Download Slow and fast lung compartments in cystic fibrosis measured by

J Appl Physiol 117: 720–729, 2014.
First published July 18, 2014; doi:10.1152/japplphysiol.01274.2013.
Slow and fast lung compartments in cystic fibrosis measured by nitrogen
multiple-breath washout
P. M. Gustafsson,1,2 P. D. Robinson,3,4 M. Gilljam,2,5 A. Lindblad,2,6 and B. K. Houltz2,7
1
Department of Pediatrics, Central Hospital, Skövde, Sweden; 2The Sahlgrenska Academy at the University of Gothenburg,
Gothenburg, Sweden; 3Department of Respiratory Medicine, The Children’s Hospital at Westmead, Sydney, New South Wales,
Australia; 4Discipline of Paediatrics and Child Health, Sydney Medical School, University of Sydney, Australia; 5Department
of Chest Medicine and Allergology, The Sahlgrenska University Hospital, Gothenburg, Sweden; 6Queen Silvia Children’s
Hospital, Gothenburg, Sweden; and 7Department of Clinical Physiology, The Sahlgrenska University Hospital/East,
Gothenburg, Sweden
Submitted 18 November 2013; accepted in final form 16 July 2014
adults; children; lung clearance index; lung function; specific ventilation; ventilation distribution.
TIDAL BREATHING INERT GAS multiple-breath washout (MBW)
tests have been used for more than 60 years to assess the
influence of airway disease on the uniformity of ventilation
distribution in the lungs (10, 12, 13). There has been a recent
explosion of interest in the technique, due to documented
strong feasibility across wide age ranges, and ability to detect
early lung disease, in important diseases such as cystic fibrosis
(CF), even when spirometry is within normal limits (4, 21).
Address for reprint requests and other correspondence: P. M. Gustafsson,
Dept. of Pediatrics, Central Hospital, Skövde, Sweden (e-mail: per.
[email protected]).
720
Technological advances have also had a considerable impact,
facilitating accurate within-breath measurements of inert gas
concentration in relation to expired flow and volume. Lung
clearance index (LCI) is a global measure of the ventilation
distribution inhomogeneity present and the most widely reported pediatric MBW index. Utility has also been demonstrated in adult CF (23, 24, 44). Strong correlation between
LCI and structural changes of lung disease on computed
tomography (CT)(22), together with demonstrated prognostic
utility (6), and a high sensitivity to detect treatment response in
small study numbers (1, 2) has enhanced interest further.
However, the additional physiological insight we gain from
LCI as a sole reported index is limited. Further insight appeared promising with analysis focused on the evolution of
concentration-normalized phase III slopes (SnIII) of successive
breaths through the MBW (termed SnIII analysis). Clinical
indices termed Scond and Sacin were derived to reflect two
mechanisms of ventilation distribution inhomogeneity generated within different zones of the lung (35, 47). Differing
patterns of Scond and Sacin abnormality were subsequently
described across important respiratory diseases (20, 46, 48).
However, the modelling of these indices was based on healthy
adult human lung data, and the integrity of this approach has
recently been questioned in more severe disease (25, 26, 45).
The advances in imaging techniques have further enhanced
our understanding of the pathophysiological processes in obstructive lung disease. Magnetic resonance imaging (MRI)
studies using hyperpolarized noble inert gases such as the
Helium isotope 3He, have shown large volumes of the lungs in
CF subjects to be either very slowly ventilated or not ventilated
at all (16, 43, 49). Regional specific ventilation measurements
are also feasible with these imaging techniques (15). Heterogeneous airway obstruction not only produces slowly cleared
subtended lung volumes, but also results in overventilated lung
units served by patent airways, which are cleared of an inert
marker gas long before the slow units. Underventilated lung
volumes have therapeutic implications on delivery of nebulized
or other inhaled medications to the targeted site of disease.
Additionally, fast emptying compartments serve as dead space
ventilation, which results in an overall increase in ventilatory
demand. However, 3He MRI imaging is not widely feasible
due to high cost and restricted access. It is also performed
supine, in contrast to upright sitting position in standard lung
function testing. Finally, only one breath of the tracer gas is
inhaled, while MBW starts after equilibration of the marker
gas in the lung. It would be highly desirable if comparable
8750-7587/14 Copyright © 2014 the American Physiological Society
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Gustafsson PM, Robinson PD, Gilljam M, Lindblad A, Houltz BK.
Slow and fast lung compartments in cystic fibrosis measured by nitrogen
multiple-breath washout. J Appl Physiol 117: 720–729, 2014. First published
July 18, 2014; doi:10.1152/japplphysiol.01274.2013.—Imaging studies
describe significant ventilation defects across a wide range of cystic
fibrosis (CF) related lung disease severity. These are unfortunately
poorly reflected by phase III slope analysis– derived Scond and Sacin
from multiple-breath washout (MBW). Methodology extending previous two-lung compartment model-based analysis is presented describing size and function of fast- and slow-ventilating lung compartments from nitrogen (N2) MBW and correlation to obstructive lung
disease severity. In 37 CF subjects (forced expiratory volume in 1 s
[FEV1] mean [SD] 84.8 [19.9] % predicted; abnormal lung clearance
index [LCI] in 36/37, range 7.28 –18.9) and 74 matched healthy
controls, volume and specific ventilation of both fast and slowly
ventilated lung compartments were derived from N2-based MBW
with commercial equipment. In healthy controls lung emptying was
characterized by a large compartment constituting 75.6 (8.4)% of
functional residual capacity (FRC) with a specific ventilation (regional alveolar tidal volume/regional lung volume) of 13.9 (3.7)% and
a small compartment with high specific ventilation (48.4 [15.7]%). In
CF the slowly ventilated lung compartment constituted 51.9(9.1)% of
FRC, with low specific ventilation of 5.3 (2.4)%. Specific ventilation
of the slowly ventilated lung compartment showed stronger correlation with LCI (r2 ⫽ 0.70, P ⬍ 0.001) vs. Sacin (r2 ⫽ 0.44, P ⬍ 0.001)
or Scond (no significant correlation). Overventilation of the fast lung
compartment was no longer seen in severe CF lung disease. Magnitude and function of under- and overventilated lung volumes can be
derived from routine N2 MBW in CF. Reported values agree with
previous modelling-derived estimates of impaired ventilation and
offer improved correlation to disease severity, compared with SnIII
analysis.
MBW-Derived Estimates of Slow Ventilated Lung Regions in CF
METHODS
CF subjects were recruited from the CF centers of the Queen Silvia
Children’s Hospital and the Sahlgrenska University Hospital, Gothenburg, at the time of annual checkup and clinical stability. Healthy
controls, matched for age and gender (at a ratio of 2:1), were recruited
from an existing larger cohort, previously identified from the general
population of Gothenburg using the Swedish population register.
Healthy subjects were defined as individuals with no known lung
disease, without evidence of recurrent respiratory symptoms, and a
negative smoking history.
Lung function testing was performed at the Department of Clinical
Physiology at the Sahlgrenska University Hospital/East, Gothenburg.
Spirometry was performed using Jaeger Masterscreen (Care Fusion,
Würzburg, Germany) in accordance with current European Respiratory Society (ERS) and American Thoracic Society (ATS) standards
(32). Spirometry data was expressed as percent predicted using
published Caucasian reference equations (41).
Gustafsson PM et al.
