Deep Learning Model of Diastolic Dysfunction Risk Stratifies the Progression of Early-Stage Aortic Stenosis

The development and progression of aortic stenosis (AS) from aortic valve (AV) sclerosis is highly variable and difficult to predict.

The authors investigated whether a previously validated echocardiography-based deep learning (DL) model assessing diastolic dysfunction (DD) could identify the latent risk associated with the development and progression of AS.

The authors evaluated 898 participants with AV sclerosis from the ARIC (Atherosclerosis Risk In Communities) cohort study and associated the DL-predicted probability of DD with 2 endpoints: 1) the new diagnosis of AS; and 2) the composite of subsequent mortality or AV interventions. Validation was performed in 2 additional cohorts: 1) in 50 patients with mild-to-moderate AS undergoing cardiac magnetic resonance (CMR) imaging and serial echocardiographic assessments; and 2) in 18 patients with AV sclerosis undergoing ¹⁸F-sodium fluoride (NaF) and ¹⁸F-fluorodeoxyglucose positron emission tomography (PET) combined with computed tomography (CT) to assess valvular inflammation and calcification.

In the ARIC cohort, a higher DL-predicted probability of DD was associated with the development of AS (adjusted HR: 3.482 [95% CI: 2.061-5.884]; P < 0.001) and subsequent mortality or AV interventions (adjusted HR: 7.033 [95% CI: 3.036-16.290]; P < 0.001). The multivariable Cox model (incorporating the DL-predicted probability of DD) derived from the ARIC cohort efficiently predicted the progression of AS (C-index: 0.798 [95% CI: 0.648-0.948]) in the CMR cohort. Moreover, the predictions of this multivariable Cox model correlated positively with valvular ¹⁸F-NaF mean standardized uptake values in the PET/CT cohort (r = 0.62; P = 0.008).

Assessment of DD using DL can stratify the latent risk associated with the progression of early-stage AS.