Artificial intelligence-enabled echocardiography as a surrogate for multi-modality aortic stenosis imaging: post-hoc analysis of a clinical trial
Oikonomou, E. K.; Craig, N. J.; Holste, G. I.; Vasisht Shankar, S.; White, A.; Mahendran, M.; Newby, D. E.; Dweck, M. R.; Khera, R.
Show abstract
BackgroundAccurate aortic stenosis (AS) phenotyping requires access to multimodality imaging which has limited availability. The Digital Aortic Stenosis Severity Index (DASSi), an AI biomarker of AS-related remodeling on 2D echocardiography, predicts AS progression independent of Doppler measurements. Whether DASSi-enhanced echocardiography provides a scalable alternative to multimodality AS imaging remains unknown. We sought to evaluate the ability of DASSi to define personalized AS progression profiles and validate its performance against multimodality imaging features of functional, structural, and biological disease severity. MethodsIn the SALTIRE-2 trial of participants with mild-or-moderate AS, we performed blinded DASSi measurements (probability of severe AS, 0-to-1) on baseline transthoracic echocardiograms. We evaluated the association between baseline DASSi and (i) disease severity by hemodynamic (peak aortic valve velocity [AV-Vmax]), structural (CT-derived aortic valve calcium score [AVCS]) and biological features ([18F]sodium fluoride [NaF] uptake on Positron Emission Tomography-CT), (ii) disease progression (change in AV-Vmax and AVCS), and (iii) incident aortic valve replacement (AVR). We used generalized linear mixed, or Cox models adjusted for risk factors and aortic valve area, as appropriate. ResultsWe analyzed 134 participants (72 [IQR: 69-78] years, 27 [20.1%] women) with a mean baseline DASSi of 0.51 (standard deviation [SD]: 0.19). DASSi was independently associated with disease severity: each SD increase was associated with higher AV-Vmax (+0.21 [95%CI: 0.12-0.30] m/sec), AVCS (+284 [95%CI: 101-467] AU) and [18F]NaF TBRmax (+0.17 [95%CI: 0.04-0.31]). Higher DASSi was also associated with disease progression by Doppler (AV-Vmax) and CT (AVCS) at 24 months (pinteraction for DASSi (x) time<0.001), and future AVR (75 events over 5.5 [IQR: 2.4-7.2] years, adj.HR 1.47 [95%CI: 1.12-1.94] per SD). ConclusionsDASSi is associated with functional, structural and biological features of AS severity as well as disease progression and outcomes. DASSi-enhanced echocardiography provides a readily accessible alternative to multimodality imaging of AS which has potential value both in clinical practice and as a clinical trial biomarker.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Transcatheter or Surgical Aortic Valve Replacement in Patients with Severe Aortic Stenosis and Small Aortic Annulus: A Randomized Clinical Trial 95%
- High Throughput Deep Learning Detection of Mitral Regurgitation 95%
- Comparison of Long-term Outcomes of Early Surgery Versus Conventional Treatment for Asymptomatic Severe Mitral Regurgitation: A Propensity Analysis 95%
Similar papers in this journal
- Development and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms 95%
- Natural Language Processing to Identify Racial and Ethnic Disparities in Aortic Stenosis 94%
- International Evaluation Of An Artificial Intelligence-Powered Ecg Model Detecting Occlusion Myocardial Infarction 93%
Similar papers in this journal
- Development and validation of imaging-free myocardial fibrosis prediction models, association with outcomes, and sample size estimation for phase 3 trials 94%
- Prognostic Value of Patient-Reported Outcomes in Predicting Long-term Mortality after Transcatheter Aortic Valve Replacement (TAVR) 94%
- The HeartMagic prospective observational study protocol - characterizing subtypes of heart failure with preserved ejection fraction 93%
Similar papers in this journal
- Sex-Related Outcomes of Transcatheter Aortic Valve Implantation with Self-Expanding or Balloon-Expandable Valves: Insights from the OPERA-TAVI Registry 95%
- A Multicenter Evaluation of the Impact of Procedural and Pharmacological Interventions on Deep Learning-based Electrocardiographic Markers of Hypertrophic Cardiomyopathy 94%
- Multiple Biomarkers to Predict Major Adverse Cardiovascular Events in Patients With Coronary Chronic Total Occlusions 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.