Vol. 90, Issue 6, 2151-2156, June 2001
Variance of ventilation during exercise
Kenneth C.
Beck1 and
Theodore A.
Wilson2
1 Division of Pulmonary and Critical Care Medicine,
Department of Internal Medicine, Mayo Clinic and Foundation,
Rochester 55905; and 2 Department of Aerospace Engineering
and Mechanics, University of Minnesota, Minneapolis, Minnesota 55455
 |
ABSTRACT |
Expired gas
concentrations were measured during a multibreath washin of He in one
female and seven male subjects at rest (seated) and during cycle
exercise at work rates of 70-210 W. In a computational model, the
ventilation distribution was represented as a log-normal distribution
with standard deviation (
); values of
were obtained by fitting the output of the model
to the data. At rest, was 0.89 ± 0.18;
during exercise, was 0.60 ± 0.13, independent of the level of exercise. These values for the width of the
functional ventilation distribution at the scale of the acinus are
approximately two times larger than those obtained from anatomic
measurements in animals at a scale of 1 cm3. The values for
, together with data from the literature on the
width of the functional ventilation-perfusion distribution, show that
ventilation and perfusion are highly correlated at rest, in agreement
with anatomic data. The structural sources of nonuniform ventilation
and perfusion and of the correlation between them are unknown.
gas mixing; ventilation-perfusion distribution; multibreath gas
washin
| |
INTRODUCTION |
FOWLER
(3) introduced the multiple breath washin or washout test
as an indicator of uniformity of ventilation in 1949. Since then, a
number of studies of ventilation distributions in normal and abnormal
subjects have been published. However, we know of no previous studies
on the dependence of the ventilation distribution on exercise or tidal
volume (VT). Here, we report the variance of ventilation
obtained by interpreting He washin tests in normal seated subjects at
rest and during exercise at work rates of 70-210 W.
In a model, the ventilation distribution was described as a log-normal
distribution with width (), and values of
were obtained by fitting the output of the model
to data on the concentration of He in the first 12-15 breaths of a
washin maneuver. We obtained a value of at rest
of 0.89 ± 0.18, which is slightly larger than but in general
agreement with the values reported by others. The value of
decreases to 0.60 ± 0.22 at a work rate of
70 W and remains essentially unchanged for higher work rates. We
ascribe this decrease to an increased uniformity of ventilation with
increasing VT. From these functional values of
and data from the literature on the variance of
regional perfusion and of ventilation-to-perfusion ratio
(A/
), we conclude that regional alveolar
ventilation and perfusion are highly correlated.
| |
METHODS |
Experiment design.
Eight subjects were studied, one was female. All eight were hospital
employees or physicians in training. All read, understood, and signed
an informed consent form that was approved by the Mayo Institutional
Review Board. Morphometric characteristics of the subjects are listed
in Table 1.
Each subject was studied in the exercise laboratory a day before the He
washin study. In the preliminary study, the subject was familiarized
with the stationary exercise cycle, and the subject's maximal exercise
capacity was measured.
Gas washin measurements were made using an apparatus that has been
described previously (11). Briefly, a Hans-Rudolf
non-rebreathing Y valve was connected directly to a low-dead-space
switching valve that allowed rapid switching of inspired gas from room
air to a gas mixture. A Hans-Rudolph pneumotachograph was connected to the mouth end of the non-rebreathing valve. The subject wore a nose
clip and breathed into the pneumotachograph through a flanged rubber
mouthpiece. The pneumotachograph was connected to a differential pressure transducer to measure flow. The pneumotachograph was linearized using the method of Yeh and colleagues (28) and
calibrated daily using a 3-liter syringe to pump test gas through the
entire valve assembly. Data for flow and gas concentration were
acquired every 8 ms and stored in computer files for later analysis.
