Article Text

## Abstract

**Background** The plethysmographic shift volume–flow loop (sR_{aw}-loop) measured during tidal breathing allows the determination of several lung function parameters such as the effective specific airway resistance (sR_{eff}), calculated from the ratio of the integral of the resistive aerodynamic specific work of breathing (sWOB) and the integral of the corresponding flow–volume loop. However, computing the inspiratory and expiratory areas of the sR_{aw}-loop separately permits the determination of further parameters of airway dynamics. Therefore, we aimed to define the discriminating diagnostic power of the inspiratory and expiratory sWOB (sWOB_{in}, sWOB_{ex}), as well as of the inspiratory and expiratory sR_{eff} (sR_{eff}
^{IN} and sR_{eff}
^{EX}), for discriminating different functional phenotypes of chronic obstructive lung diseases.

**Methods** Reference equations were obtained from measurement of different databases, incorporating 194 healthy subjects (35 children and 159 adults), and applied to a collective of 294 patients with chronic lung diseases (16 children with asthma, aged 6–16 years, and 278 adults, aged 17–92 years). For all measurements, the same type of plethysmograph was used (Jaeger Würzburg, Germany).

**Results** By multilinear modelling, reference equations of sWOB_{in}, sWOB_{ex}, sR_{eff}
^{IN} and sR_{eff}
^{EX} were derived. Apart from anthropometric indices, additional parameters such as tidal volume (V_{T}), the respiratory drive (P_{0.1}), measured by means of a mouth occlusion pressure measurement 100 ms after inspiration and the mean inspiratory flow (V_{T}/T_{I}) were found to be informative. The statistical approach to define reference equations for parameters of airway dynamics reveals the interrelationship between covariants of the actual breathing pattern and the control of breathing.

**Conclusions** We discovered that sWOB_{in}, sWOB_{ex}, sR_{eff}
^{IN} and sR_{eff}
^{EX} are new discriminating target parameters, that differentiate much better between chronic obstructive diseases and their subtypes, especially between chronic obstructive pulmonary disease (COPD) and asthma–COPD overlap (ACO), thus strengthening the concept of precision medicine.

- Lung Physiology
- Respiratory Function Test
- Asthma
- COPD Pathology
- Equipment Evaluations

## Data availability statement

Data are available in a public, open access repository. Master files have been stored and secured in the Clinical Trial Unit, Hirslanden Corporate Office, CH-8152 Glattpark, Switzerland.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

## Statistics from Altmetric.com

#### WHAT IS ALREADY KNOWN ON THIS TOPIC

Normative predicting equations for some parameters of the whole-body plethysmography are well established. However, yet hidden parameters of the plethysmographic shift volume–tidal volume (sR

_{aw}-loop) may expect further insight into the pathophysiological behaviour of lung function in patients with lung diseases. However, normative predicting equations for these new parameters are needed.

#### WHAT THIS STUDY ADDS

The new defined normative reference equations provide the computation of z-scores transitional over a wide age range minimising the age- and growth-related variability between individuals in the distribution of the reference population.

#### HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

The new defined normative predicting equations selectively for the inspiratory and expiratory parts of the sR

_{aw}-loop (sWOB_{in}, sWOB_{ex}, sR_{eff}^{IN}, sR_{eff}^{EX}) are prerequisites for studies searching for target parameters, which serve to a much better differentiation between chronic obstructive diseases and their subtypes, especially between chronic obstructive pulmonary disease (COPD) and asthma–COPD overlap, improving the concept of precision medicine.

## Introduction

There is growing interest in identifying specific functional patterns by standardised interpretation of pulmonary function tests in the diagnosis of respiratory diseases within a concept of precision medicine.1–10 There are, however, mandatory prerequisites for studies predicting disease. For the calculation of individual z-scores, as recommended for the assessment of functional severity instead of percentage predicted values, the availability of normative reference equations applied transitionally over a wide age range is essential, especially when assessing lung function data within several diagnostic classes over a longer age range.11–15 This takes into account the age- and growth-related variability between individuals in the distribution of the reference population.

