Patient-specific non-invasive estimation of the aortic blood pressure waveform by ultrasound and tonometry.

BACKGROUND AND OBJECTIVE: Aortic blood pressure (ABP) is a more effective prognostic indicator of cardiovascular disease than peripheral blood pressure. A highly accurate algorithm for non-invasively deriving the ABP wave, based on ultrasonic measurement of aortic flow combined with peripheral pulse wave measurements, has been proposed elsewhere. However, it has remained at the proof-of-concept stage because it requires a priori knowledge of the ABP waveform to calculate aortic pulse wave velocity (PWV). The objective of this study is to transform this proof-of-concept algorithm into a clinically feasible technique. METHODS: We used the Bramwell-Hill equation to non-invasively calculate aortic PWV which was then used to reconstruct the ABP waveform from non-invasively determined aortic blood flow velocity, aortic diameter, and radial pressure. The two aortic variables were acquired by an ultrasound system from 90 subjects, followed by recordings of radial pressure using a SphygmoCor device. The ABPs estimated by the new algorithm were compared with reference values obtained by cardiac catheterization (invasive validation, 8 subjects aged 62.3 ± 12.7 years) and a SphygmoCor device (non-invasive validation, 82 subjects aged 45.0 ± 17.8 years). RESULTS: In the invasive comparison, there was good agreement between the estimated and directly measured pressures: the mean error in systolic blood pressure (SBP) was 1.4 ± 0.8 mmHg; diastolic blood pressure (DBP), 0.9 ± 0.8 mmHg; mean blood pressure (MBP), 1.8 ± 1.2 mmHg and pulse pressure (PP), 1.4 ± 1.1 mmHg. In the non-invasive comparison, the estimated and directly measured pressures also agreed well: the errors being: SBP, 2.0 ± 1.4 mmHg; DBP, 0.8 ± 0.1 mmHg; MBP, 0.1 ± 0.1 mmHg and PP, 2.3 ± 1.6 mmHg. The significance of the differences in mean errors between calculated and reference values for SBP, DBP, MBP and PP were assessed by paired t-tests. The agreement between the reference methods and those obtained by applying the new approach was also expressed by correlation and Bland-Altman plots. CONCLUSION: The new method proposed here can accurately estimate ABP, allowing this important variable to be obtained non-invasively, using standard, well validated measurement techniques. It thus has the potential to relocate ABP estimation from a research environment to more routine use in the cardiac clinic. SHORT ABSTRACT: A highly accurate algorithm for non-invasively deriving the ABP wave has been proposed elsewhere. However, it has remained at the proof-of-concept stage because it requires a priori knowledge of the ABP waveform to calculate aortic pulse wave velocity (PWV). This study aims to transform this proof-of-concept algorithm into a clinically feasible technique. We used the Bramwell-Hill equation to non-invasively calculate aortic PWV which was then used to reconstruct the ABP waveform. The ABPs estimated by the new algorithm were compared with reference values obtained by cardiac catheterization or a SphygmoCor device. The results showed that there was good agreement between the estimated and directly measured pressures. The new method proposed can accurately estimate ABP, allowing this important variable to be obtained non-invasively, using standard, well validated measurement techniques. It thus has the potential to relocate ABP estimation from a research environment to more routine use in the cardiac clinic.

Wave reflection quantification analysis and personalized flow wave estimation based on the central aortic pressure waveform.