To perform the gas washin, the subject breathed normally (resting or
exercise ventilation), and the switching valve was thrown during an
expiration so that, on the next inspiration, the subject inhaled test
gas from a 60-liter bag containing 0.7% acetylene (C2H2), 9% He, 21% O2, and
balance N2. During gas washin, a computer sampled data for
flow at the mouth and fractional concentrations of O2,
CO2, N2, He, and C2H2
(mass spectrometer, Perkin-Elmer, Norwalk, CT). The subject was
encouraged to maintain a steady breathing pattern during the washin;
otherwise, no constraints on breathing were imposed. After 12-15
breaths, the subject was asked to perform a maximal inspiration to
total lung capacity (TLC), and the inspired gas was then switched back
to room air. The flow signal was integrated digitally to obtain a
continuous volume signal. These data were submitted to computerized
routines to find end-tidal gas concentrations, VT for each
breath, and end-inspiratory volume and to fit the data to a
computational model (see below). Gas washin studies were performed in
duplicate at rest and at each exercise intensity. Intensities chosen
for this study were 70, 140, and 210 W.
Within a week of the exercise session, functional residual capacity
(FRC) and TLC were measured using a constant volume body plethysmograph
(Medical Graphics, St. Paul, MN, model 1085 Dx). The subject was seated
comfortably in the plethysmograph for 2-3 min to allow thermal
equilibrium to be established. The subject breathed normally into the
mouthpiece attached to a shutter valve for four to five breaths to
establish a consistent breathing pattern. The shutter was closed at end
expiration and kept closed for 2-4 s while the subject continued
to make breathing efforts against the shutter. Thoracic gas volume was
determined from airway and plethysmograph pressure changes during these
efforts using standard procedures. The shutter was opened, and the
subject immediately inhaled fully. The flow signal was integrated, and
TLC was determined as thoracic gas volume plus the inspired volume. At
least two maneuvers were obtained for which the mouth pressure vs. box
pressure loops were closed, and values for TLC for these maneuvers were averaged.
Data analysis.
From the raw data streams, the average VT, inspiratory and
end-expiratory gas concentrations, and dead space volume
(VD) were determined. VD was obtained by mass
balance
where F
He is mixed expired
He concentration and FEHe and
FIHe represent end-expiratory and
end-inspiratory He concentrations, respectively.
FHe was determined for each
breath by taking the sum of the instantaneous expired He fraction times
the expired volume increment for each 8-ms sampling interval during
expiration and then dividing this sum by expired VT. In
each run, VD was averaged for all breaths in which
FHe was <90% of
FIHe. This exclusion was applied to avoid
problems with reduced signal-to-noise ratio as expiratory concentration
approached inspiratory concentration near the end of the maneuver.
The data were submitted to the computational model (see below) using
volumes and gas fractions expressed in STPD conditions. The
model output was a set of values for and FRC. To
validate the estimate of FRC from the model, "true" alveolar FRC
was obtained by subtracting the inspired volume obtained at the end of
the maneuver and VD from the plethysmographic TLC value for
each subject. That is, we assumed that the TLC obtained in the body
plethysmograph is accurate and that TLC does not change with exercise.
To document the significance of the changes in FRC and change in
with exercise, data for both variables were
first submitted to one-way ANOVA followed by paired t-tests
to determine significance of changes during exercise compared with preexercise.
Computational model.
Alveolar volume at end expiration (VAEE) is
imagined to be subdivided into a large number of compartments with
equal gas volumes at end expiration. During each breath, each
compartment expands from an initial volume (V0) to a final
volume (V0 + dV), where V0 is the same for
all compartments but dV differs among compartments. The fractional
volume expansion of each compartment (dV/V0) is denoted
. The distribution of volume change among compartments, f(), is
assumed to be a log-normal distribution with a peak at ln() = µ and width
|
(1)
|
VT equals the sum of the volume changes of all lung
compartments
|
(2)
|
Because of this equality, the parameter µ is related to other
variables by the following equation
|
(3)
|
It is assumed that each compartment receives gas from the dead
space in proportion to its volume expansion. Therefore, during the
first inspiration of the test gas with He concentration C0, the concentration of He in the dead space is zero and the amount of He
delivered to a compartment is C0(1 
VD/VT)dV. The compartment is assumed to be well
mixed, and the concentration of He in the compartment at the end of the
first inhalation, denoted C(,1), is computed from a mass balance for
He. The mass balance yields the following equation, where c(,1) = C(,1)/C0
|
(4)
|
During the next expiration, expired gas from all compartments is
assumed to be well mixed in the common VD. Thus the
concentration of He in the alveolar gas expired in the first breath,
denoted Cexp, is given by Eq. 5, where
cexp(1) = Cexp(1)/C0
|
(5)
|
In subsequent breaths, for instance breath number i,
the concentration in each compartment is c(,i 1)
at the beginning of inspiration, and gas from the dead space reenters
each compartment at the beginning of inspiration. It is assumed that
the concentration of He in the dead space gas equals the mixed alveolar
concentration in the previous breath,
cexp(i 1). Thus, for the ith
breath, Eq. 4 is modified to the following
|
(6)
|
The concentration of expired gas in subsequent breaths
[cexp(i)] is calculated from Eq. 5,
with c(,1) replaced by c(,i).