Chronic obstructive pulmonary disease (COPD) is a common, complex and heterogeneous disorder, characterised not only by airflow limitation but also by small airway dysfunction,16–19 pulmonary hyperinflation, gas trapping and gas exchange disturbances,20–23 due to an increased inflammatory response of the lung. Although considerable individual heterogeneity within COPD is known, reflecting different physiological mechanisms, such as endotypes and phenotypes,24 it is not surprising, that morbidity and mortality cannot be predicted from the degree of lung function impairment based only on spirometric airflow limitation in COPD alone.3 There is a need for functional algorithms for homogeneous subgroups of patients,25 as part of a concept of personalised medicine.2 3 26 27

In addition to spirometric measurements mainly obtained by forced expiratory flow-volume measurements, whole-body plethysmography is still widely used in many lung function laboratories of centres for respiratory diseases. There are numerous parameters that can be used to calculate functional characteristics of airway dynamics from the plethysmographic sR_{aw}-loops.28–30 The most promising approach is based on the integral method, originally proposed by Matthys and Orth, which provides the integral of the
-loop (the so-called sR_{aw}-loop) as sWOB, from which the effective specific airway resistance (sR_{eff}) can be calculated.31 We have recently demonstrated that good discrimination between asthma, asthma–COPD overlap (ACO)32–34 and COPD is possible based on parameters obtained from the plethysmographic sR_{aw}-loop.35 To visualise further information, not yet recognised in the sR_{aw}-loop, figure 1 shows a plethysmographic shift volume (V_{pleth}) and tidal flow (V′) plot of a patient with COPD, from which the inspiratory and expiratory parts of the breathing cycle are separately calculated. This results in parameters such as the inspiratory and expiratory sWOB (sWOB_{in}, sWOB_{ex}), as well as the inspiratory and expiratory sR_{eff} (sR_{eff}
^{IN} and sR_{eff}
^{EX}).

The aim of the present study was to establish reference equations for all parameters of the sR_{aw}-loop presented in figure 1, extended by parameters of central control of breathing such as the tidal volume (V_{T}), the respiratory drive (P_{0.1}) measured by means of a mouth occlusion pressure measurement 100 ms after inspiration, the mean inspiratory flow (V_{T}/T_{I}) and the ratio between inspiratory time and total respiratory time (T_{I}/T_{tot}) of each respiratory cycle, and hence to analyse them for their discriminatory power to define specific functional traits within the abovementioned obstructive lung diseases.

## Material and methods

### Study design

In the present study, we refer to retrospectively evaluated data obtained from five Swiss centres (University Children’s Hospital, Bern; Centre of Pulmonary Diseases, Hirslanden Hospital Group, Salem-Hospital, Bern, Switzerland; Clinic of Pneumology, Cantonal Hospital, St. Gallen, Switzerland; Centre of Pulmonology, Hirslanden Hospital Group, Clinic Hirslanden, Zürich, Switzerland; Division of Pulmonary Medicine, Clinic Barmelweid, Barmelweid, Switzerland), tested between 2006 and 2016. A healthy control collective of healthy children and adults comprises data of subjects participating in an epidemiologic study, in addition to data of healthy, no-smoking lab technicians, students, volunteers and hospital staff. The patients have been referred to the centres for extended pulmonary function testing and optimising therapy. Based on anamnestic, clinical features and functional criteria, the patients were classified by trained paediatric and adult pulmonologists into three diagnostic classes: (1) bronchial asthma, (2) COPD with coexisting asthma (ACO) and (3) COPD, the data have been exported between 2018 and 2022. The authors had no access to information that could identify individual participants during or after data collection.

### Study collective

From the previously used database defining normative equations of sWOB, sR_{eff} and the effective specific airway conductance (sG_{eff}) (n=314; children and adults),30 data from 194 healthy subjects (34 children, 19 males, 15 females, aged 6–16 years; 160 adults, 62 males, 98 females, aged 17–85 years) could be used to define the inspiratory (sWOB_{in}, sReff^{IN}) and the expiratory part (sWOB_{ex}, sReff^{EX}) of the sR_{aw}-loop.

From the previously used database defining functional predictors discriminating ACO from asthma and COPD,35 data of 294 patients (16 children with asthma, 3 males, 13 females, aged 6–16 years; and 278 adults, 112 males, 166 females, aged 17–92 years) could be included to define the inspiratory (sWOB_{in}, sReff^{IN}) and the expiratory part (sWOB_{ex}, sReff^{EX}) of the sR_{aw}-loop.

