Vol. 281, Issue 3, H1148-H1155, September 2001
Beat-to-beat stroke volume estimation from aortic
pressure waveform in conscious rats: comparison of models
C.
Cerutti,
M. P.
Gustin,
P.
Molino, and
C. Z.
Paultre
Faculty of Pharmacy, Department of Physiology and Clinical
Pharmacology, Centre National de la Recherche Scientifique UMR
5014, 69373 Lyon Cedex 08, France
 |
ABSTRACT |
Several
methods for estimating stroke volume (SV) were tested in
conscious, freely moving rats in which ascending aortic pressure and
cardiac flow were simultaneously (beat-to-beat) recorded. We compared
two pulse-contour models to two new statistical models including eight
parameters extracted from the pressure waveform in a multiple linear
regression. Global as well as individual statistical models gave higher
correlation coefficients between estimated and measured SV (model
1, r = 0.97; model 2, r = 0.96) than pulse-contour models (model 1,
r = 0.83; model 2, r = 0.91). The latter models as well as statistical model 1 used
the pulsatile systolic area and thus could be applied to only 47 ± 17% of the cardiac beats. In contrast, statistical model
2 used the pressure-increase characteristics and was therefore
established for all of the cardiac beats. The global statistical
model 2 applied to data sets independent of those used to
establish the model gave reliable SV estimates: r = 0.54 ± 0.07, a small bias between 
8% to +10%, and a mean precision of 7%. This work demonstrated the limits of pulse-contour models to estimate SV in conscious, unrestrained rats. A multivariate statistical model using eight parameters easily extracted from the
aortic waveform could be applied to all cardiac beats with good precision.
cardiac output; hemodynamics; statistical model
| |
INTRODUCTION |
ESTIMATION OF CARDIAC
OUTPUT from the arterial pressure (AP) waveform is an interesting
challenge, but it remains difficult to accurately achieve. Since the
1970s, several groups have regularly proposed new methods or
improvements of already-published strategies (1, 2, 6, 7, 21,
22). Most of these have been implemented in humans, owing to a
clear clinical interest in the noninvasive assessment of cardiovascular
function. In animals, the assessment of cardiac output from the AP
waveform is of great interest for physiological or pharmacological
experiments. Several techniques have been developed for continuous
measurement of cardiac blood flow using electromagnetic or pulsed
Doppler flow probes placed around the ascending aorta. However, these
methods require critical open-chest surgery and a long recovery period
after the surgery. Only a few old works have described methods that use pulse-wave analysis in dogs (8) or in rats (5, 13,
14) via AP measured in the ascending aorta. Recently, Yang and
Kuo (24) used a similar model in anesthetized rats via the
femoral AP.
Most of the methods developed for humans are based on pulse-contour
analysis, which relies on the windkessel arterial model, and several
groups have proposed analytical models linking AP and cardiac blood
flow (16, 21, 22). In the windkessel model, pulsatile
systolic area and stroke volume (SV) are related by means of the
characteristic impedance of the aorta (Zaort).
Several methods have been proposed for estimating
Zaort (2, 6, 8, 22). Many clinical
applications have been made using aortic (22), radial
(7, 19, 22), brachial (1), or finger (2, 17, 23) pressure waveforms. However, the validation of
models in humans is quite difficult owing to the existing techniques for measuring cardiac output. Gold standard methods are thermodilution or dye-dilution methods, which cannot provide continuous measurement of
cardiac output. In addition, in clinical research projects, data are
generally recorded in controlled conditions during short periods of
time. It appeared interesting to manage a project with rats because of
the possibility of recording and processing beat-to-beat data over long
periods of spontaneous activity and continuously measuring cardiac
blood flow.
The aim of the present work was to test the validity of
pulse-contour methods in conscious unrestrained rats for a beat-to-beat analysis using AP values that were measured in the ascending aorta and
recorded during several hours. We compared two methods derived from
human hemodynamic models that use pulse-contour analysis to two new
methods that use multivariate statistical models including a large
number of parameters extracted from the arterial pulse contour.
| |
METHODS |
Animals
Experiments were performed with male Sprague-Dawley rats (Iffa
Credo; L'Arbresle, France) that weighed 300-400 g and were housed
in controlled conditions (21 ± 1°C and a 12:12-h light-dark cycle). Rats received a standard rat chow (UAR A03;
Villemoisson-sur-Orge) containing <0.3% sodium and water ad libitum.
