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 transcatheter aortic valve replacement (TAVR). We retrospectively examined 33 cases of severe AS patients (84±6 years) 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 2-element Windkessel model. Resolution of AS resulted in direct increase in the aortic systolic pressure and maximal aortic flow (131±22 mmHg versus 157±25 mmHg and 237±49 ml/sec versus 302±69 mL/sec, p<0.001 for all), while the ejection duration decreased (p<0.001). We noted a significant decrease in the AIx (from 42±12% to 19±11%, p<0.001). Noteworthy, the arterial properties remained unchanged. There was a comparable increase in both forward (61±20 mmHg versus 77±20 mmHg, p<0.001) and backward (35±14 mmHg versus 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.

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 (Ees) from cuff-pressure and pulse wave velocity (PWV) using regression analysis. The importance of incorporating ejection fraction (EF) as additional input for estimating Ees 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 Ees (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 Ees 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, Ees cannot be predicted from pressure signals alone. Addition of the EF information greatly improves the estimated Ees. 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 Ees estimations remains to be conducted. This novel methodology has potential to improve the noninvasive monitoring of aortic hemodynamics and cardiac contractility.

Ventricular-arterial coupling is a major determinant of cardiovascular performance, however, there are still inherent difficulties in distinguishing ventricular from vascular effects on arterial pulse phenotypes. In the present study, we employed an extensive mathematical model of the cardiovascular system to investigate how sole changes in cardiac contractility might affect hemodynamics. We simulated two physiologically relevant cases of high and low contractility by altering the end-systolic elastance, Ees, (3 versus 1 mmHg/mL) under constant cardiac output and afterload, and subsequently performed pulse wave analysis and wave separation. The aortic forward pressure wave component was steeper for high Ees, which led to the change of the total pressure waveform from the characteristic Type A phenotype to Type C, and the decrease in augmentation index, AIx (-2.4% versus +18.1%). Additionally, the increase in Ees caused the pulse pressure amplification from the aorta to the radial artery to rise drastically (1.86 versus 1.39). Our results show that an increase in cardiac contractility alone, with no concomitant change in arterial properties, alters the shape of the forward pressure wave, which, consequently, changes central and peripheral pulse phenotypes. Indices based on the pressure waveform, like AIx, cannot be assumed to reflect only arterial properties.

In-vivo assessment of aortic characteristic impedance (Zao) and total arterial compliance (CT) has been hampered by the need for either invasive or inconvenient and expensive methods to access simultaneous recordings of aortic pressure and flow, wall thickness, and cross-sectional area. In contrast, regional pulse wave velocity (PWV) measurements are non-invasive and clinically available. In this study, we present a non-invasive method for estimating Zao and CT using cuff pressure, carotid-femoral PWV (cfPWV), and carotid-radial PWV (crPWV). Regression analysis is employed for both Zao and CT. The regressors are trained and tested using a pool of virtual subjects (n = 3,818) generated from a previously validated in-silico model. Predictions achieved an accuracy of 7.40%, r = 0.90, and 6.26%, r = 0.95, for Zao, and CT, respectively. The proposed approach constitutes a step forward to non-invasive screening of elastic vascular properties in humans by exploiting easily obtained measurements. This study could introduce a valuable tool for assessing arterial stiffness reducing the cost and the complexity of the required measuring techniques. Further clinical studies are required to validate the method in-vivo.

Accurate assessment of the left ventricular (LV) systolic function is indispensable in the clinic. However, estimation of a precise index of cardiac contractility, i.e., the end-systolic elastance (Ees), is invasive and cannot be established as clinical routine. The aim of this work was to present and validate a methodology that allows for the estimation of Ees from simple and readily available non-invasive measurements. The method is based on a validated model of the cardiovascular system and non-invasive data from arm-cuff pressure and routine echocardiography to render the model patient-specific. Briefly, the algorithm first uses the measured aortic flow as model input and optimizes the properties of the arterial system model in order to achieve correct prediction of the patient's peripheral pressure. In a second step, the personalized arterial system is coupled with the cardiac model (time-varying elastance model) and the LV systolic properties, including Ees, are tuned to predict accurately the aortic flow waveform. The algorithm was validated against invasive measurements of Ees (multiple pressure-volume loop analysis) taken from n=10 heart failure patients with preserved ejection fraction and n=9 patients without heart failure. Invasive measurements of Ees (median 2.4 mmHg/mL, range [1.0, 5.0] mmHg/mL) agreed well with method predictions (nRMSE=9%, ρ=0.89, bias=-0.1 mmHg/mL and limits of agreement [-0.9, 0.6] mmHg/mL). This is a promising first step towards the development of a valuable tool that can be used by clinicians to assess systolic performance of the LV in the critically ill.