### Pulmonary function procedures

In all five centres, the same type of constant-volume whole-body plethysmographs (Master Screen Body, Jaeger Würzburg, Germany) was used by standard techniques according to American Thoracic Society - European Respiratory Society criteria36 and revised Swiss guidelines.37 The exported data were obtained from the same system software (JLAB V.5.2, SentrySuite V.1.29 resp.). Inclusion criteria were reproducible baseline measurements with at least five shift volume–tidal flow loops of comparable shapes and closed expiratory part of the shift volume–tidal flow loops. Moreover, in healthy subjects, the inspiratory capacity must be within the range of normal to achieve correct total lung capacity and vital capacity values. Apart from a daily calibration procedure given by the software of the Master Screen Body, monthly so-called ‘biological controls’ were performed measuring normal values of a healthy, non-smoking technician. Apart from the extension of parameters obtained by the sR_{aw}-loop, we found it important to introduce also parameters defining the control of breathing. As initially worked out by Whitelaw *et al*,38 the respiratory drive (P_{0.1}) was measured by means of a mouth occlusion pressure measurement 100 ms after inspiration as automatic occlusion response during tidal breathing. This makes the P_{0.1} effort-independent and reproducible and minimises vagal influences because pressure swings do not lead to corresponding changes in volume.39 Since it starts from end-expiratory lung volume (EELV), any drop in P_{0.1} is independent of the recoil pressure of the lung or thorax and airway resistance because the flow is interrupted.40 Moreover, effective inspiratory impedance defined as product of P_{0.1} and the ratio between V_{T} and the inspiratory time (T_{I}) was calculated.41 42

### Mathematical and statistical approaches

To define the mathematical relationship between each lung function parameter as dependent parameters to be predicted, we first used the ‘curve estimation’ tool of the SPSS (V.29, IBM, Armonk, New York, USA) for linear, logarithmic, power and exponential regressions, as well as quadratic and cubic function for age, height and weight, as previously proposed.43 It turned out that most mathematical relationships featured power associations. Therefore, our modelling used absolute values and their natural logarithm (ln). For the evaluation of the reference equations of sWOB, sWOB_{in}, sWOB_{ex}, sReff, sR_{eff}
^{IN}, sR_{eff}
^{EX} and sR_{tot} multilevel linear models with a two-level hierarchy was used, as previously presented.30 By this mathematical modelling, the individual z-scores of each patient could be calculated instead of percentage predicted values as recommended to be used in the assessment of severity, especially if lung function data over a longer age range are assessed.15 Previous work has shown that airway resistance is highly dependent on the breathing pattern and the EELV at functional residual capacity (FRC). We searched apart from anthropometric measures (gender, age and height), also for interrelationships with parameters of the breathing pattern and the timing of breathing ((V_{T}, V_{T}/FRC, T_{I}, V_{T}/T_{I}) at rest.

## Results

### Healthy subjects and generation of normative predicting equations

The age distribution of the anthropometric measures and lung function data of the healthy subjects within six age classes expressed as per cent predicted are given in table 1. All lung function parameters presented with values within 95 percentiles equal to ±1.645 z-score of values predicted.30 43 44

### Predictive data analysis of airway dynamic parameters of the sR_{aw}-loop

From the digitalised data points of each individual’s sR_{aw}-loop shown in figure 1, we searched for inter-related parameters to define gender-specific interactions of parameters within four ‘segments’: (a) ‘anthropometry’: age, ln(age), (age)^{2}, height, ln(height) (height)^{2}, (b) ‘static lung volumes’: FRC_{pleth}, ln(FRC_{pleth}), (c) ‘ventilation’: V_{T}, ln(V_{T}) and (d) ‘control of breathing’: V_{T}/T_{I}, ln(V_{T}/T_{I}), T_{I}/T_{tot}. By multilinear modelling, we computed stepwise regressions within these ‘segments’ for sWOB and sR_{eff} and their inspiratory (sWOB_{in}, sR_{eff}
^{IN}) and expiratory parts (sWOB_{ex}, sR_{eff}
^{EX}), resolving multicollinearity by analysis of variance (ANOVA), revealing the following normative equations:

Moreover, the parameters of the control of breathing (P_{0.1}, V_{T}/T_{I}) and the inspiratory impedance defined as Z_{in}
^{pleth}=P_{0.1}/V_{T}/T_{I}) were assessed, giving the following normative equations:

The age distributions of sWOB, in relation to sWOB_{in} and sWOB_{ex} on one side and sR_{eff}
^{IN} in relation to sR_{eff}
^{EX} on the other side, over the age range of 6–86 years for healthy males and females are given in figure 2. The inspiratory sWOB_{in} contributed to 46.8%±5.8% of the sWOB (figure 2A), whereas sWOB_{ex} provided 50.2%±7.2% to the sWOB (figure 2B). Regarding sR_{eff}, the inspiratory sR_{eff}
^{IN} was part of 25.5%±8.9% of sR_{eff}, whereas sR_{eff}
^{EX} contributed 43.0%±11.8% of sR_{eff}. Finally, sWOB_{ex} was by the mean 9.4%±9.8% higher than sWOB_{in}, sR_{eff}
^{EX} by the mean 35.1% higher than sR_{eff}
^{IN}, resp. (figure 2C).