721
MBW was performed prior to spirometry in all subjects. The
commercial N2 MBW equipment used (ExhalyzerD N2, Eco Medics
AG, Duernten, Switzerland) is based on indirect inert gas (N2)
calculation using carbon dioxide (CO2) and oxygen (O2) sensors and
flow measurement from an ultrasound flow sensor. In each subject
three technically acceptable N2 MBW tests were performed, in accordance with the recently published consensus statement (39). Validation of this device, as specified in this consensus statement, has also
recently been performed (40). Further details on the device, quality
control, and test protocol used are contained in APPENDIX. While the
commercial software delivered with ExhalyzerD (Spiroware 3.1) was
used for data recording, in-house software written with a commercial
software package (TestPoint, Capital Equipment, MA) was used for
off-line analysis. The basic algorithms in our custom TestPoint software are the same as those used in the commercial Spiroware
software, and our custom software was also used for the recent device
validation studies (40). Derivations of conventional N2 MBW parameters (LCI, Sacin ⫻ VT and Scond ⫻ VT, etc.) are described in APPENDIX.
LCI, Sacin ⫻ VT, and Scond ⫻ VT data were compared with reference
values calculated in the 2:1 matched control group.
In a semilog plot of exhaled volume of N2 for each breath (Fig. 1,
closed circles), all subjects demonstrated a washout curve with two
components. The latter component containing N2 from slowly ventilating lung compartments only, and the first component containing
contributions of exhaled N2 from both fast and slowly ventilating lung
compartments. A regression line containing the contributions of the
slowly ventilated lung compartments only was then determined (Fig.
1, open triangles). This was plotted from the end of the washout
extending backwards, with the proximal end of the regression line
determined by the optimal strength of regression data fit (judged by
the r2-value). A semiautomated computer algorithm was used for this
procedure (see APPENDIX). Back extrapolation of the slowly ventilated
lung compartment regression line allowed its relative contributions to
each breath in the initial phase to be calculated. Once subtracted, only
the contributions of the fast lung compartment remained (Fig. 1, open
squares).
Calculations performed during this study are outlined below. The
functional residual capacity (FRC) of the whole lung (FRCtotal) was
calculated from the sum of the measured exhaled N2 volumes contained in all breaths (VN2,total) over the washout until 1/40th of the
initial end-tidal N2 concentration (CetN2,initial) (⬃78%), divided by
the difference in initial and final (⬃1.95%) end-tidal N2 concentration
(CetN2,final):
FRCtotal ⫽ VN2,total ⁄ 共CetN2,initial ⫺ CetN2,final兲
(1)
The regional FRCs of the fast (FRCfast) and slow compartments
(FRCslow) were then derived as follows. A power function was
applied to the regression line containing the log values of N2 volume
in each breath from the start of the washout onwards. The sum of these
N2 volumes corresponded to all N2 exhaled only from the slow
compartment (VN2,slow). The volume of N2 exhaled from the fast
compartment (VN2,fast) was then calculated as follows:
VN2,fast ⫽ VN2,total ⫺ VN2,slow
(2)
For both the fast and slow compartment, CetN2,initial must be the
same as for the lung as a whole, because these compartments are in
equilibration before start of washout. The end-tidal N2 concentration
was assumed to be 0.0% in the fast compartment when it has been
cleared of its N2 content (i.e., fast compartment CetN2,final ⫽ 0.0%).
The slow compartment CetN2,final was unknown, however, but must be
markedly greater than the measured CetN2,final for the lung as a whole.
Consequently, FRCslow cannot be directly calculated, while FRCfast
can be calculated as outlined below:
FRCfast ⫽ VN2,fast ⁄ 共CetN2,initial ⫺ 0兲
FRCslow was calculated as:
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information could be obtained from a more physiologically
relevant and also more widely feasible test. Given the recent
advances in equipment and availability of affordable validated commercial nitrogen (N2) MBW equipment, MBW
offers this potential (40).
MBW-based evaluation of the lung as differing compartments, emptying at different rates, were presented almost 60
years ago (28). This phenomenon is detectable in healthy lungs
and exaggerated by nonuniform distribution of airway obstruction in disease, which results in increased ventilation inhomogeneity among several widely distributed smaller or larger lung
units. These units can be effectively summarized into a very
well ventilated compartment and a second very slowly ventilated compartment, constituting a two-compartment lung
model. Early studies examining the characteristics of these
compartments were based on averaged or mean inert gas
concentrations during the MBW, due to the limitations of the
technology present (19, 37). Subsequent research focus
switched to the assessment of the degree of dead space ventilation in a given subject (7). Methods to calculate airway dead
space volume (VDaw) (18), corresponding to the volume of the
conducting airways, were complemented by a measure of the
Bohr dead space volume (VDBohr) (17), which encompasses
the VDaw and the alveolar dead space volume (VDalv). Using
N2 MBW, Arborelius (3) extended this work by calculating
overall effective respiratory dead space volume, which he
termed functional dead space volume (VDF). More recent
research has focused on global indices (e.g., LCI) and SnIII
analysis and the utility, and relationships to, these alternate
indices.
Addressing the functional consequences of under- and overventilated lung compartments in CF using N2 MBW has not
been performed to date. The overall aim of this study was to
demonstrate how this additional information can be easily
derived from routine N2 MBW recordings. Two specific aims
are addressed: first, to exploit the dual compartment pattern of
simple semilog N2 volume MBW curves to calculate volumes
and specific ventilation of the relatively underventilated and
remaining overventilated lung compartments; second, to explore relationships between these novel indices and disease
severity in CF, as reflected by LCI and VDF (corrected for tidal
volume, VT, i.e., VDF/VT, %), and the existing SnIII analysis
indices Scond and Sacin.
•
722
A
MBW-Derived Estimates of Slow Ventilated Lung Regions in CF
Gustafsson PM et al.
Restructuring Eq. 5,
CetN2,final,slow ⫽ CetN2,initial ⫺ VN2,slow ⁄ FRCshow
2.60
Overall washout (r2 =0.99)
Slow comparment (r2 =0.99)
2.40
Log (volume N2 expired) mL
•
Fast compartment (r2 =0.97)
2.20
Healthy female control
FRC, slow 1103 mL
FRC, fast 2124 mL
LCI 6.70
FEV1 102% predicted
2.00
1.40
1.20
1.00
10
15
20
25
30
35
FAn ⫽ FA0 ⫻ ␻n ,
Breath Number
B
2.60
Overall washout (r2 =0.96)
Slow comparment (r2 =0.98)
Log (volume N2 expired) mL
2.40
where FAn is the fractional N2 concentration after n breaths, and FA0
is that at time zero. FA decreases with each breath by a factor ␻.
In the current setting this equation can be applied to the VN2 per
breath:
VN2,n ⫽ VN2,0 ⫻ ␻n ,
(9)
␻ ⫽ n 兹 共VN2,n ⁄ VN2,0兲
(10)
Fast compartment (r2 =0.99)
2.20
Restructuring Eq. 9,
Female CF subject
FRC, slow 1010 mL
FRC, fast 1125 mL
LCI 12.7
FEV1 67% predicted
2.00
1.80
In practice, ␻ is the ratio of VN2 from any breath divided by the VN2
from the previous breath, when these volumes have been derived from
an exponential function (inverse log function) of the semilogarithmic
plot of VN2 vs. breath number.
