Corrigendum: Determination of aortic characteristic impedance and total arterial compliance from regional pulse wave velocities using machine learning: an in silico study.

[This corrects the article DOI: 10.3389/fbioe.2021.649866.].

Corrigendum: Estimation of left ventricular end-systolic elastance from brachial pressure waveform via deep learning.

[This corrects the article DOI: 10.3389/fbioe.2021.754003.].

Modulation of Pulsatile Left Ventricular Afterload by Renal Denervation in Heart Failure With Preserved Ejection Fraction.

BACKGROUND: Arterial stiffening contributes to hemodynamic derangements in heart failure with preserved ejection fraction (HFpEF). We sought to investigate the impact of renal denervation on pulsatile left ventricular loading in patients with HFpEF and hypertensive patients without heart failure (control). METHODS: Patients underwent renal denervation for treatment of hypertension and were followed up at 3 months at a single center. A validated computer model of the arterial tree, noninvasive aortic flow curves, left ventricular volumes, and E/e' as inputs were used to determine key parameters of left ventricular vascular load. RESULTS: In comparison to controls (n=30), patients with HFpEF (n=30) demonstrated lower total arterial compliance (mean difference, -0.41 [95% CI, -0.72 to -0.10] mL/mm Hg), higher impedance of the proximal aorta (Zc: 0.02; 0.01 to 0.04 mHg·s/mL), premature wave reflections (shorter backward wave transit time normalized to ejection time: -3.5; -6.5% to -0.5%), and higher wave reflection magnitude (reflection coefficient: 7.3; 2.8% to 11.9%). Overall, daytime systolic (-9.2; -12.2 to -6.2 mm Hg) and diastolic blood pressures (-5.9; -7.6 to -4.1 mm Hg) as well as blood pressure variability (-2.0; -3.0 to -0.9 mm Hg) decreased after renal denervation. In patients with HFpEF, total arterial compliance (0.42; 0.17 to 0.67 mL/mm Hg) and backward transit time normalized to ejection time (1.7; 0.4% to 3.0%) increased; Zc (-0.01; -0.02 to -0.01 mm Hg·s/mL) and reflection coefficient (-2.6; -5.0% to -0.3%) decreased after renal denervation. This was accompanied by a symptomatic improvement in patients with HFpEF. CONCLUSION: HFpEF is characterized by heightened aortic stiffness and unfavorable pulsatile left ventricular load. These abnormalities are partly normalized after renal denervation.

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

Introduction: Arterial 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 Methods: The 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. Results: Early 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 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). Conclusion: Early 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.

Prognostic Implications of the Novel Pulmonary Hypertension Definition in Patients with Aortic Stenosis after Transcatheter Valve Replacement.

Introduction: Pulmonary hypertension (PH), traditionally defined as a mean pulmonary artery pressure (PAP) ≥ 25 mmHg, is associated with poor outcomes in patients undergoing a transcatheter aortic valve replacement (TAVR) for severe aortic stenosis (AS). Recently, a novel definition for PH has been proposed, placing the cut-off value of mean PAP at 20 mmHg, and introducing pulmonary vascular resistance as an exclusive indicator for the pre-capillary involvement. In light of the novel criteria, whether PH still preserves its prognostic significance remains unknown. Methods: The study population consisted of 380 patients with AS, who underwent a right heart catheterization before TAVR. The cohort was divided according to the presence of PH (n = 174, 45.7%) or not. Patients with PH were further divided into the following groups: (1) Pre-capillary PH ((Pre-capPH), n = 46, 12.1%); (2) Isolated post-capillary PH ((IpcPH), n = 78, 20.5%); (3) Combined pre and post-capillary PH ((CpcPH), n = 82, 21.6%). The primary endpoint was all-cause mortality at 1 year. Results: A total of 246 patients (64.7%) exhibited mean PAP > 20 mmHg. Overall, the presence of PH was associated with higher 1-year mortality rates (hazard ratio (HR) 2.8, 95% CI: 1.4−5.8, p = 0.004). Compared to patients with no PH, Pre-capPH and CpcPH (but not IpcPH) were related to higher 1-year mortality (HR 2.7, 95% CI: 1.0−7.2, p = 0.041 and HR 3.9, 95% CI: 1.8−8.5, p = 0.001, respectively). This remained significant even after the adjustment for baseline comorbidities. Conclusions: Pre-interventional PH according to the novel hemodynamic criteria, is linked with poor outcomes in patients undergoing TAVR for severe AS. However, this is mainly driven by patients with mean PAP ≥ 25 mmHg. Patients with a pre-capillary PH component as defined by increased PVR present an even worse prognosis as compared to patients with isolated post-capillary or no PH who present comparable 1-year mortality rates.

