Different Types of Training Lead to Different Levels of Cardiovascular Energy Dissipation: Young Soccer Players Versus Ballet Dancers.

INTRODUCTION: Arterial wall viscosity is a source of energy dissipation that takes place during mechanical transduction. In our previous studies, a "global" damping effect in endurance training athletes was introduced, verifying that endurance-athletes dissipate greater pulsatile energy in the circulation compared with healthy untrained subjects. OBJECTIVE: To investigate the wall energy dissipation in the vascular bed for each beat and within the conceptual framework of ventricular-arterial coupling, in order to elucidate if different types of training could lead to differentiated levels of cardiovascular energy dissipation. MATERIALS AND METHODS: Data from subjects with different kinds of training (soccer players and ballet dancers) have been collected noninvasively and compared with a control group of untrained individuals to analyse the differentiating characteristics of the subjects, especially in terms of Stroke Work Dissipation (WDIS). RESULTS: In the endurance-trained individuals, an enhanced WDIS has been observed compared to the untrained individuals (p<0.05). However, non-significant differences were found regarding ballet-dancers group. CONCLUSION: Changes in wall energy dissipation are developed under high intensity endurance training routines.

Computational clustering reveals differentiated coronary artery calcium progression at prevalent levels of pulse wave velocity by classifying high-risk patients.

Many studies found that increased arterial stiffness is significantly associated with the presence and progression of Coronary Calcium Score (CCS). However, none so far have used machine learning algorithms to improve their value. Therefore, this study aims to evaluate the association between carotid-femoral Pulse Wave Velocity (cfPWV) and CCS score through computational clustering. We conducted a retrospective cross-sectional study using data from a cardiovascular risk screening program that included 377 participants. We used an unsupervised clustering algorithm using age, weight, height, blood pressure, heart rate, and cfPWV as input variables. Differences between cluster groups were analyzed through Chi-square and T-student tests. The association between (i) cfPWV and age groups, (ii) log (CCS) and age groups, and (iii) cfPWV and log(CCS) were addressed through linear regression analysis. Clusters were labeled post hoc based on cardiovascular risk. A "higher-risk group" had significantly higher left (0.76 vs. 0.70 mm, P < 0.001) and right (0.71 vs. 0.66 mm, P = 0.003) intima-media thickness, CCS (42 vs. 4 Agatston units, P = 0.012), and ascending (3.40 vs. 3.20 cm, P < 0.001) and descending (2.60 vs. 2.37 cm, P < 0.001) aorta diameters. Association with age appeared linear for cfPWV and exponential for log (CCS). The progression of the log (CCS) and cfPWV through age groups was steeper in the "higher-risk group" than in the "lower-risk group". cfPWV strongly correlated with CCS, and CCS progression over cfPWV differed among clusters. This finding could improve PWV as a "gate-keeper" of CCS testing and potentially enhance cardiovascular risk stratification.

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach.

Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure-strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure-strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm2, respectively. Regarding the pressure-strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure-strain loop of central arteries while observing pressure signals from peripheral arteries.

Predictive Cardiometabolic Risk Profiling of Patients Using Vascular Age in Liver Transplantation.

Liver transplantation is the last therapeutic option in patients with end-stage liver diseases. The adequate clinical management of transplant-patients impacts their vital prognosis and decisions on many occasions are made from the interaction of multiple variables involved in the process. This work is based on the National Liver Transplantation Program in Uruguay. We performed predictive analysis of cardiometabolic diseases on the transplanted cohort between 2014 and 2019, considering vascular age as a key factor. This aims at classification of the cohort based on the vascular age of the evaluated patients before transplantation for risk-profiling. Predicted high-risk group of the patients showed substantial deterioration of post-transplant health-conditions, including higher mortality rate. In our knowledge, this is the first study in Latin America incorporating vascular age toward predictive analysis of cardiometabolic risk factors in liver transplantations. Predictive risk-modeling using vascular age in a pre-transplantation scenario provides significant opportunity for early prediction of post-transplant risk factors, leading to efficient treatment with anticipation.