The model contains four parameters: VAEE,
VT, VD, and . Three of
these, VAEE, VT, and
VD, were experimentally determined, independently of the
values of cexp(i). Thus only one remains to be
determined from the fit of the values of
cexp(i), calculated from Eq. 5, to
the measured values of cexp(i). However, to test
the accuracy of the modeling, we purposely used values for only two of
the independently determined parameters, VT and
VD, and varied two parameters, VAEE
and , to obtain the best fit of the predicted
values of cexp(i) to the data.
The right side of Eq. 5 was evaluated numerically, and the
Powell iterative search method (17) was used to find the
values of VAEE and for
which the sum of the squared differences between the predicted and
measured values of cexp(i) was minimum. An
example of the measured and best-fit values of
cexp(i) is shown in Fig.
1. The fit to the model was excellent in
all cases (lowest r2 = 0.99 among all
subjects).
View larger version (13K):
[in this window]
[in a new window]
|
Fig. 1.
Example of He concentration values in the mixed expired
alveolar gas (end-expiratory He concentration [He]),
nondimensionalized by He concentration in the first inspiration plotted
vs. breath number. Best fit calculated from the model is shown as solid
line.
|
|
| |
RESULTS |
Values of VAEE obtained from the fit of
the model to the washin data are shown in Fig.
2, plotted vs. values obtained from the
independent measurements of TLC. The mean difference between the values
is 0.14 ± 0.43 liter (mean plethysmographic value greater than
mean washin value), which was not significantly different from 0 (P > 0.05).
View larger version (13K):
[in this window]
[in a new window]
|
Fig. 2.
Values of end-expiratory volume
(VAEE) obtained from the fit of the model
to the washin data (y-axis) vs. values measured
plethysmographically (x-axis). Plethysmographic
end-expiratory volume = TLC IC VD,
where IC is inspiratory capacity performed at the end of each maneuver,
TLC is total lung capacity, and VD is dead space volume.
Line of identity is shown. Values for each subject are connected.
Because end-expiratory volume declines with exercise, data for each
subject proceed to the left as the exercise level increases.
|
|
Values of are plotted vs. work rate in Fig.
3. In one subject, the
values were near zero at rest and at lower
levels of exercise. Including data from this subject reduced the mean
slightly; therefore, we opted to report means excluding data from this
subject. The mean and SD of the resting value of for the remaining seven subjects was 0.89 ± 0.18. Mean values of
during exercise at different levels of exercise
are significantly different from resting values, but mean
at different levels of exercise are not
significantly different. The average of for all
levels of exercise are 0.60 ± 0.13.
View larger version (17K):
[in this window]
[in a new window]
|
Fig. 3.
Values of the width of the log-normal distribution of
ventilation () as a function of work rate (W) for
8 subjects. Values for individual subjects are connected. Bold line
(±SD) indicates mean, excluding subject with
near zero.
|
|
Values of are plotted vs. VT in Fig.
4. In general,
decreased with increasing VT.
| |
DISCUSSION |
In this study, we measured expired gas concentrations at
end-expiration during a multibreath washin of He at rest and with increasing levels of exercise in eight normal subjects (one woman). We
interpreted these data to obtain values for at
different levels of exercise, and we found that decreased with increasing ventilation of exercise.