### Patients (asthma, ACO, COPD)

The age distribution of the anthropometric measures and lung function data of the patients within three diagnostic classes, expressed as z-scores, are given in table 2. The patients with asthma were significantly younger than those with ACO and COPD (p<0.001). The age distributions in the centres were similar. Regarding static lung volumes, patients with COPD presented with higher FRC_{pleth} and hence incidence of pulmonary hyperinflation (asthma 19.8%; ACO: 32.8%; COPD 78.9%). Compared with the spirometric parameters (forced expiratory volume in 1 s (FEV_{1}), forced vital capacity (FVC)/FEV_{1}, forced expiratory flow between 25% and 75% vital capacity (FEF_{25–75})), the plethysmographic parameters defining airway dynamics presented with much more pronounced differences between the three diagnostic groups, given by the *F*-value of the ANOVA, highest for sR_{tot} (*F*=541), followed by sWOB_{ex} (*F*=417).

### Distributions of each lung function parameter

Regarding potentially discriminating parameters between the diagnostic classes using the *F*-statistic of ANOVA, the z-score distributions of each lung function parameter are synoptically presented in figure 3. There are quite great differences in the z-scores between the parameters and within the diagnostic classes. Whereas parameters of forced spirometry covered only a z-score range over 12, and of static lung volumes over 11, the z-score ranges for the work of breathing covered a z-score range over 75, and of the airway resistances over 40. Using one-way ANOVA to test the statistical differences among the means of the three diagnostic classes, highest ANOVA *F*-values were found for sR_{tot} (540.9), followed by sWOB_{ex} (416.8) and d-P_{ao} (406.1). These three parameters showed also highest mean differences between ACO versus asthma and ACO versus COPD (p<0.01).

### Linear discriminant analysis (LDA)

Potentially discriminating parameters of airway dynamics (FEV_{1}, FVC/FEV_{1}, FEF_{25–75}, sWOB, sWOB_{in}, sWOB_{ex}, sR_{eff}, sR_{eff}
^{IN}, sR_{eff}
^{EX}, sR_{tot}, all parameters expressed as z-scores) were included into the model of an LDA, using the Canonical Discriminant Function tool of SPSS. The LDA based on all 10 parameters from which by stepwise exclusion 6 remained in the model is graphically represented in figure 4. The overall prediction accuracy was 84.7% (asthma: 81.2%, ACO: 81.3%, COPD: 94.7%). Based on Wilks’ lambda (Λ) test statistics six parameters sR_{tot} (Λ=0.185), sWOB_{ex} (Λ=0.163), sR_{eff} (Λ=0.152), sWOB_{in} (Λ=0.153), sR_{eff}
^{IN} (Λ=0.151) and sR_{eff}
^{EX} (Λ=0.146) discriminated between the three diagnostic classes.

## Discussion

The shape of the sR_{aw}-loop is quite complex and not a simple narrow oval loop, especially not in patients with obstructive lung diseases. Consequently, different investigators have used different portions of the loop to approximate a representative value for the entire breathing cycle. The effective specific resistance (sR_{eff}) and the total specific resistance (sR_{tot}), have been well established,31 45 46 although they have not received sufficient attention in the literature.

### The specific work of breathing assessed by plethysmography

A major step in the assessment of airway dynamics throughout the entire plethysmographic shift volume–tidal flow loop and its mathematical understanding of loop shaping was first elaborated by Matthys and Orth.31 They extended the dimensional analysis applied by Jaeger and Otis,47 to integrate these contributions to an ‘effective resistance’ that included the effects of the entire range of variable flows during tidal breathing and non-linearities of the breathing loop. The outstanding feature of these of airway dynamic parameters is their reflection of an integrative assessment of airway behaviour throughout the tidal breathing cycles. The digital integration of the respective loops improves the signal-to-noise ratio.