The study included five rats in which cardiac flow and ascending aortic
AP were simultaneously measured.
Chronic Instrumentation
An ultrasonic transit-time flow probe (model 2.5 SB;
Transonic Systems; Ithaca, NY) was placed around the ascending aorta using our previously described technique (11). Rats were
given 10-15 days to recover and to allow for the development of
fibrosis around the probe. A polyethylene catheter was then inserted
via the right common carotid artery into the ascending aorta as
previously described (11). After cannulation, rats were
placed in large cylindrical recording cages with food and water ad
libitum. After 48 h the animals had regained their initial body weight.
Signal Recording
The aortic catheter was connected to a pressure transducer
(Spectramed; Oxnard, CA) and AP signals were then amplified (model 13-4615-52; Gould; Cleveland, OH). The catheter was flushed (0.5 ml/h)
with heparinized glucose (25 IU/ml) throughout the experiment to avoid
blood diffusion and signal dampening. The cardiac flow-probe cable was
connected via a spring-guarded cable to an ultrasonic transit-time
flowmeter (model T-106; Transonic Systems). Analog-to-digital conversions of both signals were simultaneously performed on-line at
500 Hz with a personal computer equipped with an acquisition board
(AT-MIO16H-9; National Instruments; Austin, TX) and software developed
using LabView language (National Instruments).
The recording sessions began after stabilization periods of 15-30
min (when the animals were quiet and displayed normal activity) and
lasted 2 h.
Data Processing
Off-line data processing was performed on a workstation
(Ultra 5; Sun Microsystems; Mountain View, CA). For each cardiac cycle, cardiac output (defined as mean aortic flow) and SV were computed from
the aortic flow signal. AP cycles were validated after testing pulse
pressure (PP), heart rate (HR), and variation coefficients of mean AP
(MAP), HR, and PP over 30-s periods. Adequate values were 30-70
mmHg for PP, 250-500 beats/min for HR, and <10% for variation
coefficients. The AP signal exhibited some irregularities because the
measuring conditions were sometimes critical in freely moving rats.
These irregularities led us to analyze 80 ± 6% of the recording
time, and the computations were then performed on 10,000-27,000
validated cardiac cycles in each rat. Models were established using
training data obtained from the first hour of recording in each rat.
The models were then applied to data from the second hour of recording
in each rat, which were considered independent of those used to
establish the models.
SV Estimation
Hemodynamic models.
Two different hemodynamic models relying on pulse-contour methods were
used to estimate beat-to-beat SV values from the aortic waveform. In
these methods, SV was defined by the integration of AP over the
ejection phase divided by Zaort
The end of the ejection phase could be determined from the AP
curve for cardiac beats in which the characteristic incisura was
distinct enough to be detected automatically (see Fig.
1). The AP integral (SysArea) was then
defined as the area under the ejection portion of the aortic pressure
curve above a horizontal line drawn from the diastolic point and
bounded by a vertical line through the lowest point of the incisura.
The previous equation then became
In our experimental conditions with rats, the end of the
ejection phase was reliably detected in only 45% of all cardiac beats.
View larger version (45K):
[in this window]
[in a new window]
|
Fig. 1.
Example of aortic pressure waveform showing variables
included in pulse-contour models. Tsys, duration
of systole; Tdias, duration of diastole.
|
|
Zaort cannot be directly calculated, and
several authors have proposed methods for estimating this parameter.
First, we used the method proposed by Kouchoukos and colleagues
(8) for dogs and recently applied to rats by Yang and Kuo
(24). In this method, Zaort was
defined by the expression
where k was a constant, Tsys
was the duration of systole, Tdias was the
duration of diastole (determined after automatic detection of the
incisura), and T was the heart period (see Fig. 1). This
way, pulse-contour model 1 defined SV as
where K was the parameter of the model.
