Artificial Intelligence-Enabled Echocardiography as a Surrogate for Multimodality Aortic Stenosis Imaging: Post Hoc Analysis of a Clinical Trial

BACKGROUND: Accurate aortic stenosis (AS) phenotyping requires multimodality imaging which has limited availability. The digital aortic stenosis severity index (DASSi), an artificial intelligence biomarker of AS-related remodeling on single-view 2-dimensional echocardiography, predicts AS progression independent of Doppler measurements. We sought to evaluate the ability of DASSi to define personalized AS progression profiles and to validate its performance as a scalable alternative to multimodality imaging features of functional, structural, and biological AS severity. METHODS: F]sodium fluoride uptake on positron emission tomography-computed tomography); (2) longitudinal disease progression (absolute change in peak aortic valve velocity and aortic valve calcium score); and (3) incident aortic valve replacement. We used generalized linear mixed or Cox models adjusted for risk factors and aortic valve area. RESULTS: interaction for DASSi × time<0.001), and future aortic valve replacement (75 events over 5.5 [interquartile range, 2.4-7.2] years, adjusted hazard ratio, 1.42 [95% CI, 1.11-1.84] per SD). CONCLUSIONS: DASSi is associated with functional, structural and biological features of AS severity and predicts disease progression and adverse outcomes. DASSi-enhanced echocardiography may provide an accessible alternative to multimodality AS imaging and serve as a predictive enrichment biomarker in clinical trials. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02132026.