A digital biomarker for aortic stenosis development and progression using deep learning for two-dimensional echocardiography

Abstract

ABSTRACTBackgroundThe timely identification of aortic stenosis (AS) and disease stage that merits intervention requires frequent echocardiography. However, there is no strategy to personalize the frequency of monitoring neededObjectivesTo explore the role of AI-enhanced two- dimensional-echocardiography in stratifying the risk of AS development and progression.MethodsThis was a multicenter study of 12,609 patients without severe AS undergoing transthoracic echocardiography in New England (n=8,798, 71 [IQR 60-80] years, n=4250 [48.3%] women) & Cedars-Sinai, California (n=3,811, 67 [IQR 54-78] years, 1688 [44.3%] women). We examined the association of an AI-derived Digital AS Severity index (DASSi; range 0-1) with i) longitudinal changes in peak aortic valve velocity (AV Vmax; m/sec/year), and ii) all-cause mortality or aortic valve replacement (AVR) incidence, using multivariable generalized linear and Cox regression models, respectively, adjusted for age, sex, race/ethnicity, and baseline echocardiographic measurements.ResultsThe median follow-up was 4.1 [IQR 2.3-5.4] (New England) and 3.8 [IQR 3.1-4.4] years (Cedars-Sinai). Within each cohort, higher baseline DASSi was independently associated with faster progression rates in AV Vmax(for each 0.1 increment: +0.033 m/s/year [95%CI: 0.028-0.038,p<0.001], n=5,483 & +0.082 m/s/year [95%CI 0.053-0.111],p<0.001, n=1,292, respectively). Furthermore, there was a dose-response association between higher baseline DASSi and the incidence of death/AVR (adj. HR 1.10 [95%CI: 1.08-1.13],p<0.001 & 1.14 [95%CI 1.09-1.20],p<0.001, respectively). Results were consistent across severity strata, including those without hemodynamically significant AS at baseline.ConclusionsAn AI model built for two-dimensional-echocardiography can stratify the risk of AS progression, with implications for longitudinal monitoring in the community.CONDENSED ABSTRACTIn this multi-center cohort study of 12,609 patients with no, mild or moderate aortic stenosis (AS), we explored whether a deep learning-enhanced method that relies on single-view, two- dimensional videos without Doppler can stratify the risk of AS development and progression. Video-based phenotyping based on the digital AS severity index (DASSi) identified patient subgroups with distinct echocardiographic and clinical trajectories independent of the baseline AS stage and profile. The results were consistent across two geographically distinct cohorts and key clinical subgroups, supporting the use of deep learning-enhanced two-dimensional echocardiography as a supplement to the traditional assessment of AS in the community.