We adapted a second method to rats that was previously proposed by
Antonutto and colleagues (2) in which SV was estimated from Finapres AP signals in humans. The aortic impedance
Zaort was defined from a multiple linear
regression including HR, PP, and MAP by the expression
where a0, a1,
a2, a3, and
a4 are the theoretical parameters of the
equation, and the next model, called pulse-contour model 2,
then expresses SV as
where k0, k1,
k2, and k3 are the
estimated parameters of the model.
Multivariate statistical models.
In another way we proposed a new approach to estimate SV using
classical multivariate statistical models. These empirical models do
not make any hemodynamic hypothesis and rely on a multiple linear
regression analysis including several variables extracted from the AP waveform.
In addition to the variables previously defined (SysArea,
Tsys, Tdias, HR, PP, and
MAP), we also determined the following values via beat-to-beat
computations (see Fig. 2A):
1) systolic and diastolic AP (SAP and DAP, respectively);
2) three variables derived from the time derivative of AP,
including the maximum derivative (dP/dtmax), the
time of its occurrence
(TdP/dtmax), and the
pressure value at this time
(APdP/dtmax); and 3) one
feature of the diastolic relaxation, namely, the diastolic exponential
decay time (
), which was identified using the method previously
described (11).
View larger version (37K):
[in this window]
[in a new window]
|
Fig. 2.
Examples of aortic pressure waveforms showing
variables included in statistical model 1 (A) and
statistical model 2 (B).
TdP/dtmax, time of
occurrence of dP/dtmax;
TSAP, duration of pressure rise.
|
|
These 12 variables were thus considered in the elaboration of
statistical model 1: MAP, SAP, DAP, HR, PP,
dP/dtmax,
TdP/dtmax, APdP/dtmax, SysArea,
Tsys, Tdias, and .
Because the end of the ejection phase was not always reliably
detectable, we decided to propose a second model using the systolic peak instead of the lowest point of the incisura. As shown in Fig.
2B, we computed the integral PeakArea from the foot of the systolic ramp up to the systolic pressure peak (instead of SysArea) and
we considered TSAP and T TSAP (estimated from the systolic pressure peak)
instead of Tsys and
Tdias. These 12 variables were considered in the
elaboration of statistical model 2: MAP, SAP, DAP, HR, PP,
dP/dtmax,
TdP/dtmax,
APdP/dtmax, PeakArea,
TSAP, T TSAP, and .
Owing to the large number of variables first defined for both
statistical models, variables containing redundant information were
determined. The strength of the correlation between variables taken by
two was evaluated in each rat. Figure 3
provides an example of a correlation matrix obtained for variables
considered in the statistical models. DAP, SAP, and
APdP/dtmax were highly correlated
with MAP. The diastolic time, Tdias, and the
duration of the pressure decrease, T TSAP, were highly inversely correlated with HR.
In addition, SysArea and PeakArea were also well correlated. Although
some differences appeared between the rats, the variables that
exhibited the highest correlation coefficients were the same in all
rats. Couples of variables with the most significant correlation were
reduced to one of the variables. Couples of variables involving DAP,
SAP, MAP, and APdP/dtmax and those
involving T TSAP or
Tdias and HR showed mean correlation
coefficients >0.80, whereas the other mean correlation coefficients
were all <0.68. These results prompted us to establish the correlation
threshold at r = 0.80 and to select eight variables:
MAP, HR, PP, dP/dtmax, TdP/dtmax, SysArea,
Tsys, and . Statistical model 1 was then defined as
Replacing SysArea and Tsys by PeakArea
and TSAP, respectively, statistical model
2 was then defined as
View larger version (61K):
[in this window]
[in a new window]
|
Fig. 3.
