Peripheral Pressure Waveform Research Articles

Non-invasive assessment of stroke volume and cardiovascular parameters based on peripheral pressure waveform.

Cardiovascular diseases are the leading cause of death globally, making the development of non-invasive and simple-to-use tools that bring insights into the state of the cardiovascular system of utmost importance. We investigated the possibility of using peripheral pulse wave recordings to estimate stroke volume (SV) and subject-specific parameters describing the selected properties of the cardiovascular system. Peripheral pressure waveforms were recorded in the radial artery using applanation tonometry (SphygmoCor) in 35 hemodialysis (HD) patients and 14 healthy subjects. The pressure waveforms were then used to estimate subject-specific parameters of a mathematical model of pulse wave propagation coupled with the elastance-based model of the left ventricle. Bioimpedance cardiography measurements (PhysioFlow) were performed to validate the model-estimated SV. Mean absolute percentage error between the simulated and measured pressure waveforms was 4.0% and 2.8% for the HD and control group, respectively. We obtained a moderate correlation between the model-estimated and bioimpedance-based SV (r = 0.57, p<0.05, and r = 0.58, p<0.001, for the control group and HD patients, respectively). We also observed a correlation between the estimated end-systolic elastance of the left ventricle and the peripheral systolic pressure in both HD patients (r = 0.84, p<0.001) and the control group (r = 0.70, p<0.01). These preliminary results suggest that, after additional validation and possibly further refinement to increase accuracy, the proposed methodology could support non-invasive assessment of stroke volume and selected heart function parameters and vascular properties. Importantly, the proposed method could be potentially implemented in the existing devices measuring peripheral pressure waveforms.

The Effect of Bariatric Surgery on Microvascular Structure and Function, Peripheral Pressure Waveform and General Cardiovascular Risk: A Longitudinal Study

Purpose: This study aims to assess the effect of bariatric surgery on retinal microvascular calibre, peripheral microvascular function, peripheral pressure waveforms, and the general cardiovascular disease (CVD) risk in obese individuals after undergoing Roux-en-Y gastric bypass (RYGB) surgery. Methods: A total of 29 obese participants were included in the study. All of the measurements were conducted at two time points: before and one year following the bariatric surgery procedure. General anthropometric data, as well as blood markers for glucose, cholesterol, and triglycerides were assessed in all individuals. In all participants, the Framingham risk score (FRS), and retinal vessel calibre measurements, using a Zeiss fundus camera and VesselMap software (ImedosSystems, Jena, Germany), were performed. Systemic arterial stiffness was measured by pulse wave analysis (PWA), and peripheral microvascular reactivity by way of digital thermal monitoring (DTM) in all participants. Results: As expected, various general anthropometric parameters, including body mass index (BMI), waist circumference and neck circumference, were significantly decreased post-surgery comparing to baseline in all individuals (all p &lt; 0.001). In addition, their general CVD risk, as measured using FRS, was significantly improved (p &lt; 0.001). At the retinal vascular level, central retinal artery equivalent (CRAE) as well as, central retinal vein equivalent (CRVE) had increased after surgery comparing to the baseline values (p = 0.003 and p = 0.007, respectively). In addition, both systemic arterial stiffness and peripheral microvascular reactivity had improved in all participants (p &lt; 0.001 and p = 0.008 respectively). Conclusions: Our findings suggest that bariatric surgery has a positive effect on the overall vascular health, as well as on the general CVD risk of the obese patients undergoing this procedure.

Is It Feasible to Reconstruct Aortic Pressure Waveform Based On a 1d Uniform Model of the Arterial Tree?

