Non-linear parameters of specific resistance loops to characterise obstructive airways diseases
- Marko Topalovic^{1},
- Vasileios Exadaktylos^{2},
- Thierry Troosters^{1, 3},
- Geert Celis^{1},
- Jean-Marie Aerts^{2} and
- Wim Janssens^{1}Email author
Received: 19 July 2016
Accepted: 8 December 2016
Published: 9 January 2017
Abstract
Background
Specific resistance loops appear in different shapes influenced by different resistive properties of the airways, yet their descriptive ability is compressed to a single parameter - its slope. We aimed to develop new parameters reflecting the various shapes of the loop and to explore their potential in the characterisation of obstructive airways diseases.
Methods
Our study included 134 subjects: Healthy controls (N = 22), Asthma with non-obstructive lung function (N = 22) and COPD of all disease stages (N = 90). Different shapes were described by geometrical and second-order transfer function parameters.
Results
Our parameters demonstrated no difference between asthma and healthy controls groups, but were significantly different (p < 0.0001) from the patients with COPD. Grouping mild COPD subjects by an open or not-open shape of the resistance loop revealed significant differences of loop parameters and classical lung function parameters. Multiple logistic regression indicated RV/TLC as the only predictor of loop opening with OR = 1.157, 95% CI (1.064–1.267), p-value = 0.0006 and R^{2} = 0.35. Inducing airway narrowing in asthma gave equal shape measures as in COPD non-openers, but with a decreased slope (p < 0.0001).
Conclusion
This study introduces new parameters calculated from the resistance loops which may correlate with different phenotypes of obstructive airways diseases.
Keywords
Body-plethysmography Airway resistance Pulmonary function tests Chronic obstructive pulmonary disease AsthmaBackground
Obstructive lung diseases, predominantly asthma and COPD, are a group of respiratory diseases characterized by airflow limitation [1, 2]. The primary pathophysiologic impairment in these diseases is an increase of airways resistance that originates from mucosal and submucosal inflammation, bronchial constriction and airway collapse during expiration. The rise in airways resistance requires greater intrapleural pressure changes to provide sufficient pressure gradients for the initiation and maintenance of airflow. Airway resistance particularly increases during expiration when positive intrathoracic pressures are further compressing the intraluminal space of the airways [3].
Different studies have identified some limitations of the current parameterization of specific resistance loops, mainly by its variability, the necessity for artefact corrections and the clear evidence that the slope is significantly determined by small changes in the breathing frequency [9–11]. Similar observations were done with the forced oscillation technique (FOT), demonstrating important within and between-breath variation for which should be corrected to identify real airway patency or obstruction [12–15]. Other researchers have described methodologies to optimize the slope estimation as a key parameter for the calculation of airways resistance with plethysmography [7, 16, 17]. However, to the best of our knowledge, no one has explored the potential of other parameters that may better reflect the curvilinear two-dimensional shape of plethysmographic resistance loops.
We hypothesize that a more detailed mathematical modelling of the tidal breathing loops may yield new parameters which better represent the non-linear dynamics of specific resistance curves. With an empirical model-based approach our first objective was to develop geometrical parameters reflecting the shape of the loop. Secondly, we designed second-order mathematical models linking shift volumes to airflows during breathing. Finally, we checked whether these newly developed parameters associated with other important lung function measures in different clinical phenotypes of obstructive airways diseases.
Methods
Study subjects
Study population characteristics
Healthy | Asthma | COPD | |
---|---|---|---|
Subjects, n | 22 | 22 | 90 |
Gender, M/F | 16/6 | 13/9 | 67/23 |
Age, years | 60 (59–63) | 34 (22–49) | 65 (58–71) |
BMI, kg/m^{2} | 24.8 (22.9–27.1) | 23.9 (22.3–26.9) | 23.6 (21.0–27.8) |
FEV_{1}, %predicted | 114 (105–124) | 107 (93–123) | 47 (30–72) |
FEV_{1}/FVC, % | 74.5 (71.8–77.5) | 77 (73–82) | 46.5 (32–58) |
DL,_{CO}, %predicted | 94 (81–100) | 88 (81–99) | 51 (39–67) |
RV, %predicted | 101 (93–113) | 104 (92–112) | 147 (121–190) |
TLC, %predicted | 112 (106–118) | 106 (96–113) | 109 (99–127) |
FRC, %predicted | 120 (102–128) | 112 (103–120) | 142 (122–184) |
RV/TLC, % | 34.4 (32.2–36.8) | 27.4 (24.0–32.9) | 56.3 (41.6–62.7) |
R_{AW}, %predicted | 86 (76–107) | 94 (77–109) | 239 (122–333) |
sG_{AW}, %predicted | 155 (131–192) | 128 (108–171) | 47 (27–98) |
Pulmonary function tests
All pulmonary function tests were performed according to the American Thoracic Society (ATS)/ European Respiratory Society (ERS) criteria [20] using standardized equipment (Masterscreen Jaeger, Carefusion, Germany). Spirometry data are post-bronchodilator measures and are expressed, along with pre-bronchodilator plethysmography measurements of airway resistance and lung volume as percent predicted of normal reference values [21, 22]. Diffusing capacity (DL,_{CO}) was measured by the single-breath carbon monoxide gas transfer method and expressed as percent predicted of reference values [23].
