acute lung injury; airway; breathing; gas exchange; inert gas; ventilation-perfusion heterogeneity
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INTRODUCTION |
ENDOTOXIN CONTRIBUTES to gas exchange abnormalities in
experimental acute lung injury by increasing ventilation-to-perfusion ratio (
A/
)
heterogeneity and intrapulmonary shunt (8, 13, 18). Although
frequently attributed to changes in the perfusion distribution (19),
A/ heterogeneity is
determined by changes in alveolar ventilation
(A) heterogeneity, perfusion
heterogeneity, and the correlation between regional ventilation and
perfusion (29). Because the distributions of ventilation and perfusion have not been independently measured during endotoxemia, the precise changes in ventilation and perfusion that lead to
A/ heterogeneity are unknown.
The purpose of this study was to determine how endotoxin changes the
distributions of regional ventilation and perfusion and the
contribution of these changes to
A/ heterogeneity. We independently measured regional ventilation and perfusion using aerosolized and injected microspheres in endotoxemic pigs and quantified the relative importance of changes in ventilation
heterogeneity, perfusion heterogeneity, and correlation between
regional ventilation and perfusion in determining
A/ heterogeneity during endotoxemia.
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METHODS |
Animal Preparation
The study was approved by the University of Washington Animal Care
Committee. Seven pathogen-free pigs weighing 21-25 kg were chemically restrained with intramuscular ketamine (20 mg/kg) and xylazine (2 mg/kg). Anesthesia was induced with intravenous thiopental (~4-8 mg/kg) and maintained with a thiopental infusion
sufficient to produce a surgical plane of anesthesia and suppress
spontaneous ventilation (~10-17
mg · kg
1 · h1).
Pigs breathed air and were mechanically ventilated via tracheostomy without positive end-expiratory pressure. Tidal volume was set at
11-12 ml/kg, and respiratory rate was adjusted to keep arterial PCO2 between 30 and 35 Torr before
endotoxin infusion (i.e., respiratory rate was not changed after
endotoxin infusion began). Minute ventilation was measured with a drum
spirometer (Collins, Boston, MA). Catheters were placed in one carotid
and femoral artery and in both femoral veins. A flow-directed pulmonary artery catheter was introduced through the external jugular vein. Lungs
were hyperinflated to twice the tidal volume every 15 min to prevent
atelectasis. Exhaled CO2 and expiratory flow were digitally sampled with an infrared CO2 detector (model 1260, Novametrix Medical Systems, Wallingford, CT) and pneumotach,
respectively, for later determination of anatomic dead space. Animals
were placed in the prone posture, and a solution of six inert gases
(sulfur hexafluoride, ethane, cyclopropane, halothane, diethyl ether, and acetone) was infused for at least 30 min before the study protocol
was begun.
Study Protocol
Data were collected at three time points during the study with the end
of data collection for one time point and the start of data collection
for the subsequent time point always separated by 40 min. The first two
time points occurred before endotoxemia (i.e., baseline 1 and
baseline 2), and data collection for the third began after 30 min of endotoxin infusion. E. coli O55:B5 endotoxin (Sigma
Chemical, St. Louis, MO) was infused at 2.5 µg · kg1 · h1
through a femoral venous catheter. Normal saline (500 ml) was given
intravenously if systemic blood pressure fell to 80% of its
preendotoxin level. The rate of endotoxin infusion was halved if
systemic blood pressure did not respond to fluids.
Regional ventilation was measured at each time point by aerosolizing
yellow, orange, or yellow-green 1-µm-diameter fluorescent microspheres (FluoSpheres, Molecular Probes, Eugene, OR) in the ventilator circuit (25), while regional perfusion was simultaneously measured by injection of crimson, blue-green, or green 15-µm
fluorescent microspheres (FluoSpheres, Molecular Probes) through a
femoral venous catheter. Microspheres were sonicated and vortexed
immediately before administration. Fifteen-micrometer microspheres were
suspended in normal saline to a total volume of 10 ml, manually
injected in small intermittent boluses (in an attempt to achieve as
constant an infusion as possible), and agitated frequently during
injection to prevent settling. Microspheres were administered over a
10-min time period in the first five animals and, because of
improvements in aerosol delivery, over 5 min in the last two animals.