### Findings of the present study

Normative values of the airway dynamic parameters depend not only on anthropometric measures but also on parameters of the breathing pattern, timing of breathing and central control of breathing. This applies not only to sWOB and sR_{eff}, as previously reported,30 but also to the inspiratory and expiratory parts of the sR_{aw}-loop, which are indicated by sWOB_{in}, sWOB_{ex,} sR_{eff}
^{IN} and sR_{eff}
^{EX}. Therefore, we postulate, that the effort of breathing to move the lung, and hence the sWOB obtained by plethysmography allows an estimation of the gas dynamic, resistive effort integrating the needed plethysmographic shift volume over the tidal volume. In a constant volume whole-body plethysmograph, the shift volume refers to the size of the lung volume that decreases on compression and increases on decompression, and is proportional to the underpressure and overpressure in the lung and the absolute, ventilated and non-ventilated, lung volume. It follows that the specific gas-dynamic work performed during tidal breathing at rest can be estimated by simultaneously assessing the plethysmographic shift volume and the corresponding tidal volume. By this way, the sWOB can be considered as an approximation of the total gas-dynamic work, performed during a complete breathing cycle.

Most importantly, this kind of modelling has been shown to be predictive for a large age range from childhood to adulthood.35 In fact, the healthy human body has a wide range of regulatory mechanisms during normal breathing and can serve as a model to understand what the interactions would look like in patients with respiratory disorders. The pattern of breathing and airway resistance during exercise in terms of the relationships between inspiratory time (T_{I}), tidal volume (V_{T}) and EELV has been studied in detail by Hesser and Lind,48–50 showing the interrelationship between T_{I} and V_{T} at different ranges. This is important for assessing the overall understanding of how airway dynamics relate to the distending forces of the thoraco-pulmonary system, especially in diseased subjects with pulmonary hyperinflation, small airway dysfunction and/or pulmonary restriction. Apart from the advantage that airway dynamics can be assessed in close relation to these promoting factors of actual breathing, plethysmographic measurements offer the advantage that they can be performed during tidal breathing, requiring little cooperation from the subject and, therefore are effort independent. For such measurements, deep inspiration and forced breathing manoeuvres that influence the regional distribution of the air are not required, and such side effects can be avoided.

### Impact of the ageing pulmonary system on airway dynamics

Ageing is associated with the loss of lung elastic recoil and stiffening of the chest wall as well as decreased maximum respiratory pressure-generating capacity, airway calibre and expiratory flow rates during exercise.51–54 The mechanisms responsible for the elevated sWOB with age can be better understood, if sWOB is partitioned into the inspiratory (sWOB_{in}) and expiratory (sWOB_{ex}) part, related to age and other linked parameters. In fact, as shown in figure 2 both sWOB_{in} and sWOB_{ex} significantly increase with age curvilinearly (*F*=16.7; p<0.001), and additionally, sWOB_{ex} increases significantly more than sWOB_{in} (*F*=378.9; p<0.001). This finding confirms previous results obtained in exercise studies, that the smaller airways and greater mechanical constraints during exercise likely result in increased aerodynamic sWOB in the older compared with the younger adults.54 It is well documented that in normal subjects at rest, sWOB_{in} and sR_{eff}
^{IN} and hence the work needed against lung and chest wall inspiratory resistance is a minor component of the work of breathing.55 The effective resistance of the relaxed chest wall is caused by pressure–volume hysteresis measured as sR_{eff}
^{IN}. However, sR_{eff}
^{IN} is small at normal breathing rates,56 the diameter of the bronchi enlarges during inspiration and consequently sWOB_{in} is lower than sWOB_{ex}. As the regression analyses indicate, there are two other mechanisms, which must be considered. The ratio between V_{T} and FRC_{pleth} (ventilation in relation to the EELV) decreases dramatically in young age, remaining thereafter more or less stable. This parameter could play an important role in patients with obstructive lung disease, if the EELV is increased due to pulmonary hyperinflation, or trapped gases are present.

### Relevance to differentiate parameters of the inspiratory and expiratory parts separately

As it could be shown by the canonical discriminant analysis and based on Wilks’ lambda (Λ) test statistics, sR_{tot} presented with the most discriminative power followed by sWOB_{ex}, sR_{eff}, sWOB_{in}, sR_{eff}
^{IN} and sR_{eff}
^{EX} differentiating between the three diagnostic classes. Noteworthy, the three spirometric parameters FEV_{1}, FEV_{1}/FVC and FEF_{25–75} were excluded from the model. Furthermore, the rating list of discriminative lung function parameters revealed that the inclusion of aerodynamic parameters separating the inspiratory from the expiratory limb of the sR_{aw}-loop is highly recommended.