Darling et al. (14) described that ␻ can be determined by the
relationship between lung volume and alveolar ventilation:
1.60
1.40
1.20
␻ ⫽ V 0 ⁄ 共 V 0 ⫹ V T ⫺ V D兲 ,
1.00
0.80
0
5
10
15
20
25
30
(8)
35
40
Breath Number
Fig. 1. Multiple-breath washout (MBW) test plots of log values of measured
volume of N2 expired (ml) (closed circles) against breath number from two
representative subjects: a healthy 19-year-old female (A) and a 19-year-old
female cystic fibrosis (CF) subject (B). Open triangles represent the breathby-breath contribution from the slowly ventilated lung compartment (based on
its extrapolated regression line). Open squares represent the breath-by-breath
contribution from the remaining fast emptying compartment (calculated by
subtracting the slow compartment contribution from the overall lung emptying). The overall emptying curve of the healthy control appears almost
monophasic but does contain two distinct components. In contrast, the overall
emptying curve of the CF subject appears to be triphasic but can in fact be
reconstructed from a slow and a fast linear semilog regression line. While the
emptying rate of the fast compartment appears to be similar in the two subjects,
the clearance of the slow compartment is markedly reduced in the CF subject
with a high lung clearance index (LCI). For further details on calculation
principles, see METHODS.
FRCslow ⫽ FRCtotal ⫺ FRCfast
(4)
Applying Eq. 1 allowed CetN2,final for the slow compartment to be
calculated:
FRCslow ⫽ VN2,slow ⁄ 共CetN2,initial ⫺ CetN2,final,slow兲
(5)
(11)
where, V0 is end-expiratory lung volume, VT is overall tidal volume,
and VD represents the dead space volume of the breath, where VT-VD
represents VTA.
In the current setting this relationship can be expressed as:
␻ ⫽ FRC ⁄ 共FRC ⫹ VTA兲
(12)
VTA ⫽ 共FRC ⁄ ␻兲 ⫺ FRC
(13)
Restructuring Eq. 11,
This can then be applied on the fast and slow compartments, respectively, giving the regional (r) VTA:
rVTA,fast ⫽ 共rFRC,fast ⁄ r␻,fast兲 ⫺ rFRC,fast
(14)
rVTA,slow ⫽ 共rFRC,slow ⁄ r␻,slow兲 ⫺ rFRC,slow
(15)
Regional specific ventilation (rV=/V) of each compartment can then be
given:
rV⬘ / Vfast ⫽ rVTA,fast ⁄ rFRC,fast
(16)
rV⬘/ Vslow ⫽ rVTA,slow ⁄ rFRC,slow
(17)
Functional dead space volume (VDF/VT %) was calculated as
outlined by Arborelius et al.(3). In their experiments, expired gas was
collected through the MBW in Douglas bags and analyzed at the end
of washout, rather than breath-by-breath as in modern day experiments. For a given washout, where the lungs empty as a single
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After determining the volume of the two compartments, the clearance rate of the two compartments was determined to allow for
calculation of the respective alveolar tidal volumes (VTA) and their
specific ventilations (rV=/V), i.e., regional VTA/regional FRC.
The VN2 per breath from the fast component from breath number
one onward was calculated from the overall VN2 per breath minus the
calculated contributions from the slow compartment (see above). The
exponential decay rates described by the semilog plots of VN2 per
breath vs. breath number were then used to derive a dilution factor ␻
for the two compartments as described in principle by Fowler et al. in
1952 (19):
1.60
5
As a quality control step, at this stage, the estimate of CetN2,final for
the lung as a whole was calculated from CetN2,final,slow, assuming no
N2 was present in the fast compartment. This was then compared with
measured overall CetN2,final.
Estimated overall CetN2,final ⫽ FRCslow ⫻ CetN2,final,slow ⁄ FRCtotal(7)
1.80
0
(6)
MBW-Derived Estimates of Slow Ventilated Lung Regions in CF
Table 1. Demographic and spirometry data for healthy
control and cystic fibrosis cohorts in the study
Cystic Fibrosis
74 (50)
23.0 (7.3)
175.2 (9.4)
69.2 (12.0)
22.4 (2.6)
101.9 (11.2)
101.9 (11.6)
99.7 (7.1)
99.9 (25.6)
37 (25)
23.0 (7.7)
173.0 (11.8)
64.9 (13.9)
21.6 (3.0)
94.9 (12.9)*
84.8 (19.9)**
88.3 (13.5)**
70.2 (36.8)**
Data expressed as means (SD) unless stated otherwise. BMI, body mass
index; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 s;
MMEF, maximal mid expiratory flow. *P ⬍ 0.01, **P ⬍ 0.001.
compartment, the ideal N2 concentration (CidN2, %) of collected
expired gas over the washout as a whole was calculated from ␻ as:
VDF/ VT (%) is then defined as:
(19)
where CEN2 represents the actual N2 concentration of collected
expired gas over the entire MBW.
Finally, for comparison, another index which can be derived from
semilog plots of this nature, termed curvilinearity (or curve index),
and postulated to reflect heterogeneity in specific ventilation, was
derived as previously described by Verbanck et al. (44).
Statistical analysis of the data generated was performed using
Statistica 7 (StatSoft, Tulsa, OK). Student’s t-tests and Mann Whitney
tests were performed on parametric and nonparametric data, respectively. Simple linear Pearson regression analysis was used on parametric data, and, if required, nonparametric data was log transformed
prior to correlation analysis. Correlation strength was defined based
on published recommendations (34). A two-tailed P value ⬍ 0.05 was
considered statistically significant. Ethics approval for this study was
gained from the Ethics Committee at the University of Gothenburg,
Sweden. Written, informed consent was obtained from participants.
VT mean, ml
FRC, ml
VT/FRC
LCI, lung volume turnovers
CV LCI, %
VDF/VT, %
Scond x VT
Sacin x VT
Curve index
r VTA, slow, ml
r VTA, fast, ml
␻
r␻, slow
r␻, fast
r VTA, slow/VT, full, %
r VTA, fast/VT, full, %
rV=/V, slow, %
rV=/V, fast (%)
Ratio rV=/V, fast:slow
rFRC, slow/FRC, full (%)
rFRC, fast/FRC, full (%)
Healthy Control
Cystic Fibrosis
936 (174)
3456 (889)
0.29 (0.07)
6.67 (0.35)
3.7 (2.0)
39.5 (3.3)
0.017 (0.003)
0.059 (0.015)
0.39 (0.06)
350 (106)
366 (141)
0.86 (0.03)
0.88 (0.03)
0.68 (0.07)
37.7 (8.2)
38.5 (8.8)
13.9 (3.7)
48.4 (15.7)
3.46 (0.40)
75.6 (8.4)
24.4 (8.4)
934 (206)
3228 (985)
0.31 (0.09)
12.16 (3.33)**
4.5 (3.3)
64.7 (9.5)**
0.061 (0.020)**
0.176 (0.118)**
0.67 (0.08)**
82 (37)
499 (161)
0.91 (0.04)**
0.95 (0.02)***
0.75 (0.06)***
8.9 (3.7)***
53.3 (11.2)***
5.3 (2.4)***
34.0 (9.3)*
7.20 (2.10)***
51.9 (9.1)***
48.1 (9.1)***
Data expressed as means (SD). VT, tidal volume; FRC, functional residual
capacity; LCI, lung clearance index; CV, coefficient of variation; VdF, functional dead space; VTA, alveolar tidal volume; ␻, dilution factor; V=/V,
specific ventilation. “r” prior to indices denotes regional. *P ⬍ 0.01, **P ⬍
0.001, ***P ⬍ 0.0001.
one subject had both a normal LCI and FEV1 (Fig. 2), while
25/37 subjects (68%) had a normal FEV1 but abnormal LCI.