Different color microspheres were used to measure ventilation and
perfusion at each time point, and color assignment varied across
experiments. Arterial, mixed-venous, and exhaled gas samples were
collected for determination of inert gas concentrations by use of a gas chromatograph (Varian 3300; Walnut Creek, CA) (15) immediately after
each microsphere administration, and blood-gas measurements were made
on arterial and venous samples (ABL 4, Radiometer, Copenhagen, Denmark).
Core body temperature, mean arterial pressure, pulmonary artery
pressure, pulmonary artery occlusion pressure, peak and end-inspiratory pause airway pressures, cardiac output (in triplicate), respiratory exchange ratio, and arterial blood gases were measured immediately before and after each microsphere administration, and average values
were used for comparisons between time points. Cardiac output was
measured by thermodilution (Edward's COM 2, Baxter, Irvine, CA).
Hematocrit was determined by centrifugation of duplicate samples drawn
after each microsphere administration. The respiratory exchange ratio
was calculated by using fractional O2 and CO2
contents in inspired and mixed expired gas that were measured with a
mass spectrometer (MGA1100, Perkin-Elmer, Norwalk, CT).
Animals were given heparin (10,000 units) and papaverine (2 mg/kg) and
then killed by exsanguination under deep anesthesia. Median sternotomy
was performed, large-bore catheters were placed in the left atrium and
main pulmonary artery, and the lungs were perfused with 2% dextran in
normal saline until free of blood. The right kidney was removed from
five animals and analyzed for fluorescence to determine whether
right-to-left shunting was present.
Lungs were removed from the chest, inflated with 25 cmH2O
airway pressure, and air dried. They were encased in foam while suspended vertically in a squared box and then cut into
1.2-cm3-thick transverse slices, and each slice was cut
into ~1.7-cm3 cubes in a miter box, yielding
851-1,221 lung pieces per animal. Pieces were visually scored for
airway content and weighed, with pieces less than 8 mg excluded from
the data set to minimize error due to uncertainty in flow or weight.
Fluorescent intensities for each color were determined by extracting
fluorescent dye from each lung piece with the organic solvent
2-ethoxyethyl acetate (Aldrich Chemical, Milwaukee, WI). Dye
concentration was read in a fluorimeter (LS50B, Perkin-Elmer) and
corrected for background signal and spillover from adjacent colors
(11). Based on the mean fluorescent intensity for each color in each
animal and standard values for fluorescent intensity per microsphere,
the mean number of microspheres per piece was ~1,300 for crimson,
1,200 for blue-green, 1,000 for green, 39,000 for yellow, 37,000 for
yellow-green, and 56,000 for orange. Kidney fluorescence was determined
by dissolving tissue with 4 M KOH, filtering the suspension with a
10-µm pore filter, extracting fluorescent dye from the filter with
2-ethoxyethyl acetate, and determining dye concentration in a
fluorimeter (11).
Data Processing
Fluorescence.
Fluorescent intensities were converted to flows in milliliters per
minute by dividing the fluorescent intensity within each piece by the
sum of intensities for that color in all pieces and then multiplying by
total ventilation (for ventilation) or cardiac output (for perfusion).
Ventilation was calculated by estimating anatomic dead space in three
consecutive breaths from plots of exhaled CO2 concentration
vs. exhaled volume as described by Fowler (9).
Ventilation and perfusion (in units of ml/min) were used to predict gas
exchange (2), to determine A- and
-weighted A/
distributions, and to determine redistribution of ventilation and
perfusion. To compensate for differences in piece size, ventilation and
perfusion were weight normalized
(ml · min1 · g1)
before coefficients of variation (standard deviation/mean) and variances for the ventilation and perfusion distributions were calculated. Correlation between regional ventilation and perfusion was
also calculated by using weight-normalized data.