Pulse wave reflections reflect cardiac afterload and perfusion, which yield valid indicators for monitoring cardiovascular status. Accurate quantification of pressure wave reflections requires the measurement of aortic flow wave. However, direct flow measurement involves extra equipment and well-trained operator. In this study, the personalized aortic flow waveform was estimated from the individual central aortic pressure waveform (CAPW) based on pressure-flow relations. The separated forward and backward pressure waves were used to calculate wave reflection indices such as reflection index (RI) and reflection magnitude (RM), as well as the central aortic pulse transit time (PTT). The effectiveness and feasibility of the method were validated by a set of clinical data (13 participants) and the Nektar1D Pulse Wave Database (4,374 subjects). The performance of the proposed personalized flow waveform method was compared with the traditional triangular flow waveform method and the recently proposed lognormal flow waveform method by statistical analyses. Results show that the root mean square error calculated by the personalized flow waveform approach is smaller than that of the typical triangular and lognormal flow methods, and the correlation coefficient with the measured flow waveform is higher. The estimated personalized flow waveform based on the characteristics of the CAPW can estimate wave reflection indices more accurately than the other two methods. The proposed personalized flow waveform method can be potentially used as a convenient alternative for the measurement of aortic flow waveform.

Estimation of central pulse wave velocity from radial pulse wave analysis.

BACKGROUND AND OBJECTIVE: Arterial stiffness, commonly assessed by carotid-femoral pulse wave velocity (cfPWV), is an independent biomarker for cardiovascular disease. The measurement of cfPWV, however, has been considered impractical for routine clinical application. Pulse wave analysis using a single pulse wave measurement in the radial artery is a convenient alternative. This study aims to identify pulse wave features for a more accurate estimation of cfPWV from a single radial pulse wave measurement. METHODS: From a dataset of 140 subjects, cfPWV was measured and the radial pulse waveform was recorded for 30 s twice in succession. Features were extracted from the waveforms in the time and frequency domains, as well as by wave separation analysis. All-possible regressions with bootstrapping, McHenry's select algorithm, and support vector regression were applied to compute models for cfPWV estimation. RESULTS: The correlation coefficients between the measured and estimated cfPWV were r = 0.81, r = 0.81, and r = 0.8 for all-possible regressions, McHenry's select algorithm, and support vector regression, respectively. The features selected by all-possible regressions are physiologically interpretable. In particular, the amplitude ratio of the diastolic peak to the notch of the radial pulse waveform (Rn,dr,P) is shown to be correlated with cfPWV. This correlation was further evaluated and found to be independent of wave reflections using a dataset (n = 3,325) of simulated pulse waves. CONCLUSIONS: The proposed method may serve as a convenient surrogate for the measurement of cfPWV. Rn,dr,P is associated with aortic pulse wave velocity and this association may not be dependent on wave reflection.

Noninvasive estimation of aortic pressure waveform based on simplified Kalman filter and dual peripheral artery pressure waveforms.

BACKGROUND AND OBJECTIVE: Aortic pressure (Pa) is important for the diagnosis of cardiovascular disease. However, its direct measurement is invasive, not risk-free, and relatively costly. In this paper, a new simplified Kalman filter (SKF) algorithm is employed for the reconstruction of the Pa waveform using dual peripheral artery pressure waveforms. METHODS: Pa waveforms obtained in a previous study were collected from 25 patients. Simultaneously, radial and femoral pressure waveforms were generated from two simulation experiments, using transfer functions. In the first, the transfer function is a known finite impulse response; and in the second, it is derived from a tube-load model. To analyze the performance of the proposed SKF algorithm, variable amounts of noise were added to the observed output signal, to give a range of signal-to-noise ratios (SNRs). Additionally, central aortic, brachial and femoral pressure waveforms were simultaneously collected from 2 Sprague-Dawley rats and the measured and reconstructed Pa waveforms were compared. RESULTS: The proposed SKF algorithm outperforms canonical correlation analysis (CCA), which is the current state-of-the-art blind system identification method for the non-invasive estimation of central aortic blood pressure. It is also shown that the proposed SKF algorithm is more noise-tolerant than the CCA algorithm over a wide range of SNRs. CONCLUSION: The simulations and animal experiments illustrate that the proposed SKF algorithm is accurate and stable in the face of low SNRs. Improved methods for estimating central blood pressure as a measure of cardiac load adds to their value as a prognostic and diagnostic tool.