Scond ⫻ VT and Sacin ⫻ VT were abnormal in 36/37 (97%, ULN
0.023, 0.022– 0.101) and 27/37 (73%, ULN 0.088, range
0.038 – 0.486) CF subjects, respectively. The relationships of
Scond ⫻ VT and Sacin ⫻ VT to LCI in the CF cohort are shown
in Fig. 3. Scond ⫻ VT appeared to increase early in disease
severity but not in a linear way at higher LCI values, resulting
FEV1 LLN
20.0
RESULTS
18.0
16.0
14.0
LCI
Thirty-seven CF subjects (12 females) were recruited and all
provided three technically acceptable MBW and spirometry
data. Seventy-four age and gender matched healthy controls
were selected (in a 2:1 ratio for each CF subject) for inclusion
in the study as a healthy group for comparison. The demographics of the two cohorts are shown in Table 1. Within the
CF cohort, 10/37 (4 females) were aged 11.0 to 17.8 years
(median 15.5), and the remaining 27 subjects were 18.0 to 45.4
years old (median 24.4 years), 23/37 (62%) were pF508.del
homozygous, 33/37 (89%) were pancreatic insufficient, and
16/37 (43%) were chronically colonized with Pseudomonas
aeruginosa. CF subjects had significantly lower spirometry
values (all P ⬍ 0.001) with FEV1 values predominantly in the
mild to moderate range of obstructive lung disease (range
34.2–111.7% predicted; only four subjects had an FEV1 ⬍60%
predicted) (Table 1). No difference in nutritional status was
detected between groups.
As expected, MBW values were increased in CF compared
with healthy control subjects (Table 2). LCI was abnormal in
36/37 CF subjects (97%, ULN 7.36, range 7.28 –18.89). Only
12.0
10.0
8.0
LCI ULN
6.0
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
FEV1 (z-score)
Fig. 2. The relationship between LCI (actual values) and forced expiratory
volume in 1 s (FEV1) (expressed as z-scores) in the 37 CF subjects (r2 ⫽ 0.64,
high correlation, P ⬍ 0.001). The lower limit of normal (LLN) for FEV1 and
the upper limit of normal (ULN) for LCI are shown as vertical and horizontal
dotted lines, respectively.
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CidN2共%兲 ⫽ 共100 ⫻ ␻ ⫹ 100 ⫻ ␻2 . . . . . . ⫹100 ⫻ ␻n兲 ⁄ n
(18)
VDF ⁄ VT共%兲 ⫽ 100 ⫺ CEN2共%兲 ⁄ CidN2共%兲 ,
723
Gustafsson PM et al.
Table 2. Derived multiple breath washout parameters for
healthy control and cystic fibrosis cohorts
Healthy Control
Number, males
Age, yr
Height, cm
Weight, kg
BMI, kg/m2
FVC, % predicted
FEV1, % predicted
FEV1/FVC, % predicted
MMEF, % predicted
•
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MBW-Derived Estimates of Slow Ventilated Lung Regions in CF
0.600
LCI ULN
Scond x VT
Scond x VT or Sacin x VT
0.500
Sacin x VT
0.400
0.300
0.200
0.100
Sacin x VT ULN
Scond x VT ULN
0.000
6.0
8.0
10.0
12.0
14.0
16.0
18.0
•
Gustafsson PM et al.
increased (r2 ⫽ 0.27, low correlation, P ⬍ 0.001 [Fig. 4A]). A
similar relationship with Sacin ⫻ VT was observed (r2 ⫽ 0.39,
moderate correlation, P ⬍ 0.001; r2 ⫽ 0.46, after log transformation, P ⬍ 0.001). No correlation was observed with Scond ⫻
VT (r2 ⫽ 0.01, P ⫽ 0.90 [Fig. 4B]).
Specific ventilation of the slower compartment ranged between 1.8 and 13.1% in CF and was reduced compared with the
healthy cohort (6.8 to 23.2%; P ⬍ 0.001). Mean (SD) withinsession SD was 0.6 (0.6) in CF, vs. 1.3 (1.2) in the healthy
cohort. LCI increased as specific ventilation of the slowly
ventilated lung compartment decreased (r2 ⫽ 0.70, very high
correlation, P ⬍ 0.001 [Fig. 5A]). A similar but weaker
relationship with Sacin ⫻ VT was seen (r2 ⫽ 0.44, moderate
correlation, P ⬍ 0.001). No significant correlation was seen
with Scond ⫻ VT (r2 ⫽ 0.11 [Fig. 5B]).
20.0
LCI
A
20.0
18.0
16.0
in low overall correlation to LCI (r ⫽ 0.11, P ⫽ 0.049),
compared with Sacin ⫻ VT, which became abnormal at higher
LCI and showed high correlation to LCI (r2 ⫽ 0.60, P ⬍
0.0001). Log transformation of Sacin ⫻ VT values did not
strengthen this correlation (r2 ⫽ 0.55, P ⬍ 0.0001).
As described in historical data, healthy control subjects also
had two detectable lung compartments, one small very well
ventilated compartment and one large with “normal” ventilation (Fig. 1A) (19, 28). Healthy subjects showed significant fit
to the two-compartment model in all cases with overall mean
(SD) r2 of 0.997 (0.003), P ⬍ 0.001, with mean (SD) for r2
CV% of 0.17 (0.15). Mean (SD) of r2 for fast and slow
regression lines were 0.981 (0.024) and 0.988 (0.007), with
mean (SD) for CV% of r2 values of 1.4 (1.7) and 0.4(0.3),
respectively. Mean (SD) of within-session SD for fast and slow
compartments r2 were 0.013 (0.016) and 0.004 (0.003), respectively. CF subjects showed strong fit to the two-compartment
model in all cases, with overall mean (SD) r2 of 0.961 (0.028),
P ⬍ 0.001, and mean (SD) for r2 values CV% of 1.65 (1.27).
Mean (SD) of r2 for fast and slow regression lines were 0.961
(0.044) and 0.913 (0.051), with mean (SD) for CV% of r2 of
3.9 (4.8) and 4.4(3.4), respectively. Mean (SD) of withinsession SD for fast and slow compartments r2 were 0.025
(0.041) and 0.039 (0.029), respectively.
Derived MBW results expressed as mean (SD) for healthy
control and CF cohorts are given in Table 2. In CF, the
measured volume of the slowly ventilated lung compartment
varied from 34 to 67% of the full FRC (vs. 46 to 92% in
healthy cohort, P ⬍ 0.001). Mean (SD) within-session SD was
3.8 (2.0)% in CF vs. 4.9 (4.7)% in controls (nonsignificant). In
the healthy cohort, regional alveolar tidal volume (VTA) was
similar between the two compartments with VTA, slow of
mean (SD) 350 (106) ml and VTA, fast 366 (141) ml (nonsignificant). In CF significantly smaller VTA was delivered to the
slowly vs. the better ventilated compartment (82 [37] vs. 499
[161] ml [P ⬍ 0.001]). LCI increased as the relative volume of
this slowly ventilated lung compartment (i.e., % of full FRC)
LCI
2
14.0
12.0
10.0
8.0
LCI ULN
6.0
30
35
40
45
50
55
60
65
70
rFRCslow / FRCfull (%)
B
0.600
Scond x VT
Sacin x VT
Scond x VT or Sacin x VT
0.500
0.400
0.300
0.200
0.100
Sacin x VT ULN
Scond x VT ULN
0.000
30
35
40
45
50
55
60
65
70
rFRCslow / FRCfull (%)
Fig. 4. Relationships between LCI (A) or SnIII indices (B) and the volume of
the slowly ventilated lung compartment (expressed as a percentage of full
functional residual capacity [FRC], i.e., rFRCslow/FRCtotal) in 37 CF subjects.
Dotted horizontal lines indicate the upper limit of normal (ULN) for respective
indices. For LCI, r2 ⫽ 0.27 (low correlation, P ⬍ 0.001); for Sacin ⫻ VT, r2 ⫽
0.39 (moderate correlation, P ⬍ 0.001); and for Scond ⫻ VT, r2 ⫽ 0.01 (no
significant correlation).
