In vitro investigation of the impact of aortic valve stenosis severity on left coronary artery flow.

Patients with aortic valve stenosis (AS) may experience angina pectoris even if they have angiographically normal coronary arteries. Angina is associated with a marked increase in the risk of sudden death in AS patients. Only a few in vitro models describing the interaction between the left ventricular and aortic pressures, and the coronary circulation have been reported. These models were designed for specific research studies and they need to be improved or modified when other specific studies are required. Consequently, we have developed an in vitro model that is able to mimic the coronary circulation in presence of aortic stenosis. First, we have validated the model under physiological conditions. Then, we have examined and quantified the hemodynamic effects of different degrees of AS (from normal to severe AS) on the coronary flow using a model of the normal left coronary artery. In the coronary in vitro model without AS (normal valve), the amplitude and shape of coronary flow were similar to those observed in in vivo measurements obtained under physiological conditions, as described by Hozumi et al. (1998, "Noninvasive Assessment of Significant Left Anterior Descending Coronary Artery Stenosis by Coronary Flow Velocity Reserve With Transthoracic Color Doppler Echocardiography," Circulation, 97, pp. 1557-1562). The presence of an AS induced an increase in the maximum and mean coronary flow rates (97% and 73%, respectively, for a very severe AS). Furthermore, when AS was very severe, a retrograde flow occurred during systole. This study allowed us to validate our coronary in vitro model under physiological conditions, both in the absence and presence of AS. These changes could explain the fact that even if patients have angiographically normal epicardial coronary arteries, we can observe the occurrence of angina pectoris in these patients in the presence of an AS.

Optimization of Doppler velocity echocardiographic measurements using an automatic contour detection method.

Intra- and inter-observer variability in Doppler velocity echocardiographic measurements (DVEM) is a significant issue. Indeed, imprecisions of DVEM can lead to diagnostic errors, particularly in the quantification of the severity of heart valve dysfunction. To minimize the variability and rapidity of DVEM, we have developed an automatic method of Doppler velocity wave contour detection, based on active contour models. To validate our new method, results obtained with this method were compared to those obtained manually by an experienced echocardiographer on Doppler echocardiographic images of left ventricular outflow tract and transvalvular flow velocity signals recorded in 30 patients, 15 with aortic stenosis and 15 with mitral stenosis. We focused on three essential variables that are measured routinely by Doppler echocardiography in the clinical setting: the maximum velocity, the mean velocity and the velocity-time integral. Comparison between the two methods has shown a very good agreement (linear correlation coefficient R(2) = 0.99 between the automatically and the manually extracted variables). Moreover, the computation time was really short, about 5s. This new method applied to DVEM could, therefore, provide a useful tool to eliminate the intra- and inter-observer variabilities associated with DVEM and thereby to improve the diagnosis of cardiovascular disease. This automatic method could also allow the echocardiographer to realize these measurements within a much shorter period of time compared to standard manual tracing method. From a practical point of view, the model developed can be easily implanted in a standard echocardiographic system.

Impact of systemic hypertension on the assessment of aortic stenosis.

OBJECTIVE: To determine the effect of systemic arterial hypertension on the indices of aortic stenosis (AS) severity. METHODS: A severe supravalvar AS was created in 24 pigs. The maximum and mean pressure gradients across the stenosis were measured by Doppler echocardiography and by catheterisation. Both echocardiography and catheter data were used to calculate stenosis effective orifice area, energy loss coefficient, and peak systolic left ventricular wall stress. Measurements were taken both at normal aortic pressures and during hypertension induced by banding of the distal thoracic aorta in 14 pigs and by intravenous administration of phenylephrine in 10 pigs. RESULTS: During hypertension, systemic arterial resistance downstream from the stenosis increased greatly (all animals: 71 (40)%), whereas total systemic arterial compliance decreased significantly (-38 (21)%). Hypertension resulted in a moderate increase in effective orifice area (29 (14)%) and energy loss coefficient (25 (17)%) and substantial decreases in catheter gradients (maximum: -40 (20)%; mean: -43 (20)%; peak to peak: -70 (23)%) and Doppler gradients (maximum: -35 (17)%; mean: -37 (16)%). In multivariate analysis, peak to peak gradient was significantly (p < 0.001) related to the energy loss coefficient, mean flow rate, and arterial compliance, whereas maximum and mean catheter gradients were related only to the energy loss coefficient and flow rate. Of major importance, maximum systolic left ventricular wall stress increased greatly during hypertension (43 (23)%). CONCLUSIONS: The severity of AS may be partially masked by the presence of coexisting hypertension. The markers of AS severity should thus be interpreted with caution in hypertensive patients and be re-evaluated when the patient is in a normotensive state.