Abstract Based on a 1D uniform model of the arterial tree, various machine-learning techniques have been explored to reconstruct aortic pressure waveform (APW) from peripheral pressure waveform (PPW). This study aims to examine the feasibility of such reconstruction. Based on a 1D uniform vibrating-string model, transfer function (TF) of PPW to APW contains four harmonics-dependent parameters: value and phase of reflection coefficient (i.e., load impedance) at periphery and transmission parameter and transmission loss in the aorta-periphery section, and they are all harmonics-dependent. Pressure waveforms and blood velocity waveforms at the ascending aorta (AA), the carotid artery (CA), and the radial artery (RA) at different ages in a database are analyzed to calculate 1) reflection coefficient at the CA and the RA as two peripheries, 2) TF for the AA-CA and AA-RA sections, and 3) transmission parameter and transmission loss in the two sections. Harmonics-dependence of the four parameters varies with aging for both sections, revealing unpracticality of any mathematical model for harmonics-dependence of load impedance. Compared with fluid-loading, arterial non-uniformity significantly affects wave transmission. Transmission loss dramatically alters reconstructed APW, relative to higher harmonics. A 1D uniform model allows accurate reconstruction of APW from PPW, with a caveat that baseline values for the four parameters at different harmonics under different cardiovascular (CV) conditions need to be established a priori. Alternatively, based on the baseline values, PPW can be directly utilized for inferring the CV conditions.

Mock circulatory loop generated database for dynamic characterization of pressure-based cardiac output monitoring systems

Pulse contour cardiac output monitoring systems allow real-time and continuous estimation of hemodynamic variables such as cardiac output (CO) and stroke volume variation (SVV) by analysis of arterial blood pressure waveforms. However, evaluating the performance of CO monitoring systems to measure the small variations in these variables sometimes used to guide fluid therapy is a challenge due to limitations in clinical reference methods. We developed a non-clinical database as a tool for assessing the dynamic attributes of pressure-based CO monitoring systems, including CO response time and CO and SVV resolutions. We developed a mock circulation loop (MCL) that can simulate rapid changes in different parameters, such as CO and SVV. The MCL was configured to simulate three different states (normovolemic, cardiogenic shock, and hyperdynamic) representing a range of flow and pressure conditions. For each state, we simulated stepwise changes in the MCL flow and collected datasets for characterizing pressure-based CO systems. Nine datasets were generated that contain hours of peripheral pressure, central flow and pressure waveforms. The MCL-generated database is provided open access as a tool for evaluating dynamic characteristics of pressure-based CO algorithms and systems in detecting variations in CO and SVV indices. In an example application of the database, a CO response time of 10 s, CO and SVV resolutions with lower and upper limits of (−9.1%, 8.4%) and (−5.0%, 3.8%), respectively, were determined for a pressure-based CO benchtop system. This tool will support a more comprehensive assessment of pressure-based CO monitoring systems and algorithms.

A Vibrating-String Model for Closed-Loop Wave Transmission and Reflection Between the Aorta and Periphery

Abstract A tube-load model is used to reconstruct aortic pressure waveform from peripheral pressure waveform. Yet, the reconstructed aortic pressure waveform is greatly affected by load impedance used. In this work, a vibrating-string model for closed-loop wave transmission and reflection between the aorta and periphery is developed to examine the roles of all the parameters involved in aortic pressure waveform. The arterial pulsatile wave theory gives rise to the standard one-dimensional wave equation for a vibrating string. A vibrating-string model based on radial displacement of the arterial wall is developed to relate aortic pressure waveform to peripheral pressure waveform, relate load impedance to input impedance, and derive theoretical expressions for associated clinical indices. The vibrating-string model is extended to incorporate blood velocity and is further connected to the left ventricle (LV) to study the role of the LV in aortic pressure waveform. The difference between the vibrating-string model and the tube-load model is also examined. Load impedance is identified as an indispensable independent parameter for reconstruction of aortic pressure waveform with accuracy, and its physiologically realistic harmonic dependence can only be obtained from the measured input impedance. The derived expressions for clinical indices interpret some clinical findings and underscore the role of harmonics in clinical indices. Some misconceptions in the tube-load model are revealed, including load impedance and characteristic impedance. This work clarifies the role of harmonics-dependence of load impedance and harmonics of aortic pressure waveform in determining clinical indices.