A Delineator for Arterial Blood Pressure Waveform Analysis Based on a Deep Learning Technique.

A deep learning technique based on semantic segmentation was implemented into the blood pressure detection points field. Two models were trained and evaluated in terms of a reference detector. The proposed methodology outperforms the reference detector in two of the three classic benchmarks and on signals from a public database that were modified with realistic test maneuvers and artifacts. Both models differentiate regions with valid information and artifacts. So far, no other delineator had shown this capacity.

Energy Dissipation in the Arterial Wall Analyzed by Allometric Relationships.

INTRODUCTION: Allometry describes the disproportionate changes in shape, size or function that are observed when comparing separate isolated features in animals spanning a range of body sizes. Scaling of the energy dissipation has been also observed in warm blooded animals, essentially varying as mammal's body mass (BM). Part of the energy stored in the arterial wall during elastic distension corresponding to the viscous deformation is dissipated within the arterial wall. OBJECTIVE: To elucidate the allometric existing relationship between BM and arterial wall viscosity, as a measure of energy dissipation. MATERIAL AND METHODS: Arterial viscous dissipation (WVD) was assessed in dogs, sheep, and humans in terms of BM and heart rate (HR) variations. RESULTS: An allometric law was found between WVD and BM, jointly with the assessment of WVD in terms of HR. CONCLUSION: The existence of a power-law link for viscous dissipation and BM that involve different mammals was demonstrated.

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism.

Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.

Parameter estimation to study the immediate impact of aortic cross-clamping using reduced order models.

Aortic cross-clamping is a common strategy during vascular surgery, however, its instantaneous impact on hemodynamics is unknown. We, therefore, developed two numerical models to estimate the immediate impact of aortic clamping on the vascular properties. To assess the validity of the models, we recorded continuous invasive pressure signals during abdominal aneurysm repair surgery, immediately before and after clamping. The first model is a zero-dimensional (0D) three-element Windkessel model, which we coupled to a gradient-based parameter estimation algorithm to identify patient-specific parameters such as vascular resistance and compliance. We found a 10% increase in the total resistance and a 20% decrease in the total compliance after clamping. The second model is a nine-artery network corresponding to an average human body in which we solved the one-dimensional (1D) blood flow equations. With a similar parameter estimation method and using the results from the 0D model, we identified the resistance boundary conditions of the 1D network. Determining the patient-specific total resistance and the distribution of peripheral resistances through the parameter estimation process was sufficient for the 1D model to accurately reproduce the impact of clamping on the pressure waveform. Both models gave an accurate description of the pressure wave and had a high correlation (R2 > .95) with experimental blood pressure data.

Internet of Things and Artificial Intelligence in Healthcare During COVID-19 Pandemic-A South American Perspective.

The shudders of the COVID-19 pandemic have projected newer challenges in the healthcare domain across the world. In South American scenario, severe issues and difficulties have been noticed in areas like patient consultations, remote monitoring, medical resources, healthcare personnel etc. This work is aimed at providing a holistic view to the digital healthcare during the times of COVID-19 pandemic in South America. It includes different initiatives like mobile apps, web-platforms and intelligent analyses toward early detection and overall healthcare management. In addition to discussing briefly the key issues toward extensive implementation of eHealth paradigms, this work also sheds light on some key aspects of Artificial Intelligence and the Internet of Things along their potential applications like clinical decision support systems and predictive risk modeling, especially in the direction of combating the emergent challenges due to the COVID-19 pandemic.

Evaluation of transit time-based models in wearable central aortic blood pressure estimation.