Geometrical modelling
- 1/Area of the expiratory loop: it stands for total surface covered by the expiratory phase of the breathing manoeuver (Fig. 2, panel I.A). Area of loop (AOL) per se is effort depended, as deeper breathing will cause larger loop surfaces. Therefore, we normalized each loop to a range [0, 1] for both flows and shift volumes.
- 2/Roundness of the expiratory loop: In essence, this parameter measures how closely the expiratory loop approaches to a circle (Fig. 2, panel I.B). It appears in an interval from 0 to 1, where 0 means complete closure of the loop (flat line) while 1 means a perfect circle. Smooth eclipses will have a low roundness and an open loop is never expected to reach a perfect circle. Roundness (Rnd) is defined based on (Eq. 1):$$ Rnd=\frac{4\uppi \ast \mathrm{A}\mathrm{O}\mathrm{L}}{\mathrm{Perimete}{\mathrm{r}}^2} $$(1)
where AOL represents the complete area of the normalised expiratory loop, and Perimeter is the perimeter of the same loop.
- 3/
Median point of the expiratory loop: it is a two dimensional measure, with the values coming from both, X axis (median volume shift = point X) and Y axis (median flow = point Y) in the expiratory phase (Fig. 2, panel I.C). Ordinarily, both axes have a negative value as somewhat longer expiration and larger opening of the loop pulls the point lower to the third quadrant.
- 4/
Asynchrony between volume shift and flow: The nonlinearity in the relationship between volume shift and flow lies in the asynchrony between this two factors. It corresponds to airflow drops despite increasing volume shifts (Fig. 2, panel I.D, it can be also depicted from Fig. 2, panel II.C). Asynchrony is a measure expressed as time difference of volume shift peak and peak of the flow.
- 5/
SG _{ 0.5 } : Similar to sG_{AW}, yet limited to the linear drop between right-handside inspiratory and expiratory flow rates of ±0.5 L/s (Fig. 2, panel I.E.) [6].
Transfer function modelling
Development of the data-based input–output transfer function models was performed in an offline framework in MATLAB (8.3, The MathWorks, Natick, Massachusetts) using the System Identification Toolbox [24]. From the Masterlab system, measurements of tidal expiration were exported at a sampling rate of 100Hz. Shape differences of specific resistance loop were described via relationship of their two creating factors: flow and volume shift. Volume shift was used as a model input, while the result of that generated pressure, flow, was used as a model output (input–output relationship shown in Fig. 2, panel II).
- 1/the steady state gain (SSG), defined as a ratio of the steady state output and the input (Eq. 3), which in simplified sense represents volume shift multiplication to reach a certain flow level in steady state conditions.$$ SSG=\frac{\varDelta F(z)}{\varDelta Vs(z)}=\frac{{\displaystyle {\sum}_{i=0}^2{b}_i}}{1+{\displaystyle {\sum}_{i=1}^2{a}_i}} $$(3)
- 2/using denominator coefficients, two dynamic components of tidal expiration were derived: namely Pole _{ 1 } and Pole _{ 2 } (i.e. the roots of the denominator) (Eq. 4).$$ Pol{e}_{1,2} = \frac{a_1\pm \sqrt{{a_1}^2-4{a}_2}}{2} $$(4)
- 3/
finally, the numerator coefficients (b _{ 0 } , b _{ 1 } , and b _{ 2 }), were used for group comparison.