Personalized aortic pressure waveform estimation from brachial pressure waveform using an adaptive transfer function

Background and objectiveThe aortic pressure waveform (APW) provides reliable information for the diagnosis of cardiovascular disease. APW is often measured using a generalized transfer function (GTF) applied to the peripheral pressure waveform acquired noninvasively, to avoid the significant risks of invasive APW acquisition. However, the GTF ignores various physiological conditions, which affects the accuracy of the estimated APW. To solve this problem, this study utilized an adaptive transfer function (ATF) combined with a tube-load model to achieve personalized and accurate estimation of APW from the brachial pressure waveform (BPW). MethodsThe proposed method was validated using APWs and BPWs from 34 patients. The ATF was defined using a tube-load model in which pulse transit time and reflection coefficients were determined from, respectively, the diastolic-exponential-pressure-decay of the APW and a piece-wise constant approximation. The root-mean-square-error of overall morphology, mean absolute errors of common hemodynamic indices (systolic blood pressure, diastolic blood pressure and pulse pressure) were used to evaluate the ATF. ResultsThe proposed ATF performed better in estimating diastolic blood pressure and pulse pressure (1.63 versus 1.94 mmHg, and 2.37 versus 3.10 mmHg, respectively, both P < 0.10), and produced similar errors in overall morphology and systolic blood pressure (3.91 versus 4.24 mmHg, and 2.83 versus 2.91 mmHg, respectively, both P > 0.10) compared to GTF. ConclusionUnlike the GTF which uses fixed parameters trained on existing clinical datasets, the proposed method can achieve personalized estimation of APW. Hence, it provides accurate pulsatile hemodynamic measures for the evaluation of cardiovascular function.

A strategy to personalize a 1D pulse wave propagation model for estimating subject-specific central aortic pressure waveform

The central aortic pressure (CAP) provides insights into the prediction, prevention, diagnosis, and treatment of cardiovascular disease, but can't be directly measured non-invasively. Therefore, the development of a noninvasive CAP estimation method based on the non-invasively measured peripheral pressure waveform is critical for clinical decisions based on the CAP. Some existing widely applied methods, such as the generalized transfer function (GTF) method relating measured peripheral pressure to the CAP, do not or only partly account for inter-subject or intra-subject variability of the cardiovascular system. To overcome this pitfall, we propose a subject-specific central aortic pressure estimation method in this paper. The novel method presented can derive an accurate aortic pressure from the peripheral pressure based on an individualized pulse wave propagation model using a GTF method as a first guess. To develop a strategy to personalize a pulse wave propagation model one usually needs to optimize many input parameters. Therefore, we present a two-step approach with the screening method of Morris and the adaptive sparse generalized polynomial chaos expansion (agPCE) algorithm for the sensitivity analysis of the wave propagation model. First, for a-priori defined output of the model, a subset of important parameters is identified using the screening method of Morris. Next, a quantitative variance-based sensitivity analysis is performed using agPCE. This approach is applied to a 1D pulse wave propagation model to get the personalized parameters of the pulse wave propagation model for the estimation of a subject-specific central aortic pressure waveform and is validated with 26 patients. Compared with the GTF method, the proposed method showed better performance in estimating the central aortic pulse wave and predicting the parameters.

Simultaneous adaption of the gain and phase of a generalized transfer function for aortic pressure waveform estimation

GoalThis paper proposes and validates a completely adaptive transfer function (CATF) based on an autoregressive exogenous (ARX) model which adjusts the gain and phase of a generalized transfer function (GTF) simultaneously to estimate the aortic pressure waveform from a brachial pressure waveform. MethodsInvasive aortic and brachial pressure waveforms were recorded from 34 subjects for the validation of the proposed method. Individual transfer functions (ITFs) were trained based on the pressure waveforms using an ARX model. The GTF was derived by averaging the ITFs. CATF was then obtained by adjusting both the gain and phase of the GTF using regression formulas calculated from the ITFs and brachial hemodynamic parameters. Meanwhile the quantitative contributions of the adaption of gain and phase of the GTF were investigated respectively. The root-mean-square-error of the total waveform and absolute errors of common hemodynamic indices including systolic and diastolic blood pressures (SBP and DBP, respectively), pulse pressure (PP) and augmentation index were used to evaluate the performance of the proposed method in the data divided into low, middle and high PP amplification groups. ResultsThe CATF achieved lower errors for DBP and PP in the low PP amplification group (1.79 versus 2.10 mmHg and 5.08 versus 6.23 mmHg, respectively, both P < 0.05) and PP in the middle amplification group (1.43 versus 1.92 mmHg, P < 0.05) compared with the GTF. SignificanceThe proposed method provides a step towards the development of an improved and clinically useful non-invasive approach for estimating the aortic pressure waveform from a peripheral pressure waveform.