Correlation between ventilation and perfusion and measures of
heterogeneity (coefficients of variation and standard deviations) were
calculated after lung pieces that received no ventilation or perfusion
were excluded. Specifically, pieces were excluded for a given time
point if ventilation or perfusion signals were <0.05 times the mean
signal for those colors. This resulted in exclusion of 5 ± 3, 5 ± 2, and 9 ± 7% (means ± SD) of pieces in baseline 1,
baseline 2, and endotoxemia, respectively. In comparison, using
A/ < 0.01 and
A/ > 100 to
define shunt and dead space would have resulted in exclusion of 3 ± 2, 2 ± 2, and 6 ± 6% of pieces in baseline 1, baseline
2, and endotoxemia. We excluded pieces with fluorescent signals
<0.05 times mean signal because signals <0.05 times mean cannot be
distinguished from zero when the mean signal has a raw fluorescent
intensity less than ~20, as was the case for some colors in this
study. Experimental error in pieces with low fluorescent signals was
estimated by simultaneously aerosolizing 1-µm microspheres of four
different colors and determining variability between colors within each piece using previously published data (1).
A- and
-weighted
A/ distributions.
Inert-gas data for an animal were excluded if the remaining sum of
squares at any time point exceeded 10. Retentions and excretions of
inert gases were used to compute 50-compartment
A- and
-weighted A/ distributions as
described by Wagner et al. (27). Standard deviations of
A- and -weighted
A/ distributions
(logSD and logSD) were
calculated by using all
A/ bins
except shunt and dead space and again after exclusion of secondary
A/ peaks (i.e.,
those smaller than the main peak) in high
A/ regions.
A- and
-weighted
A/
distributions were also calculated from microsphere data after pieces
representing shunt and dead space were excluded.
Predicting gas exchange.
Arterial PO2,
PCO2, and alveolar-arterial
O2 differences were predicted from
A/ distributions
measured with microspheres, mixed venous blood gases, Hb concentration, and body temperature as described by Altemeier et al. (2). Briefly,
end-capillary O2 and CO2 contents and regional
alveolar PO2 and
PCO2 were calculated in each lung
piece by using its
A/ and solving mass
balance equations for O2, CO2, and
N2. End-capillary gas contents were perfusion weighted and
summed to give arterial gas contents, and regional alveolar gas
tensions were ventilation weighted and summed to give mixed alveolar
gas tensions. Arterial O2 and CO2 contents were
converted to gas tensions by using oxygen- and carbon dioxide-Hb
dissociation curves for pigs.
Statistical Analysis
All data are reported as means ± SD. Differences between baseline
1 and baseline 2 reflect time and method error and were therefore used as within-animal controls for evaluating differences between baseline 2 and endotoxemia. Statistical significance
was assumed if P < 0.05 unless otherwise stated.
Comparisons between baselines 1 and 2 and between
baseline 2 and endotoxemia were made by using two-tailed paired
t-tests for physiological, gas exchange, and inert gas data and
for coefficients of variation and standard deviations of the
A, , and
A/ distributions.
Paired t-tests were also used to compare results of inert gas
and microsphere techniques and predicted and measured gas exchange.
Linear correlation coefficients were calculated for measurement of
regional A and
within the same piece at the same time point, and differences were
evaluated by using paired t-tests after Fisher's z transformation.
Determinants of ventilation-perfusion heterogeneity.