Pearson correlation matrix obtained from one rat for all variables
used in the multivariate statistical models. Shaded areas indicate
correlation coefficients with absolute value >0.80.
dP/dtmax, maximum derivative of arterial
pressure; APdP/dtmax, value of
arterial pressure at
TdP/dtmax; SysArea,
systolic area; PeakArea, pressure area from diastolic to systolic
pressure. *Variables that were selected for inclusion in the models.
|
|
Statistical Tools
The parameters (K, k0,
k1, ...) of the four models were estimated
with multiple linear regression analysis using Systat 9.0 software
(SPSS; Chicago, IL). Individual models were estimated for each rat
using individual beat-to-beat data of the training data set. Global
models were established with beat-to-beat training data for all rats
pooled together thus allowing the creation of a unique file containing
the entire training data set. The quality of the models was given by
the correlation coefficients computed between estimated and measured SV
values. Considering other individual data sets, the estimation of SV
with the different models was compared.
The distributions of values for all variables were determined. All were
close to normality except
TdP/dtmax, which exhibited a lognormal distribution, and a logarithmic transformation was applied to this variable. Pearson correlation analysis between variables taken by two was performed to eliminate highly correlated variables with r > 0.80. The models were then
estimated using multiple linear regression with stepwise forward
entries. The rank of entry of each variable was considered. The
Bland-Altman method (3) was used to determine the bias and
precision of SV values estimated with statistical model 2.
| |
RESULTS |
Mean values of all cardiovascular parameters obtained in the
training data sets and other data sets used to test the models are given in Table 1. No statistical
difference was observed between these two data sets.
Determination of Individual Models
The optimal parameters and correlation coefficients obtained for
each rat in each model are given in Table
2. These parameters and correlation
coefficients show interindividual variability. Correlation coefficients
were specifically different between rats for pulse-contour model
1. Table 3 indicates that both the
mean percentages of cardiac beats included in the individual models and
the mean correlation coefficients were different between the models.
Statistical model 2, which did not use the ejection time, was the only model that could be estimated with all of the cardiac beats. The other models used <50% of the cardiac beats.
Pulse-contour model 2 as well as both statistical models
gave similar correlation coefficients, which were higher than those
obtained with pulse-contour model 1.
View this table:
[in this window]
[in a new window]
|
Table 2.
Parameters of individual models including one constant and K value
associated to each mentioned variable and correlation coefficients of
models obtained in each rat for each model
|
|
View this table:
[in this window]
[in a new window]
|
Table 3.
Quality of models assessed with percentage of cardiac beats and
correlation coefficient between estimated and measured SV values taken
into account
|
|
Determination of Global Models
As shown in Table 3, both statistical models yielded high
correlation coefficients between estimated and measured SV values; however, pulse-contour model 1 was much worse than the other
models. Differences between the correlation coefficients were highly
significant due to the large amount of data (>60,000 values) used to
establish the models.
Table 4 shows the ranks of entry for
pressure variables in the two statistical models established with the
pooled data. In both models, PP was the most significant variable
because it was entered first in the stepwise multiple regression, with
r = 0.92 for statistical model 1 and
r = 0.86 for statistical model 2. dP/dtmax, MAP, HR, , SysArea,
TdP/dtmax, and
Tsys were then successively entered in
statistical model 1, with r increasing clearly
for the first five steps and reaching 0.97. For the determination of statistical model 2, MAP,
dP/dtmax, HR, , PeakArea,
TSAP, and
TdP/dtmax were
successively entered in this order, with r reaching 0.96 at
step 7. The entry ranks of the variables were very close in
the two models, and r reached similar values. The regression
coefficients of the statistical models and the corresponding standard
errors are given in Table 4.
View this table:
[in this window]
[in a new window]
|
Table 4.
Results of stepwise multiple regressions to estimate global statistical
models: entry rank of each pressure variable, correlation coefficient
of regression at each step, and final regression coefficients with SE
|
|
Application of Global Models to Other Individual Data
Quality of estimated SV values.
To test the validity of the models, the different global models were
applied to validated cardiac beats from the training data set and from
the second hour of recording for each rat. The quality of the fits is
given in Table 5 in terms of percentage of cardiac beats with SV estimate and correlation between estimated and
measured SV values. Percentages of beats were similar for both data
sets, with statistical model 2 allowing the estimation of SV
in all of the validated cardiac beats. Although no significant difference was found between r values obtained for the two
conditions, those obtained for the second data set tended to be lower
the training set values.
View this table:
[in this window]
[in a new window]
|
Table 5.