Estimation of Left Ventricular End-Systolic Elastance From Brachial Pressure Waveform via Deep Learning.

Determination of left ventricular (LV) end-systolic elastance (E es ) is of utmost importance for assessing the cardiac systolic function and hemodynamical state in humans. Yet, the clinical use of E es 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 E es via 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 E es . Predictions were found to be in good agreement with the reference E es 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 E es for monitoring the contractile state of the heart in the real-life setting.

The effect of left ventricular contractility on arterial hemodynamics: A model-based investigation.

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.

Acute and Long-Term Effects of Aortic Compliance Decrease on Central Hemodynamics: A Modeling Analysis.

Aortic compliance is an important determinant of cardiac afterload and a contributor to cardiovascular morbidity. In the present study, we sought to provide in silico insights into the acute as well as long-term effects of aortic compliance decrease on central hemodynamics. To that aim, we used a mathematical model of the cardiovascular system to simulate the hemodynamics (a) of a healthy young adult (baseline), (b) acutely after banding of the proximal aorta, (c) after the heart remodeled itself to match the increased afterload. The simulated pressure and flow waves were used for subsequent wave separation analysis. Aortic banding induced hypertension (SBP 106 mmHg at baseline versus 152 mmHg after banding), which was sustained after left ventricular (LV) remodeling. The main mechanism that drove hypertension was the enhancement of the forward wave, which became even more significant after LV remodeling (forward amplitude 30 mmHg at baseline versus 60 mmHg acutely after banding versus 64 mmHg after remodeling). Accordingly, the forward wave's contribution to the total pulse pressure increased throughout this process, while the reflection coefficient acutely decreased and then remained roughly constant. Finally, LV remodeling was accompanied by a decrease in augmentation index (AIx 13% acutely after banding versus -3% after remodeling) and a change of the central pressure wave phenotype from the characteristic Type A ("old") to Type C ("young") phenotype. These findings provide valuable insights into the mechanisms of hypertension and provoke us to reconsider our understanding of AIx as a solely arterial parameter.

On the assessment of arterial compliance from carotid pressure waveform.

In a progressively aging population, it is of utmost importance to develop reliable, noninvasive, and cost-effective tools to estimate biomarkers that can be indicative of cardiovascular risk. Various pathophysiological conditions are associated to changes in the total arterial compliance (CT), and thus, its estimation via an accurate and simple method is valuable. Direct noninvasive measurement of CT is not feasible in the clinical practice. Previous methods exist for indirect estimation of CT, which, however, require noninvasive, yet complex and expensive, recordings of the central pressure and flow. Here, we introduce a novel, noninvasive method for estimating CT from a single carotid waveform measurement using regression analysis. Features were extracted from the carotid wave and were combined with demographic data. A prediction pipeline was adopted for estimating CT using, first, a feature-based regression analysis and, second, the raw carotid pulse wave. The proposed methodology was appraised using the large human cohort (N = 2,256) of the Asklepios study. Accurate estimates of CT were yielded for both prediction schemes, namely, r = 0.83 and normalized root mean square error (nRMSE) = 9.58% for the feature-based model, and r = 0.83 and nRSME = 9.67% for the model that used the raw signal. The major advantage of this method pertains to the simplification of the technique offering easily applicable and convenient CT monitoring. Such an approach could offer promising applications, ranging from fast and cost-efficient hemodynamical monitoring by the physician to integration in wearable technologies.NEW & NOTEWORTHY This article introduces a novel artificial intelligence method to estimate total arterial compliance (CT) via exploiting the information provided by an uncalibrated carotid blood pressure waveform as well as typical clinical variables. The major finding of this study is that CT, which is usually acquired using both pressure and flow waveforms, can be accurately derived by the use of the pressure wave alone. This method could potentially facilitate easily applicable and convenient monitoring of CT.

Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study.

In-vivo assessment of aortic characteristic impedance (Z ao ) and total arterial compliance (C T ) 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 Z ao and C T using cuff pressure, carotid-femoral PWV (cfPWV), and carotid-radial PWV (crPWV). Regression analysis is employed for both Z ao and C T . 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 Z ao , and C T , 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.

AI-Based Estimation of End-Systolic Elastance From Arm-Pressure and Systolic Time Intervals.

Left ventricular end-systolic elastance (Ees) is a major determinant of cardiac systolic function and ventricular-arterial interaction. Previous methods for the Ees estimation require the use of the echocardiographic ejection fraction (EF). However, given that EF expresses the stroke volume as a fraction of end-diastolic volume (EDV), accurate interpretation of EF is attainable only with the additional measurement of EDV. Hence, there is still need for a simple, reliable, noninvasive method to estimate Ees. This study proposes a novel artificial intelligence-based approach to estimate Ees using the information embedded in clinically relevant systolic time intervals, namely the pre-ejection period (PEP) and ejection time (ET). We developed a training/testing scheme using virtual subjects (n = 4,645) from a previously validated in-silico model. Extreme Gradient Boosting regressor was employed to model Ees using as inputs arm cuff pressure, PEP, and ET. Results showed that Ees can be predicted with high accuracy achieving a normalized RMSE equal to 9.15% (r = 0.92) for a wide range of Ees values from 1.2 to 4.5 mmHg/ml. The proposed model was found to be less sensitive to measurement errors (±10-30% of the actual value) in blood pressure, presenting low test errors for the different levels of noise (RMSE did not exceed 0.32 mmHg/ml). In contrast, a high sensitivity was reported for measurements errors in the systolic timing features. It was demonstrated that Ees can be reliably estimated from the traditional arm-pressure and echocardiographic PEP and ET. This approach constitutes a step towards the development of an easy and clinically applicable method for assessing left ventricular systolic function.

In vivo application and validation of a novel noninvasive method to estimate the end-systolic elastance.

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 noninvasive measurements. The method is based on a validated model of the cardiovascular system and noninvasive 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 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 patients with heart failure 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 (normalized root mean square error = 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 toward the development of a valuable tool that can be used by clinicians to assess systolic performance of the LV in the critically ill.NEW & NOTEWORTHY In this study, we present a novel model-based method to estimate the left ventricular (LV) end-systolic elastance (Ees) according to measurement of the patient's arm-cuff pressure and a routine echocardiography examination. The proposed method was validated in vivo against invasive multiple-loop measurements of Ees, achieving high correlation and low bias. This tool could be most valuable for clinicians to assess the cardiovascular health of critically ill patients.

Validation of a Non-invasive Inverse Problem-Solving Method for Stroke Volume.