The interpretation of washin and washout data by compartmental models
has a long history. Fowler and colleagues (4) used two-
and three-compartment models to interpret data on nitrogen washout,
and, since then, others have used models of varying complexity (7, 12). Our model is similar to these. We assume that
terminal alveolar units are connected to the mouth via a common dead
space. Transport by diffusion is not explicitly described, but the dead space and alveolar compartments are assumed to be well-mixed. The
reinspiration of gas from the dead space is included in the model. We
assume that VD is redistributed to the alveolar
compartments in the same proportion as VT. Asynchronies of
filling and emptying are not included, and the model cannot account for
the slope of phase III of expiratory gas concentration. We describe the
distribution of regional ventilation by log-normal distribution with
one adjustable parameter, the . With this model, the fits to the data were very good (r = 0.998 ± 0.002). We also tried a more complicated model, namely, a two-parameter
logarithmic distribution with skew. However, the fits of that model to
the data were only marginally improved, and the inferred values of the
were not substantially different. Thus, although
the log-normal distribution might be too limited to describe the
ventilation distribution in patients with respiratory disease, it seems
to describe the ventilation distribution in normal subjects adequately.
Although we had an independent experimental measurement of
VAEE, we purposely included
VAEE as a parameter that was determined by
fitting the model to the washin data because a comparison of the value
of VAEE inferred from the washin data with the
plethysmographically measured value of VAEE
(TLC IC VD), where IC is inspiratory capacity, provides a test of the model. The agreement between the two,
shown in Fig. 2, is quite good. As a result, we have some confidence in
the validity of the model.
Our values of can be compared with those
reported by others. Although the distribution function used by Gomez
and colleagues (7) is somewhat different from ours, the
relative half-width of their distribution (0.78 ± 0.28) is
comparable to our value (0.89 ± 0.18).
Lewis and colleagues (12) used a 50-compartment model to
fit their N2 washout data and obtained SD of log()
values of 0.56 ± 0.13 in young subjects and 0.86 ± 0.19 in
older subjects. Recently, Prisk and colleagues (18)
applied the Lewis model to data obtained from subjects who flew on the
space shuttle. They obtained preflight control values of 0.73 ± 0.05, 0.64 ± 0.02, and 0.73 ± 0.03 from data on washout of
N2 and washin of He and SF6, respectively.
VT for the Gomez study varied considerably but averaged >1
liter. The VT values in the Lewis and Prisk studies were
500 and 700 ml, respectively. Our value of for
resting subjects is slightly higher than the values reported by others.
VT values for our resting subjects were larger than the
average of Lewis and colleagues but in the range of values found by
Gomez and associates. Our values of at
VT of 700-1,400 ml are much the same as theirs.
The first studies of the anatomic distributions of regional ventilation
measured washin or washout of radioactive gases detected by rather
coarse-grained external counters. The results of these studies are
summarized in the review article by Milic-Emili (14). This
technique revealed a vertical gradient of ventilation, but, as Piiper
and Scheid (16) pointed out, the variance of ventilation implied from these data (25) is considerably smaller than
the value obtained from gas mixing studies; this implies the existence of nonuniformities in addition to the gravitational gradient.
The first direct observation of a nongravitational component of
nonuniform ventilation was reported by Hubmayr and colleagues (10). They used parenchymal markers and biplane video
fluoroscopy to measure regional expansion at a scale of ~1
cm3. Since then, Treppo and colleagues (24)
measured regional ventilation and perfusion simultaneously using
positron emission tomography, and others (1, 13, 20) have
measured regional ventilation by fluorescent aerosol deposition. The
resolution of these techniques is about the same as that of the
parenchymal marker technique, but these methods provide data that
sample the lung more uniformly and thoroughly. These methods are
restricted to use in animals. More recently, another method, computed
tomographic measurements of regional washin of a radiodense gas, has
been developed (23); this method is potentially applicable
to humans.