Improving the accuracy and robustness of carotid-femoral pulse wave velocity measurement using a simplified tube-load model.

Arterial stiffness, as measured by pulse wave velocity, for the early non-invasive screening of cardiovascular disease is becoming ever more widely used and is an independent prognostic indicator for a variety of pathologies including arteriosclerosis. Carotid-femoral pulse wave velocity (cfPWV) is regarded as the gold standard for aortic stiffness. Existing algorithms for cfPWV estimation have been shown to have good repeatability and accuracy, however, further assessment is needed, especially when signal quality is compromised. We propose a method for calculating cfPWV based on a simplified tube-load model, which allows for the propagation and reflection of the pulse wave. In-vivo cfPWV measurements from 57 subjects and numerical cfPWV data based on a one-dimensional model were used to assess the method and its performance was compared to three other existing approaches (waveform matching, intersecting tangent, and cross-correlation). The cfPWV calculated using the simplified tube-load model had better repeatability than the other methods (Intra-group Correlation Coefficient, ICC = 0.985). The model was also more accurate than other methods (deviation, 0.13 ms-1) and was more robust when dealing with noisy signals. We conclude that the determination of cfPWV based on the proposed model can accurately and robustly evaluate arterial stiffness.

Ultrasound-based method for individualized estimation of central aortic blood pressure from flow velocity and diameter.

Central aortic blood pressure (CABP) is a better predictor for cardiovascular events than brachial blood pressure. However, direct CABP measurement is invasive. The objective of this paper is to develop an ultrasound-based method using individualized Windkessel (WK) models for non-invasive estimation of CABP. Three WK models (with two-, three- and four-element WK, named, WK2, WK3 and WK4, respectively) were created and the model parameters were individualized based on aortic flow velocity and diameter waveforms measured by ultrasound (US). Experimental data were acquired in 42 subjects aged 21-67 years. The CABP estimated by WK models was compared with the reference CABP obtained using a commercial system. The results showed that the overall performance of the WK3 and WK4 models was similar, outperforming the WK2 model. The estimated CABP based on WK3/WK4 model showed good agreement with the reference CABP: the absolute errors of systolic blood pressure (SBP), 2.4 ± 2.1/2.4 ± 2.0 mmHg; diastolic blood pressure (DBP), 1.4 ± 1.1/1.7 ± 1.5 mmHg; mean blood pressure (MBP), 1.3 ± 0.8/1.3 ± 0.8 mmHg; pulse pressure (PP), 3.0 ± 2.3/3.2 ± 2.6 mmHg; the root mean square error (RMSE) of the waveforms, 2.5 ± 1.0/2.6 ± 1.1 mmHg. Therefore, the proposed method can provide a non-invasive CABP estimation during routine cardiac US examination.

A review on low-dimensional physics-based models of systemic arteries: application to estimation of central aortic pressure.

The physiological processes and mechanisms of an arterial system are complex and subtle. Physics-based models have been proven to be a very useful tool to simulate actual physiological behavior of the arteries. The current physics-based models include high-dimensional models (2D and 3D models) and low-dimensional models (0D, 1D and tube-load models). High-dimensional models can describe the local hemodynamic information of arteries in detail. With regard to an exact model of the whole arterial system, a high-dimensional model is computationally impracticable since the complex geometry, viscosity or elastic properties and complex vectorial output need to be provided. For low-dimensional models, the structure, centerline and viscosity or elastic properties only need to be provided. Therefore, low-dimensional modeling with lower computational costs might be a more applicable approach to represent hemodynamic properties of the entire arterial system and these three types of low-dimensional models have been extensively used in the study of cardiovascular dynamics. In recent decades, application of physics-based models to estimate central aortic pressure has attracted increasing interest. However, to our best knowledge, there has been few review paper about reconstruction of central aortic pressure using these physics-based models. In this paper, three types of low-dimensional physical models (0D, 1D and tube-load models) of systemic arteries are reviewed, the application of three types of models on estimation of central aortic pressure is taken as an example to discuss their advantages and disadvantages, and the proper choice of models for specific researches and applications are advised.