Statistical analysis
JMP Pro version 12, (SAS Institute, Cary, USA) was used to perform statistical analysis. The Shapiro-Wilk test was used to inspect normality of the groups. To control differences between two groups (paired or unpaired, where appropriate) with parametric and non-parametric distribution T-test and Mann–Whitney test were used, respectively. In the case of multiple group comparisons, ANOVA or Kruskal-Wallis tests were performed, depending of group distribution. The logistic-regression model was applied for binary variables analyses, where in addition stepwise selection was used to identify the subset of variables that had the strongest relation to outcome, using default criteria of significance at the 0.25 level to enter and 0.5 level to leave the model. The model consisted of lung function parameters: FVC, %pred, FEV_{1}, %pred, FEV_{1}/FVC, %pred, PEF, %pred. TLC, %pred, RV, %pred, FRC, %pred., DL,_{CO}, %pred, K_{CO}, %pred., RV/TLC. To determine normal value range, two-sided prediction interval of 99% was applied.
Results
Models
The assessment of the geometrical parameters was possible with the data of all subjects. Due to instable results of TF model parameters (|z| > 1), one healthy and ten COPD subjects were excluded from further analysis [26]. In general, confirmation of the appropriate model selection was demonstrated with a high goodness of fit expressed as normalized root mean square error (NRMSE) of 90 (85–92)% (values are median and IQR) for complete dataset. A visualisation of the model performance with its accuracy is shown in Fig. 2, Panel III.
Resistive parameters in different groups
Healthy | Asthma | COPD | p value (H vs. A) | p value (H vs. C) | p value (A vs. C) | |
---|---|---|---|---|---|---|
Roundness | 0.04 (0.03–0.07) | 0.07 (0.04–0.09) | 0.41 (0.17–0.58) | 0.7900 | <0.0001 | <0.0001 |
Area of Loop | 0.07 (0.05–0.10) | 0.12 (0.08–0.16) | 0.38 (0.21–0.51) | 0.9189 | <0.0001 | <0.0001 |
Asynchrony, msec | 10 (0–20) | 15 (0–33) | 70 (30–140) | 0.9064 | <0.0001 | <0.0001 |
Point X, ml | −29 (−67–−15) | −26 (−79–−6) | −128 (−318–−48) | 0.9999 | 0.0006 | 0.0005 |
Point Y, ml/sec | −243 (−470–−25) | −195 (−440–37) | −331 (−443–−209) | 0.9653 | 0.5014 | 0.5014 |
b _{ 0 } | 5.66 (3.78–7.43) | 5.77 (4.64–7.09) | 1.51 (0.84–3.27) | 0.9999 | <0.0001 | <0.0001 |
b _{ 1 } | −8.88 (−11.8–−5.0) | −8.57 (−11.5–−5.6) | −2.36 (−4.43–−0.17) | 0.8356 | <0.0001 | <0.0001 |
b _{ 2 } | 4.22 (2.39–5.21) | 3.19 (1.10–4.97) | 0.90 (−0.18–1.82) | 0.9999 | <0.0001 | <0.0001 |
SSG, 1/sec | 5.48 (4.62–6.21) | 5.18 (4.11–6.16) | 1.34 (0.62–2.61) | 0.9999 | <0.0001 | <0.0001 |
Pole1 | 0.94 (0.90–0.97) | 0.95 (0.89–0.97) | 0.91 (0.82–0.96) | 0.9999 | 0.4899 | 0.4068 |
Pole2 | 0.93 (0.80–0.96) | 0.92 (0.59–0.95) | 0.78 (0.53–0.91) | 0.6674 | 0.0241 | 0.7444 |
sG_{AW}, 1/ kPa*sec | 1.32 (1.12–1.63) | 1.09 (0.92–1.45) | 0.40 (0.23–0.83) | 0.9635 | <0.0001 | <0.0001 |
sG_{0.5} , 1/ kPa*sec | 1.42 (1.15–1.57) | 1.33 (1.24–1.47) | 0.75 (0.49–1.14) | 0.9999 | <0.0001 | <0.0001 |
Resistance loop in COPD
Comparison of patients with opened vs. unopened loop in mild to moderate COPD
Non-openers | Openers | p value | |
---|---|---|---|
Subjects, n | 22 | 17 | |
BMI, kg/m^{2} | 24.9 (22.0–29.4) | 26.8 (24.1–27.8) | 0.6093 |
FEV_{1}, %predicted | 83 (76–93) | 65 (61–76) | 0.0018 |
DL_{CO}, %predicted | 67 (55–79) | 69 (53–75) | 0.6774 |
RV, %predicted | 115 (106–133) | 134 (120–150) | 0.0368 |