Monitoring respiratory rate based on tracheal sounds. First experiences.

The objective was to develop a non-invasive method for continuously monitoring respiratory rate (RR) based on tracheal sounds. 25 volunteers and 36 patients with chronic pulmonary diseases were enrolled in a clinical study. Tracheal sounds were acquired using a contact piezoelectric sensor placed on the examinee's throat and analyzed using a combined investigation of the sound envelope and frequency content. RR estimates were compared to reference measurements taken from a pneumotachometer coupled to a face mask worn by the examinee. RR was also manually counted by a respiratory technician. Two types of breathing (mouth and nose) and three different positions were studied (fowler, semi-fowler and supine). RR estimated in volunteers had a success rate (SR) of 96%, a correlation coefficient (r) of 0.99 and a standard error of the estimate (SEE) of 0.56. The RR estimated in patients was comparable or slightly better (SR = 85%, r = 0.93 and SEE = 1.49) than those obtained by manual count (SR = 82%, r = 0.91, SEE = 1.58), which is the method widely used in clinical settings. No significant difference in the capacity to estimate RR was found related to posture and breathing type, making this method useful for continuous monitoring.

Automated extraction of aortic and pulmonary components of the second heart sound for the estimation of pulmonary artery pressure.

The second heart sound, S2, is generally believed to be comprised of aortic (A2) and pulmonary (P2) components. Previously, the normalized splitting interval (NSI) between the A2 and P2 components has been shown to be proportional to the pulmonary artery pressure (PAP). A set of fully automated algorithms based on adaptive modeling of A2/P2 components using chirplets were developed to provide real-time estimates of PAP. The method was tested on 16 pigs which were administered drugs to induce pulmonary hypertension. Simultaneous reference pressure measurements were obtained with a pulmonary artery catheter (PAC). Estimation of PAP in pigs using the new techniques resulted in a correlation coefficient (r) of 0.84 and standard error (SEE) of 9.2 mm Hg. This is in line with echocardiography studies, which have a performance ranging from r=0.69-0.91 and SEE from 5 to 12 mm Hg when compared to PAC measurements. It is also consistent with previous results based on a manual estimation of PAP derived through image processing methods. Based on these findings, this method has the potential to offer continuous noninvasive monitoring of PAP.

Estimation of pulmonary arterial pressure by a neural network analysis using features based on time-frequency representations of the second heart sound.

The objective of the study was to develop a non-invasive method for the estimation of pulmonary arterial pressure (PAP) using a neural network (NN) and features extracted from the second heart sound (S2). To obtain the information required to train and test the NN, an animal model of pulmonary hypertension (PHT) was developed, and nine pigs were investigated. During the experiments, the electrocardiogram, phonocardiogram and PAP were recorded. Subsequently, between 15 and 50 S2 heart sounds were isolated for each PAP stage and for each animal studied. A Coiflet wavelet decomposition and a pseudo smoothed Wigner-Ville distribution were used to extract features from the S2 sounds and train a one-hidden-layer NN using two-thirds of the data. The NN performance was tested on the remaining one-third of the data. NN estimates of the systolic and mean PAPs were obtained for each S2 and then ensemble averaged over the 15-50 S2 sounds selected for each PAP stage. The standard errors between the mean and systolic PAPs estimated by the NN and those measured with a catheter were 6.0 mmHg and 8.4 mmHg, respectively, and the correlation coefficients were 0.89 and 0.86, respectively. The classification accuracy, using 23 mmHg mean PAP and 30 mmHg systolic PAP thresholds between normal PAP and PHT, was 97% and 91%, respectively.

