Abstract
Background The multiple breath nitrogen washout (N2MBW) technique is increasingly used to assess the degree of ventilation inhomogeneity in school-aged children with lung disease. However, reference values for healthy children are currently not available. The aim of this study was to generate reference values for N2MBW outcomes in a cohort of healthy Caucasian school-aged children.
Methods N2MBW data from healthy Caucasian school-age children between 6 and 18 years old were collected from four experienced centres. Measurements were performed using an ultrasonic flowmeter (Exhalyzer D, Eco Medics AG, Duernten, Switzerland) and were analysed with commercial software (Spiroware version 3.2.1, Eco Medics AG). Normative values and upper limits of normal (ULN) were generated for lung clearance index (LCI) at 2.5% (LCI2.5%) and at 5% (LCI5%) of the initial nitrogen concentration and for moment ratios (M1/M0 and M2/M0). A prediction equation was generated for functional residual capacity (FRC).
Results Analysis used 485 trials from 180 healthy Caucasian children aged from 6 to 18 years old. While LCI increased with age, this increase was negligible (0.04 units·year–1 for LCI2.5%) and therefore fixed ULN were defined for this age group. These limits were 7.91 for LCI2.5%, 5.73 for LCI5%, 1.75 for M1/M0 and 6.15 for M2/M0, respectively. Height and weight were found to be independent predictors of FRC.
Conclusion We report reference values for N2MBW outcomes measured on a commercially available ultrasonic flowmeter device (Exhalyzer D, Eco Medics AG) in healthy school-aged children to allow accurate interpretation of ventilation distribution outcomes and FRC in children with lung disease.
Abstract
This study provides reference values for nitrogen multiple breath washout outcomes in healthy Caucasian children from 6 to 18 years old, measured with a commercially available device http://bit.ly/2rsab8v
Introduction
The multiple breath washout (MBW) test provides lung volume and ventilation distribution outcomes that are more sensitive than conventional lung function outcomes for detecting lung disease in children with cystic fibrosis (CF) [1–3] and potentially other respiratory disorders [4, 5]. The lung clearance index (LCI) is a global marker of ventilation distribution derived from the MBW test that is reproducible [6, 7], discriminates between health and disease [3], and correlates with the extent of structural lung disease [2, 8] in children with CF. These data have led to LCI being used as the primary endpoint in observational studies and interventional trials in patients with CF [9–11]. It may potentially also be a future factor for the clinical surveillance of children with CF [12].
LCI is traditionally calculated as the number of lung turnovers required to wash out a tracer gas to one fortieth (1/40th) of its starting concentration (LCI2.5%) [13]. The number of lung turnovers required to wash out a tracer gas to one twentieth (1/20th) of its initial concentration (LCI5%) has also been calculated [14, 15]. Moment ratios (M1/M0 and M2/M0) describe the degree of skewness of the washout curve and may be more sensitive for detecting ventilation inhomogeneity in the periphery of the lung [2, 15]. In addition, the MBW test measures the functional residual capacity (FRC) of the lung, which may indicate some degree of lung hyperinflation [1].
Despite the increasing use of MBW, reference values for MBW outcomes in children are scarce [16, 17]. Several commercially available and custom-made MBW devices are currently in use but outcomes are generally not interchangeable between them [18–20]. In addition, different software versions and system settings can influence the calculation of MBW outcomes [21, 22]. As a result, published normative values are only applicable for the specific device, software and tracer gas used [17, 21, 23]. For this reason, studies evaluating MBW outcomes using commercially available equipment have required data collection in age-matched healthy controls [2, 12].
The aim of our study was to provide normative values for multiple breath nitrogen washout (N2MBW) outcomes (LCI, moment ratios and FRC) in school-aged children, as measured with a commercially available device (the Exhalyzer D ultrasonic flowmeter (Eco Medics AG, Duernten, Switzerland)) and to investigate the association of MBW outcomes with anthropometric and physiological parameters, including tidal volume (VT) and equipment-related dead space volume (VD) [24, 25]. To achieve this, we collected data from healthy children in four different centres using the same MBW device and protocol, and analysed these data using the same software and system settings.
Methods
Study subjects
For this study we used MBW measurements from healthy school-aged Caucasian children collected between 2011 and 2016 from four paediatric centres specialised in N2MBW, including Inselspital (Bern, Switzerland), SickKids (Toronto, Canada), University Children's Hospital (Heidelberg, Germany) and Telethon Kids Institute (Perth, Australia). We used the following exclusion criteria: non-Caucasians, age less than 6 years or greater than 18 years, chronic respiratory or cardiac disease, respiratory infection during the last 4 weeks prior to measurement and other major systemic diseases with potential influence on lung function [26]. Healthy individuals from each centre took part in prospective observational studies and therefore some data have been published previously [2, 8, 12, 18, 27, 28]. The study was approved by local ethics committees (Ethics Committee of the Canton of Bern, Bern, Switzerland; Research Ethics Board at SickKids, Toronto, Canada; Ethics Committee of the University of Heidelberg, Heidelberg, Germany; Princess Margaret Hospital Human Ethics Committee, Perth, Australia). Parents or caregivers provided informed written consent.