TLC, %predicted | 109 (99–115) | 107 (102–110) | 0.6793 |
FRC, %predicted | 122 (101–135) | 131 (120–142) | 0.1578 |
RV/TLC, % | 38.7 (35.7–40.4) | 46.6 (41.5–53.9) | 0.0007 |
sG_{AW}, 1/ kPa*sec | 1.03 (0.90–1.21) | 0.61 (0.46–0.74) | <0.0001 |
sG_{0.5} , 1/ kPa*sec | 1.23 (1.06–1.38) | 1.00 (0.81–1.15) | 0.0047 |
Roundness | 0.09 (0.06–0.12) | 0.31 (0.22–0.41) | <0.0001 |
Area of Loop | 0.14 (0.10–0.20) | 0.31 (0.23–0.44) | <0.0001 |
Asynchrony, msec | 10 (0–40) | 40 (30–70) | 0.0097 |
Point X, ml | −38 (−93–−19) | −92 (−209–−31) | 0.0827 |
Point Y, ml/sec | −251 (−379–−177) | −251 (−406–−139) | 0.7107 |
b _{ 0 } | 4.10 (2.92–5.56) | 1.63 (1.18–2.58) | 0.0013 |
b _{ 1 } | −3.47 (−8.11–1.16) | −2.40 (−3.95–0.20) | 0.3080 |
b _{ 2 } | 1.42 (−2.87–3.27) | 0.77 (−0.46–1.97) | 0.5132 |
SSG, 1/sec | 4.03 (2.76–4.54) | 1.67 (1.50–2.21) | <0.0001 |
Pole1 | 0.84 (0.67–0.96) | 0.93 (0.82–0.97) | 0.2301 |
Pole2 | 0.64 (0.26–0.89) | 0.86 (0.49–0.95) | 0.1216 |
Relationship between pulmonary function parameters and the opening of resistance loops in multiple logistic regression with stepwise selection: A/ in mild to moderate COPD, B/ in all COPD
Variables | Odds Ratio (95% Confidence limit) | p value |
---|---|---|
Mild COPD | ||
RV/TLC, % | 1.157 (1.064–1.267) | 0.0006 |
COPD | ||
RV/TLC, % | 1.168 (1.092–1.259) | <0.0001 |
FEV_{1}/FVC, % | 0.866 (0.746–0.949) | 0.0004 |
Changes in resistance loops during bronchoconstriction
Lung function and resistance parameters in the subgroup of asthma patients with pre and post methacholine challenge test
Pre | Post | p value | |
---|---|---|---|
Subjects, n | 11 | 11 | |
Openers, n | 0 | 4 | |
FEV_{1}, %predicted | 108 (91–115) | 75 (67–89) | <0.0001 |
RV, %predicted | 107 (102–118) | 132 (115–162) | 0.0188 |
TLC, %predicted | 106 (95–113) | 102 (89–115) | 0.2014 |
FRC, %predicted | 116 (110–120) | 130 (123–140) | 0.0508 |
RV/TLC, % | 27.4 (26.4–32.5) | 32.1 (27.9–48.4) | 0.1289 |
sG_{AW}, 1/ kPa*sec | 1.02 (0.88–1.10) | 0.45 (0.38–0.51) | 0.0010 |
sG_{0.5} , 1/ kPa*sec | 1.28 (1.24–1.34) | 0.63 (0.57–0.69) | 0.0010 |
Roundness | 0.08 (0.02–0.09) | 0.13 (0.12–0.27) | 0.0029 |
Area of Loop | 0.14 (0.02–0.18) | 0.14 (0.13–0.19) | 0.1678 |
Asynchrony, msec | 20 (0–20) | 20 (20–50) | 0.1339 |
Point X, ml | −14 (−27–1) | −115 (−172–3) | 0.0137 |
Point Y, ml/sec | −158 (−188–73) | −258 (−399–−42) | 0.2447 |
b _{ 0 } | 6.04 (4.68–7.03) | 2.03 (1.67–4.11) | 0.0049 |
b _{ 1 } | −7.72 (−11.14–−5.20) | −2.37 (−4.45–0.06) | 0.0105 |
b _{ 2 } | 2.92 (0.87–4.15) | 0.91 (−0.88–2.09) | 0.1242 |
SSG, 1/sec | 5.00 (4.16–5.89) | 2.13 (1.41–2.90) | 0.0020 |
Pole1 | 0.95 (0.86–0.97) | 0.90 (0.57–0.94) | 0.3203 |
Pole2 | 0.85 (0.16–0.95) | 0.71 (0.45–0.91) | 0.8984 |
Comparison of groups where flow limitation is undoubted
Post methacholine Asthma (A) | Non-Openers mild COPD (B) | Openers mild COPD (C) | p value (A vs. B) | p value (A vs. C) | |
---|---|---|---|---|---|
Subjects, n | 11 | 22 | 17 | ||
FEV_{1}, %predicted | 75 (67–89) | 83 (76–93) | 65 (61–76) | 0.3757 | 0.0937 |
FEV_{1}/FVC, % | 65 (60–69) | 62 (58–68) | 55 (48–61) | 0.7242 | 0.0027 |
RV/TLC, % | 32.1 (27.9–48.4) | 38.7 (35.7–40.4) | 46.6 (41.5–53.9) | <0.0001 | <0.0001 |
sG_{AW}, 1/ kPa*sec | 0.45 (0.38–0.51) | 1.03 (0.90–1.21) | 0.61 (0.46–0.74) | <0.0001 | 0.2420 |
sG_{0.5} , 1/ kPa*sec | 0.63 (0.57–0.69) | 1.23 (1.06–1.38) | 1.00 (0.81–1.15) | <0.0001 | 0.0033 |
Roundness | 0.13 (0.12–0.27) | 0.09 (0.06–0.12) | 0.31 (0.22–0.41) | 0.0309 | 0.0656 |