We partitioned changes in
A/ heterogeneity
during endotoxemia into those attributable only to changes in
A heterogeneity, only to changes in
heterogeneity, and only to changes in correlation between regional A and
(i.e.,
A- correlation) by
using the mathematical expression that relates these variables (29)
![&sfgr;<SUP>2</SUP><SUB><A><AC>V</AC><AC>˙</AC></A><SC>a</SC>/<A><AC>Q</AC><AC>˙</AC></A></SUB> = F[&sfgr;<SUB><A><AC>V</AC><AC>˙</AC></A><SC>a</SC></SUB>, &sfgr;<SUB><A><AC>Q</AC><AC>˙</AC></A></SUB>, &rgr;] = &sfgr;<SUP>2</SUP><SUB><A><AC>V</AC><AC>˙</AC></A><SC>a</SC></SUB> + &sfgr;<SUP>2</SUP><SUB><A><AC>Q</AC><AC>˙</AC></A></SUB> − 2 ⋅ &sfgr;<SUB><A><AC>V</AC><AC>˙</AC></A><SC>a</SC></SUB> ⋅ &sfgr;<SUB><A><AC>Q</AC><AC>˙</AC></A></SUB> ⋅ &rgr;](/content/vol88/issue6/fulltext/1933/img001.gif)
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(1)
|
where
F describes the variance of the
A/ distribution in
the natural log domain
(
), 
is the correlation between ln A and
ln within a piece, and
and 
are the variances of the A and
distributions in the natural log domain. Specifically, effects of
, , and
were quantified in each animal by calculating the hypothetical change
in
that would have resulted had endotoxin changed only one of the three
variables. Each hypothetical change, H, was calculated by taking the
difference between
during baseline 2 (calculated by inserting values for
, 
, and from
baseline 2 into Eq. 1), and
that would have resulted had endotoxin changed only one of the three
variables (calculated by inserting appropriate values for , , and into
Eq. 1)
where
subscripts E and 2 denote distributions measured during endotoxemia and
baseline 2. ANOVA using a randomized complete block design was
used to assess differences between hypothetical changes in
due to , , and , and post
hoc comparisons between individual changes were evaluated with
Fisher's protected least significant difference test.
Because
is also affected by the interaction between simultaneous changes in
, , and , we
calculated hypothetical changes, H, in
attributable to the interaction between simultaneous changes in and
(interaction [A-]),
and simultaneous changes in and
, and and (interaction
[-A,]).
H (interaction
[A-])
was calculated by taking the change in
due to simultaneous changes in
and 
, and
subtracting from it the change in
due to endotoxin-induced changes in
, and the
change in
due to endotoxin-induced changes in
. H (interaction
[-A ,])
was calculated in an analogous fashion. In their simplest
form, expressions for the interaction terms are written as follows
The sum of these five hypothetical changes in
gives the total change in
between baseline 2 and endotoxemia. In all animals, this sum
equaled the change in
between baseline 2 and endotoxemia calculated directly from the ln A/
distributions, empirically verifying Eq. 1.
Hypothetical changes in
due to endotoxin's effects on
, , , and interactions between variables were reported as percentages of the
total change in
within each animal. To calculate percentages, each hypothetical change
was divided by the sum of the absolute values of all hypothetical changes and multiplied by 100. Absolute rather than raw values were
used to calculate the denominator of this expression to avoid the
statistically unstable situation in which the denominator is extremely
small relative to the numerator (e.g., when individual hypothetical
changes are large but have opposite signs so that their sum is small).
Percent changes in
due to each hypothetical change were evaluated for difference from zero
with unpaired t-tests.
Redistribution of ventilation and perfusion.
Redistribution was defined as the decrease in correlation between pre-
and postendotoxin measurements (e.g., [AE,
A2]) compared with
the correlation between measurements made during baselines 1 and 2 (e.g., [A1,
A2]). Redistribution
was considered significant if the confidence interval for the
difference in correlation coefficients did not include zero. The
magnitudes of ventilation and perfusion redistribution were considered
significantly different if confidence intervals describing ventilation
and perfusion redistribution did not overlap.
Confidence intervals were generated using the bootstrap technique (3,
7) because this technique is free of assumptions about the distribution
of data or independence of data points. Conventional methods of
generating confidence intervals about a correlation coefficient assume
that all measurements are independent, but this assumption is violated
because regional pulmonary blood flow is spatially correlated (10). The
bootstrap technique calculates confidence intervals by constructing
hypothetical data sets from an experimental data set with n
lung pieces by grouping each piece with its 29 closest neighbors and
selecting n/30 groups with replacement. Linear correlation
coefficients or their differences are calculated for each hypothetical
data set, and 95% confidence intervals are defined by excluding the
highest and lowest 2.5% of values.
Effects of airway deposition.
To evaluate the effect of airway content on ventilation and perfusion
distributions, we recalculated coefficients of variation and
redistribution for ventilation and perfusion after excluding lung
pieces judged to contain more than 25% airways by volume.
Mechanisms of redistribution.
