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.].

Novel theory and potential applications of central diastolic pressure decay time constant.

Central aortic diastolic pressure decay time constant ( τ ) is according to the two-element Windkessel model equal to the product of total peripheral resistance ( R ) times total arterial compliance ( C ). As such, it is related to arterial stiffness, which has considerable pathophysiological relevance in the assessment of vascular health. This study aimed to investigate the relationship of the constant τ with the product T MBP cPP , given by heart period ( T ) times the ratio of mean blood pressure (MBP) to central pulse pressure ( cPP ). The relationship was derived by performing linear fitting on an in silico population of n1 = 3818 virtual subjects, and was subsequently evaluated on in vivo data (n2 = 2263) from the large Asklepios study. The resulted expression was found to be τ = k ' T MBP cPP , with k ' = 0.7 (R2 = 0.9). The evaluation of the equation on the in vivo human data reported high agreement between the estimated and reference τ values, with a correlation coefficient equal to 0.94 and a normalized RMSE equal to 5.5%. Moreover, the analysis provided evidence that the coefficient k ' is age- and gender-independent. The proposed formula provides novel theoretical insights in the relationship between τ and central blood pressure features. In addition, it may allow for the evaluation of τ without the need for acquiring the entire central blood pressure wave, especially when an approximation of the cPP is feasible. This study adds to the current literature by contributing to the accessibility of an additional biomarker, such as the central diastolic pressure decay time constant, for the improved assessment of vascular ageing.

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.

Design and computational optimization of compliance-matching aortic grafts.

Introduction: Synthetic vascular grafts have been widely used in clinical practice for aortic replacement surgery. Despite their high rates of surgical success, they remain significantly less compliant than the native aorta, resulting in a phenomenon called compliance mismatch. This incompatibility of elastic properties may cause serious post-operative complications, including hypertension and myocardial hypertrophy. Methods: To mitigate the risk for these complications, we designed a multi-layer compliance-matching stent-graft, that we optimized computationally using finite element analysis, and subsequently evaluated in vitro. Results: We found that our compliance-matching grafts attained the distensibility of healthy human aortas, including those of young adults, thereby significantly exceeding the distensibility of gold-standard grafts. The compliant grafts maintained their properties in a wide range of conditions that are expected after the implantation. Furthermore, the computational model predicted the graft radius with enough accuracy to allow computational optimization to be performed effectively. Conclusion: Compliance-matching grafts may offer a valuable improvement over existing prostheses and they could potentially mitigate the risk for post-operative complications attributed to excessive graft stiffness.

Wet adhesive hydrogels to correct malacic trachea (tracheomalacia) A proof of concept.

Tracheomalacia (TM) is a condition characterized by a weak tracheal cartilage and/or muscle, resulting in excessive collapse of the airway in the newborns. Current treatments including tracheal reconstruction, tracheoplasty, endo- and extra-luminal stents have limitations. To address these limitations, this work proposes a new strategy by wrapping an adhesive hydrogel patch around a malacic trachea. Through a numerical model, first it was demonstrated that a hydrogel patch with sufficient mechanical and adhesion strength can preserve the trachea's physiological shape. Accordingly, a new hydrogel providing robust adhesion on wet tracheal surfaces was synthesized employing the hydroxyethyl acrylamide (HEAam) and polyethylene glycol methacrylate (PEGDMA) as main polymer network and crosslinker, respectively. Ex vivo experiments revealed that the adhesive hydrogel patches can restrain the collapsing of malacic trachea under negative pressure. This study may open the possibility of using an adhesive hydrogel as a new approach in the difficult clinical situation of tracheomalacia.

On the similarity between aortic and carotid pressure diastolic decay: a mathematical modelling study.

Aortic diastolic pressure decay (DPD) has been shown to have considerable pathophysiological relevance in the assessment of vascular health, as it is significantly affected by arterial stiffening. Nonetheless, the aortic pressure waveform is rarely available and hence the utility of the aortic DPD is limited. On the other hand, carotid blood pressure is often used as a surrogate of central (aortic) blood pressure in cardiovascular monitoring. Although the two waveforms are inherently different, it is unknown whether the aortic DPD shares a common pattern with the carotid DPD. In this study, we compared the DPD time constant of the aorta (aortic RC) and the DPD time constant of the carotid artery (carotid RC) using an in-silico-generated healthy population from a previously validated one-dimensional numerical model of the arterial tree. Our results demonstrated that there is near-absolute agreement between the aortic RC and the carotid RC. In particular, a correlation of ~ 1 was reported for a distribution of aortic/carotid RC values equal to 1.76 ± 0.94 s/1.74 ± 0.87 s. To the best of our knowledge, this is the first study to compare the DPD of the aortic and the carotid pressure waveform. The findings indicate a strong correlation between carotid DPD and aortic DPD, supported by the examination of curve shape and the diastolic decay time constant across a wide range of simulated cardiovascular conditions. Additional investigation is required to validate these results in human subjects and assess their applicability in vivo.