Application of global models to individual data sets: percentages of
cardiac beats with SV estimate and correlation coefficients between
estimated and measured SV values
|
|
Bias and precision of statistical model 2.
SV values estimated with statistical model 2 were compared
with measured SV values using the Bland-Altman method. Bias and precision of the estimation were determined for each rat using all of
the cardiac beats. Figure 4 provides an
example of a scatterplot showing bias as a function of the average
value of estimated and measured SV in one rat. In this example, bias
was +20.0 µl (+9.2% of the mean measured SV value), which reflects a
slight underestimation of SV by the model. Precision, defined as the
standard deviation of the bias, was 13.7 µl (6.5%). Considering the
five rats, mean bias was 7.2 ± 7.6 µl, which represents
2.2 ± 3.4% of the measured SV mean value, and precision
was 6.6 ± 0.3%. The percentage of cardiac beats for which the
absolute value of bias was <10% was computed for each rat; this value
ranged between 47% and 87%, and the mean percentage computed for the
five rats was 65 ± 8%.
View larger version (37K):
[in this window]
[in a new window]
|
Fig. 4.
Bias of stroke volume (SV) estimate obtained with global
statistical model 2 in one rat during 1 h of recording
expressed in absolute (A) and percentage values
(B) and plotted against the average of measured and
estimated SV values. Distribution of each variable is given along the
corresponding axis; horizontal lines indicate mean bias and 95%
confidence interval.
|
|
| |
DISCUSSION |
This work aimed at demonstrating the feasibility of SV estimation
from the arterial waveform analysis during long periods of spontaneous
activity in conscious, unrestrained rats. Using aortic pressure
measurements, we compared two hemodynamic models used for humans with
two new models that are based on a multivariate statistical model. The
results show that our statistical models were clearly better than the
pulse-contour models. In contrast with the other models,
statistical model 2 could be applied to all the cardiac
beats. Thus a global model was estimated that yielded low bias and good
precision for an indirect method.
Since the 1970s, several methods have been developed in humans for
obtaining SV and cardiac output estimates from the pressure waveform
(either aortic or peripheral) obtained either intraarterially or
noninvasively using Finapres. These methods rely on knowledge models,
which are associated to biophysical representations (6, 16, 21,
22). The methods provide analytical expressions of all of the
variables included in a model, and the parameters of the models result
from mechanical hypotheses. At the time of the development of these
models, the computation means were weak; thus analytical expressions of
the variables were essential. Surprisingly, such models have not often
been tested and applied to rats for hemodynamic studies in physiology
or pharmacology. Few studies using pulse-contour methods on
anesthetized rats have been published; for instance, three studies were
published >20 years ago (5, 13, 14) and only one recent
work (24) using pulse-contour model 1 for a
pharmacological study was found in the literature. In the present work
we have tested this latter model, which was first described by
Kouchoukos and colleagues (8), and pulse-contour model 2, which was proposed by Antonutto and co-workers
(2), for humans. This model appeared easy to transpose to
rats because it did not include noncardiovascular variables.
These hemodynamic models (as well as others) include an operational
approach to estimating Zaort. A multiple linear
regression involving MAP, HR, and PP was used in pulse-contour
model 2, and a linear combination of MAP, HR, and age multiplied
by an individual calibration factor was used by other authors (6,
21). These methods, therefore, combine a pure hemodynamic model
and a statistical model to estimate some hemodynamic parameters. In the
present work, we proposed two statistical models that did not rely on any hemodynamic model but were designed to be predictive. We did not
aim to provide a new knowledge model of the cardiovascular system. We
developed pure statistical models using multiple linear regression
applied to a large training set of data. Whether computed from
individual data or from the entire set of data, these models better
fitted the data than did pulse-contour model 1, but results were close to those obtained with pulse-contour model 2.