Linear regression analysis was used to determine whether changes in
predicted alveolar PO2 (2) within a
lung piece [(predicted alveolar
PO2 during endotoxemia) 
(predicted alveolar PO2 during
baseline 2)] were predictive of changes in perfusion due
to endotoxin in that piece
[100 · (E 2)/2]. Pieces with flows during baseline 2 that were less than 0.05 times mean flow were excluded from this analysis. Slopes of regression lines were evaluated for difference from zero with unpaired
t-tests.
To determine whether changes in regional ventilation and perfusion
during endotoxemia were matched, linear correlation coefficients were
calculated for the percent change in regional ventilation (100 · [AE A2]/A2)
and the percent change in regional perfusion
(100 · [E 2]/2)
within a piece. Pieces with flows during baseline 2 that were
<0.05 times mean flow were excluded from this analysis. Because the
correlation was positive in every animal, the coefficient of
determination (r2) was used to describe the
strength of the association between changes in regional ventilation and perfusion.
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RESULTS |
Physiological Data
On average, endotoxemia caused mean pulmonary artery and
pulmonary artery occlusion pressures to triple and cardiac output to
halve (Table 1). Mean arterial pressure was
not changed significantly at 30 min, but five of seven animals
developed hypotension before that time point and were given intravenous
fluids. The rate of endotoxin infusion was halved in three animals
because hypotension did not resolve promptly with intravenous fluids.
Endotoxemia decreased arterial and mixed venous
PO2 and increased mixed venous
PCO2 and the alveolar-arterial O2 difference. Arterial
PCO2 did not increase significantly (Table 1). Endotoxin also increased peak airway and end-inspiratory hold pressure and resulted in hemoconcentration and acidemia. Physiological data did not change significantly between baseline 1 and baseline 2.
Microsphere Data: Ventilation, Perfusion, and
Ventilation-Perfusion Distributions
Before endotoxemia, ventilation and perfusion were heterogeneously
distributed and highly correlated (Table
2). During endotoxemia, the correlation
between regional ventilation and perfusion decreased (Fig.
1) and perfusion heterogeneity increased,
but ventilation heterogeneity did not change significantly (Table 2).
When lung regions judged to contain the more than 25% airways by
volume were excluded from the analysis, ventilation and perfusion
heterogeneity decreased to ~98% and ~97%, respectively, of
previous values, and statistical comparisons between time points were
unchanged. Endotoxin increased
A/ heterogeneity (Table
2; see also Table 4) but did not significantly change intrapulmonary
shunt. There were no significant differences between baselines
1 and 2.
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Fig. 1.
Regional alveolar ventilation (A) vs.
regional perfusion () within same lung piece measured
during baseline 2 (A) and endotoxemia (B).
Subscripts 2 and E denote time points baseline 2 and
endotoxemia, respectively. Each point represents a single
1.7-cm3 lung region (n = 1,221). Correlation
between regional A and
is initially high but decreases during endotoxemia.
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Inert-Gas Data: Ventilation-Perfusion Heterogeneity
Inert-gas data from two animals were excluded because the remaining sum
of squares for these animals exceeded 10 (remaining sum of squares was
1.5 ± 2.0 for included data). Endotoxemia increased heterogeneity of
the -weighted
A/ distribution
(P = 0.007) (logSD), but intrapulmonary
shunt (P = 0.11) and heterogeneity of the
A-weighted
A/ distribution (logSD) (P = 0.12) did not change significantly (Table 4). There were no significant differences between
baseline 1 and baseline 2.
Determinants of Ventilation-Perfusion Heterogeneity
Changes in ventilation heterogeneity, perfusion heterogeneity, and
A- correlation had
significantly different effects on
A/ heterogeneity
(P = 0.025). The decrease in
A- correlation had a
greater impact on
A/
heterogeneity than did the increase in ventilation (P = 0.018)
or perfusion heterogeneity (P = 0.016) (Fig.
2). Only the decrease in
A- correlation (48 ± 22%, P = 0.001) and interaction between changes in
A- correlation and
A and heterogeneity
(20 ± 8%, P = 0.0006) significantly increased
A/ heterogeneity.
Although A/
heterogeneity tended to increase as a result of changes in perfusion
heterogeneity (15 ± 17%, P = 0.06) and ventilation
heterogeneity (5 ± 11%, P = 0.24) and to decrease as a
result of the interaction between changes in
A and heterogeneity
(6 ± 9%, P = 0.12), these effects did not achieve
statistical significance.