A new, simple, and accurate method for non-invasive estimation of pulmonary arterial pressure.

OBJECTIVE: To develop and validate a new non-invasive method for the estimation of pulmonary arterial pressure (PAP) based on advanced signal processing of the second heart sound. DESIGN: Prospective comparative study. SETTING: Referral cardiology centre. PATIENTS: This method was first tested in 16 pigs with experimentally induced pulmonary hypertension and then in 23 patients undergoing pulmonary artery catheterisation. METHODS: The heart sounds were recorded at the surface of the thorax using a microphone connected to a personal computer. The splitting time interval between the aortic and the pulmonary components of the second heart sound was measured using a computer assisted spectral dechirping method and was normalised for heart rate. RESULTS: The systolic PAP varied between 14-73 mm Hg in pigs and between 20-70 mm Hg in patients. The normalised splitting interval was measurable in 97% of the recordings made in pigs and 91% of the recordings made in patients. There was a strong relation between the normalised splitting interval and the systolic PAP (pigs: r = 0.94, standard error of the estimate (SEE) = 5.3 mm Hg; patients: r = 0.84, SEE = 7.8 mm Hg) or the mean pulmonary pressure (pigs: r = 0.94, SEE = 4.1 mm Hg; patients: r = 0.85, SEE = 5.8 mm Hg). CONCLUSIONS: This study shows that this new non-invasive method based on advanced signal processing of the second heart sound provides an accurate estimation of the PAP.

Comparison of valve resistance with effective orifice area regarding flow dependence.

Aortic valve resistance has been proposed to represent the severity of aortic stenosis because some studies observed that it was less affected by change in flow than the valve-effective orifice area, but this issue remains controversial. The objective of this study was to systematically analyze the theoretical and practical determinants of these parameters in relation to changes in flow. Valve area and resistance in different valves were studied in vitro in a pulse duplicator system at different flow rates and in vivo in 90 subjects referred to either exercise or dobutamine infusion. Theoretical analysis and experimental results both demonstrated a unique relation between resistance (RES), valve-effective orifice area (EOA), and flow rate (Q): RES = K x (Q/EOA(2)). Accordingly, in fixed stenoses or in mechanical valves, resistance increased markedly with flow rate both in vitro (+0.88 +/- 0.26%/% of flow increase) and in vivo (mechanical valves: +2.09 +/- 4.61, fixed stenotic valves: +0.59 +/- 0.32%/%), whereas valve area did not change significantly (<0.2%/%). In contrast, in valves with a flexible orifice (bioprostheses and some patients with aortic stenosis), resistance was less increased due to the increase in valve area. Thus, both from a theoretical and a practical standpoint, valve resistance is much more flow dependent than valve area, particularly in fixed stenoses. Situations in which resistance does not increase with flow rate are unpredictable and are found in flexible valves when there is a concomitant increase in valve area.

Extraction of the aortic and pulmonary components of the second heart sound using a nonlinear transient chirp signal model.

The objective of this paper is to adapt and validate a nonlinear transient chirp signal modeling approach for the analysis and synthesis of overlapping aortic (A2) and pulmonary (P2) components of the second heart sound (S2). The approach is based on the time-frequency representation of multicomponent signals for estimating and reconstructing the instantaneous phase and amplitude functions of each component. To evaluate the accuracy of the approach, a simulated S2 with A2 and P2 components having different overlapping intervals (5-30 ms) was synthesized. The simulation results show that the technique is very effective for extracting the two components, even in the presence of noise (-15 dB). The normalized root-mean-squared error between the original A2 and P2 components and their reconstructed versions varied between 1% and 6%, proportionally to the duration of the overlapping interval, and it increased by less than 2% in the presence of noise. The validated technique was then applied to S2 components recorded in pigs under normal or high pulmonary artery pressures. The results show that this approach can successfully isolate and extract overlapping A2 and P2 components from successive S2 recordings obtained from different heartbeats of the same animal as well from different animals.

