Abstract
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 modeling where AVA saturates with increasing Q, contradicting the assumption of linear Q-AVA relation currently predominant in the literature. The objective of this study is the development of a new method for the quantification of AS. In 141 patient undergoing transesophageal echocardiography, AVA was obtained frame-by-frame over an entire systole from 3-D echocardiograms using multiplanar reconstruction. Q was retrieved by Doppler- and 3-D 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 modeling was used to look for predictors of rehospitalization-free survival. Compared with 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 receiver operating characteristic (ROC)-analysis revealed a rAVS value of 1.51 as the best cut-off for distinguishing severe from nonsevere AS. Cox proportional hazard modeling showed its value as an independent predictor of rehospitalization-free survival. We present a new method for the quantification of AS that could help simplify the diagnosis of patients with low-flow, low-gradient AS.NEW & NOTEWORTHY Aortic stenosis (AS) quantification relies on transvalvular gradients or aortic valve opening area (AVA). We present a new quantification method for AS based on new modeling of the aortic valve opening behavior. We fitted a sigmoid relation between flow rate and AVA and retrieved the "relative aortic valve stiffness" (rAVS). We could demonstrate its superior association with clinical endpoints compared with previous models in AS and provide a simple tool for its calculation using echocardiography.