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Fig. 2.
Percent changes in ratio of A to
(A/)
heterogeneity attributable to change in
A- correlation (change
in ), A heterogeneity (change in
A), heterogeneity
(change in ), interaction between simultaneous
changes in A and
heterogeneity [interaction
(A-)], and
interaction between simultaneous changes in
A- correlation and
A and heterogeneity
[interaction
(-A,)]. On
average, decrease in A-
correlation had a greater impact on
A/ heterogeneity than
did changes in A or
heterogeneity. Interaction
(-A,) also
significantly increased
A/ heterogeneity
because a decrease in
A- correlation
magnifies the effect that increases in A
and heterogeneity have on
A/ heterogeneity. Note
that interaction (A-)
decreased A/
heterogeneity because
A/
heterogeneity achieves a local minimum when
A and heterogeneity
are similar, and A and
heterogeneities are more alike when changes due to endotoxin are
considered together. See text and Fig. 5 for further discussion.
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Redistribution of Ventilation and Perfusion
Endotoxemia caused significant redistribution of perfusion between lung
regions in all animals and significant redistribution of ventilation in
five of seven animals. In each animal, redistribution of perfusion was
significantly greater than redistribution of ventilation (Table
3, Fig.
3). When lung regions judged
to contain more than 25% airways by volume were excluded from the
analysis, ventilation and perfusion redistribution increased by ~1
and ~5%, respectively, of previous values.
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Fig. 3.
A: regional during baseline 1 (1) vs. regional
2. B: regional
during endotoxemia (E) vs. regional
2. C: regional
at baseline 1 (A1) vs. regional
A2.
D: regional AE vs.
regional
A2.
Each point represents a single 1.7-cm3 lung region
(n = 913). Note linear correlation coefficient
(r) and 95% confidence intervals (given in parentheses).
Endotoxemia causes significant redistribution of perfusion, but
redistribution of ventilation is modest.
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Regional perfusion did not preferentially decrease in regions predicted
to have the largest decrease in alveolar
PO2 (mean slope = 1.1 ± 0.4 Torr1, P = 0.0005 for difference from zero) (Fig.
4A). Changes in regional ventilation and perfusion due to endotoxin were poorly correlated (mean r2 = 0.09, 95% confidence interval 0.06 0.25) (Fig. 4B) even when animals with no or
minimal ventilation redistribution were excluded from the
analysis.
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Fig. 4.
A: percent change in regional
[100 · (E 2)/2]
vs. change in predicted alveolar PO2
(PAO2) in the same lung
piece [(predicted PAO2
during endotoxemia) (predicted
PAO2 during baseline
2)]. Note regression line. Perfusion does not preferentially
decrease in regions with the greatest decrease in
PAO2, suggesting that
hypoxic pulmonary vasoconstriction does not determine perfusion
redistribution during endotoxemia. B: percent change in
regional A
[100 · (E 2)/2]
vs. percent change in regional
[100 · (E 2)/2]
in same lung piece. Note coefficient of determination
R2. Changes in regional ventilation and perfusion
are poorly correlated, suggesting that mechanisms that match
ventilation and perfusion do not determine redistribution of
ventilation or perfusion during endotoxemia. Each point represents a
single 1.7-cm3 lung region (n = 913).
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Comparison of Microsphere and Inert-Gas Data
A- and -weighted
A / distributions
derived from microsphere data underestimated
A/ heterogeneity
compared with the multiple inert-gas elimination technique (MIGET).
When secondary A/ peaks
in high A/
regions were excluded from MIGET data, the discrepancy between the two
techniques decreased somewhat but remained statistically
significant (Table 4). MIGET and
microsphere techniques yielded estimates of intrapulmonary shunt
that were not significantly different, although inert-gas estimates
tended to be greater during endotoxemia (P = 0.11).
Predicted vs. Measured Gas Exchange
A/ distributions
derived from microsphere data accurately predicted arterial
PO2,
PCO2, and the alveolar-arterial O2 difference before endotoxemia (Table
5). However, microsphere data overestimated
arterial PO2 and underestimated the alveolar-arterial O2 difference during endotoxemia.