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Fig. 3. Relationships between LCI and SnIII indices Scond ⫻ VT (open circles,
r2 ⫽ 0.11, low correlation, P ⫽ 0.049) and Sacin ⫻ VT (closed circles, r2 ⫽
0.60, high correlation, P ⬍ 0.001) in the 37 CF subjects. The ULN for Scond
and Sacin are shown as horizontal dotted lines, while the ULN for LCI is shown
as the vertical dotted line.
MBW-Derived Estimates of Slow Ventilated Lung Regions in CF
Gustafsson PM et al.
725
DISCUSSION
A
20.0
18.0
LCI
16.0
14.0
12.0
10.0
8.0
6.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
B
0.600
Scond x VT
Sacin x VT
0.500
0.400
0.300
0.200
0.100
0.000
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
rV´/V (slow compartment) (%)
Fig. 5. Relationships between LCI (A) or SnIII indices (B) and regional specific
ventilation (rV=/V) of the slowly ventilated lung compartment (%) in the 37 CF
subjects. LCI had a very high correlation to rV=/V of the slow compartment
(r2 ⫽ 0.70, P ⬍ 0.001; and r2 ⫽ 0.76, P ⬍ 0.001, after log transformation).
Sacin ⫻ VT had a moderate correlation to rV=/V of the slow compartment (r2 ⫽
0.44; P ⬍ 0.001). Scond ⫻ VT had no significant correlation (r2 ⫽ 0.11).
A very strong curvilinear relationship was seen between
VDF/VT and LCI (r2 ⫽ 0.96, very high correlation, after log
transformation of LCI, P ⬍ 0.001 [Fig. 6]).
VDF/VT (%) and specific ventilation of the slow compartment were very highly correlated in a linear manner to VDF/VT
(r2 ⫽ 0.80, P ⬍ 0.001 [Fig. 7]). Curve index did not correlate
with the relative volume of the slow compartment (r2 ⫽ 0.00)
but showed moderate correlation with the specific ventilation
of the slow compartment (r2 ⫽ 0.48, P ⬍ 0.001).
The observed CetN2,final was on average marginally lower
than the estimated CetN2,final (Eq. 7) (Mean [SD] 1.86 [0.05]%
vs. 1.93 [0.20]%, P ⫽ 0.031). Mean (SD) within-session SD of
estimated CetN2,final was 0.24 (0.15), suggesting good quality
of the modelling.
This study represents the first published effort to describe the
quantitative and functional assessment of the slowly ventilated
lung compartment based on N2 MBW analysis in subjects with
CF. The novel indices presented add to information provided
by global measures of ventilation inhomogeneity, such as LCI.
These indices also suggest that as CF lung disease severity
increases, lung regions with marked underventilation are
formed. This has not been shown before. The new indices show
a strong relationship with LCI across disease severity, in
contrast to SnIII– based indices, particularly Scond ⫻ VT. Accuracy of our size estimates of the slowly ventilated lung
compartment is suggested by good agreement with previous
modelling and imaging studies (27, 44). Importantly, these
easily derived new indices are potentially automatable from
routine N2 MBW tests.
Ventilation distribution inhomogeneity is a common finding
in respiratory disease, especially in CF, where abnormal LCI
values have been reported in 60% of preschoolers (42) and
95% of school aged children (5). SnIII analysis is challenging
in younger children, despite proposed VT correction of SnIII
values (39). In infants, SnIII analysis is further compromised by
the relatively short phase III slope, due to the larger airway size
to lung volume ratio and smaller amount of lung parenchyma
present in this age group (38). For this reason we chose a
predominantly adult cohort (but included some older pediatric
subjects) where SnIII analysis was highly feasible. Recent work
has highlighted that the modelling, on which SnIII analysis is
based, may not hold in more severe disease (45). We describe
the same problematic relationships of LCI and SnIII parameters
within our CF subjects reported previously in the adult CF
literature (25). Poor correlation of Scond was observed in more
advanced disease (LCI values ⬎ 12), reaching an apparent
“ceiling” or even decreasing as disease severity increased. In
contrast, stronger correlation was observed with Sacin as LCI
increased (Fig. 3). Imaging studies have shown that significant
interregional differences in ventilation distribution occur in
more severe CF lung disease (31). Scond is proposed to reflect
ventilation inhomogeneity arising due to changes in specific
ventilation and sequencing among lung units sharing branch
points proximal to the diffusion-convection front. However,
Scond failed to reflect this accurately in this setting, as discussed
in recent literature (33, 45).
The indices we present extend the previous literature exploring information contained in semilog plot analysis, originally
described almost 60 years ago (8, 9, 19, 28, 37). Comparison to
other MBW indices based on semilog analysis highlights
important limitations of currently reported indices. The initial
rapidly declining portion of the semilog plot of end-tidal N2
values used for curve index (44) and Slope ratio (11, 36)
include contributions from both the fast and slow compartments. However, the corresponding plot of exhaled N2 volume
used for the current compartment analysis has been cleared of
slow compartment contributions. This important modification
may produce an improved index of interregional inhomogeneities. Currently used indices also fail to quantify the volume or
specific ventilation of this impaired region, which constitute
the main focus of the present study. Our proposed indices may
provide insight into the underlying process that occurs with
advancing disease severity in CF, as they display a more
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rV´/V (slow compartment) (%)
Scond x VT or Sacin x VT
•
726
MBW-Derived Estimates of Slow Ventilated Lung Regions in CF
•
Gustafsson PM et al.
LCI LLN
80
LCI ULN
Controls
70
CF
60
VDF / VT (%)
Fig. 6. Relationship between functional respiratory dead space volume (expressed as percentage of full VT, i.e., VDF/VT, %) and LCI in the
74 healthy control subjects (open circles) and 37
subjects with CF (closed circles). The lower
limits of normal (LLN) and ULN for LCI and
VDF/VT are shown as vertical and horizontal
dotted lines, respectively, and define the normal
range. Very high correlation was observed (r2 ⫽
0.96 after log transformation of VDF/VT values,
P ⬍ 0.001).
50
VDF / VT (%) ULN
40
VDF / VT (%) LLN
30
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
LCI
consistent correlation across disease severity than SnIII indices
(Figs. 4 and 5). Importantly, these indices can be derived from
modern tidal breathing MBW tests and offer potential for
automated calculation, avoiding reliance on clear phase III
slopes. Feasibility of calculation of these new indices may not
be related to age; however, this needs to be confirmed in future
studies. Intuitively, knowledge of the size and extent of the
slowly ventilated lung compartment may inform the clinician
of issues regarding possible efficacy of inhaled medication
delivery to regions of disease, although regional imaging
studies are required to further investigate what a given change
(or lack of) in these indices represents.
Recent modelling work based on MBW or imaging data has
suggested that these slowly ventilated lung regions contribute
significantly to the ventilation inhomogeneity present in obstructive lung disease. Verbanck et al. (44) modelled MBW
data to show that LCI values of ⬃9 could be generated by the
presence of a slowly ventilated lung compartment comprising
50% of the lung volume and receiving 10% of overall venti80
VDF / VT (%)
70
60
50
40
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
rV´ / Vslow
Fig. 7. Relationship between functional respiratory dead space volume (expressed as a percentage of VT, i.e., VDF/VT, %) and regional specific ventilation (rV=/V) of the slowly ventilated compartment (%) in the 37 CF subjects
(r2 ⫽ 0.80, very high correlation, P ⬍ 0.001).
lation. Imaging work has described ventilation defects affecting on average 30% of the lung volume in a small group (n ⫽
5) of adult CF subjects (27). Our estimates are in agreement
with this previous work in CF and also the early historical
estimates of the relative magnitude of these two compartments
in health (19, 37). In our study, a slowly ventilated lung
compartment was found in all CF subjects and ranged from 34
to 67% of the full FRC, with a measured specific ventilation of
the slow compartment ranging between 1.8 and 13.1%.
