Why the study?
Current aortic stenosis quantification relies on flow-dependent transvalvular gradients or opening area assuming a linear relation, prompting development of a new quantification method where opening area saturates with increasing flow.
Does a sigmoid Q-AVA relation model calculating relative aortic valve stiffness (rAVS) better predict measured AVA and rehospitalization-free survival compared to a linear model in patients undergoing transesophageal echocardiography?
Does a sigmoid Q-AVA relation model calculating relative aortic valve stiffness (rAVS) better predict measured AVA and rehospitalization-free survival compared to a linear model in patients undergoing transesophageal echocardiography?
A novel relative aortic valve stiffness (rAVS) parameter based on a sigmoid flow-area relationship improves aortic stenosis quantification and independently predicts rehospitalization-free survival, potentially simplifying diagnosis of low-flow, low-gradient aortic stenosis.
Background: Aortic stenosis (AS) quantification relies on transvalvular gradients or aortic valve opening area (AVA), two measures that inherently depend on the instantaneous transvalvular flow rate (Q). We present a new quantification method for AS based on new modelling where AVA saturates with increasing Q, contradicting the assumption of linear Q-AVA-relation currently predominant in the literature. Objective: Development of a new method for the quantification of AS. Methods: In 141 patient undergoing transesophageal echocardiography, AVA was obtained frame-by-frame over an entire systole from 3D echocardiograms using multiplanar reconstruction. Q was retrieved by Doppler- and 3D echocardiography of the left ventricular outflow tract. A sigmoid Q-AVA relation was fitted with a machine learning algorithm. The measured and the predicted AVA were compared between a parametrized linear and sigmoid Q-AVA-relation model. A relative aortic valve stiffness (rAVS) was calculated, and an isostiffness nomogram was constructed. Cox proportional hazard modelling was used to look for predictors of rehospitalization-free survival Results: Compared to the linear model, the sigmoid model consistently better predicted measured AVA in all AS severity groups. Within severity groups, rAVS remained robust and constant, a property not shared by the linear model. A ROC-Analysis revealed a rAVS-value of 1.51 as the best cut-off for distinguishing severe from non-severe AS. Cox proportional hazard modelling showed its value as an independent predictor of rehospitalization-free survival. Conclusions: We present a new method for the quantification of AS that could help simplify the diagnosis of patients with low-flow, low-gradient AS.
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Buffle et al. (2026) studied this question.
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