Performance of time-frequency representation techniques to measure blood flow turbulence with pulsed-wave Doppler ultrasound.

The current processing performed by commercial instruments to obtain the time-frequency representation (TFR) of pulsed-wave Doppler signals may not be adequate to characterize turbulent flow motions. The assessment of the intensity of turbulence is of high clinical importance and measuring high-frequency (small-scale) flow motions, using Doppler ultrasound (US), is a difficult problem that has been studied very little. The objective was to optimize the performance of the spectrogram (SPEC), autoregressive modeling (AR), Choi-Williams distribution (CWD), Choi-Williams reduced interference distribution (CW-RID), Bessel distribution (BD), and matching pursuit method (MP) for mean velocity waveform estimation and turbulence detection. The intensity of turbulence was measured from the fluctuations of the Doppler mean velocity obtained from a simulation model under pulsatile flow. The Kolmogorov spectrum, which is used to determine the frequency of the fluctuations and, thus, the scale of the turbulent motions, was also computed for each method. The best set of parameters for each TFR method was determined by minimizing the error of the absolute frequency fluctuations and Kolmogorov spectral bandwidth measured from the simulated and computed Doppler spectra. The results showed that different parameters must be used for each method to minimize the velocity variance of the estimator, to optimize the detection of the turbulent frequency fluctuations, and to estimate the Kolmogorov spectrum. To minimize the variance and to measure the absolute turbulent frequency fluctuations, four methods provided similar results: SPEC (10-ms sine-cosine windows), AR (10-ms rectangular windows, model order = 8), CWD (w(N) and w(M) = 10-ms rectangular windows, sigma = 0.01), and BD (w(N) = 10-ms rectangular windows, alpha = 16). The velocity variance in the absence of turbulence was on the order of 0.04 m/s (coefficient of variation ranging from 8.0% to 14.5%, depending on the method). With these spectral techniques, the peak of the turbulence intensity was adequately estimated (velocity bias < 0.01 m/s). To track the frequency of turbulence, the best method was BD (w(N) = 2-ms rectangular windows, alpha = 2). The bias in the estimate of the -10 dB bandwidth of the Kolmogorov spectrum was 354 +/- 51 Hz in the absence of turbulence (the true bandwidth should be 0 Hz), and -193 +/- 371 Hz with turbulence (the simulated -10-dB bandwidth was estimated at 1256 Hz instead of 1449 Hz). In conclusion, several TFR methods can be used to measure the magnitude of the turbulent fluctuations. To track eddies ranging from large vortex to small turbulent fluctuations (wide Kolmogorov spectrum), the Bessel distribution with appropriate set of parameters is recommended.

Quantitative assessment of in vitro jets based on three-dimensional color Doppler reconstruction.

Three-dimensional (3-D) color Doppler imaging of flow jets was performed to investigate the effects of flow rate and orifice size on jet volumes. Flow jets were generated using a flow model to simulate mitral regurgitation. This flow model consisted of a ventricular chamber, a valvular plate and an atrial chamber. Steady flow was driven through circular orifices having diameters of 2.5, 3.5, 4.5, and 6 mm, respectively, with flow rates of 5, 10, 15, 20, and 25 mL/s to form free jets in the atrial chamber. An ATL Ultramark 9 HDI system was used to perform 3-D color Doppler imaging of the flow jets. A transesophageal probe was rotated by a stepper motor to create 3-D color Doppler images of the jets. The color jet volumes for different hemodynamic conditions were measured and then compared with the theoretical predictions. Results showed that the jet volume estimated from the 3-D color Doppler was directly proportional to the flow rate and inversely proportional to the orifice size. The estimated jet volumes correlated well (r > 0.95) with theoretical predictions. This study supports the use of color jet volume as a parameter to quantify mitral regurgitation.

Analysis of the first heart sound using the matching pursuit method.

