Arterial Wave Reflection and Aortic Valve Stenosis: Diagnostic Challenges and Prognostic Significance

Vasiliki Bikia, Stamatia Zoi Pagoulatou, Georgios Rovas, Nikolaos Stergiopulos
FRONTIERS MEDIA SA, 2022
Article

Résumé

IntroductionArterial wave reflection is an important component of the left ventricular afterload, affecting both pressure and flow to the aorta. The aim of the present study was to evaluate the impact of wave reflection on transvalvular pressure gradients (TPG), a key parameter for the evaluation of aortic valve stenosis (AS), as well as its prognostic significance in patients with AS undergoing a transcatheter aortic valve replacement (TAVR). Materials and MethodsThe study population consisted of 351 patients with AS (mean age 84 +/- 6 years, 43% males) who underwent a complete hemodynamic evaluation before the TAVR. The baseline assessment included right and left heart catheterization, transthoracic echocardiography, and a thorough evaluation of the left ventricular afterload by means of wave separation analysis. The cohort was divided into quartiles according to the transit time of the backward pressure wave (BWTT). Primary endpoint was all-cause mortality at 1 year. ResultsEarly arrival of the backward pressure wave was related to lower cardiac output (Q1: 3.7 +/- 0.9 lt/min vs Q4: 4.4 +/- 1.0 lt/min, p < 0.001) and higher aortic systolic blood pressure (Q1: 132 +/- 26 mmHg vs Q4: 117 +/- 26 mmHg, p < 0.001). TPG was significantly related to the BWTT, patients in the arrival group exhibiting the lowest TPG (mean TPG, Q1: 37.6 +/- 12.7 mmHg vs Q4: 44.8 +/- 14.7 mmHg, p = 0.005) for the same aortic valve area (AVA) (Q1: 0.58 +/- 0.35 cm(2) vs 0.61 +/- 0.22 cm(2), p = 0.303). In multivariate analysis, BWTT remained an independent determinant of mean TPG (beta 0.3, p = 0.002). Moreover, the prevalence of low-flow, low-gradient AS with preserved ejection fraction was higher in patients with early arterial reflection arrival (Q1: 33.3% vs Q4: 14.9%, p = 0.033). Finally, patients with early arrival of the reflected wave (Q1) exhibited higher all-cause mortality at 1 year after the TAVR (unadjusted HR: 2.33, 95% CI: 1.17-4.65, p = 0.016). ConclusionEarly reflected wave arrival to the aortic root is associated with poor prognosis and significant aortic hemodynamic alterations in patients undergoing a TAVR for AS. This is related to a significant decrease in TPG for a given AVA, leading to a possible underestimation of the AS severity.

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Vasiliki Bikia, Stamatia Zoi Pagoulatou, Georgios Rovas, Nikolaos Stergiopulos
FRONTIERS MEDIA SA, 2022
Article

Résumé

Official source

https://infoscience.epfl.ch/record/295508?ln=fr

À propos de ce résultat

Concepts associés (14)

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Publications associées (19)

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Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning

Vasiliki Bikia, Stamatia Zoi Pagoulatou, Georgios Rovas, Nikolaos Stergiopulos

Cardiac and aortic characteristics are crucial for cardiovascular disease detection. However, noninvasive estimation of aortic hemodynamics and cardiac contractility is still challenging. This paper investigated the potential of estimating aortic systolic pressure (aSBP), cardiac output (CO), and end-systolic elastance (E-es) from cuff-pressure and pulse wave velocity (PWV) using regression analysis. The importance of incorporating ejection fraction (EF) as additional input for estimating E-es was also assessed. The models, including Random Forest, Support Vector Regressor, Ridge, Gradient Boosting, were trained/validated using synthetic data (n = 4,018) from an in-silico model. When cuff-pressure and PWV were used as inputs, the normalized-RMSEs/correlations for aSBP, CO, and E-es (best-performing models) were 3.36 +/- 0.74%/0.99, 7.60 +/- 0.68%/0.96, and 16.96 +/- 0.64%/0.37, respectively. Using EF as additional input for estimating E-es significantly improved the predictions (7.00 +/- 0.78%/0.92). Results showed that the use of noninvasive pressure measurements allows estimating aSBP and CO with acceptable accuracy. In contrast, E-es cannot be predicted from pressure signals alone. Addition of the EF information greatly improves the estimated E-es. Accuracy of the model-derived aSBP compared to in-vivo aSBP (n = 783) was very satisfactory (5.26 +/- 2.30%/0.97). Future in-vivo evaluation of CO and E-es estimations remains to be conducted. This novel methodology has potential to improve the noninvasive monitoring of aortic hemodynamics and cardiac contractility.