Changes in curve index were driven by changes in specific
ventilation of this slow compartment (r2 ⫽ 0.48, P ⬍ 0.001)
rather than changes in its relative volume (r2 ⫽ 0.00, P ⫽
0.93), suggesting utility in quantifying this functional component. In addition, our cross-sectional data provides potential
insight into the overall functional response that occurs as this
slowly ventilated lung compartment develops. As the slowly
ventilated lung compartment increased across subjects within
our cross-sectional CF cohort, there also appeared to be increased overventilation of the remaining lung units (data not
shown). This overventilation was not present at greater lung
disease severity (higher LCI values) (Fig. 8). This may reflect
a smaller available volume of better functioning lung. Longitudinal studies are needed to confirm this hypothesis.
The previous focus of work by Arborelius et al. (3) on the
role of the respiratory dead space suggested that functional
dead space measures derived from N2 MBW should increase as
ventilation distribution inhomogeneity increases. Our data confirms the strong curvilinear relationship between LCI and
functional respiratory dead space volume (VDF/VT%) (Fig. 6)
initially described by Arborelius et al. The data presented in
our manuscript extends this earlier work by describing a more
linear relationship between increasing VDF/VT% and reduced
specific ventilation of this slow compartment than is evident
with LCI (Fig. 7 and Fig. 5, respectively). This suggests that
VDF/VT%, as a global index of ventilation distribution inhomogeneity, should be considered as a reported outcome, particularly in severe disease. This ongoing decrease in specific
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20
MBW-Derived Estimates of Slow Ventilated Lung Regions in CF
60.0
14.0
Slow compartment
50.0
12.0
10.0
40.0
8.0
30.0
6.0
20.0
4.0
10.0
2.0
0.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
rV´/ V (slow compartment)
rV´/ V (fast compartment)
Fast compartment
0.0
20.0
LCI
Fig. 8. Relationships between regional V=/V% of fast (open circles, r2 ⫽ 0.33,
low correlation, P ⬍ 0.001) and slowly ventilated lung compartments (closed
circles, r2 ⫽ 0.70, high correlation, P ⬍ 0.001) and LCI in 37 CF subjects.
Gustafsson PM et al.
727
more severely obstructed subjects. The chosen cohort did not
affect our ability to replicate the issues previously described
with SnIII indices and advanced lung disease. This also limits
our ability to discuss utility in very early CF disease with this
data. Because the calculation of global indices of inhomogeneity, SnIII data, and compartment indices were undertaken in
one sequence, there was no blinding to severity of disease.
In conclusion, additional mechanistic data can be derived
from N2 MBW tests to provide both quantitative and functional
information about the slowly ventilating lung compartments in
CF lung disease. These presented indices were strong drivers
of the observed change in global ventilation heterogeneity
(LCI) in our cohort of CF subjects and showed a more
consistent relationship across disease severity than the previously proposed SnIII analysis variables. These indices offer
useful additional information to indices such as LCI and the
curve index, and we would advocate their inclusion to existing
reported indices as they provide a more complete picture of the
impact of the underlying disease process. Importantly, these
indices offer potential for automated calculation. Future studies
are required to validate measurement accuracy and stability
and explore clinical utility, but good agreement with values
generated by previous modelling studies is encouraging.
APPENDIX
Quality control performed at the time of MBW testing.
All gas and flow signals in the Exhalyzer D were sampled at 200
Hz. Calibrations of the ultrasonic flow meter, O2 and CO2 analyzers,
and gas and flow signals synchronization were performed, in accordance with manufacturer’s recommendations, prior to each subject
test session. All test subjects wore a nose clip and breathed via a
trimmed silicon mouthpiece (to minimize equipment-related dead
space volume) attached to a bacterial filter (Air Safety Ltd, Morecambe, UK) in line with the recording system. The set 3 dead space
reducer (Ecomedics AG, Duernten, Switzerland) was used to reduce
the equipment dead space volume. The measured pre-gas-sampling
point external dead space volume of the system was 35 ml and the
post-gas-sampling point dead space volume was 22 ml. This was ⱕ2
ml/kg body wt for all but one of the subjects tested (99.8%). N2 MBW
was performed in the upright sitting position during relaxed tidal
breathing. On-line tidal volume monitoring was performed by the
operator such that the phase III slope of each breath (reflecting
alveolar gas concentration) constituted approximately half the breath.
A minimum interval of the preceding washout time was observed
between tests to ensure resting end-tidal N2 concentration values had
returned to normal levels prior to the next test.
Derivation of reported conventional N2 MBW parameters.
This information is provided in keeping with the recommendation
of the recent consensus statement (39). FRC derivation is outlined in
the main manuscript. Lung volume turnover values (TO) were calculated for each breath over the washout as the cumulative expired
volume (CEV) of air at the point of the lips divided by FRC. The CEV
of each breath was corrected for total equipment-related dead space
volume. LCI was calculated as TO value of the first of three consecutive breaths where end-tidal N2 concentration had fallen below 1/40th
of starting end-tidal N2 concentration.
The phase III slope of each breath was determined from 50 to 95%
of the exhaled volume of air by linear regression over the slowly
rising portion of the breath containing alveolar gas. By visual inspection, care was taken to avoid influence of the gas from the bronchial
phase (phase II) or the fast rising phase IV, occurring due to airway
closures. The concentration-normalized phase III slope (SnIII) was
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ventilation to an increasing volume of slowly emptying lung
drives the increase seen in Sacin in our cohort.
There are several limitations of the underlying assumptions
on which the new indices are based. The two-compartment
model is obviously a simplification of the widely differing
specific ventilations among thousands of lung units with different mechanical properties (i.e., regional compliance and
resistance). The label “two-compartment model” may be misleading, as it is in reality is a mathematical two-component
model. Nevertheless, the model provides additional insights
into the lung pathophysiology in CF, which may help the
clinician understand the disease process. Another limitation is
that the current modelling work does not take into account N2
dissolved in blood and other tissue, which will diffuse into the
alveoli when pure O2 is breathed. This, however, is thought to
contribute only to a small proportion of the expired N2 over the
first minutes of washout (29) and has been shown not to affect
SnIII data significantly (11). Last, lung units with fast turnover
and units more slowly ventilated have a common dead space
volume in the central airways, a fact that the present model
does not consider. When well-ventilated lung units have been
washed out of most or all their alveolar N2, alveoli in poorly
ventilated lung units will continue to deliver N2 to the expirate.
The common dead space volume will speed up the washout of
the slow compartments and delay clearance of the fast (30).
Despite all shortcomings and limitations of the current model,
the relatively good agreement between the observed and calculated overall final end-tidal N2 concentrations suggest that it
delivers fairly representative and robust measures. Several
aspects of the indices reported here, and the hypotheses generated, remain to be addressed in future studies. These studies
will need to validate the accuracy of these new measures
against functional imaging-based measurements, feasibility in
younger age groups, and assess the repeatability and clinical
utility of the new indices generated. The CF cohort studied
here included mainly subjects with either normal FEV1 values
or mild obstructive disease. The integrity of the relationships
between the novel indices, LCI and VDF/VT in moderate and
severe obstructive lung disease, has not been shown, but it
should be noted that the utility of MBW is felt to lie predominantly in mild disease due to the increasing test duration in
•
728
MBW-Derived Estimates of Slow Ventilated Lung Regions in CF
calculated by dividing the phase III slope by the mean N2 concentration over the slope. Before calculation of Scond and Sacin, SnIII values
were further normalized by multiplying them by their respective tidal
volume (SnIII ⫻ VT), i.e., simulating a VT of 1000 ml, to account for
differences in size between subjects and for variations in VT during
the washout. Data from the three MBW recordings were averaged
before Scond and Sacin calculation (39). Scond was calculated by linear
regression from a plot of SnIII vs. TO as the increase of SnIII from 1.5
to 6 TO. Sacin was calculated as the first breath SnIII minus the Scond
contribution to SnIII.