It is acknowledged that the first heart sound S1 consists of two major, high-frequency components M1 and T1, corresponding, respectively, to the vibrations of the mitral and tricuspid valves and their surrounding tissues following valve closure in early systole. In this study, the matching pursuit (MP) method was used to decompose S1 into a series of time-frequency atoms. M1 and T1 were separated from the parameterised atoms of S1. The first two dominant frequencies of M1 were identified and used as features of a linear classifier to diagnose mitral valve abnormality. This method was applied to two sets of S1 data recorded from 15 patients with normal, and 15 patients with abnormal, bioprosthetic mitral valves, respectively. It was found that the two features exhibit significant differences between the normal and abnormal sets (p< 0.001). Using these two features, a correct classification of 93% was obtained. In addition, when the Wigner distribution of S1 was calculated from the decomposed atoms and compared with a spectrogram, the MP method provided better results. The study demonstrates that the MP method may be a promising technique for heart sound analysis.

A virtual instrument for acquisition and analysis of the phonocardiogram and its internet-based application.

The objective of this study is to develop a phonocardiogram (PCG) acquisition and analysis instrument using virtual instrumentation technology and investigate its Internet-based application. The PCG instrument was developed using a Pentium 200 computer, a data acquisition board, and a two-channel custom designed bio-signal preamplifier. LabVIEW was used to create the instrument's front panels. Spectral and joint time-frequency analyses were implemented into the instrument. This instrument can be used to display the PCG and to analyze the individual heart sound and murmur for the detection of heart valve diseases. Using a test-bed, the PCG data acquisition and analysis were performed remotely over the Internet. Through the main PCG panel, an operator can control the acquisition and analysis of PCG signals. In the remote test, real-time transmission of the PCG signal over the Internet was possible. Remote operators were able to view smoothly scrolling PCG waveforms and could control all the acquisition parameters and perform spectral and time-frequency analyses on the acquired heart sound. This study demonstrated that a LabVIEW-based medical virtual instrument provides a low-cost and flexible solution for data acquisition and analysis of PCG. It also showed that the current Internet supports the transmission of real-time PCG signals. Compared with other telemedicine systems, this application transfers not only the medical data, but also the virtual instrument and its signal processing capability through the Internet.

Nonlinear transient chirp signal modeling of the aortic and pulmonary components of the second heart sound.

This paper describes a new approach based on the time-frequency representation of transient nonlinear chirp signals for modeling the aortic (A2) and the pulmonary (P2) components of the second heart sound (S2). It is demonstrated that each component is a narrow-band signal with decreasing instantaneous frequency defined by its instantaneous amplitude and its instantaneous phase. Each component is also a polynomial phase signal, the instantaneous phase of which can be accurately represented by a polynomial having an order of thirty. A dechirping approach is used to obtain the instantaneous amplitude of each component while reducing the effect of the background noise. The analysis-synthesis procedure is applied to 32 isolated A2 and 32 isolated P2 components recorded in four pigs with pulmonary hypertension. The mean +/- standard deviation of the normalized root-mean-squared error (NRMSE) and the correlation coefficient (rho) between the original and the synthesized signal components were: NRMSE = 2.1 +/- 0.3% and rho = 0.97 +/- 0.02 for A2 and NRMSE = 2.52 +/- 0.5% and rho = 0.96 +/- 0.02 for P2. These results confirm that each component can be modeled as mono-component nonlinear chirp signals of short duration with energy distributions concentrated along its decreasing instantaneous frequency.

Assessment of aortic valve stenosis severity: A new index based on the energy loss concept.

BACKGROUND: Fluid energy loss across stenotic aortic valves is influenced by factors other than the valve effective orifice area (EOA). We propose a new index that will provide a more accurate estimate of this energy loss. METHODS AND RESULTS: An experimental model was designed to measure EOA and energy loss in 2 fixed stenoses and 7 bioprosthetic valves for different flow rates and 2 different aortic sizes (25 and 38 mm). The results showed that the relationship between EOA and energy loss is influenced by both flow rate and aortic cross-sectional area (A(A)) and that the energy loss is systematically higher (15+/-2%) in the large aorta. The coefficient (EOAxA(A))/(A(A)-EOA) accurately predicted the energy loss in all situations (r(2)=0.98). This coefficient is more closely related to the increase in left ventricular workload than EOA. To account for varying flow rates, the coefficient was indexed for body surface area in a retrospective study of 138 patients with moderate or severe aortic stenosis. The energy loss index measured by Doppler echocardiography was superior to the EOA in predicting the end points, which were defined as death or aortic valve replacement. An energy loss index

Assessment of arterial stenosis in a flow model with power Doppler angiography: accuracy and observations on blood echogenicity.