Evidence suggests that central aortic blood pressure (CABP) may provide a more accurate prognosis of cardiovascular events than peripheral pressure. The capability of monitoring CABP in a continuous, wearable, unobtrusive way might have a significant impact on hypertension management. The purpose of this study is to experimentally explore whether a wearable device equipped with an electrocardiogram (ECG) and ballistocardiogram (BCG) acquisition system could be used to predict CABP. This is based on state-of-the-art results on the relationship between transit time extracted from these signals and CABP. Ten young, healthy volunteers participated in the study where data-sets were acquired during three hemodynamic interventions, i.e., breath-holding, Valsalva maneuver, and cold pressor. Each data-set included ECG and BCG waveforms acquired by the wearable device and a CABP assessment from a cuff-based device. A total of nine PTT-based models (PBMs) derived from pulse transit time methodology were considered. Each PBM was tested with three alternative feature times extracted from the recorded waveforms PBMs were calibrated with data-sets acquired at baseline state, which were not considered for testing the PBM estimation performance. Four of the nine tested models presented a proper agreement in estimating CABP through the acquired signals, after the calibration procedure with baseline-state data. Results in one of these promising models are the following. Mean estimation error (95% confidence interval), systolic: 0 to 1.7 mmHg, diastolic: 0.4 to 2.3 mmHg, Pearson correlation: 0.82 systolic and 0.78 diastolic (p < 0.001). The proposed methodology may lead to continuous wearable BP monitoring.

Effects of Cross-Clamping on Vascular Mechanics: Comparing Waveform Analysis With a Numerical Model.

BACKGROUND: Immediate changes in vascular mechanics during aortic cross-clamping remain widely unknown. By using a numerical model of the arterial network, vascular compliance and resistance can be estimated and the time constant of pressure waves can be calculated and compared with results from the classic arterial waveform analysis. METHODS: Experimental data were registered from continuous invasive radial artery pressure measurements from 11 patients undergoing vascular surgery. A stable set of beats were chosen immediately before and after each clamping event. Through the arterial waveform analysis, the time constant was calculated for each individual beat and for a mean beat of each condition as to compare with numerical simulations. Overall proportional changes in resistance and compliance during clamping and unclamping were calculated using the numerical model. RESULTS: Arterial waveform analysis of individual beats indicated a significant 10% median reduction in the time constant after clamping, and a significant 17% median increase in the time constant after unclamping. There was a positive correlation between waveform analysis and numerical values of the time constant, which was moderate (ρ = 0.51; P = 0.01486) during clamping and strong (ρ = 0.77; P ≤ 0.0001) during unclamping. After clamping, there was a significant 16% increase in the mean resistance and a significant 23% decrease in the mean compliance. After unclamping, there was a significant 19% decrease in the mean resistance and a significant 56% increase in the mean compliance. CONCLUSIONS: There are significant hemodynamic changes in vascular compliance and resistance during aortic clamping and unclamping. Numerical computer models can add information on the mechanisms of injury due to aortic clamping.

Analysis of ischaemic crisis using the informational causal entropy-complexity plane.

In the present work, an ischaemic process, mainly focused on the reperfusion stage, is studied using the informational causal entropy-complexity plane. Ischaemic wall behavior under this condition was analyzed through wall thickness and ventricular pressure variations, acquired during an obstructive flow maneuver performed on left coronary arteries of surgically instrumented animals. Basically, the induction of ischaemia depends on the temporary occlusion of left circumflex coronary artery (which supplies blood to the posterior left ventricular wall) that lasts for a few seconds. Normal perfusion of the wall was then reestablished while the anterior ventricular wall remained adequately perfused during the entire maneuver. The obtained results showed that system dynamics could be effectively described by entropy-complexity loops, in both abnormally and well perfused walls. These results could contribute to making an objective indicator of the recovery heart tissues after an ischaemic process, in a way to quantify the restoration of myocardial behavior after the supply of oxygen to the ventricular wall was suppressed for a brief period.

Quantitative Vascular Evaluation: From Laboratory Experiments to Point-of-Care Patient (Clinical Approach).

To enhance the efficiency of patient-specific risk stratification and diagnosis, an assessment of arterial structural and functional changes associated to a vascular disease in both early and advanced stages have been proposed, with the objective of limiting the progression or revert vascular alterations. In this connection, an interdisciplinary international partnership made up by research institutions from France, Argentina, Uruguay and Spain was established, with the objective of contributing to the evaluation and follow-up of factors involved in the physiopathology of cardiometabolic diseases and human aging. Several studies, such as the effect of hypertension in large arteries, alterations in arterial wall viscosity, stiffness and inertia, endothelial function and vascular reactivity, cardiovascular risk improvement, vascular age assessment and cryografts vascular response evaluation were carried out as a result of this international collaboration during the last twenty-five years.