3.
4.
5.
6.
Determination of the fast and slowly ventilated lung
compartments.
A semiautomatic TestPoint algorithm was used to decide the
optimal starting point for the regression line delineating the sole
contribution from the slowly ventilated lung compartments to the
overall semilog washout curve (Fig. 1, closed circles). This was
performed by scanning in increasing segments of 0.05 (i.e., 5% of the
total number of breaths) from 90% of the washout back to 10% of the
washout. The peak r2 value obtained was used to define the starting
point of this regression line. The operator then reviewed this visually
and confirmed the starting point. Back extrapolation of this regression
line to the first breath of the washout was performed to calculate the
contribution of these slowly ventilated lung regions across the full
washout (Fig. 1, open triangles). Once subtracted, only the contributions of the fast lung compartments remained (Fig. 1, open squares).
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
GRANTS
This study was supported by grants from the Västra Götaland Research
Council, Gothenburg, Sweden.
17.
DISCLOSURES
18.
No conflicts of interest, financial or otherwise, are declared by the author(s).
19.
AUTHOR CONTRIBUTIONS
Author contributions: P.M.G., P.R., M.G., A.L., and B.K.H. conception and
design of research; P.M.G. and B.K.H. performed experiments; P.M.G., P.R.,
and B.K.H. analyzed data; P.M.G., P.R., M.G., A.L., and B.K.H. interpreted
results of experiments; P.M.G. and P.R. prepared figures; P.M.G. and P.R.
drafted manuscript; P.M.G., P.R., M.G., A.L., and B.K.H. edited and revised
manuscript; P.M.G., P.R., M.G., A.L., and B.K.H. approved final version of
manuscript.
REFERENCES
20.
21.
22.
23.
1. Amin R, Subbarao P, Jabar A, Balkovec S, Jensen R, Kerrigan S,
Gustafsson P, Ratjen F. Hypertonic saline improves the LCI in paediatric
CF patients with normal lung function. Thorax 65: 379 –383, 2010.
2. Amin R, Subbarao P, Lou W, Jabar A, Balkovec S, Jensen R,
Kerrigan S, Gustafsson P, Ratjen F. The effect of dornase alfa on
24.
Gustafsson PM et al.
ventilation inhomogeneity in patients with cystic fibrosis. Eur Respir J 37:
806 –812, 2011.
Arborelius M, Rosberg HE, Wiberg R. Multiple breath nitrogen dead
space. Clin Physiol 8: 561–576, 1988.
Aurora P, Bush A, Gustafsson P, Oliver C, Wallis C, Price J,
Stroobant J, Carr S, Stocks J. Multiple-breath washout as a marker of
lung disease in preschool children with cystic fibrosis. Am J Respir Crit
Care Med 171: 249 –256, 2005.
Aurora P, Gustafsson P, Bush A, Lindblad A, Oliver C, Wallis CE,
Stocks J. Multiple breath inert gas washout as a measure of ventilation
distribution in children with cystic fibrosis. Thorax 59: 1068 –1073, 2004.
Aurora P, Stanojevic S, Wade A, Oliver C, Kozlowska W, Lum S,
Bush A, Price J, Carr SB, Shankar A, Stocks J. Lung clearance index
at 4 years predicts subsequent lung function in children with cystic
fibrosis. Am J Respir Crit Care Med 183: 752–758, 2011.
Bouhuys A. Respiratory dead space. In: Handbook of Physiology, edited
by Fenn WO and Rahn H. Washington, DC: Am Physiol Soc, 1964, p.
699 –714.
Bouhuys A, Hagstam KE, Lundin G. Efficiency of pulmonary ventilation during rest and light exercise; a study of alveolar nitrogen wash-out
curves in normal subjects. Acta Physiol Scand 35: 289 –303, 1956.
Briscoe WA, Cournand A. Uneven ventilation of normal and diseased
lungs studied by an open-circuit method. J Appl Physiol 14: 284 –290,
1959.
Cournand A, Baldwin ED, Darling RC, Richards DW. Studies on
intrapulmonary mixture of gases. IV. The significance of the pulmonary
emptying rate and a simplified open circuit measurement of residual air. J
Clin Invest 20: 681–689, 1941.
Crawford AB, Makowska M, Paiva M, Engel LA. Convection- and
diffusion-dependent ventilation maldistribution in normal subjects. J Appl
Physiol 59: 838 –846, 1985.
Darling RC, Cournand A, Mansfield JS, Richards DW. Studies on the
intrapulmonary mixture of gases. I. Nitrogen elimination from blood and
body tissues during high oxygen breathing. J Clin Invest 19: 591–597,
1940.
Darling RC, Cournand A, Richards DW. Studies on the intrapulmonary
mixture of gases. III. An open circuit method for measuring residual air.
J Clin Invest 19: 609 –618, 1940.
Darling RC, Cournand A, Richards DW, Domanski B. Studies on
intrapulmonary mixture of gases. V. Forms of inadequate ventilation in
normal and emphysematous lungs, analyzed by means of breathing pure
oxygen. J Clin Invest 23: 55–67, 1944.
Deninger AJ, Mansson S, Petersson JS, Pettersson G, Magnusson P,
Svensson J, Fridlund B, Hansson G, Erjefeldt I, Wollmer P, Golman
K. Quantitative measurement of regional lung ventilation using 3He MRI.
Magn Reson Med 48: 223–232, 2002.
Donnelly LF, MacFall JR, McAdams HP, Majure JM, Smith J, Frush
DP, Bogonad P, Charles HC, Ravin CE. Cystic fibrosis: combined
hyperpolarized 3He-enhanced and conventional proton MR imaging in the
lung—preliminary observations. Radiology 212: 885–889, 1999.
Fletcher R, Jonson B. A new method for calculation of ventilatory
deadspace. Br J Anaesth 63: 639 –640, 1989.
Fowler WS. Lung function studies; the respiratory dead space. Am J
Physiol 154: 405–416, 1948.
Fowler WS, Cornish ER Jr, Kety SS. Lung function studies. VIII.
Analysis of alveolar ventilation by pulmonary N2 clearance curves. J Clin
Invest 31: 40 –50, 1952.
Gustafsson PM. Peripheral airway involvement in CF and asthma compared by inert gas washout. Pediatr Pulmonol 42: 168 –176, 2007.
Gustafsson PM, Aurora P, Lindblad A. Evaluation of ventilation maldistribution as an early indicator of lung disease in children with cystic
fibrosis. Eur Respir J 22: 972–979, 2003.
Gustafsson PM, De Jong PA, Tiddens HA, Lindblad A. Multiple-breath
inert gas washout and spirometry versus structural lung disease in cystic
fibrosis. Thorax 63: 129 –134, 2008.
Horsley A. Lung clearance index in the assessment of airways disease.
Respir Med 103: 793–799, 2009.
Horsley AR, Gustafsson PM, Macleod KA, Saunders C, Greening AP,
Porteous DJ, Davies JC, Cunningham S, Alton EW, Innes JA. Lung
clearance index is a sensitive, repeatable and practical measure of airways
disease in adults with cystic fibrosis. Thorax 63: 135–140, 2008.
