New estimate of valvuloarterial impedance in aortic valve stenosis: A cardiac magnetic resonance study.


PURPOSE Valvuloarterial impedance (ZVA ), estimating left ventricle (LV) afterload, has been proposed in transthoracic echocardiography (TTE) as a predictor of mortality in aortic valve stenosis (AVS). However, its calculation differs from arterial characteristic impedance (ZC ). Our aim was to apply the concept of ZC calculation to estimate ZVA from MR with carotid tonometry and to evaluate these indices through their associations with symptoms, LV diastolic function and aortic stiffness. MATERIALS AND METHODS In 40 patients with AVS (76 ± 13 years), ZVA-TI derived from velocity time integral and E/Ea were estimated by TTE. ZVA-INS , based on ZC formula, calculated as the instantaneous pressure gradient to peak flow ratio and aortic compliance were estimated by using MRI at 1.5 Tesla. RESULTS Both ZVA estimates were higher in symptomatic than asymptomatic patients (707 ± 22 versus 579 ± 53 dyne.s/cm5 , P = 0.031 for ZVA-INS and 4.35 ± 0.16 versus 3.33 ± 0.38 mmHg.m2 /mL, P = 0.018 for ZVA-TI ). Although they were both associated with aortic compliance (r = -0.45; P = 0.006 for ZVA-INS and r = -0.43; P = 0.008 for ZVA-TI ) only ZVA-INS was associated with E/Ea (r = 0.50; P < 0.001). In multivariate analysis to identify determinants of E/Ea, a model including age, mean blood pressure, LV ejection fraction, LV mass, and aortic valve area was performed (R2  = 0.41; P < 0.01). When ZVA-INS was added to the model, its overall significance was higher R2  = 0.56 (P < 0.01) and ZVA-INS and LV mass were the only significant determinants. CONCLUSION ZVA-INS was more strongly associated with diastolic dysfunction than usual parameters quantifying AVS severity. This new ZVA estimate could improve LV afterload evaluation. LEVEL OF EVIDENCE 1 J. Magn. Reson. Imaging 2017;45:795-803.

DOI: 10.1002/jmri.25399

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@article{Soulat2017NewEO, title={New estimate of valvuloarterial impedance in aortic valve stenosis: A cardiac magnetic resonance study.}, author={Gilles Soulat and Nadjia Kachenoura and E. Bollache and L. Perdrix and Benoit Diebold and Valentina Zhygalina and Christian Latr{\'e}mouille and St{\'e}phane Laurent and J Fabiani and {\'E}lie Mousseaux}, journal={Journal of magnetic resonance imaging : JMRI}, year={2017}, volume={45 3}, pages={795-803} }