| Title: | A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression |
| Journal: | JAMA Cardiology |
| Published: | 6 Apr 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38581644/ |
| DOI: | https://doi.org/10.1001/jamacardio.2024.0595 |
Publication 9952
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Abstract
Importance: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization.</p>
Objective: To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression.</p>
Design, Setting, and Participants: This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024.</p>
Exposure: DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos.</p>
Main Outcomes and Measures: Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR).</p>
Results: A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors.</p>
Conclusions and Relevance: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.</p>
17 Authors
- Evangelos K. Oikonomou
- Gregory Holste
- Neal Yuan
- Andreas Coppi
- Robert L. McNamara
- Norrisa A. Haynes
- Amit N. Vora
- Eric J. Velazquez
- Fan Li
- Venu Menon
- Samir R. Kapadia
- Thomas M. Gill
- Girish N. Nadkarni
- Harlan M. Krumholz
- Zhangyang Wang
- David Ouyang
- Rohan Khera
1 Application
| Application ID | Title |
|---|---|
| 71033 | A framework to assess causal effects in observational data through deep digital phenotyping and creating digital twins |
Enabling scientific discoveries that improve human health