Wilson and colleagues (21, 27) noted that the variance of
regional volume measured by the parenchymal marker technique depended
on the size of the region sampled, with variance increasing as sample
size decreased. They argued that the observed variance was the residue
of a larger variance at a smaller scale. This question has continued to
vex the field: To what extent do anatomic measurements at a scale of 1 cm3 underestimate the functional variance of ventilation at
the scale of the acinus? The question can be addressed by comparing the functional and anatomic values of . The results
of Hubmayr and colleagues (10) are described in somewhat
different terms from ours, and their data must be reinterpreted before
they can be compared with ours. Hubmayr and associates measured
regional volume as a fraction of volume at TLC during deflation from
TLC. Plots of fractional regional volume vs. lung volume were linear,
and they report the variance of the slopes of these plots. Thus the
volume excursions in these experiments were large, and regional volume
change was normalized by regional volume at TLC, not by volume at end
expiration. They report a distribution of slopes with a standard
deviation of ~0.12. To account for the fact that these data are
normalized by TLC rather than by FRC, they should be multiplied by
~2.5. Thus we obtain an estimate for at FRC of
~0.3 for their data. The data of Treppo and associates
(24) describe specific regional ventilation, and their
value of can be compared directly with ours.
Their value for is ~0.25, in reasonable
agreement with the value inferred from the data of Hubmayr and
colleagues. Measurements of regional ventilation by the aerosol
deposition technique in goats (13) and pigs
(20) give larger values of , namely, ~0.40. Perhaps these slightly higher values are caused by a species dependence of , or perhaps they are the result of
the fact that the mechanics of aerosol deposition and gas transport are
different. Thus the comparison between the functional value of
in humans and the value obtained from anatomic
measurements in animals indicates that the value measured at a scale of
1-2 cm3 is approximately two times smaller than the
value at the scale of the acinus. Gas mixing studies in excised dog
lobes (27) also showed that a standard deviation of
regional volume of approximately two times that measured at the scale
of 1 cm3 would be required to explain the mixing
efficiency, and computer tomographic measurements of regional
volume confirmed this estimate of small-scale variability
(21). Thus comparison of gas mixing studies with studies
of anatomic heterogeneity of regional ventilation lead us to conclude
that the functional unit of gas exchange is smaller than the 1-2
cm3 of most anatomic studies.
The value of has implications for gas exchange.
The effectiveness of transport between respired gases and the blood
depends primarily on the distribution of A/. The width of the A/ distribution
(/) depends on the widths of the
ventilation and perfusion distributions, and
, and on the correlation between ventilation and
perfusion, 
. The relation between the widths of the three
distributions, each described as a log-normal distribution, and is
given in Eq. 7 (26)
|
(7)
|
Our data provide values of , and values of
/ are available in the literature.
Reported values for / at rest are
0.4-0.5 in resting subjects and 0.4-0.6 during exercise (5, 6, 8, 9, 19, 22). Thus / is smaller than , and, from Eq. 7, it
follows that must be of the same magnitude as
and that must be high. The relations between
and that are obtained by substituting the
values of and / for
resting ( = 0.89, / = 0.45) and exercise
( = 0.60, / = 0.5) into Eq. 7 are
shown in Fig. 5. For resting conditions, the range of possible values for is fairly wide,
but the value of is more restricted: > 0.85. Simultaneous
anatomic measurements of ventilation and perfusion in animals allow a
direct evaluation of the correlation between ventilation and perfusion.
Values obtained by positron emission tomography and microsphere
techniques are 0.7-0.8 (1, 13, 15, 20, 24). The
possible values of and during exercise cover
a broader range (Fig. 5). It is clear that , like
, might decrease during exercise and that
/ rises slightly because the correlation
between ventilation and perfusion decreases.
View larger version (10K):
[in this window]
[in a new window]
|
Fig. 5.
Values of the coefficient of correlation between
ventilation (A) and perfusion () plotted
against values of the width of the perfusion distribution
(). These curves were obtained from the relation
between the variables in Eq. 7, with
= 0.89 and
/ = 0.45 for rest (solid line) and
= 0.60 and
/ = 0.50 for exercise (dashed line).
Note the wider range of possible values for both and
during exercise, caused largely by the decrease
in that we documented.
|
|
The value of that we measure at rest is large.
The decrease with increasing VT is caused, at least in
part, by the decrease in the vertical gradient of ventilation with
increasing VT (2). Our value of
at higher VT agrees with the value
obtained by Prisk and colleagues (18) in microgravity. At
higher VT, the remaining nonuniformity appears to be caused
by lung expansion that is nonuniform at the scale of the acinus.