Analysis of kidney fluorescence demonstrated right-to-left shunting in
two of five animals during endotoxemia but not during baselines
1 or 2. When data from these two animals were excluded,
predicted and measured PO2 were no
longer significantly different during endotoxemia (P = 0.13),
although microsphere data continued to underestimate the
alveolar-arterial O2 difference (P = 0.02).
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DISCUSSION |
The main findings of this study are the following: 1)
Endotoxemia decreases the correlation between regional ventilation
and perfusion and increases perfusion heterogeneity. 2)
Decrease in correlation between regional ventilation and perfusion
during endotoxemia substantially increases
A/ heterogeneity,
whereas increase in perfusion heterogeneity has less effect.
3) Endotoxemia has only modest effects on the
ventilation distribution. Therefore, the decrease in correlation
between regional ventilation and perfusion during endotoxemia results
primarily from changes in perfusion.
Despite a significant increase in perfusion heterogeneity, the increase
in A/ heterogeneity
during endotoxemia was determined principally by the decrease in
A-
correlation. The importance of the
A- correlation follows
directly from the mathematical relationship describing
A/ heterogeneity as a
function of ventilation and perfusion heterogeneity and
A- correlation (Eq. 1) (29) that is illustrated graphically in Fig.
5.
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Fig. 5.
Contour plots showing
A/ heterogeneity for 2 different A-
correlations: A-
correlation before endotoxemia
(A- correlation = 0.88;
A), and A-
correlation during endotoxemia
(A- correlation = 0.56;
B). Specifically, contour plots show
A/ heterogeneity
(variance A-) as a
function of A heterogeneity (variance
A), heterogeneity
(variance ), and
A- correlation as
described in Eq. 1 (see Statistical Analysis). When the
A- correlation is high
(A), A/
heterogeneity achieves local minima in the region in which
A and
heterogeneity are similar (i.e., in the "valley" of the
contour plot). In this region, changes in
A and heterogeneity
have little impact on
A/ heterogeneity. When
the A- correlation
decreases (B),
A/ heterogeneity
becomes more sensitive to changes in A
and heterogeneity, and
A/ heterogeneity
increases considerably even if A and
heterogeneity are unchanged.
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When the A- correlation
is high (Fig. 5A),
A/ heterogeneity is
relatively low in the region in which ventilation and perfusion
heterogeneity are similar (i.e., in the "valley" of the contour
plot). In this region, even large increases in ventilation and
perfusion heterogeneity have relatively little impact on
A/ heterogeneity. In
contrast, A/
heterogeneity increases dramatically when the
A- correlation
decreases (Fig. 5B), even if ventilation and perfusion
heterogeneity do not change. When the
A- correlation is low
or ventilation and perfusion heterogeneity are very dissimilar, changes
in ventilation and perfusion heterogeneity have a greater impact on
A/ heterogeneity.
The importance of the
A- correlation to
A/ heterogeneity has
not been previously emphasized. Historically,
A/ heterogeneity has
been attributed primarily to heterogeneity in the distributions of
ventilation and perfusion because the
A- correlation was
thought to be weak (29). When regional ventilation and perfusion are
directly and independently measured, however, the
A- correlation is
strong (3, 21, 25). Melsom et al. (21) found that this strong
correlation was associated with a narrow
A- distribution even
though the individual distributions of ventilation and perfusion were
broad. We extend their observations by showing that
A/ heterogeneity
increases during endotoxemia principally as a result of the decrease in
the previously strong correlation between regional ventilation and
perfusion. The importance of the
A- correlation is
unlikely to be unique to endotoxemia. Because we and others (3, 21, 25)
have demonstrated that normal lung (i.e., before injury) is
characterized by a strong A- correlation and has
similar ventilation and perfusion heterogeneities, A/ heterogeneity should
be relatively sensitive to changes in
A- correlation and
insensitive to changes in ventilation and perfusion heterogeneity
regardless of their cause.