Area of Loop | 0.14 (0.13–0.19) | 0.14 (0.10–0.20) | 0.31 (0.23–0.44) | 0.6732 | <0.0001 |
SSG, 1/sec | 2.13 (1.41–2.90) | 4.03 (2.76–4.54) | 1.67 (1.50–2.21) | 0.0022 | 0.2651 |
Point X, ml | −115 (−172–3) | −38 (−93–−19) | −92 (−29–−31) | - | - |
Asynchrony, msec | 20 (20–50) | 10 (0–43) | 40 (30–65) | 0.9999 | 0.4266 |
Discussion
In this study we describe specific resistance loops measured by body-plethysmography by using geometrical analyses and second-order transfer functions. The newly developed parameters reflect the curvilinear aspect and rotation of the resistance loops and typically associate with different lung function characteristics. Loops that are mathematically identified by an open appearance as measured by roundness typically occur in the majority of COPD subjects with hyperinflation, whereas rotation of the slope without opening is apparent during bronchoconstriction, as demonstrated in asthma.
Opening of the loops in COPD was significantly associated with RV/TLC ratio, much more than with FEV_{1} or FEV_{1}/FVC ratio. In numerous studies the RV/TLC ratio has been linked to air trapping and hyperinflation, together with increases of RV and FRC [27–29]. In fact, the RV/TLC ratio reflects the proportion of trapped lung volume that cannot be mobilized by maximal breathing. Increases in static RV/TLC ratio are inversely correlated with maximal inspiratory capacity, which often declines during exercise by dynamic hyperinflation and strongly associates with breathing discomfort and dyspnea. From a mechanistic point of view, we can only speculate on the reasons why RV/TLC ratio is the best predictor for loop opening and asynchrony between alveolar pressures and flows. One possible explanation may be found in the competition for space between lung areas that are emptied during expiration with areas that are trapped with air and progressively compress adjacent airways during expiration. As such the heterogeneity of airflow may contribute to a wide distribution of time constants for gas emptying and thus asynchrony, typically occurring with hyperinflation. Heterogeneity in the ventilation may also occur following bronchoconstriction, but with limited hyperinflation. As methacholine challenge in the asthma group did not induce important asynchrony, one may hypothesize that a more homogenous reduction in flow was obtained. A last explanation may be found in increased airway collapse by reduced airway tethering with the loss of alveolar tissue. We did not observe any significant relationship between roundness and DL_{CO} or K_{CO}, as indicative lung function markers of emphysema. Unfortunately, in the absence of CT measures we were not able to study the relationship with emphysema in more depth. From a clinical point of view the strong association between the roundness of resistance loops and hyperinflation is very attractive. The reduction of static hyperinflation is a main target of several COPD treatments including lung volume reduction surgery, endoscopic valve displacement and bronchodilators. Bronchodilator responses are usually evaluated on the expiratory volumes of spirometry, although different studies suggest that their impact on static lung volumes may be much larger [30–32]. Therefore, the monitoring of hyperinflation during tidal breathing may become a promising evaluation tool for treatment responses in specific phenotypes.