2020

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Transcatheter aortic valve replacement (TAVR) is increasingly used to treat severe aortic stenosis (AS) patients. However, little is known regarding the direct effect of TAVR on the ventricular-aortic interaction. In the present study, we aimed to investigate changes in central hemodynamics after successful TAVR. We retrospectively examined 33 cases of severe AS patients (84 +/- 6 yr) who underwent TAVR. Invasive measurements of left ventricular and aortic pressures as well as echocardiographic aortic flow were acquired before and after TAVR (maximum within 5 days). We examined alterations in key features of central pressure and flow waveforms, including the aortic augmentation index (AIx), and performed wave separation analysis. Arterial parameters were determined via parameter-fitting on a two-element Windkessel model. Resolution of AS resulted in direct increase in the aortic systolic pressure and maximal aortic flow (131 +/- 22 vs. 157 +/- 25 mmHg and 237 +/- 49 vs. 302 +/- 69 mL/s, P < 0.001 for all), whereas the ejection duration decreased (P < 0.001). We noted a significant decrease in the AIx (from 42 +/- 12 to 19 +/- 11%, P < 0.001). Of note, the arterial properties remained unchanged. There was a comparable increase in both forward (61 +/- 20 vs. 77 +/- 20 mmHg, P < 0.001) and backward ( 35 +/- 14 vs. 42 +/- 10 mmHg, P = 0.013) pressure wave amplitudes, while their ratio, i.e., the reflection coefficient, was preserved. Our results highlight the impact of TAVR on the ventricular-aortic interaction by affecting the amplitude, shape, and related attributes of the aortic pressure and flow pulse and challenge the interpretation of AIx as a solely vascular measure in AS patients. NEW & NOTEWORTHY Transcatheter aortic valve replacement (TAVR) is linked with an immediate increase in aortic systolic blood pressure and maximal flow, as well as steeper aortic pressure and flow wave upstrokes. After TAVR, the forward wave pumped by the heart is enhanced. Although the arterial properties remain unchanged, the central augmentation index (AIx) is markedly decreased after TAVR. This challenges the interpretation of AIx as a solely vascular measure in patients with aortic valve stenosis.

AMER PHYSIOLOGICAL SOC2020

Chargement

In a progressively aging population, it is of utmost importance to develop reliable, non-invasive, and cost-effective tools to estimate biomarkers that can be indicative of cardiovascular risk. Clinical parameters directly measured in the heart or the aorta are crucial for the diagnosis and management of disease. However, their clinical use is severely hampered by their invasive nature, cost, or need for special equipment. Aortic systolic blood pressure (aSBP), cardiac output (CO), end-systolic elastance (Ees), and arterial stiffness indices provide valuable information about the cardiovascular state in humans and are strongly associated with clinical outcomes. This thesis presents original predictive algorithms suitable for estimating such cardiovascular biomarkers from readily available, non-invasive clinical data.The first aim of this thesis is to develop and validate methods to estimate central hemodynamics. Firstly, an inverse problem-solving method is presented to estimate aSBP and CO from non-invasive measurements of cuff pressure and pulse wave velocity (PWV). The method relies on the adjustment of a previously validated computational arterial tree model. Assessment of the method indicates a high performance on a large human cohort. The second approach involves the machine learning-based estimation of aSBP and CO using again cuff pressure and PWV. Validation of the method on in silico data shows that machine learning offers a greatly accurate alternative for monitoring aSBP and CO. Additionally, we develop and test a gamut of machine learning frameworks for predicting Ees. First, a machine learning model is trained/tested using as inputs cuff pressure and PWV. The importance of incorporating ejection fraction (EF) as additional input is also assessed. Results indicate that Ees cannot be predicted from pressure-based data alone, while the addition of the EF is indispensable. Alternatively, we propose a novel artificial intelligence-based approach to estimate Ees using the information embedded in clinically relevant systolic timing intervals. Lastly, by means of a deep learning algorithm, we demonstrate that Ees can be accurately predicted using the morphology of the brachial blood pressure waveform. Furthermore, we aim to improve in vivo assessment of aortic characteristic impedance (Zao) and total arterial compliance (CT). Given that regional PWV measurements are non-invasive and clinically available, we present a non-invasive method for estimating Zao and CT using cuff pressure, cfPWV, and carotid-radial PWV via regression analysis. In silico validation verifies that the method may offer a valuable tool for assessing arterial stiffness while reducing the cost and the complexity of the existing techniques. As a step forward, we introduce a non-invasive method for estimating CT from a single carotid waveform using artificial neural networks. The proposed methodology is appraised using the large Asklepios human cohort. Precise estimates of CT are yielded, indicating that such an approach could offer promising applications, ranging from fast and cost-efficient hemodynamical monitoring by the physician to integration in wearable technologies. Finally, in view of the conflicting clinical and experimental evidence regarding the influence of HR on arterial stiffness, we highlight the importance of accounting for the effect of HR-induced blood pressure effects on the PWV measurement.

EPFL2021

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AMER PHYSIOLOGICAL SOC2020

EPFL2021