The objective of the project was to study the influence of various hemodynamic and rheologic factors on the accuracy of 3-D power Doppler angiography (PDA) for quantifying the percentage of area reduction of a stenotic artery along its longitudinal axis. The study was performed with a 3-D power Doppler ultrasound (US) imaging system and an in vitro mock flow model containing a simulated artery with a stenosis of 80% area reduction. Measurements were performed under steady and pulsatile flow conditions by circulating, at different flow rates, four types of fluid (porcine whole blood, porcine whole blood with a US contrast agent, porcine blood cell suspension and porcine blood cell suspension with a US contrast agent). A total of 120 measurements were performed. Computational simulations of the fluid dynamics in the vicinity of the axisymmetrical stenosis were performed with finite-element modeling (FEM) to locate and identify the PDA signal loss due to the wall filter of the US instrument. The performance of three segmentation algorithms used to delineate the vessel lumen on the PDA images was assessed and compared. It is shown that the type of fluid flowing in the phantom affects the echoicity of PDA images and the accuracy of the segmentation algorithms. The type of flow (steady or pulsatile) and the flow rate can also influence the PDA image accuracy, whereas the use of US contrast agent has no significant effect. For the conditions that would correspond to a US scan of a common femoral artery (whole blood flowing at a mean pulsatile flow rate of 450 mL min(-1)), the errors in the percentages of area reduction were 4.3 +/- 1.2% before the stenosis, -2.0 +/- 1.0% in the stenosis, 11.5 +/- 3.1% in the recirculation zone, and 2.8 +/- 1.7% after the stenosis, respectively. Based on the simulated blood flow patterns obtained with FEM, the lower accuracy in the recirculation zone can be attributed to the effect of the wall filter that removes low flow velocities. In conclusion, the small errors reported in vitro may support the clinical use of this technique.

Hemodynamic and physical performance during maximal exercise in patients with an aortic bioprosthetic valve: comparison of stentless versus stented bioprostheses.

OBJECTIVES: The objective of this study was to compare stentless bioprostheses with stented bioprostheses with regard to their hemodynamic behavior during exercise. BACKGROUND: Stentless aortic bioprostheses have better hemodynamic performances at rest than stented bioprostheses, but very few comparisons were performed during exercise. METHODS: Thirty-eight patients with normally functioning stentless (n = 19) or stented (n = 19) bioprostheses were submitted to a maximal ramp upright bicycle exercise test. Valve effective orifice area and mean transvalvular pressure gradient at rest and during peak exercise were successfully measured using Doppler echocardiography in 30 of the 38 patients. RESULTS: At peak exercise, the mean gradient increased significantly less in stentless than in stented bioprostheses (+5 +/- 3 vs. +12 +/- 8 mm Hg; p = 0.002) despite similar increases in mean flow rates (+137 +/- 58 vs. +125 +/- 65 ml/s; p = 0.58); valve area also increased but with no significant difference between groups. Despite this hemodynamic difference, exercise capacity was not significantly different, but left ventricular (LV) mass and function were closer to normal in stentless bioprostheses. Overall, there was a strong inverse relation between the mean gradient during peak exercise and the indexed valve area at rest (r = 0.90). CONCLUSIONS: Hemodynamics during exercise are better in stentless than stented bioprostheses due to the larger resting indexed valve area of stentless bioprostheses. This is associated with beneficial effects with regard to LV mass and function. The relation found between the resting indexed valve area and the gradient during exercise can be used to project the hemodynamic behavior of these bioprostheses at the time of operation. It should thus be useful to select the optimal prosthesis given the patient's body surface area and level of physical activity.

Performance of short-time spectral parametric methods for reducing the variance of the Doppler ultrasound mean instantaneous frequency estimation.