Transfer-function-free technique for the noninvasive determination of the human arterial pressure waveform.

The estimation of central aortic blood pressure is a cardinal measurement, carrying effective physiological, and prognostic data beyond routine peripheral blood pressure. Transfer function‐based devices effectively estimate aortic systolic and diastolic blood pressure from peripheral pressure waveforms, but the reconstructed pressure waveform seems to preserve features of the peripheral waveform. We sought to develop a new method for converting the local diameter distension waveform into a pressure waveform, through an exponential function whose parameters depend on the local wave speed. The proposed method was then tested at the common carotid artery. Diameter and blood velocity waveforms were acquired via ultrasound at the right common carotid artery while simultaneously recording pressure at the left common carotid artery via tonometer in 203 people (122 men, 50 ± 18 years). The wave speed was noninvasively estimated via the lnDU‐loop method and then used to define the exponential function to convert the diameter into pressure. Noninvasive systolic and mean pressures estimated by the new technique were 3.8 ± 21.8 (p = 0.015) and 2.3 ± 9.6 mmHg (p = 0.011) higher than those obtained using tonometery. However, differences were much reduced and not significant in people >35 years (0.6 ± 18.7 and 0.8 ± 8.3 mmHg, respectively). This proof of concept study demonstrated that local wave speed, estimated from noninvasive local measurement of diameter and flow velocity, can be used to determine an exponential function that describes the relationship between local pressure and diameter. This pressure‐diameter function can then be used for the noninvasive estimation of local arterial pressure.

Anesthetics affect peripheral venous pressure waveforms and the cross-talk with arterial pressure

Analysis of peripheral venous pressure (PVP) waveforms is a novel method of monitoring intravascular volume. Two pediatric cohorts were studied to test the effect of anesthetic agents on the PVP waveform and cross-talk between peripheral veins and arteries: (1) dehydration setting in a pyloromyotomy using the infused anesthetic propofol and (2) hemorrhage setting during elective surgery for craniosynostosis with the inhaled anesthetic isoflurane. PVP waveforms were collected from 39 patients that received propofol and 9 that received isoflurane. A multiple analysis of variance test determined if anesthetics influence the PVP waveform. A prediction system was built using k-nearest neighbor (k-NN) to distinguish between: (1) PVP waveforms with and without propofol and (2) different minimum alveolar concentration (MAC) groups of isoflurane. 52 porcine, 5 propofol, and 7 isoflurane subjects were used to determine the cross-talk between veins and arteries at the heart and respiratory rate frequency during: (a) during and after bleeding with constant anesthesia, (b) before and after propofol, and (c) at each MAC value. PVP waveforms are influenced by anesthetics, determined by MANOVA: p value < 0.01, η2 = 0.478 for hypovolemic, and η2 = 0.388 for euvolemic conditions. The k-NN prediction models had 82% and 77% accuracy for detecting propofol and MAC, respectively. The cross-talk relationship at each stage was: (a) ρ = 0.95, (b) ρ = 0.96, and (c) could not be evaluated using this cohort. Future research should consider anesthetic agents when analyzing PVP waveforms developing future clinical monitoring technology that uses PVP.