Quantitative Vascular Evaluation: From Laboratory Experiments to Point-of-Care Patient (Experimental Approach).

This paper illustrates the evolution of our knowledge of arterial mechanics from our initial research works up to the present time. Several techniques focusing on this topic in terms of our experience are discussed. An interdisciplinary team composed by different institutions from Argentina, Uruguay, France and Spain was created to conduct research, to train human resources and to fulfill the inevitable social role of gaining access to technological innovation to improve public health.

A Novel Interpretation for Arterial Pulse Pressure Amplification in Health and Disease.

Arterial pressure waves have been described in one dimension using several approaches, such as lumped (Windkessel) or distributed (using Navier-Stokes equations) models. An alternative approach consists of modeling blood pressure waves using a Korteweg-de Vries (KdV) equation and representing pressure waves as combinations of solitons. This model captures many key features of wave propagation in the systemic network and, in particular, pulse pressure amplification (PPA), which is a mechanical biomarker of cardiovascular risk. The main objective of this work is to compare the propagation dynamics described by a KdV equation in a human-like arterial tree using acquired pressure waves. Furthermore, we analyzed the ability of our model to reproduce induced elastic changes in PPA due to different pathological conditions. To this end, numerical simulations were performed using acquired central pressure signals from different subject groups (young, adults, and hypertensive) as input and then comparing the output of the model with measured radial artery pressure waveforms. Pathological conditions were modeled as changes in arterial elasticity (E). Numerical results showed that the model was able to propagate acquired pressure waveforms and to reproduce PPA variations as a consequence of elastic changes. Calculated elasticity for each group was in accordance with the existing literature.

Low group delay signal conditioning for wearable central blood pressure monitoring device.

Pulse transit time (ptt) is a widely researched approach for wearable, unobtrusive, blood pressure monitoring. The estimation of ptt, being a delay measurement, may be affected by the group delay introduced by the signal conditioning chain. In this work a previously reported method for estimating central (aortic) blood pressure from ptt at aortic domain, using ECG R wave and BCG J wave detection, is considered. A simple design approach for the signal conditioning chain, which is suitable for a wearable device and that takes care of minimizing the impact of eventually introduced spurious delays is presented. The design provides less than 2ms group-delay. The design of a wearable device prototype for ECG, BCG and ptt acquisition and experimental results of its application are reported, showing the effectiveness of the proposed approach.

Aortic-Radial Pulse Wave Velocity Ratio in End-stage Renal Disease Patients: Association with Age, Body Tissue Hydration Status, Renal Failure Etiology and Five Years of Hemodialysis.

INTRODUCTION: The etiology of the end-stage renal disease (ESRD) and the hydration status may be involved in the arterial stiffening process observed in hemodialyzed patients. The ratio between carotid-femoral and carotid-radial pulse wave velocity (PWV ratio) was recently proposed to characterize the patient-specific stiffening process. AIMS: to analyze: (1) the PWV-ratio in healthy and hemodialyzed subjects, analyzing potential changes associated to etiologies of the ESRD, (2) the PWV-ratio and hydration status using multiple-frequency bioimpedance and, (3) the effects of hemodialysis on PWV-ratio in a 5-year follow-up. METHODS: PWV-ratio was evaluated in 151 patients differentiated by the pathology determining their ESRD. Total body fluid (TBF), intra and extra cellular fluid (ICF, ECF) were measured in 65 of these patients using bioelectrical-impedance. The association between arterial, hemodynamic or fluid parameters was analyzed. PWV-ratio was evaluated in a group of patients (n = 25) 5 years later (follow-up study). RESULTS: PWV-ratio increased in the ESRD cohort with respect to the control group (1.03 ± 0.23 vs. 1.31 ± 0.37; p < 0.001). PWV-ratio in the diabetic nephropathy group was higher than in all other etiological groups (1.61 ± 0.33; p < 0.05). PWV-ratio was associated with TBF (r = -0.238; p < 0.05), ICF (r = -0.323; p < 0.01), ECF/ICF (r = 0.400; p < 0.001) and ECF/TBF (r = 0.403; p < 0.001). PWV-ratio calculated in ESRD patients in 2007 increased 5 years later (1.14 ± 0.32 vs. 1.43 ± 0.44; p < 0.005). CONCLUSIONS: PWV-ratio increased the most in patients with diabetic nephropathy. PWV ratio was significantly associated with age and body hydration status, but not with the blood pressure. PWV-ratio could be considered a blood pressure-independent parameter, associated with the age and hydration status of the patient.