Stroke volume (SV) is a major biomarker of cardiac function, reflecting ventricular-vascular coupling. Despite this, hemodynamic monitoring and management seldomly includes assessments of SV and remains predominantly guided by brachial cuff blood pressure (BP). Recently, we proposed a mathematical inverse-problem solving method for acquiring non-invasive estimates of mean aortic flow and SV using age, weight, height and measurements of brachial BP and carotid-femoral pulse wave velocity (cfPWV). This approach relies on the adjustment of a validated one-dimensional model of the systemic circulation and applies an optimization process for deriving a quasi-personalized profile of an individual's arterial hemodynamics. Following the promising results of our initial validation, our first aim was to validate our method against measurements of SV derived from magnetic resonance imaging (MRI) in healthy individuals covering a wide range of ages (n = 144; age range 18-85 years). Our second aim was to investigate whether the performance of the inverse problem-solving method for estimating SV is superior to traditional statistical approaches using multilinear regression models. We showed that the inverse method yielded higher agreement between estimated and reference data (r = 0.83, P < 0.001) in comparison to the agreement achieved using a traditional regression model (r = 0.74, P < 0.001) across a wide range of age decades. Our findings further verify the utility of the inverse method in the clinical setting and highlight the importance of physics-based mathematical modeling in improving predictive tools for hemodynamic monitoring.

The effect of the elongation of the proximal aorta on the estimation of the aortic wall distensibility.

The compliance of the proximal aortic wall is a major determinant of cardiac afterload. Aortic compliance is often estimated based on cross-sectional area changes over the pulse pressure, under the assumption of a negligible longitudinal stretch during the pulse. However, the proximal aorta is subjected to significant axial stretch during cardiac contraction. In the present study, we sought to evaluate the importance of axial stretch on compliance estimation by undertaking both an in silico and an in vivo approach. In the computational analysis, we developed a 3-D finite element model of the proximal aorta and investigated the discrepancy between the actual wall compliance to the value estimated after neglecting the longitudinal stretch of the aorta. A parameter sensitivity analysis was further conducted to show how increased material stiffness and increased aortic root motion might amplify the estimation errors (discrepancies between actual and estimated distensibility ranging from - 20 to - 62%). Axial and circumferential aortic deformation during ventricular contraction was also evaluated in vivo based on MR images of the aorta of 3 healthy young volunteers. The in vivo results were in good qualitative agreement with the computational analysis (underestimation errors ranging from - 26 to - 44%, with increased errors reflecting higher aortic root displacement). Both the in silico and in vivo findings suggest that neglecting the longitudinal strain during contraction might lead to severe underestimation of local aortic compliance, particularly in the case of women who tend to have higher aortic root motion or in subjects with stiff aortas.

Acute effects of transcatheter aortic valve replacement on the ventricular-aortic interaction.

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.

Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning.

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.

Biomechanics of diastolic dysfunction: a one-dimensional computational modeling approach.

Diastolic dysfunction (DD) is a major component of heart failure with preserved ejection fraction (HFpEF). Accordingly, a profound understanding of the underlying biomechanical mechanisms involved in DD is needed to elucidate all aspects of HFpEF. In this study, we have developed a computational model of DD by leveraging the power of an advanced one-dimensional arterial network coupled to a four-chambered zero-dimensional cardiac model. The two main pathologies investigated were linked to the active relaxation of the myocardium and the passive stiffness of the left ventricular wall. These pathologies were quantified through two parameters for the biphasic delay of active relaxation, which simulate the early and late-phase relaxation delay, and one parameter for passive stiffness, which simulates the increased nonlinear stiffness of the ventricular wall. A parameter sensitivity analysis was conducted on each of the three parameters to investigate their effect in isolation. The three parameters were then concurrently adjusted to produce the three main phenotypes of DD. It was found that the impaired relaxation phenotype can be replicated by mainly manipulating the active relaxation, the pseudo-normal phenotype was replicated by manipulating both the active relaxation and passive stiffness, and, finally, the restricted phenotype was replicated by mainly changing the passive stiffness. This article presents a simple model producing a holistic and comprehensive replication of the main DD phenotypes and presents novel biomechanical insights on how key parameters defining the relaxation and stiffness properties of the myocardium affect the development and manifestation of DD.NEW & NOTEWORTHY This study uses a complete and validated computational model of the cardiovascular system to simulate the two main pathologies involved in diastolic dysfunction (DD), i.e., abnormal active relaxation and increased ventricular diastolic stiffness. The three phenotypes of DD were successfully replicated according to literature data. We elucidate the biomechanical effect of the relaxation pathologies involved and how these pathologies interact to create the various phenotypes of DD.