Effects of exercise modalities on central hemodynamics, arterial stiffness and cardiac function in cardiovascular disease: Systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Exercise is accepted as an important contribution to the rehabilitation of patients with cardiovascular disease (CVD). This study aims to better understand the possible causes for lack of consensus and reviews the effects of three exercise modalities (aerobic, resistance and combined exercise) on central hemodynamics, arterial stiffness and cardiac function for better rehabilitation strategies in CVD. METHODS: The electronic data sources, Cochrane Library, MEDLINE, Web of Science, EBSCO (CINAHL), and ScienceDirect from inception to July 2017 were searched for randomized controlled trials (RCTs) investigating the effect of exercise modalities in adult patients with CVD. The effect size was estimated as mean differences (MD) with 95% confidence intervals (CI). Subgroup analysis and meta-regression were used to study potential moderating factors. RESULTS: Thirty-eight articles describing RCTs with a total of 2089 patients with CVD were included. The pooling revealed that aerobic exercise [MD(95%CI) = -5.87 (-8.85, -2.88), P = 0.0001] and resistance exercise [MD(95%CI) = -7.62 (-10.69, -4.54), P<0.00001] significantly decreased aortic systolic pressure (ASP). Resistance exercise significantly decreased aortic diastolic pressure [MD(95%CI) = -4(-5.63, -2.37), P<0.00001]. Aerobic exercise significantly decreased augmentation index (AIx) based on 24-week exercise duration and patients aged 50-60 years. Meanwhile, aerobic exercise significantly improved carotid-femoral pulse wave velocity (cf-PWV) [MD(95%CI) = -0.42 (-0.83, -0.01), P = 0.04], cardiac output (CO) [MD(95% CI) = 0.36(0.08, 0.64), P = 0.01] and left ventricular ejection fraction (LVEF) [MD(95%CI) = 3.02 (2.11, 3.93), P<0.00001]. Combined exercise significantly improved cf-PWV [MD(95%CI) = -1.15 (-1.95, -0.36), P = 0.004] and CO [MD(95% CI) = 0.9 (0.39, 1.41), P = 0.0006]. CONCLUSIONS: Aerobic and resistance exercise significantly decreased ASP, and long-term aerobic exercise reduced AIx. Meanwhile, aerobic and combined exercise significantly improved central arterial stiffness and cardiac function in patients with CVD. These findings suggest that a well-planned regime could optimize the beneficial effects of exercise and can provide some evidence-based guidance for those involved in cardiovascular rehabilitation of patients with CVD.

Validation of an Adaptive Transfer Function Method to Estimate the Aortic Pressure Waveform.

Aortic pulse wave reflects cardiovascular status, but, unlike the peripheral pulse wave, is difficult to be measured reliably using noninvasive techniques. Thus, the estimation of aortic pulse wave from peripheral ones is of great significance. This study proposed an adaptive transfer function (ATF) method to estimate the aortic pulse wave from the brachial pulse wave. Aortic and brachial pulse waves were derived from 26 patients who underwent cardiac catheterization. Generalized transfer functions (GTF) were derived based on the autoregressive exogenous model. Then, the GTF was adapted by its peak resonance frequency. And the optional peak resonance frequency for an individual was determined by regression formulas using brachial systolic blood pressure. The method was validated using the leave-one-out cross validation method. Compared with previous studies, the ATF method showed better performance in estimating the aortic pulse wave and predicting the feature parameters. The prediction error of the aortic systolic blood pressure and pulse pressure were 0.2 ± 3.1 and -0.9 ± 3.1 mmHg, respectively. The percentage errors of augmentation index, percentage notch amplitude, and ejection duration were -2.1 ± 32.7%, 12.4 ± 9.2%, and -2.4 ± 3.3%, respectively.