Presumably, this nonuniform expansion is the result of nonuniform
parenchymal compliance at that scale. The range of from one SD
above the mean to one SD below the mean covers a factor of six at rest
and three during exercise. The identity of the structural component
that is the source of this nonuniformity, as well as the source of the
equally large and highly correlated nonuniformity of perfusion, is unknown.
| |
ACKNOWLEDGEMENTS |
We thank C. M. Swee and K. A. O'Malley for technical
assistance and L. L. Oeltjenbruns for manuscript preparation.
| |
FOOTNOTES |
This work was supported in part by a General Clinical Research Center
grant from the National Institutes of Health (RR-00585) awarded to the
Mayo Clinic, Rochester, Minnesota, and National Institutes of Health
Grant HL-52230.
Address for reprint requests and other correspondence: K. C. Beck, Rm. 4-411 Alfred Bldg., Mayo Clinic, 200 First St. SW, Rochester, MN 55905 (E-mail: beckk2{at}mayo.edu).
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 7 August 2000; accepted in final form 10 January 2001.
| |
REFERENCES |
1.
Altemeier, WA,
McKinney S,
and
Glenny RW.
Fractal nature of regional ventilation distribution.
J Appl Physiol
88:
1551-1557,
2000[Abstract/Free Full Text].
2.
Bryan, AC,
Bentivoglio LG,
Beerel F,
MacLeish H,
Zidulka A,
and
Bates DV.
Factors affecting regional distribution of ventilation and perfusion in the lung.
J Appl Physiol
19:
395-402,
1964[Abstract/Free Full Text].
3.
Fowler, WS.
Lung function studies. III. Uneven pulmonary ventilation in normal subjects and in patients with pulmonary disease.
J Appl Physiol
2:
283-299,
1949[Free Full Text].
4.
Fowler, WS,
Cornish ER,
and
Kety JJ.
Lung function study. VIII. Analysis of ventilation by pulmonary N2 clearance curves.
J Clin Invest
31:
40-50,
1952.
5.
Gale, GE,
Torre-Buene JR,
Moon RE,
Saltzman HA,
and
Wagner PD.
Ventilation-perfusion inequality in normal humans during exercise at sea level and simulated altitude.
J Appl Physiol
58:
978-988,
1985[Abstract/Free Full Text].
6.
Gledhill, N,
Froese AB,
Buick FJ,
and
Bryan AC.
A/ inhomogeneity and A-DO2 in man during exercise: Effect of SF6 breathing.
J Appl Physiol
45:
512-515,
1978[Abstract/Free Full Text].
7.
Gomez, DM,
Briscoe WA,
and
Cumming G.
Continuous distribution of specific tidal volume throughout the lung.
J Appl Physiol
19:
683-692,
1964[Abstract/Free Full Text].
8.
Hammond, MD,
Gale GE,
Kapitan KS,
Ries A,
and
Wagner PD.
Pulmonary gas exchange in humans during normobaric hypoxic exercise.
J Appl Physiol
61:
1749-1757,
1986[Abstract/Free Full Text].
9.
Hopkins, SR,
Gavin TP,
Siafakas NM,
Haseler LJ,
Olfert IM,
Wagner H,
and
Wagner PD.
Effect of prolonged, heavy exercise on pulmonary gas exchange in athletes.
J Appl Physiol
85:
1523-1532,
1998[Abstract/Free Full Text].
10.
Hubmayr, RD,
Walters BJ,
Chevalier PA,
Rodarte JR,
and
Olson LE.
Topographical distribution of regional lung volume in anesthetized dogs.
J Appl Physiol
54:
1048-1056,
1983[Abstract/Free Full Text].
11.
Johnson, BD,
Beck KC,
Proctor DN,
Miller J,
Dietz NM,
and
Joyner MJ.
Cardiac output during exercise by open circuit acetylene washin method: comparison with direct Fick.
J Appl Physiol
88:
1650-1658,
2000[Abstract/Free Full Text].
12.
Lewis, SM,
Evans JW,
and
Jalowayski AA.
Continuous distributions of specific ventilation recovered from inert gas washout.
J Appl Physiol
44:
416-423,
1978[Abstract/Free Full Text].
13.