Cardiac output estimated from an uncalibrated radial blood pressure waveform: validation in an in-silico-generated population.

Background: Cardiac output is essential for patient management in critically ill patients. The state-of-the-art for cardiac output monitoring bears limitations that pertain to the invasive nature of the method, high costs, and associated complications. Hence, the determination of cardiac output in a non-invasive, accurate, and reliable way remains an unmet need. The advent of wearable technologies has directed research towards the exploitation of wearable-sensed data to improve hemodynamical monitoring. Methods: We developed an artificial neural networks (ANN)-enabled modelling approach to estimate cardiac output from radial blood pressure waveform. In silico data including a variety of arterial pulse waves and cardiovascular parameters from 3,818 virtual subjects were used for the analysis. Of particular interest was to investigate whether the uncalibrated, namely, normalized between 0 and 1, radial blood pressure waveform contains sufficient information to derive cardiac output accurately in an in silico population. Specifically, a training/testing pipeline was adopted for the development of two artificial neural networks models using as input: the calibrated radial blood pressure waveform (ANNcalradBP), or the uncalibrated radial blood pressure waveform (ANNuncalradBP). Results: Artificial neural networks models provided precise cardiac output estimations across the extensive range of cardiovascular profiles, with accuracy being higher for the ANNcalradBP. Pearson's correlation coefficient and limits of agreement were found to be equal to [0.98 and (-0.44, 0.53) L/min] and [0.95 and (-0.84, 0.73) L/min] for ANNcalradBP and ANNuncalradBP, respectively. The method's sensitivity to major cardiovascular parameters, such as heart rate, aortic blood pressure, and total arterial compliance was evaluated. Discussion: The study findings indicate that the uncalibrated radial blood pressure waveform provides sample information for accurately deriving cardiac output in an in silico population of virtual subjects. Validation of our results using in vivo human data will verify the clinical utility of the proposed model, while it will enable research applications for the integration of the model in wearable sensing systems, such as smartwatches or other consumer devices.

The Impact of Left Ventricular Performance and Afterload on the Evaluation of Aortic Valve Stenosis: A 1D Mathematical Modeling Approach.

The transaortic valvular pressure gradient (TPG) plays a central role in decision-making for patients suffering from severe aortic stenosis. However, the flow-dependence nature of the TPG makes the diagnosis of aortic stenosis challenging since the markers of cardiac performance and afterload present high physiological interdependence and thus, isolated effects cannot be measured directly in vivo. We used a validated 1D mathematical model of the cardiovascular system, coupled with a model of aortic stenosis, to assess and quantify the independent effect of the main left ventricular performance parameters (end-systolic (Ees) and end-diastolic (Eed) elastance) and principal afterload indices (total vascular resistance (TVR) and total arterial compliance (TAC)) on the TPG for different levels of aortic stenosis. In patients with critical aortic stenosis (aortic valve area (AVA) ≤ 0.6 cm2), a 10% increase of Eed from the baseline value was associated with the most important effect on the TPG (-5.6 ± 0.5 mmHg, p < 0.001), followed by a similar increase of Ees (3.4 ± 0.1 mmHg, p < 0.001), in TAC (1.3 ±0.2 mmHg, p < 0.001) and TVR (-0.7 ± 0.04 mmHg, p < 0.001). The interdependence of the TPG left ventricular performance and afterload indices become stronger with increased aortic stenosis severity. Disregarding their effects may lead to an underestimation of stenosis severity and a potential delay in therapeutic intervention. Therefore, a comprehensive evaluation of left ventricular function and afterload should be performed, especially in cases of diagnostic challenge, since it may offer the pathophysiological mechanism that explains the mismatch between aortic severity and the TPG.

Quantification of the Phenomena Affecting Reflective Arterial Photoplethysmography.

Photoplethysmography (PPG) is a widely emerging method to assess vascular health in humans. The origins of the signal of reflective PPG on peripheral arteries have not been thoroughly investigated. We aimed to identify and quantify the optical and biomechanical processes that influence the reflective PPG signal. We developed a theoretical model to describe the dependence of reflected light on the pressure, flow rate, and the hemorheological properties of erythrocytes. To verify the theory, we designed a silicone model of a human radial artery, inserted it in a mock circulatory circuit filled with porcine blood, and imposed static and pulsatile flow conditions. We found a positive, linear relationship between the pressure and the PPG and a negative, non-linear relationship, of comparable magnitude, between the flow and the PPG. Additionally, we quantified the effects of the erythrocyte disorientation and aggregation. The theoretical model based on pressure and flow rate yielded more accurate predictions, compared to the model using pressure alone. Our results indicate that the PPG waveform is not a suitable surrogate for intraluminal pressure and that flow rate significantly affects PPG. Further validation of the proposed methodology in vivo could enable the non-invasive estimation of arterial pressure from PPG and increase the accuracy of health-monitoring devices.