Uncertainty Quantification in a Patient-Specific One-Dimensional Arterial Network Model: EnKF-Based Inflow Estimator.

Successful clinical use of patient-specific models for cardiovascular dynamics depends on the reliability of the model output in the presence of input uncertainties. For 1D fluid dynamics models of arterial networks, input uncertainties associated with the model output are related to the specification of vessel and network geometry, parameters within the fluid and wall equations, and parameters used to specify inlet and outlet boundary conditions. This study investigates how uncertainty in the flow profile applied at the inlet boundary of a 1D model affects area and pressure predictions at the center of a single vessel. More specifically, this study develops an iterative scheme based on the ensemble Kalman filter (EnKF) to estimate the temporal inflow profile from a prior distribution of curves. The EnKF-based inflow estimator provides a measure of uncertainty in the size and shape of the estimated inflow, which is propagated through the model to determine the corresponding uncertainty in model predictions of area and pressure. Model predictions are compared to ex vivo area and blood pressure measurements in the ascending aorta, the carotid artery, and the femoral artery of a healthy male Merino sheep. Results discuss dynamics obtained using a linear and a nonlinear viscoelastic wall model.

Hemodialysis Decreases the Etiologically-Related Early Vascular Aging Observed in End-Stage Renal Disease: A 5-Year Follow-Up Study.

AIMS: To analyze the early vascular aging (EVA) in end-stage renal disease (ESRD) patients, attempting to determine a potential association between EVA and the etiology of ESRD, and to investigate the association of hemodialysis and EVA in ESRD patients during a 5-year follow-up period. METHODS: Carotid-femoral pulse wave velocity (cfPWV) was obtained in 151 chronically hemodialyzed patients (CHP) and 283 control subjects, and in 25 CHP, who were followed-up after a 5-year lapse. RESULTS: cfPWV increased in ESRD patients compared to control subjects. The cfPWV-age relationship was found to have a steeper increase in ESRD patients. The highest cfPWV and EVA values were observed in patients with diabetic nephropathy. Regression analysis demonstrated a significant reduction of the EVA in HD patients on a 5-year follow-up. CONCLUSION: Patients in ESRD showed higher levels of EVA. cfPWV and EVA differed in ESRD patients depending on their renal failure etiology. CHP showed an EVA reduction after a 5-year follow-up period.

Linear and Nonlinear Viscoelastic Arterial Wall Models: Application on Animals.

This work deals with the viscoelasticity of the arterial wall and its influence on the pulse waves. We describe the viscoelasticity by a nonlinear Kelvin-Voigt model in which the coefficients are fitted using experimental time series of pressure and radius measured on a sheep's arterial network. We obtained a good agreement between the results of the nonlinear Kelvin-Voigt model and the experimental measurements. We found that the viscoelastic relaxation time-defined by the ratio between the viscoelastic coefficient and the Young's modulus-is nearly constant throughout the network. Therefore, as it is well known that smaller arteries are stiffer, the viscoelastic coefficient rises when approaching the peripheral sites to compensate the rise of the Young's modulus, resulting in a higher damping effect. We incorporated the fitted viscoelastic coefficients in a nonlinear 1D fluid model to compute the pulse waves in the network. The damping effect of viscoelasticity on the high-frequency waves is clear especially at the peripheral sites.