Our statistical models take into account more information extracted
from the pressure waveform than the pulse-contour models. In an
additive manner, our models consider eight parameters extracted from
the aortic waveform, which are included in a multivariate linear
regression. The amount of information appears to be essential and
compensates for lack of physical hypotheses. Among the eight parameters
included in the multiple regression, MAP was chosen rather than DAP,
SAP, or APdP/dtmax, because it
appeared less noisy than the others and it had been used in
pulse-contour models. For the same reasons, HR was preferred to
Tdias or T TSAP. The replacing of MAP or HR by one of the
correlated variables led to similar models with similar r
values and entry ranks of the variables. In addition, the pulse-contour
models as well as our statistical model 1 use the pulsatile
systolic area SysArea. However, in many cardiac cycles there was no
clear pressure incisura reflecting the aortic valve closing but only a
slight change in the time derivative of the decreasing part of the
pressure curve. Therefore, SysArea could reliably be determined in
<50% of the recorded beats. Statistical model 2 did not
use this variable, and thus it was the only model that could be applied
to all of the cardiac beats.
In humans, comparing the results given by the models to direct cardiac
flow measurements has validated the proposed models. However, standard
dilution techniques (1, 7, 21, 22) as well as new
noninvasive methods (2, 4, 17, 18) only yield discrete
cardiac output values. Validation studies were carried out either in
critically ill patients (19, 22) or in healthy subjects
(2, 6) but at rest or during controlled conditions
(17, 23). The models theoretically allowed beat-to-beat calculation of SV, but most often values were averaged over one or two
respiratory cycles or several cardiac cycles. Study periods were
generally very short (2, 21, 23), and comparisons with the
reference method were necessarily made on a small number of direct
measures. When long periods were studied, recalibration was performed
at regular intervals (every 1 or 2 h) to recalculate Zaort (19). Because the techniques
used for direct blood flow measurement did not allow for continuous
measurements, all studies involving comparisons of methods used a
restricted number of data selected in controlled hemodynamic
conditions. This fact may partly explain the excellent correlations
that have been found.
In rats, cardiac blood flow can be continuously measured with flow
probes using ultrasonic or electromagnetic techniques that can be
chronically implanted in rats (9, 10, 15). Nevertheless, these techniques are difficult to use in routine experiments and produce constraining conditions for the rat. Noninvasive Doppler echocardiographic methods have been adapted to rats (12,
20), but they are little used due to technical difficulties in
signal processing and also because a restraining device is required for conscious rats. Therefore, we thought it particularly useful to validate the assessment of SV from the aortic waveform by means of the
continuous measurement of cardiac blood flow in freely moving rats.
Considering the individual models, some differences appeared between
the parameters of the models obtained in each of the rats, and some of
these differences were rather large. These differences may be partly
due to the variable quality of the individual models (mainly for
pulse-contour model 1). In addition, in the statistical models including eight variables and thus eight parameters plus one
constant, one can accept that the eight parameters are not independent
and that little variation of one parameter may be associated with more
or less large variations of the other parameters. Some of the
individual parameters were also different from global parameters. This
observation might be explained by the fact that the cardiovascular
state slightly differed between rats although they were studied in
similar experimental conditions. The global models took into account
both interindividual and individual variability over time, because we
had about 10,000 cardiac beats per rat. Therefore, the differences in
the model parameters are likely to reflect individual characteristics
that must be considered to establish a reliable global model. Of
course, the application of the global model in one rat yields a less
good estimation than the individual model, but this is the cost of the
determination of a general model.
The two global statistical models exhibited similar quality, and the
main difference comes from the fact that only statistical model
2 could be applied to all of the cardiac beats. In both models, PP
was the most significant variable because it was entered first in the
stepwise regressions with high correlation. The other variables were
entered in a nearly identical order in both models. Although is not
used in hemodynamic models, its rank of 5 in both models shows that
peripheral vascular properties are of importance in the determination
of SV.
The application of the four global models to data sets different from
the one used to establish the models resulted in SV estimates that were
logically slightly less good than SV estimates computed in the training
data set. The Bland-Altman analysis was used to compare SV estimates
obtained with global statistical model 2 to measured SV
values. It disclosed the existence of a small bias between estimated
and measured SV values, which expressed either over- or
underestimation (<10%). For each rat the precision was <8%, which
is quite good for an indirect method. Large differences in bias values
were observed between rats (ranging between 8 and 10%), whereas
precision was much more homogeneous. These observations suggest that
information is lacking for the determination of the absolute SV level,
whereas variability aspects are better estimated.