Our data clearly indicate that bronchoconstriction due to airway muscle contraction and mucosal oedema in asthma is resulting in significant changes in input related parameters such as SSG, point X, b _{ 0 } and b _{ 1 }. These changes correspond to an increased volume shift in phase with the flow, which obviously result in a reduction of the slope. Although most of our asthma patients did not show opening, 4 subjects had significant increases in roundness after the bronchial challenge, which is not surprising as hyperinflation can occur during bronchoconstriction and heterogeneity in airflow during exhalation may occur with increased bronchoconstriction [33]. When using sG_{AW} as best correlate for the slope, we accept that volume shift and airflow have a linear relationship, which is often not the case. SSG, which is highly correlated to sG_{AW}, accounts for pressure multiplication to reach the level of exhaled flow but under steady state condition. Both SSG and sG_{AW} ignore much of the system’s dynamics, but as they are computed differently they may still be influenced by other factors in the flow volume shift relationship. Looking at the slope per se, our data suggest to use sG_{0.5} instead of or with sG_{AW} (computed as sG_{mid}) due to its potential to discriminate obstructive asthma from mild COPD. As sG_{0.5} practically represents the slope of the right hand side of the loop, it will be little influenced by resistive mechanisms that open the loop.
Previous studies with FOT have demonstrated that resistance measures can vary within and between breath as they are flow- and volume dependent. In particular, bronchomotor challenge can alter the ventilation which translates into changes of airway resistance and obscures the true changes in airway patency. Moreover, it has been shown that in patients with COPD the degree of flow limitation is varying between breaths [12, 34]. In the transition period of mild disease the detection of flow limitation may therefore require the monitoring of several breathing loops. For FOT different mathematical models have been proposed that correct for loop and flow dependency during tidal breathing [12, 15]. Our approach with plethysmography is adjusting flows for pressure changes during the whole breathing cycle and is taking the non-linearity of these dynamics into account. Indirectly, it also provides correction for between-breath differences as a representative breathing cycle of overlapping loops was carefully selected. Taken together, our data imply that airways are not just simple tubes, but flexible structures that may enlarge or compress in function of the manoeuver.
The new parameters that we have developed are potentially important as they are able to identify different phenotypes within the spectrum of obstructive airways diseases. Hence, it is only with interventions and longitudinal follow-up that we will be able to identify their exact clinical relevance. One weakness of the study is the smaller number of healthy subjects, since a larger number would secure confidence in defining the normality range for each parameter. Similarly, comparisons of postbronchodilator with prebronchodilator measures, particularly in asthma may reveal more subtle differences between asthma and healthy controls. Another weakness may be the lack of data from CT scans, as that would allow the correlation of newly developed parameters with CT parameters of hyperinflation, airtrapping, bronchial inflammation and emphysema [35–37].
Conclusion
We developed new parameters which are reflecting the dynamic relationships between alveolar pressures and flows. Some of them are potentially important as they are linked to specific phenotypes within the spectrum of obstructive airways diseases. Mechanistic and prospective follow-up studies are now needed to determine the true validity of these parameters in respiratory medicine.
Declarations
Acknowledgements
We thank Mrs. Kristien De Bent, Mr. Willem De Wit and Mr. Rafi Zonanashvili for the provided technical support and help with data collection.
Funding
This work was supported by an Astra Zeneca Chair 2013–2015. WJ is supported the Flemish Research Foundation (FWO).
Availability of data and materials
Data will not be shared.