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.

Low-Intensity Electrostimulation Enhances Neuroregeneration and Improves Erectile Function in a Rat Model of Cavernous Nerve Injury.

BACKGROUND: Neurogenic erectile dysfunction (ED) following radical prostatectomy (RP) is a frequent complication often leading to erectile tissue remodeling and permanent ED. Low-intensity electrostimulation (LIES) has been shown to enhance peripheral nerve regeneration, however, its application on cavernous nerves (CN) has never been investigated. AIMS: To investigate whether LIES enhances CN regeneration, improves erectile function (EF) recovery, and prevents corpora cavernosal remodeling after CN injury, which is a principal factor for ED following RP. METHODS: Adult male Sprague-Dawley rats were divided into Sham, Bilateral Cavernous Nerve Injury (BCNI), and BCNI + LIES (1V, 0.1ms, 12Hz, 1h/day). After 7days, EF was assessed (ICP measurement). Penes and CN were collected for molecular analyses of TGF-ß1, Il-6, CRP, eNOS, ERK and AKT protein levels in corpus cavernosum (CC), and immunohistological analysis of DHE, total collagen and α-SMA in CC and S-100, Tub-III, DAPI, TUNEL, and nNOS in CN. OUTCOMES: Effects of LIES on EF, erectile tissue remodeling and CN structure. RESULTS: EF was decreased (P < .05) 7 days after BCNI and increased (P < .05) by LIES. Intracavernosal reactive oxygen species (DHE) was increased (P < .05) after BCNI and normalized by LIES. Protein expressions of TGF-ß1, IL-6, and CRP were increased in the penis (P < .05) after BCNI and normalized by LIES. The α-SMA and/or total collagen ratio was decreased (P < .05) after BCNI in the penis and normalized by LIES. Protein expression ratio of p-ERK/ERK and p-AKT/AKT did not change after BCNI but increased (P < .05) in LIES group. Myelination and number of nNOS positive cells in the CN were decreased (P < .05) after BCNI and normalized by LIES. The number of apoptotic nerve cells within the dorsal penile nerve was increased (P < .05) after BCNI and decreased (P < .05) by LIES compared to the BCNI group. There were no differences in eNOS expression in the penis between study groups. CLINICAL TRANSLATION: LIES may offer a potential new tool for penile rehabilitation and ED management following RP, potentially enhancing EF recovery and minimizing the side effects of this surgery. STRENGTHS & LIMITATIONS: This study provides evidence of the protective effect of LIES on EF and tissue remodeling following CN injury; nevertheless, this study has been conducted on animals and the translation to humans remains to be demonstrated. Further research to identify the underlying mechanisms of action is required. CONCLUSION: This study demonstrates that LIES of the CN after CN injury protects CN structure, enhances EF recovery, and prevents corpora cavernosal remodeling. Sturny M, Karakus S, Fraga-Silva R, et al. Low-Intensity Electrostimulation Enhances Neuroregeneration and Improves Erectile Function in a Rat Model of Cavernous Nerve Injury. J Sex Med 2022;19:686-696.

MRI after successful eyeWatchTM implantation.

The eyeWatchTM is a new glaucoma drainage device that includes an adjustable mechanism that can vary the resistance to aqueous humor outflow during the postoperative period to reduce the burden of postoperative intraocular pressure (IOP) management. The mechanism contains a magnetic rotor that can be adjusted using an external magnetic control unit. Adjustments of the position of the rotor are performed mostly in the initial postoperative follow-up period in order to reach the target IOP. However, for some patients, it might be necessary to perform MRI for the sake of medical investigations. As the MRI is creating a strong magnetic field, this magnetic field is likely to interact with the adjustable rotor of the eyeWatchTM, resulting in modification of the IOP. We report the case of an 82-old female patient successfully operated with the implantation of an eyeWatchTM. The patient underwent a cerebral MRI for persistent headache. Shortly after the MRI procedure, the patient was checked at the eye clinic to assess the position of the rotor and to measure the IOP. The eyeWatchTM was readjusted to the former position set before undergoing the MRI. No complications were reported in the follow-up after MRI. This case demonstrates that MRI examinations can be safely performed after glaucoma surgery using an eyeWatchTM without compromising on the quality of the imaging or the stability of the IOP. This is a complication-free procedure that only requires checking the new position of the rotor and re-adjusting the implant, if necessary, to achieve the target IOP.

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.

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.

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.