To what extent are changes in the ventilation and perfusion
distributions independently responsible for the decrease in
A- correlation during
endotoxemia? The contribution of each to the decrease in
A- correlation is
roughly reflected by the magnitude of ventilation and perfusion
redistribution. Because perfusion redistribution was significantly
greater than ventilation redistribution, changes in perfusion are
primarily responsible for the decrease in the
A- correlation.
Redistribution roughly reflects effects on the
A- correlation because
the A- correlation was
strong before endotoxemia and changes in ventilation and perfusion
within each piece during endotoxemia were weakly correlated. Therefore, redistribution of ventilation and perfusion is much more likely to
decrease than to increase
A- correlation.
Although mechanisms determining redistribution of perfusion during
endotoxemia are unclear, redistribution is unlikely to be explained by
regional hypoxic pulmonary vasoconstriction. Perfusion did not decrease
in regions predicted to have the greatest decrease in alveolar
PO2 (Fig. 4A), and changes in
regional ventilation and perfusion within each piece were not strongly
correlated (Fig. 4B). This suggests that hypoxic pulmonary
vasoconstriction or other mechanisms that preserve
A/ matching do not
determine redistribution of ventilation or perfusion during
endotoxemia. Release of thromboxane A2 has been shown to
mediate endotoxin-induced pulmonary vasoconstriction (17, 26) and could
potentially mediate heterogeneous changes in perfusion via regional
differences in thromboxane A2 release or responsiveness of
the pulmonary vasculature. The presence of physiologically significant
spatial heterogeneity in biochemical and cellular mediators of
vasoregulation awaits direct confirmation.
Our conclusions rely on the ability of microspheres to accurately
measure regional ventilation and perfusion. Fluorescent microspheres
are a well-established method for measuring regional pulmonary blood
flow. Microspheres of 15-µm diameter lodge in pulmonary
capillaries in proportion to regional blood flow (4, 14, 20). In
addition, fluorescent-labeled microspheres have been validated as
markers of regional pulmonary blood flow by comparison with
radiolabeled microspheres in injured (16) and uninjured (11) lungs.
Although lung pieces with low relative blood flows may have contained
fewer than 400 microspheres of a single color in this study,
heterogeneity and correlation coefficients are well determined even
when the "400-microsphere rule" is violated (24).
Aerosolized, 1-µm-diameter fluorescent microspheres have been
validated as markers of regional ventilation in normal lung (25). They
deposit nearly exclusively in gas-exchanging regions of the lung,
provide reproducible measurements, and (combined with measurements of
regional blood flow) can accurately predict respiratory and inert-gas
exchange (2). Because deposition patterns of aerosolized microspheres
have not been determined during lung injury, increased deposition in
non-gas-exchanging regions (i.e., airways) during endotoxemia may have
confounded estimates of regional ventilation. Although we cannot
definitively exclude this possibility, changes in ventilation
heterogeneity and redistribution were minimal when we excluded lung
pieces with high airway content from our analysis.
A/ distributions
generated from microsphere data most likely underestimate
A/ heterogeneity during
endotoxemia. Both overestimation of arterial
PO2 and underestimation of A/ heterogeneity
compared with inert-gas data support this belief. Underestimation of
A/
heterogeneity is most likely explained by development of
A/ heterogeneity on a
scale that is beneath the spatial resolution of our methods
(i.e., within 1.7-cm3 lung pieces). This interpretation is
consistent with that of Altemeier et al. (3), who showed that
microsphere data accurately predicted arterial
PO2 in uninjured lungs but
overestimated arterial PO2 after
vascular bead embolism.
A/
heterogeneity in
endotoxemia
TOP
ABSTRACT
INTRODUCTION
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![H(interaction [&rgr;-<A><AC>V</AC><AC>˙</AC></A><SC>a</SC>,<A><AC>Q</AC><AC>˙</AC></A>]) = F(&sfgr;<SUB><A><AC>V</AC><AC>˙</AC></A><SC>a</SC><SUB>E</SUB></SUB>, &sfgr;<SUB><A><AC>Q</AC><AC>˙</AC></A><SUB>E</SUB></SUB>, &rgr;<SUB>E</SUB>)](/content/vol88/issue6/fulltext/1933/img013.gif)