Precision calibration of peripheral pressure waveforms using intra-arterial blood pressure reveals the need for improved ways to accurately estimate aortic blood pressure

BackgroundEstimating aortic blood pressure (BP) non-invasively requires peripheral waveform calibration using cuff systolic (SBP) and diastolic (DBP). Accuracy of estimated aortic BP has never been determined when peripheral waveforms are precision calibrated using peripheral intra-arterial SBP/DBP. This is relevant to understanding the best methods to estimate aortic BP accurately and was the aim of this study. We also determined how other calibrations influence estimated aortic BP accuracy.MethodsAscending aortic, brachial and radial artery intra-arterial BP was measured among 104 patients (61.8 ± 10 years, 66% male) undergoing coronary angiography. Intra- arterial aortic SBP was compared with estimated aortic SBP by generalised transfer function (SphygmoCor) using: (1) intraarterial brachial pressure waveforms calibrated with intra-arterial brachial SBP/DBP; (2) intra-arterial radial pressure waveforms calibrated with intra-arterial brachial SBP/DBP and (3) radial SBP/DBP and; (4) intra-arterial aortic mean arterial pressure (MAP)/DBP.ResultsAll intra-arterial SBP/DBP peripheral waveform calibrations significantly underestimated intra-arterial aortic SBP ((1) −4.5 ± 7.0 mmHg; (2) −8.8 ± 8.0 mmHg and (3) −5.4 ± 7.6 mmHg; p < 0.0001 all). Conversely, intra-arterial aortic MAP/DBP calibration (4) accurately estimated aortic SBP (0.03 ± 4.6 mmHg, p = 0.95). Underestimation of intra-arterial aortic SBP was related to lower aortic-to-brachial SBP amplification (r > 0.25, p < 0.009 all calibrations).ConclusionEven when using accurate (intra-arterial) SBP/DBP for precision peripheral waveform calibration, aortic SBP was significantly underestimated. Intra-arterial aortic MAP/DBP was the most accurate calibration, but is not feasible for non-invasive use. These findings highlight the need for improved ways to accurately estimate aortic SBP.

PP.19.27] APPLICATION OF NEURAL NETWORKS FOR ESTIMATION OF AORTIC SYSTOLIC PRESSURE FROM PERIPHERAL SYSTOLIC AND DIASTOLIC PRESSURE

Objective: Methods for estimation of aortic systolic blood pressure (SBP) require recording of a peripheral pressure waveform, which requires specialised devices for brachial volumetric or radial tonometric pulse measurement. This study investigates the possibility of aortic SBP estimation from peripheral SBP and diastolic blood pressure (DBP) using artificial neural networks (ANN) with (ANN[SBP,DBP,HR]) and without (ANN[SBP,DBP]) heart rate (HR). These parameters were investigated as they are readily available in conventional brachial sphygmomanometry. As a proof of concept and to remove measurement error, the theory was investigated using invasive measurements of blood pressure. Design and method: Ten-fold cross validation was applied to invasive, simultaneously recorded aortic and radial pressure during rest and vasodilation following nitroglycerin (6 g/kg/min)) infusion in subjects (n =62) drawn from a patient cohort previously reported. The results of the ANN models were compared to an ANN model using additional waveform features (ANNwaveform), to an N-point moving average method (NPMA) and to an existing, validated generalized transfer function (GTF). Results: Estimated aortic SBP for all methods was on average <1 mmHg different to measured aortic SBP with the exception of NPMA (difference 2.0 ± 3.5 mmHg, p = 0.62). Variability of the difference was significantly greater in ANN[SBP,DBP,HR] and ANN[SBP,DBP] (both S.D. of ± 5.9 mmHg, p < 0.001 compared to the GTF method, ± 4.0 mmHg, p < 0.001). Inclusion of waveform features decreased the variability (ANNwaveform ± 3.9 mmHg, p = 0.264). Estimated aortic SBP in all models was correlated with measured SBP, with ANN models providing statistically similar results to the GTF method, only the NPMA being statistically different (p = 0.031). Conclusions: These findings indicate that ANN applied to peripheral SBP, DBP and HR alone can provide aortic SBP estimation comparable to the GTF, albeit with slightly greater variance. Pending noninvasive validation using brachial sphygmomanometry techniques, the ANN method provides plausible aortic SBP estimation without waveform analysis, allowing potential inclusion in conventional brachial sphygmomanometer devices.