Further studies are thus needed to understand the differences between
rats in the quality of the fit to individual and global models. More
precisely, we plan to find an explanation of the bias by modeling it as
a function of the pressure variables that we considered. In addition,
because all of the rats were freely moving during the recording
session, the physical activity is likely to be of importance in the
definition of the model. Therefore, further studies will be designed to
define models specific to each activity state. In addition, because the
measurement of AP in the ascending aorta is rather critical, it appears
also necessary to adapt the statistical models to the AP waveform
obtained in the abdominal aorta.
In conclusion, this work presents rigorous comparisons of methods that
were applied to large sets of data. We demonstrated the limits of
hemodynamic models in estimating SV in a continuous way during long
periods in conscious freely moving rats. A statistical model using
eight parameters easily extracted from the aortic pressure waveform was
designed to be applied to all cardiac beats, and it gave SV estimates
with good global precision. The precise conditions of use for this
model in freely moving rats remain to be clarified.
| |
ACKNOWLEDGEMENTS |
P. Molino was supported by a grant from the Communauté de
Travail des Alpes Occidentales and the Région Rhône-Alpes, France.
| |
FOOTNOTES |
Address for reprint requests and other correspondence: C. Cerutti, Département de Physiologie et Pharmacologie Clinique, CNRS UMR 5014, Faculté de Pharmacie, 8 Ave. Rockefeller, 69373 Lyon Cedex 08, France (E-mail: cerutti{at}univ-lyon1.fr).
The costs of publication of this
article were defrayed in part by the
payment of page charges. The article
must therefore be hereby marked
"advertisement"
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 25 October 2000; accepted in final form 27 March 2001.
| |
REFERENCES |
1.
Alicandri, C,
Fariello R,
Boni E,
Zaninelli A,
and
Muiesan G.
Possibility of cardiac output monitoring from the intra-arterial blood pressure profile.
Clin Exp Hypertens A
7:
345-353,
1985[Web of Science][Medline].
2.
Antonutto, G,
Girardis M,
Tuniz D,
Petri E,
and
Capelli C.
Assessment of cardiac output from noninvasive determination of arterial pressure profile in subjects at rest.
Eur J Appl Physiol
69:
183-188,
1994.
3.
Bland, JM,
and
Altman DG.
Statistical methods for assessing agreement between two methods of clinical measurement.
Lancet
1:
307-310,
1986[Web of Science][Medline].
4.
Gratze, G,
Fortin J,
Holler A,
Grasenick K,
Pfurtscheller G,
Wach P,
Schönegger J,
Kotanko P,
and
Skrabal F.
A software package for non-invasive, real-time beat-to-beat monitoring of stroke volume, blood pressure, total peripheral resistance and for assessment of autonomic function.
Comput Biol Med
28:
121-142,
1998[Web of Science][Medline].
5.
Iriuchijima, J,
Numao Y,
and
Suga H.
Hemodynamics of experimentally hypertensive rats in conscious and anesthetized states.
Jpn Heart J
17:
80-87,
1976[Medline].
6.
Jansen, JRC,
Wesseling KH,
Settels JJ,
and
Schreuder JJ.
Continuous cardiac output monitoring by pulse contour analysis during cardiac surgery.
Eur Heart J
2:
26-35,
1990.
7.
Jardin, F,
Farcot JC,
Gueret P,
Prost JF,
Ozier Y,
and
Bourdarias JP.
Cyclic changes in arterial pulse during respiratory support.
Circulation
68:
266-274,
1983[Free Full Text].
8.
Kouchoukos, NT,
Sheppard LC,
and
McDonald DA.
Estimation of stroke volume in the dog by a pulse contour method.
Circ Res
26:
611-623,
1970[Abstract/Free Full Text].
9.
Létienne, R,
Barrès C,
Cerutti C,
and
Julien C.
Short-term hemodynamic variability in the conscious areflexic rat.
J Physiol
506:
263-274,
1998[Abstract/Free Full Text].
10.
Li, SG,
Randall DC,
and
Brown DR.
