: | 1 Apr 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40175013/ |
| DOI: | https://doi.org/10.1016/j.jacc.2025.01.035 |
Publication 14851
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Abstract
BACKGROUND: Mild aortic stenosis (AS) is associated with adverse outcomes but is incompletely defined.</p>
OBJECTIVES: The purpose of this study was to examine the epidemiology of AV function measured without clinical indications.</p>
METHODS: We developed a deep learning model to measure aortic valve (AV) area, peak velocity, and mean gradient in velocity-encoded cardiac magnetic resonance imaging in 62,902 UK Biobank participants. Study findings were externally validated in NEDA (National Echo Database Australia), a clinical cohort of 365,870 people.</p>
RESULTS: From measuring reference ranges of AV function in a healthy subcohort (n = 41,859), we observed a natural boundary between normal and abnormal AV hemodynamics (>95th percentile) that we refer to as "mild ASproposed": peak velocity >1.65 m/s, mean gradient >4.9 mm Hg, or aortic valve area <2.1 cm2 (men) or <1.7 cm2 (women). In the full cohort, 3,676 (5.8%) participants met these novel criteria; the HR for a subsequent AV replacement for each severity category was 31.7 (mild ASproposed), 522.4 (moderate AS), and 3,057.4 (severe AS), all P < 0.001. Over a mean 3.9 years of follow-up, those with mild ASproposed also had a higher risk of atrial fibrillation (110 events; HR: 1.86; P = 1.4 × 10-9) and heart failure (70 events; HR: 2.37; P = 5.9 × 10-11) compared with those without AS. In NEDA, the 101,335 participants with mild ASproposed identified with echocardiography using the same cardiac magnetic resonance imaging-defined criteria had increased all-cause mortality (HR: 1.25; 95% CI: 1.24-1.27).</p>
CONCLUSIONS: We report a large-scale study of AV hemodynamics and identify a population threshold between normal and abnormal AV function. Mild AS, as defined by the proposed criteria, was linked to adverse outcomes in the UK Biobank and in NEDA.</p>
12 Keywords
- Aged
- Aortic Valve
- Aortic Valve Stenosis
- Deep Learning
- Female
- Humans
- Magnetic Resonance Imaging
- Magnetic Resonance Imaging, Cine
- Male
- Middle Aged
- Severity of Illness Index
- United Kingdom
18 Authors
- Shinwan Kany
- Joel T Rämö
- David Playford
- Geoff Strange
- Cody Hou
- Sean J Jurgens
- Victor Nauffal
- Jonathan W Cunningham
- Emily S Lau
- Atul J Butte
- Jennifer E Ho
- Jeffrey E Olgin
- Sammy Elmariah
- Mark E Lindsay
- Yih-Kai Chan
- Simon Stewart
- Patrick T Ellinor
- James P Pirruccello
1 Application
| Application ID | Title |
|---|---|
| 41664 | Machine learning from complex and genetic data |
Enabling scientific discoveries that improve human health