Authors’ contributions
MT performed the data analysis, contributed to the study design and wrote the manuscript. VE contributed in the data analysis and critically reviewed the manuscript. GC, TT and JA critically reviewed the manuscript. WJ takes responsibility for the content of the manuscript, contributed to the study design, assisted in the data analysis and critically reviewed the manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
All patients provided informed consent for the use of lung function and clinical data and the protocol was approved by the ethics committee of the University hospital of Leuven, Belgium.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
References
- Decramer M, Janssens W, Miravitlles M. Chronic obstructive pulmonary disease. Lancet. 2012;379:1341–51.View ArticlePubMedGoogle Scholar
- Decramer M, Selroos O. Asthma and COPD: differences and similarities. With special reference to the usefulness of budesonide/formoterol in a single inhaler (Symbicort) in both diseases. Int J Clin Pract. 2005;59:385–98.View ArticlePubMedGoogle Scholar
- O’Leary JPTA, Capote LR. The physiologic basis of surgery. 4th ed. 2008.Google Scholar
- Bateman ED, Hurd SS, Barnes PJ, Bousquet J, Drazen JM, FitzGerald M, Gibson P, Ohta K, O’Byrne P, Pedersen SE, et al. Global strategy for asthma management and prevention: GINA executive summary. Eur Respir J. 2008;31:143–78.View ArticlePubMedGoogle Scholar
- Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, Calverley P, Fukuchi Y, Jenkins C, Rodriguez-Roisin R, van Weel C, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med. 2007;176:532–55.View ArticlePubMedGoogle Scholar
- Goldman M, Smith H, Ulmer W. Whole-body plethysmography. Eur Res Monograph. 2005;31:15.Google Scholar
- Criee C, Sorichter S, Smith H, Kardos P, Merget R, Heise D, Berdel D, Köhler D, Magnussen H, Marek W. Body plethysmography–its principles and clinical use. Respir Med. 2011;105:959–71.View ArticlePubMedGoogle Scholar
- Topalovic M, Derom E, Osadnik CR, Troosters T, Decramer M, Janssens W, Belgian Pulmonary Function Study I. Airways resistance and specific conductance for the diagnosis of obstructive airways diseases. Respir Res. 2015;16:88.View ArticlePubMedPubMed CentralGoogle Scholar
- Peslin R, Duvivier C, Malvestio P, Benis AR. Correction of thermal artifacts in plethysmographic airway resistance measurements. J Appl Physiol (1985). 1996;80:2198–203.Google Scholar
- Peslin R, Duvivier C, Malvestio P, Benis AR, Polu JM. Frequency dependence of specific airway resistance in a commercialized plethysmograph. Eur Respir J. 1996;9:1747–50.View ArticlePubMedGoogle Scholar
- Peslin R, Gallina C, Rotger M. Methodological factors in the variability of lung volume and specific airway resistance measured by body plethysmography. Bull Eur Physiopathol Respir. 1987;23:323–7.PubMedGoogle Scholar
- Dellaca RL, Duffy N, Pompilio PP, Aliverti A, Koulouris NG, Pedotti A, Calverley PM. Expiratory flow limitation detected by forced oscillation and negative expiratory pressure. Eur Respir J. 2007;29:363–74.View ArticlePubMedGoogle Scholar
- Oostveen E, MacLeod D, Lorino H, Farre R, Hantos Z, Desager K, Marchal F. The forced oscillation technique in clinical practice: methodology, recommendations and future developments. Eur Respir J. 2003;22:1026–41.View ArticlePubMedGoogle Scholar
- Peslin R, Duvivier C, Gallina C. Total respiratory input and transfer impedances in humans. J Appl Physiol (1985). 1985;59:492–501.Google Scholar
- Tomalak W, Peslin R, Duvivier C. Variations in airways impedance during respiratory cycle derived from combined measurements of input and transfer impedances. Eur Respir J. 1998;12:1436–41.View ArticlePubMedGoogle Scholar
- Islam M, Ulmer W. Diagnostic value of ‘closing volume’ in comparison to ‘airway resistance/lung volume plot’. Respiration. 1974;31:449–58.View ArticlePubMedGoogle Scholar
- Matthys H, Orth U. Comparative measurements of airway resistance. Respiration. 1975;32:121–34.View ArticlePubMedGoogle Scholar