Validation of a novel nonlinear black box Wiener System model for arterial pulse transmission

Numerous linear dynamic models exist for describing the arterial pulse transmission phenomenon. We introduce a novel Wiener system based model in which a linear filter representing large arteries is coupled with a hysteresis-free nonlinear function representing complex wave transmission of branching arteries and the periphery. Experimental datasets (n = 7) are used to first estimate the Wiener model with linear, quadratic and cubic function for the aorta to radial artery pulse transmission and aorta to femoral artery pulse transmission. To model the nonlinear memoryless monotonic function in the Wiener System model, a correlation study is performed for linear finite impulse response (FIR) filter simulated peripheral pressure vs. measured peripheral pressure waveform. Each of this correlation curves were fitted to linear, quadratic and cubic polynomial equation. Wiener model is then simulated for aortic-to-radial artery as well as aortic-to-femoral artery to reconstruct radial and femoral pressure waveform. It was found that Wiener model with 3rd order polynomial function yielded better modeling accuracy (with average RMSE = 2.187 mmHg for radial and 4.281 mmHg for femoral pressure) than that from 2nd order polynomial function (with average RMSE = 2.242 mmHg for radial and 4.355 mmHg for femoral pressure) which in turn was better than mere linear FIR filter (with average RMSE = 2.604 mmHg for radial and 4.810 mmHg for femoral pressure).

Hemodynamic Impact of the C-Pulse Cardiac Support Device: A One-Dimensional Arterial Model Study.

The C-Pulse is a novel extra-aortic counter-pulsation device to unload the heart in patients with heart failure. Its impact on overall hemodynamics, however, is not fully understood. In this study, the function of the C-Pulse heart assist system is implemented in a one-dimensional (1-D) model of the arterial tree, and central and peripheral pressure and flow waveforms with the C-Pulse turned on and off were simulated. The results were studied using wave intensity analysis and compared with in vivo data measured non-invasively in three patients with heart failure and with invasive data measured in a large animal (pig). In all cases the activation of the C-Pulse was discernible by the presence of a diastolic augmentation in the pressure and flow waveforms. Activation of the device initiates a forward traveling compression wave, whereas a forward traveling expansion wave is associated to the device relaxation, with waves exerting an action in the coronary and the carotid vascular beds. We also found that the stiffness of the arterial tree is an important determinant of action of the device. In settings with reduced arterial compliance, the same level of aortic compression demands higher values of external pressure, leading to stronger hemodynamic effects and enhanced perfusion. We conclude that the 1-D model may be used as an efficient tool for predicting the hemodynamic impact of the C-Pulse system in the entire arterial tree, complementing in vivo observations.

OP.6B.01] DIRECT ESTIMATION OF CENTRAL SYSTOLIC PRESSURE FROM PERIPHERAL PRESSURES

Objective: The amplification of systolic pressure from aorta to the brachial artery depends on many factors including age, gender, patient's height, heart rate and arterial stiffness. Non-invasive estimation of central pressure usually requires a transfer function and/or the morphological analysis of peripheral pressure waveforms (e.g., as obtained by using arterial tonometry). We have developed a new method (DCBP) to estimate central systolic blood pressure (cSBP) directly from peripheral pressure values. Design and method: The accuracy of the DCBP method was tested in the present meta-analysis in which only invasive studies with high fidelity pressure recordings at both the central aortic level and brachial level were included. Results: Five studies fulfilled the inclusion criteria with a total of 282 subjects (77.3% male). All measurements were obtained during diagnostic cardiac catheterization for suspected coronary artery disease (CAD). Mean age ± SD was 63.3 ± 13.2 years, heart rate was 67.1 ± 11.3 bpm and high fidelity invasive brachial pressures were 137.9 ± 19.9 mmHg, 70.9 ± 10.2 mmHg and 97.1 ± 11.7 mmHg for systolic, diastolic and mean pressures respectively. The measured invasive cSBP was 131.1 ± 19.9 mmHg (amplification of systolic pressure = 6.8 mmHg). The mean cSBP estimated by the DCBP method was 132.9 mmHg. The mean difference compared to invasive value was 1.8 mmHg (1.4%). Conclusions: This meta-analysis from invasive high-fidelity pressure data from the literature showed that our DCBP method may accurately estimate cSBP from peripheral pressure values, without the need to perform a pressure waveform analysis or to use a transfer function. The results pertained strictly to the studied population (patients aged 63.3 years on average and patients with a high suspicion of CAD). Further prospective studies are required to confirm our results and also to document the accuracy and precision of our DCBP method in various populations as well as the sensitivity of DCBP to the peripheral pressure measurement method.