The impact of heart rate on pulse wave velocity: an in-silico evaluation.

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 ±â€Š0.24 m/s per 10 bpm, P < 0.001) was observed. This effect was reduced to 0.21 ±â€Š0.14 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 ±â€Š0.3 ml/mmHg (0.26 ±â€Š0.15 m/s per 10 bpm, P = 0.014) than the case of higher C = 1.7 ±â€Š0.5 ml/mmHg (0.16 ±â€Š0.07 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.

Noninvasive Cardiac Output and Central Systolic Pressure From Cuff-Pressure and Pulse Wave Velocity.

GOAL: We introduce a novel approach to estimate cardiac output (CO) and central systolic blood pressure (cSBP) from noninvasive measurements of peripheral cuff-pressure and carotid-to-femoral pulse wave velocity (cf-PWV). METHODS: The adjustment of a previously validated one-dimensional arterial tree model is achieved via an optimization process. In the optimization loop, compliance and resistance of the generic arterial tree model as well as aortic flow are adjusted so that simulated brachial systolic and diastolic pressures and cf-PWV converge towards the measured brachial systolic and diastolic pressures and cf-PWV. The process is repeated until full convergence in terms of both brachial pressures and cf-PWV is reached. To assess the accuracy of the proposed framework, we implemented the algorithm on in vivo anonymized data from 20 subjects and compared the method-derived estimates of CO and cSBP to patient-specific measurements obtained with Mobil-O-Graph apparatus (central pressure) and two-dimensional transthoracic echocardiography (aortic blood flow). RESULTS: Both CO and cSBP estimates were found to be in good agreement with the reference values achieving an RMSE of 0.36 L/min and 2.46 mmHg, respectively. Low biases were reported, namely -0.04 ± 0.36 L/min for CO predictions and -0.27 ± 2.51 mmHg for cSBP predictions. SIGNIFICANCE: Our one-dimensional model can be successfully "tuned" to partially patient-specific standards by using noninvasive, easily obtained peripheral measurement data. The in vivo evaluation demonstrated that this method can potentially be used to obtain central aortic hemodynamic parameters in a noninvasive and accurate way.

On the importance of the nonuniform aortic stiffening in the hemodynamics of physiological aging.

Mathematical models of the arterial tree constitute a valuable tool to investigate the hemodynamics of aging and pathology. Rendering such models as patient specific could allow for the assessment of central hemodynamic variables of clinical interest. However, this task is challenging, particularly with respect to the tuning of the local area compliance that varies significantly along the arterial tree. Accordingly, in this study, we demonstrate the importance of taking into account the differential effects of aging on the stiffness of central and peripheral arteries when simulating a person's hemodynamic profile. More specifically, we propose a simple method for effectively adapting the properties of a generic one-dimensional model of the arterial tree based on the subject's age and noninvasive measurements of aortic flow and brachial pressure. A key element for the success of the method is the implementation of different mechanisms of arterial stiffening for young and old individuals. The designed methodology was tested and validated against in vivo data from a population of n = 20 adults. Carotid-to-femoral pulse wave velocity was accurately predicted by the model (mean error = 0.14 m/s, SD = 0.77 m/s), with the greatest deviations being observed for older subjects. In regard to aortic pressure, model-derived systolic blood pressure and augmentation index were both in good agreement (mean difference of 2.3 mmHg and 4.25%, respectively) with the predictions of a widely used commercial device (Mobil-O-Graph). These preliminary results encourage us to further validate the method in larger samples and consider its potential as a noninvasive tool for hemodynamic monitoring.NEW & NOTEWORTHY We propose a technique for adapting the parameters of a validated one-dimensional model of the arterial tree using noninvasive measurements of aortic flow and brachial pressure. Emphasis is given on the adjustment of the arterial tree distensibility, which incorporates the nonuniform effects of aging on central and peripheral vessel elasticity. Our method could find application in the derivation of important hemodynamic indices, paving the way for novel diagnostic tools.