### Limits of the methods

In modern plethysmographs, the thermos-hygrometric artefact from inspiration to expiration is automatically corrected by algorithms however, different depending on the manufacturers. It is understandable that the details of these algorithms are not published or provided with the manuals of the plethysmograph. Therefore, it was essential to have in each centre the same plethysmograph and the same software. It follows that the reference equations are valid only for the Jaeger plethysmograph used to collect these data.

### Perspectives and clinical implications

We have recently demonstrated that parameters of airway dynamics are important diagnostic tools as target parameters both, the assessment of the bronchodilator response39 and the assessment of airway hyper-reactivity by methacholine challenge test in patients with asthma, ACO and COPD.57 Both test techniques are principally based on defining airway patency, and hence changes of airway dynamics during these test procedures. In so far, the specific aerodynamic work of breathing could well be a new reliable parameter to define specific disease endophenotypes.

The availability of normative reference equations applied over a wide age range are prerequisites for studies predicting disease progression in obstructive lung diseases. Not only spirometric parameters but also plethysmographic parameters of airway dynamics evaluated from the sR_{aw}-loop feature new insights into the physiopathology of these diseases. There is increasing interest in incorporating independent discriminatory parameters within new concepts of ‘artificial intelligence’,4 58 highlighting and comparing the various functional facets and the physiological complexity within obstructive lung diseases. Therefore, using extended sets of spirometric and plethysmographic parameters in a multivariate approach, can enable the identification of functional traits within the diagnosis of obstructive pulmonary diseases. A new option now is to use these normative equations for target parameters to differentiate the functional physiopathology between different airway diseases. Preliminary results of an ongoing study reveal that, if a whole set of holistically evaluated parameters of spirometry and plethysmography are well-tailored and introduced within a multidimensional perception, treatable trait strategies as new concepts towards precision medicine can be developed.7 10 It follows that the analysis of new parameters obtained by the plethysmographic sR_{aw}-loop may well discriminate between different obstructive lung diseases and their subtypes.

## Conclusions

There are many advantages using the plethysmographic parameters (sWOB, sR_{eff},) derived from the sR_{aw}-loop subdividing the inspiratory from the expiratory part of the breathing cycle. Our work presented here provides normative equations allowing the introduction of new parameters such as sWOB_{in}, sWOB_{ex}, sR_{eff}
^{IN} and sR_{eff}
^{EX}. Moreover, the study demonstrates that parameters of airway dynamics are highly inter-related with parameters of the central control of breathing such as P_{0.1}, V_{T}/T_{I} and T_{I}/T_{tot}. Depending on which functional characteristics are evaluated in the assessment of functional derangements in patients with obstructive lung diseases, we discovered that sWOB_{in}, sWOB_{ex}, sR_{eff}
^{IN} and sR_{eff}
^{EX} are new discriminating target parameters, which serve as a much better differentiation between chronic obstructive diseases and their subtypes, especially between COPD and ACO, improving the concept of precision medicine.

## Data availability statement

Data are available in a public, open access repository. Master files have been stored and secured in the Clinical Trial Unit, Hirslanden Corporate Office, CH-8152 Glattpark, Switzerland.

## Ethics statements

### Patient consent for publication

### Ethics approval

The study was planned according to the Federal Law of Human Research, conceptualised according to the Swiss Ethics Committees on research involving humans and was conducted in accordance with the tenets of the Declaration of Helsinki. The study is part of the framework of the project entitled “Functional diversification of the Asthma-ACO-COPD multi-centre study” (ID 2017-00259), approved by the Governmental Ethics Committees of the State of Bern, the State of St. Gallen and the State of Zürich (Project KEK-BE PB_2017-00104). Written informed consent was waived because of the retrospective study design, which follows the institutional and national policies concerning research approvals. Participants gave informed consent to participate in the study before taking part.

## Acknowledgments

We thank all the participants, the parents of children and the technicians who volunteered for lung function testing diligently devoting their time.

## References

## Footnotes

Contributors RK designed, coordinated and anaysed the data, drafted the manuscript, and acted as guarantor of this publication. H-JS and JR gave advice on the technical parts of the data acquisition and took part in the interpretation of data and revising. SG and HM edited and revised the manuscript. All authors approved the final version of the manuscript.

Funding The data collection was part of various research projects, supported by grants of the Swiss National Science Foundation (SNF 32-040681.94 and SNF 32-040562.95).

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.