Clinical Assessment of Central Blood Pressure

Central aortic blood pressure (CBP) is increasingly considered a better cardiovascular prognostic marker than conventional cuff brachial blood pressure. Because CBP cannot be directly measured noninvasively, it has to be estimated from peripheral pressure pulses. To assess estimated CBP appropriately, the accuracy and features of the estimation method should be considered. The aim of this review is to provide basic knowledge and information useful for interpreting and assessing estimated CBP from a methodological point of view. Precise peripheral pressure pulse recording has been enabled by the introduction of arterial applanation tonometry, for which the radial artery may be the optimal site. An automated tonometry device utilizing a sensor array is preferable in terms of reproducibility and objectivity. Calibration of a peripheral pressure waveform has unresolved problems for any estimation method, due to imperfect brachial sphygmomanometry. However, if central and peripheral pressure calibrations are equivalent, two major methods to estimate CBP—those based on generalized pressure transfer function or radial late systolic pressure—may be comparable in their accuracy of CBP parameter estimation.

Pulse pressure amplification in relation to body fatness

Aortic-brachial pulse pressure amplification (PPA) is a measure of arterial elasticity and it is also an independent cardiovascular risk factor. The PPA is mainly determined by age, height, central and peripheral pressure waveforms characteristics, including measures of arterial stiffness and wave reflection. In this study, however, we demonstrate that PPA is also significantly associated with indirect indices of body fatness. As the body fatness is treatable, our findings might be used as a reference for future studies on the effects of body fat reduction on PPA and the PPA-related cardiovascular risk. AIMS Arterial pressure transfer to the periphery is accompanied by pulse pressure amplification (PPA). Pulse pressure is influence by body fat. The purpose of the present study was to evaluate any possible inter-relation between body fatness and PPA in healthy subjects. Haemodynamic and wave reflection indices were estimated by pulse wave analysis. Body fat was measured by bio-impedance. A total of 367 healthy volunteers (136 men and 231 women) was studied. Pulse pressure amplification correlated significantly with percentage of body fat (r=-0.53, P < 0.0001), age (r=-0.62, P < 0.0001), height (r= 0.43, P < 0.0001), heart rate (r= 0.28, P < 0.0001) and mean blood pressure (r=-0.29, P < 0.0001). The association of PPA with body fat was also significant in a multiple linear regression model. Age was an independent predictor of PPA and analysis of study subjects subdivided into two groups, those <50 years and those >50 years showed that body fatness correlated inversely and significantly with PPA in individuals both younger and older than 50 years (r=-0.44, P < 0.0001, r=-0.37, P < 0.0001 respectively). Augmentation pressure was also associated significantly with percentage of body fat in both subgroups (r= 0.48, P < 0.0001 and r= 0.49, P < 0.0001 respectively). This study performed on healthy subjects showed that pulse pressure amplification is related to body fatness over a wide age range. Percentage body fat is significantly associated with augmentation pressure, a component of central pulse pressure.

