A new sigmoid Q-AVA model quantifies aortic stenosis better than linear models, with relative aortic valve stiffness (rAVS) >1.51 independently predicting rehospitalization-free survival.
Does a sigmoid Q-AVA relation model calculating relative aortic valve stiffness (rAVS) improve the quantification of aortic stenosis compared to a linear model?
A novel relative aortic valve stiffness parameter based on a sigmoid flow-area relationship improves aortic stenosis quantification and predicts rehospitalization-free survival, potentially simplifying diagnosis in low-flow, low-gradient AS.
Absolute Event Rate: 0% vs 0%
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.
Buffle et al. (Fri,) reported a other. A new sigmoid Q-AVA model quantifies aortic stenosis better than linear models, with relative aortic valve stiffness (rAVS) >1.51 independently predicting rehospitalization-free survival.