Background: Clinical and experimental evidence regarding the influence of heart rate (HR) on arterial stiffness and its surrogate marker carotid-to-femoral pulse wave velocity (cf-PWV) is conflicting. We aimed to evaluate the effect of HR on cf-PWV measurement under controlled haemodynamic conditions and especially with respect to blood pressure (BP) that is a strong determinant of arterial stiffness. Method: Fifty-nine simulated cases were created using a previously validated in-silico model. For each case, cf-PWV was measured at five HR values, 60, 70, 80, 90, 100 bpm. With increasing HR, we assessed cf-PWV under two scenarios: with BP free to vary in response to HR increase, and with aortic DBP (aoDBP) fixed to its baseline value at 60 bpm, by modifying total peripheral resistance accordingly. Further, we quantified the importance of arterial compliance (C) on cf-PWV changes caused by increasing HR. Results: When BP was left free to vary with HR, a significant HR-effect on cf-PWV (0.66 ± 0.24 m/s per 10 bpm, P < 0.001) was observed. This effect was reduced to 0.21 ± 0.14 m/s per 10 bpm (P = 0.048) when aoDBP was maintained fixed with increasing HR. The HR-effect on the BP-corrected cf-PWV was higher in the case of low C = 0.8 ± 0.3 ml/mmHg (0.26 ± 0.15 m/s per 10 bpm, P = 0.014) than the case of higher C = 1.7 ± 0.5 ml/mmHg (0.16 ± 0.07 m/s per 10 bpm, P = 0.045). Conclusion: Our findings demonstrated that relatively small HR changes may only slightly affect the cf-PWV. Nevertheless, in cases wherein HR might vary at a greater extent, a more clinically significant impact on cf-PWV should be considered.

Determination of left ventricular (LV) end-systolic elastance (Ees) is of utmost importance for assessing the cardiac systolic function and hemodynamical state in humans. Yet, the clinical use of Ees is not established due to the invasive nature and high costs of the existing measuring techniques. The objective of this study is to introduce a method to assess cardiac contractility, using as a sole measurement an arterial blood pressure (BP) waveform. Particularly, we aim to provide evidence on the potential in using the morphology of the brachial BP waveform and its time derivative for predicting LV Eesvia convolution neural networks (CNNs). The requirement of a broad training dataset is addressed by the use of an in silico dataset (n = 3,748) which is generated by a validated one-dimensional mathematical model of the cardiovasculature. We evaluated two CNN configurations: 1) a one-channel CNN (CNN1) with only the raw brachial BP signal as an input, and 2) a two-channel CNN (CNN2) using as inputs both the brachial BP wave and its time derivative. Accurate predictions were yielded using both CNN configurations. For CNN1, Pearson’s correlation coefficient (r) and RMSE were equal to 0.86 and 0.27 mmHg/ml, respectively. The performance was found to be greatly improved for CNN2 (r = 0.97 and RMSE = 0.13 mmHg/ml). Moreover, all absolute errors from CNN2 were found to be less than 0.5 mmHg/ml. Importantly, the brachial BP wave appeared to be a promising source of information for estimating Ees. Predictions were found to be in good agreement with the reference Ees values over an extensive range of LV contractility values and loading conditions. Therefore, the proposed methodology could be easily transferred to the bedside and potentially facilitate the clinical use of Ees for monitoring the contractile state of the heart in the real-life setting.

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 &lt; 0.001) and higher aortic systolic blood pressure (Q1: 132 ± 26 mmHg vs Q4: 117 ± 26 mmHg, p &lt; 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 cm2 vs 0.61 ± 0.22 cm2, 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.