Melsom, M,
Kramer-Johansen J,
Flatebo J,
Muller T,
and
Nicolayson C.
Distribution of pulmonary ventilation and perfusion measured simultaneously in awake goats.
Acta Physiol Scand
159:
199-208,
1997[ISI][Medline].
14.
Milic-Emili, J.
Static distribution of lung volumes.
In: Handbook of Physiology. Mechanics of Breathing. Bethesda, MD: Am. Physiol. Soc, 1986, sect. 3, vol. III, pt. 2, chapt. 31, p. 561-574.
15.
Mure, M,
Domino KB,
Lindahl SGE,
Hlastala MP,
Altemeier WA,
and
Glenny RA.
Regional ventilation-perfusion distribution is more uniform in the prone position.
J Appl Physiol
88:
1076-1083,
2000[Abstract/Free Full Text].
16.
Piiper, J,
and
Scheid P.
Respiration: alveolar gas exchange.
Annu Rev Physiol
33:
131-154,
1971[ISI][Medline].
17.
Press, WH,
Flannery BP,
Teukolsky SA,
and
Vetterling WT.
Numerical Recipes in C. Cambridge, MA: Cambridge Univ. Press, 1988.
18.
Prisk, GK,
Elliott AR,
Guy HJ,
Verbanck S,
Paiva M,
and
West JB.
Multiple-breath washin of helium and sulfur hexafluoride in sustained microgravity.
J Appl Physiol
84:
244-252,
1998[Abstract/Free Full Text].
19.
Rice, AJ,
Thornton AT,
Gore CJ,
Scroop GC,
Greville HW,
Wagner H,
Wagner PD,
and
Hopkins SR.
Pulmonary gas exchange during exercise in highly trained cyclists with arterial hypoxemia.
J Appl Physiol
87:
1802-1812,
1999[Abstract/Free Full Text].
20.
Robertson, HT,
Glenny RW,
Stanford D,
McInnes LM,
Luchtel DL,
and
Covert D.
High-resolution maps of regional ventilation utilizing inhaled fluorescent microspheres.
J Appl Physiol
82:
943-953,
1997[Abstract/Free Full Text].
21.
Rodarte, JR,
Chaniotakis M,
and
Wilson TA.
Variability of parenchymal expansion measured by computed tomography.
J Appl Physiol
67:
226-231,
1989[Abstract/Free Full Text].
22.
Schaffartzik, W,
Poole DC,
Derion T,
Tsukimoto Hogan MC K,
Arcos JP,
Bebout DE,
and
Wagner PD.
A/ distribution during heavy exercise and recovery in humans: implications for pulmonary edema.
J Appl Physiol
72:
1657-1667,
1992[Abstract/Free Full Text].
23.
Tajik, JK,
Tran BQ,
and
Hoffman EA.
Xenon enhanced CT imaging of local pulmonary ventilation.
Proc SPIE
2709:
40-54,
1996.
24.
Treppo, S,
Mijailovich SM,
and
Venegas J.
Contributions of pulmonary perfusion and ventilation to heterogeneity in A/ measured by PET.
J Appl Physiol
82:
1163-1176,
1997[Abstract/Free Full Text].
25.
West, JB.
Regional differences in gas exchange in the lung of erect man.
J Appl Physiol
17:
893-898,
1962[Abstract/Free Full Text].
26.
Wilson, TA,
and
Beck KC.
Contributions of ventilation and perfusion inhomogeneities to the A/ distribution.
J Appl Physiol
76:
2298-2304,
1992[Abstract/Free Full Text].
27.
Wilson, TA,
Olson LE,
and
Rodarte JR.
Effect of variable parenchymal expansion on gas mixing.
J Appl Physiol
62:
634-639,
1987[Abstract/Free Full Text].
28.
Yeh, MP,
Gardner RM,
Adams TD,
and
Yanowitz FG.
Computerized determination of pneumotachometer characteristics using a calibrated syringe.
J Appl Physiol
53:
280-285,
1982[Abstract/Free Full Text].
J APPL PHYSIOL 90(6):2151-2156
8750-7587/01 $5.00
Copyright © 2001 the American Physiological Society
TOP
ABSTRACT
INTRODUCTION