E-005 ESTIMATION OF CENTRAL PULSE PRESSURE FROM PERIPHERAL PRESSURE WAVEFORMS

Objectives The aim of this study was to identify the major source of errors and to improve the accuracy for the noninvasive estimation of central aortic pulse pressure (PP). Background The clinical value of central PP may have been underestimated due to the inaccuracy of its measurement. Methods Invasive high-fidelity right brachial and central aortic pressure tracings and left-brachial pulse volume plethysmography (PVP) waveforms from an oscillometric blood pressure monitor were all digitized simultaneously in 40 patients during cardiac catheterization. Invasive brachial pressure waveforms were used to estimate the central PP by both the generalized transfer function (GTF) and the pressure waveform analysis (PWA) approaches. A noninvasive PWA prediction model was also constructed using the PVP waveforms calibrated by the oscillometric brachial pressures. Results Errors between the central PP and invasive estimates by GTF and PWA were −0.8 ± 3.8 mmHg and 0.0 ± 3.5 mmHg, respectively. However, the oscillometric brachial PP underestimated the invasive brachial PP by −7.4 ± 11.7 mmHg. When the brachial pressure waveforms were purposefully calibrated using oscillometric brachial pressures, the errors of the estimation of central PP were markedly inflated (−7.3 ± 8.4 mmHg by GTF, and −11.7 ± 8.6 mmHg by PWA, respectively). In contrast, the error of the noninvasive PVP model was 0.0 ± 6.7 mmHg. Conclusions Large errors are introduced into the central PP estimates from the oscillometric brachial pressures. The accuracy may be improved when the oscillometric pressures calibrated peripheral pressure waveforms are used in the construction of the prediction models.

Closer to Noninvasive Out-of-Office Aortic Blood Pressure Assessment

See related article, pp 825–832 The noninvasive assessment of central (aortic or carotid) blood pressure (BP) is a challenge to the clinical investigation of arterial hypertension. The importance of central BP was recognized decades ago on the basis of invasive hemodynamic studies1 but has become widely acknowledged during the last 15 years.2 This is largely attributed to the work of highly respected pioneers, like Michael O'Rourke's team, who created the first commercial device (SphygmoCor, AtCor, Sydney, Australia), enabling the wide application of the noninvasive assessment of aortic BP.3 In this issue of Hypertension , Weber et al4 present the validation of a novel BP recording device that noninvasively assesses aortic BP based on the brachial cuff oscillometric method and the application of mathematical transformation. The present invasive and noninvasive validation is of importance because it is a straightforward demonstration of the strengths and disadvantages of a new generation of BP devices that opens the way to out-of-office aortic BP self-assessment. After briefly reviewing the reasons for estimating central BP, we discuss the novelty of this device, the findings, and limitations of this study.4 and some of the major issues that should arise in future clinical research and practice. ### Why Assess Central Pressure? The reasons have been extensively addressed in recent expert opinions and reviews1,5 and are only summarized here. Normally, central systolic BP (SBP) and pulse pressure (PP) are lower than peripheral SBP and PP (from 1 to 30 mm Hg). This PP disparity, defined as PP amplification,1 is an undisputable physiological phenomenon and depends on the individual's hemodynamic and pathophysiological characteristics.1,6 Notably, PP amplification exhibits substantial within-subject time variability (eg, from 5 to 20 mm Hg; Figure) that is mainly attributed to variations in heart rate, pressure wave reflections, and arterial …

Pulse Wave Analysis

See related article, pp 1108–1116 Applanation tonometry, a simple noninvasive technique for recording a high-fidelity waveform from an accessible pulse, has facilitated pulse wave analysis in large population studies. Most interest has focused on estimating an aortic or “central” waveform by the application of a filter or generalized transfer function applied to a radial waveform.1 Systolic pressure and pulse pressure differ between central and peripheral arteries as a result of peripheral amplification. By contrast, there is little regional variation in mean and diastolic pressures within large arteries and conduit arteries. A synthesized aortic waveform can, thus, be calibrated on the assumption of equality of mean and diastolic pressures at central and peripheral sites and used to estimate central systolic and pulse pressures. The main limitation of this approach, at least for the estimation of central systolic blood pressure, is the accuracy of the peripheral blood pressure measurements used to calibrate the waveform.2 Most studies and a recent meta-analysis suggest that central pressures provide a better prediction of cardiovascular risk than peripheral pressures,3 a finding intuitively rationalized in terms of the load on the left ventricle and coronary and carotid arteries. In addition to central blood pressure, a variety of indices can be obtained relating to inflection points or shoulders in the arterial waveform. Central augmentation pressure (AP) is the height above the first systolic shoulder of the aortic waveform and central augmentation index (AI; cAI) the ratio of AP to central pulse pressure (Figure). AP is thought to relate to pressure wave reflection from the periphery of the circulation and cAI to be an index of pressure wave reflection. However, this interpretation has been challenged, and it has been suggested …