- Crapo RO, Casaburi R, Coates AL, Enright PL, Hankinson JL, Irvin CG, MacIntyre NR, McKay RT, Wanger JS, Anderson SD, et al. Guidelines for methacholine and exercise challenge testing-1999. This official statement of the American Thoracic Society was adopted by the ATS Board of Directors, July 1999. Am J Respir Crit Care Med. 2000;161:309–29.View ArticlePubMedGoogle Scholar
- Vestbo J, Hurd SS, Agusti AG, Jones PW, Vogelmeier C, Anzueto A, Barnes PJ, Fabbri LM, Martinez FJ, Nishimura M, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med. 2013;187:347–65.View ArticlePubMedGoogle Scholar
- Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, Crapo R, Enright P, van der Grinten CP, Gustafsson P, et al. Standardisation of spirometry. Eur Respir J. 2005;26:319–38.View ArticlePubMedGoogle Scholar
- Quanjer PH. Standardized lung function testing. Report working party. Bull Eur Physiopathol Respir. 1983;19 Suppl 5:1–95.Google Scholar
- Quanjer PH, Tammeling GJ, Cotes JE, Pedersen OF, Peslin R, Yernault JC. Lung volumes and forced ventilatory flows. Work Group on Standardization of Respiratory Function Tests. European Community for Coal and Steel. Official position of the European Respiratory Society. Rev Mal Respir. 1994;11 Suppl 3:5–40.PubMedGoogle Scholar
- Miller A, Thornton JC, Warshaw R, Anderson H, Teirstein AS, Selikoff IJ. Single breath diffusing capacity in a representative sample of the population of Michigan, a large industrial state: predicted values, lower limits of normal, and frequencies of abnormality by smoking history 1–3. Am Rev Respir Dis. 1983;127:270–7.PubMedGoogle Scholar
- Ljung L. System Identification Toolbox for Use with {MATLAB}. 2007.Google Scholar
- Ljung L. Prediction error estimation methods. Circ Syst Signal Processing. 2002;21:11–21.View ArticleGoogle Scholar
- Schüssler HW. A stability theorem for discrete systems. Acoustics, Speech and Signal Processing, IEEE Transactions on. 1976;24:87–9.View ArticleGoogle Scholar
- Gibson G. Pulmonary hyperinflation a clinical overview. Eur Respir J. 1996;9:2640–9.View ArticlePubMedGoogle Scholar
- O’Donnell DE, Webb KA, Neder JA. Lung hyperinflation in COPD: applying physiology to clinical practice. COPD Research and Practice. 2015;1:1.Google Scholar
- Rubinsztajn R, Przybyłowski T, Maskey-Warzęchowska M, Paplińska-Goryca M, Karwat K, Nejman-Gryz P, Chazan R. Correlation between hyperinflation defined as an elevated RV/TLC ratio and body composition and cytokine profile in patients with chronic obstructive pulmonary disease. Pneumologia i Alergologia Polska. 2015;83:120–5.View ArticleGoogle Scholar
- Ferguson GT. Why does the lung hyperinflate? Proc Am Thorac Soc. 2006;3:176–9.View ArticlePubMedGoogle Scholar
- Newton MF, O’Donnell DE, Forkert L. Response of lung volumes to inhaled salbutamol in a large population of patients with severe hyperinflation. CHEST. 2002;121:1042–50.View ArticlePubMedGoogle Scholar
- O’Donnell D, Flüge T, Gerken F, Hamilton A, Webb K, Aguilaniu B, Make B, Magnussen H. Effects of tiotropium on lung hyperinflation, dyspnoea and exercise tolerance in COPD. Eur Respir J. 2004;23:832–40.View ArticlePubMedGoogle Scholar
- Lougheed MD, Fisher T, O’Donnell DE. Dynamic hyperinflation during bronchoconstriction in asthma: implications for symptom perception. CHEST. 2006;130:1072–81.View ArticlePubMedGoogle Scholar
- Dellaca RL, Santus P, Aliverti A, Stevenson N, Centanni S, Macklem PT, Pedotti A, Calverley PM. Detection of expiratory flow limitation in COPD using the forced oscillation technique. Eur Respir J. 2004;23:232–40.View ArticlePubMedGoogle Scholar
- Baroni RH, Feller-Kopman D, Nishino M, Hatabu H, Loring SH, Ernst A, Boiselle PM. Tracheobronchomalacia: comparison between end-expiratory and dynamic expiratory CT for evaluation of central airway collapse 1. Radiology. 2005;235:635–41.View ArticlePubMedGoogle Scholar
- Wagnetz U, Roberts HC, Chung T, Patsios D, Chapman KR, Paul NS. Dynamic airway evaluation with volume CT: initial experience. Can Assoc Radiol J. 2010;61:90–7.View ArticlePubMedGoogle Scholar
- Wielpütz MO, Eberhardt R, Puderbach M, Weinheimer O, Kauczor H-U, Heussel CP. Simultaneous assessment of airway instability and respiratory dynamics with low-dose 4D-CT in chronic obstructive pulmonary disease: a technical note. Respiration. 2014;87:294–300.View ArticlePubMedGoogle Scholar