J Appl Physiol • doi:10.1152/japplphysiol.01274.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.5 on June 16, 2017
Off-line analysis of MBW tests.
The off-line data analysis in the TestPoint software started with a
synchronization of the CO2 and O2 signals to the flow signal for each
recording. The delay time of the gas signals was defined as the time
between the moment when the post-gas-sampling dead space volume
(measured to be 22 ml) had been inhaled until the 50% deflection
point of the CO2 and O2 signals, respectively. The median lag time
value for all breaths over the three recordings was applied. Typically
lag times varied less than 10 ms for both gases in each of the two
recording devices over the 18 mo period that recordings were done.
After synchronization of CO2 and O2 signals, the N2 concentration
was calculated N2% ⫽ 100% ⫺ O2% ⫺ CO2% ⫺ Ar%, where Argon
(Ar) was assumed to form a fixed portion of the N2 signal (39).
Analysis of the MBW recordings involved visual check of the
quality of each breath and correction of default estimation of phase III
slopes (e.g., error due to cardiogenic oscillations).
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MBW-Derived Estimates of Slow Ventilated Lung Regions in CF
Gustafsson PM et al.
729
39. Robinson PD, Latzin P, Verbanck S, Hall GL, Horsley A, Gappa M,
Thamrin C, Arets HG, Aurora P, Fuchs S, King G, Lum S, M, Paiva
M, Pillow JJ, Ranganathan S, Ratjen F, Singer F, Sonnappa S, Stocks
J, Subbarao P, Thompson B, Gustafsson PM KA. ERS/ATS Consensus
statement for inert gas washout measurement using multiple and single
breath tests. Eur Respir J 41: 507–522, 2013.
40. Singer F, Houltz B, Latzin P, Robinson P, Gustafsson P. A realistic
validation study of a new nitrogen multiple-breath washout system. PLoS
One 7: e36083, 2012.
41. Stanojevic S, Wade A, Stocks J, Hankinson J, Coates AL, Pan H,
Rosenthal M, Corey M, Lebecque P, Cole TJ. Reference ranges for
spirometry across all ages: a new approach. Am J Respir Crit Care Med
177: 253–260, 2008.
42. Subbarao P, Stanojevic S, Brown M, Jensen R, Rosenfeld M, Davis S,
Brumback L, Gustafsson P, Ratjen F. Lung clearance index as an
outcome measure for clinical trials in young children with cystic fibrosis.
A pilot study using inhaled hypertonic saline. Am J Respir Crit Care Med
188: 456 –460, 2013.
43. van Beek EJ, Hill C, Woodhouse N, Fichele S, Fleming S, Howe B,
Bott S, Wild JM, Taylor CJ. Assessment of lung disease in children with
cystic fibrosis using hyperpolarized 3-Helium MRI: comparison with
Shwachman score, Chrispin-Norman score and spirometry. Eur Radiol 17:
1018 –1024, 2007.
44. Verbanck S, Paiva M, Paeps E, Schuermans D, Malfroot A, Vincken
W, Vanderhelst E. Lung clearance index in adult cystic fibrosis patients:
the role of convection-dependent lung units. Eur Respir J 42: 380 –388,
2013.
45. Verbanck S, Paiva M, Schuermans D, Malfroot A, Vincken W,
Vanderhelst E. Acinar and conductive ventilation heterogeneity in severe
CF lung disease: Back to the model. Respir Physiol Neurobiol 188:
124 –132, 2013.
46. Verbanck S, Schuermans D, Paiva M, Vincken W. Nonreversible
conductive airway ventilation heterogeneity in mild asthma. J Appl
Physiol 94: 1380 –1386, 2003.
47. Verbanck S, Schuermans D, Van Muylem A, Paiva M, Noppen M,
Vincken W. Ventilation distribution during histamine provocation. J Appl
Physiol 83: 1907–1916, 1997.
48. Verbanck S, Schuermans D, Vincken W. Small airways ventilation
heterogeneity and hyperinflation in COPD: response to tiotropium bromide. Int J Chron Obstruct Pulmon Dis 2: 625–634, 2007.
49. Woodhouse N, Wild JM, van Beek EJ, Hoggard N, Barker N, Taylor
CJ. Assessment of hyperpolarized 3He lung MRI for regional evaluation
of interventional therapy: a pilot study in pediatric cystic fibrosis. J Magn
Reson Imaging 30: 981–988, 2009.
J Appl Physiol • doi:10.1152/japplphysiol.01274.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.5 on June 16, 2017
25. Horsley AR, Macleod KA, Robson AG, Lenney J, Bell NJ, Cunningham S, Greening AP, Gustafsson PM, Innes JA. Effects of cystic
fibrosis lung disease on gas mixing indices derived from alveolar slope
analysis. Respir Physiol Neurobiol 162: 197–203, 2008.
26. Kaminsky DA. Multiple breath nitrogen washout profiles in asthmatic
patients: what do they mean clinically? J Allergy Clin Immunol 131:
1329 –1330, 2013.
27. Kirby M, Heydarian M, Svenningsen S, Wheatley A, McCormack
DG, Etemad-Rezai R, Parraga G. Hyperpolarized 3He magnetic resonance functional imaging semiautomated segmentation. Acad Radiol 19:
141–152, 2012.
28. Lundin G. Alveolar ventilation (in normal subjects) analyzed breath by
breath as nitrogen elimination during oxygen breathing. Scand J Clin Lab
Invest 7, Suppl 20: 39 –51, 1955.
29. Lundin G. Nitrogen elimination during oxygen breathing. Acta Physiol
Scand Suppl 111: 130 –143, 1953.
30. Martin CJ, Young AC. Lobar ventilation in man. Am Rev Tuberc 73:
330 –337, 1956.
31. Mentore K, Froh DK, de Lange EE, Brookeman JR, Paget-Brown
AO, Altes TA. Hyperpolarized HHe 3 MRI of the lung in cystic fibrosis:
assessment at baseline and after bronchodilator and airway clearance
treatment. Acad Radiol 12: 1423–1429, 2005.
32. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates
A, Crapo R, Enright P, van der Grinten CP, Gustafsson P, Jensen R,
Johnson DC, MacIntyre N, McKay R, Navajas D, Pedersen OF,
Pellegrino R, Viegi G, Wanger J. Standardisation of spirometry. Eur
Respir J 26: 319 –338, 2005.
33. Mitchell JH, Hoffman EA, Tawhai MH. Relating indices of inert gas
washout to localised bronchoconstriction. Respir Physiol Neurobiol 183:
224 –233, 2012.
34. Munro BH. Correlations. In: Statistical Methods for Health Care Research, edited by Munro BH, Visintainer MA, and Page EB. Philadelphia,
PA: JB Lippincott, 1993, p. 181.
35. Paiva M. Theoretical studies of gas mixing in the lung. In: Gas Mixing
and Distribution in the Lung, edited by Engel LA and Paiva M. New York:
Marcel Dekker, 1985, p. 221–285.
36. Prisk GK, Guy HJ, Elliott AR, Paiva M, West JB. Ventilatory inhomogeneity determined from multiple-breath washouts during sustained
microgravity on Spacelab SLS-1. J Appl Physiol 78: 597–607, 1995.
37. Robertson JS, Siri WE, Jones HB. Lung ventilation patterns determined
by analysis of nitrogen elimination rates; use of mass spectrometer as a
continuous gas analyzer. J Clin Invest 29: 577–590, 1950.
38. Robinson PD, Latzin P, Gustafsson P. Multiple-breath washout. In:
European Monograph 47: Paediatric Lung Function, edited by Frey U
and Merkus PJFM. ERS Journals, 2010, p. 87–104.
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