Roles of cardiac output and peripheral resistance in mediating blood pressure response to stress in rats.
Am J Physiol Regulatory Integrative Comp Physiol
274:
R1065-R1069,
1998[Abstract/Free Full Text].
11.
Molino, P,
Cerutti C,
Julien C,
Cuisinaud G,
Gustin MP,
and
Paultre C.
Beat-to-beat estimation of windkessel model parameters in conscious rats.
Am J Physiol Heart Circ Physiol
274:
H171-H177,
1998[Abstract/Free Full Text].
12.
Nakamura, T,
Matsumuro A,
Kuribayashi T,
Matsubara K,
Shima M,
Shimoo K,
Katsume H,
and
Nakagawa M.
Echocardiographic determination of stroke volume during rapid atrial pacing and volume loading in normal rats.
Cardiovasc Res
26:
765-769,
1992[Abstract/Free Full Text].
13.
Nikolic, S,
Susic D,
and
Kentera D.
Evaluation of the pulse contour method in beat-to-beat determination of the cardiac output in small laboratory animals.
Bibl Cardiol
37:
169-173,
1979.
14.
Numao, Y,
Suga H,
and
Iriuchijima J.
Hemodynamics of spontaneously hypertensive rats in conscious state.
Jpn Heart J
16:
719-730,
1975[Medline].
15.
Oosting, J,
Struijker-Boudier HA,
and
Janssen BJ.
Circadian and ultradian control of cardiac output in spontaneous hypertension in rats.
Am J Physiol Heart Circ Physiol
273:
H66-H75,
1997[Abstract/Free Full Text].
16.
Redling, JD,
and
Akay M.
Noninvasive cardiac output estimation: a preliminary study.
Biol Cybern
77:
111-122,
1997[Web of Science][Medline].
17.
Stok, WJ,
Baisch F,
Hillebrecht A,
Schulz H,
Meyer M,
and
Karemaker JM.
Noninvasive cardiac output measurement by arterial pulse analysis compared with inert gas rebreathing.
J Appl Physiol
74:
2687-2693,
1993[Abstract/Free Full Text].
18.
Stok, WJ,
Stringer RCO,
and
Karemaker JM.
Noninvasive cardiac output measurement in orthostasis: pulse contour analysis compared with acetylene rebreathing.
J Appl Physiol
87:
2266-2273,
1999[Abstract/Free Full Text].
19.
Tannenbaum, GA,
Mathews D,
and
Weissman C.
Pulse contour cardiac output in surgical intensive care unit patients.
J Clin Anesth
5:
471-478,
1993[Web of Science][Medline].
20.
Van Gorp, AD,
Van Ingen Schenau DS,
Willigers J,
Hoeks APG,
De Mey JGR,
Struyker-Boudier HAJ,
and
Reneman RS.
A technique to assess aortic distensibility and compliance in anesthetized and awake rats.
Am J Physiol Heart Circ Physiol
270:
H780-H786,
1996[Abstract/Free Full Text].
21.
Wesseling, KH,
de Wit B,
Weber JAP,
and
Ty Smith N.
A simple device for the continuous measurement of cardiac output.
Adv Cardiovasc Phys
5:
16-52,
1983.
22.
Wesseling, KH,
Jansen JRC,
Settels JJ,
and
Schreuder JJ.
Computation of aortic flow from pressure in humans using a nonlinear three-element model.
J Appl Physiol
74:
2566-2573,
1993[Abstract/Free Full Text].
23.
Wieling, W,
Veerman DP,
Dambrink JHA,
and
Imholz BPM
Disparities in circulatory adjustment to standing between young and elderly subjects explained by pulse contour analysis.
Clin Sci
83:
149-155,
1992[Medline].
24.
Yang, CCH,
and
Kuo TBJ
Assessment of cardiac sympathetic regulation by respiratory-related arterial pressure variability in the rat.
J Physiol
515:
887-896,
1999[Abstract/Free Full Text].
Am J Physiol Heart Circ Physiol 281(3):H1148-H1155
0363-6135/01 $5.00
Copyright © 2001 the American Physiological Society
TOP
ABSTRACT
INTRODUCTION