To achieve an accurate estimation of the instantaneous turbulent velocity fluctuations downstream of prosthetic heart valves in vivo, the variability of the spectral method used to measure the mean frequency shift of the Doppler signal (i.e. the Doppler velocity) should be minimised. This paper investigates the performance of various short-time spectral parametric methods such as the short-time Fourier transform, autoregressive modelling based on two different approaches, autoregressive moving average modelling based on the Steiglitz-McBride method, and Prony's spectral method. A simulated Doppler signal was used to evaluate the performance of the above mentioned spectral methods and Gaussian noise was added to obtain a set of signals with various signal-to-noise ratios. Two different parameters were used to evaluate the performance of each method in terms of variability and accurate matching of the theoretical Doppler mean instantaneous frequency variation within the cardiac cycle. Results show that autoregressive modelling outperforms the other investigated spectral techniques for window lengths varying between 1 and 10 ms. Among the autoregressive algorithms implemented, it is shown that the maximum entropy method based on a block data processing technique gives the best results for a signal-to-noise ratio of 20 dB. However, at 10 and 0 dB, the Levinson-Durbin algorithm surpasses the performance of the maximum entropy method. It is expected that the intrinsic variance of the spectral methods can be an important source of error for the estimation of the turbulence intensity. The range of this error varies from 0.38% to 24% depending on the parameters of the spectral method and the signal-to-noise ratio.

Review of the assessment of single level and multilevel arterial occlusive disease in lower limbs by duplex ultrasound.

The purpose of this article is to review the performance of duplex ultrasound scanning in assessing lower limb arterial disease with emphasis on patients with multisegmental occlusive lesions. Several studies have reported that duplex scanning can be as accurate as angiography to localize arterial stenoses. In spite of these promising results, there still remain some difficulties and controversies. Among them, it has been reported that multisegmental disease may affect the accuracy of duplex scanning. Indeed, some studies have indicated a lower sensitivity for detecting significant stenoses distal to severe or total occlusions. It also was demonstrated that second-order stenoses were detected with lower sensitivity compared to first-order stenoses. The main reason proposed to explain this lower sensitivity is that the highly reduced flow distal to occluded or highly stenotic segments increases the difficulty of detecting significant Doppler velocity changes in the distal or secondary stenoses. The intrinsic limitations of the peak systolic velocity ratio used as a classification criterion are presented. Finally, new and promising developments in power Doppler imaging and ultrasound contrast agents are discussed, because they may allow expansion of the capabilities of current ultrasound scanning systems and provide more accurate diagnosis of patients with multiple disease.

Usefulness of the indexed effective orifice area at rest in predicting an increase in gradient during maximum exercise in patients with a bioprosthesis in the aortic valve position.

This study examines the hemodynamic behavior of aortic bioprosthetic valves during maximum exercise. Nineteen patients with a normally functioning stented bioprosthetic valve and preserved left ventricular function were submitted to maximum ramp bicycle exercise. In 14 of the 19 patients, valve effective orifice area and mean gradient were measured at rest and during exercise using Doppler echocardiography. At peak exercise (mean maximal workload 118 +/- 53 W), the cardiac index increased by 122 +/- 34% (+3.18 +/- 0.71 L/min/ m2, p <0.001), whereas mean gradient increased by 94 +/- 49% (+12 +/- 8 mm Hg, p <0.001), and effective orifice area by 9 +/- 13% (+0.15 +/- 0.22 cm2, p = 0.02). A strong correlation was found between the increase in mean gradient during maximum exercise and the valve area at rest indexed for body surface area (r = 0.84, p <0.0001). Due to the increase in valve area, the increase in gradient was less (-9 +/- 7 mm Hg, -41 +/- 33%, p = 0.0006) than theoretically predicted assuming a fixed valve area. These results suggest that the effective orifice area of the bioprostheses has the capacity to increase during exercise; therefore, limiting the increase in gradient. The relation found between the indexed effective orifice area at rest and the increase in gradient during exercise should be useful in predicting the hemodynamic behavior of a stented bioprosthesis during exercise.