MBW measurements
We performed N2MBW measurements using an ultrasonic flowmeter (Exhalyzer D, Eco Medics AG, Duernten, Switzerland) and the software provided by the manufacturer (Spiroware version 3.1.6, Eco Medics AG) as previously described [12, 18]. All centres used the same equipment and performed the calibration and measurement using the same protocol (details are available in the supplementary material).
MBW analysis
N2MBW data originally recorded in Spiroware version 3.1.6 were reloaded and analysed using the updated version of the software provided by the manufacturer at the time the study was performed (Spiroware version 3.2.1, Eco Medics AG). Further details are provided in the supplementary material.
MBW outcomes and physiological indices
FRC, LCI2.5%, LCI5% and moment ratios (M1/M0 and M2/M0) were calculated according to current recommendations [13, 14]. The mean VT for each trial was provided by the software. In order to investigate the effect of breathing pattern [29] and equipment-related VD [30] on MBW outcomes, the mean ratio of VT to FRC (VT/FRC) and VD to VT (VD/VT) were calculated per trial (expressed as percentages).
Quality control of MBW trials
Quality control of MBW trials was assessed by an experienced operator at the Inselspital, Bern (P. Anagnostopoulou) according to the 2013 American Thoracic Society (ATS)/European Respiratory Society (ERS) MBW consensus guidelines [13] and additional criteria proposed by Jensen et al. [31]. Further details are provided in the supplementary material. Tests with at least two technically acceptable trials with FRC values within 25% of the mean were included for analysis.
Statistics
Statistical analysis of the data was performed using R version 3.4.3 (The R Project for Statistical Computing, www.r-project.org) and Stata statistical software, release 14 (StataCorp LP, College Station, TX, USA). Graphs were generated using GraphPad Prism version 5.0 (GraphPad Software, San Diego, CA, USA). Normality of data distribution was assessed visually and using the Shapiro–Wilk test. The upper and lower limits of normal (ULN and LLN, respectively) correspond to the 97.5th percentile (mean±1.96SD), as previously described [17]. The intra-subject variability was defined as the mean relative difference for subjects with two trials ((trial1–trial2)/mean (%)) or the coefficient of variation (CV; sd/mean (%)) for those with at least three trials. We used one-way ANOVA with a Bonferroni post-hoc test for between-centre comparisons. Multiple linear regression was performed to assess the associations of normally distributed primary outcomes (LCI2.5%, LCI5%, M1/M0, M2/M0 and FRC) with demographic factors (age, weight, height and sex) and physiological factors (VT/FRC and VD/VT). All of the variables considered were selected for biological reasons. Multicollinearity between independent variables was assessed by checking the variance inflation factor and was not present in the final models. The homoscedasticity and normality of residuals for the models were assessed using White's test and the Shapiro–Wilk test, respectively. In the final models, only significant factors were considered to facilitate easy application of the reference equations. The statistical significance level was set to p<0.05.
Results
Study subjects
N2MBW data from 285 healthy children were assessed for eligibility. Of these, 67 did not fulfil the inclusion criteria and 38 did not pass the quality control (success rate: 82.6%) (figure 1). Thus, 485 MBW trials from 180 children were used for analysis: 82 children from the University Children's Hospital in Bern, Switzerland (mean age: 10.8 years; range: 6.0–17.8 years), 33 children from the University Children's Hospital Heidelberg, Germany (mean age: 12.0 years; range: 7.1–17.0 years), 28 children from the SickKids in Toronto, Canada (mean age: 12.5 years; range: 6.5–17.2 years) and 37 children from the Telethon Kids Institute, Perth, Australia (mean age: 9.5 years; range: 6.0–13.6 years). Of the 180 study participants, 38.9% had two acceptable MBW trials, 53.9% had three acceptable trials and 7.2% had four or more acceptable trials.
Flow chart of healthy subjects who participated in the study
Centre differences
Study demographics and anthropometrics for each centre are reported in table 1. Participants from Perth were significantly younger compared with those from Toronto and Heidelberg (p<0.001) (supplementary figure S1). Participants from Toronto had higher height z-scores compared with those from Heidelberg (p=0.02) but there were no differences in weight z-score between centres. There were no differences in LCI or moment ratio outcomes between centres; however, there were significant differences in FRC, whereby children from Toronto had higher FRC values compared to children from Perth (p=0.02) (supplementary figure S1).
Demographic and anthropometric characteristics of the study population per centre
Lung clearance index and moment ratios
In univariable regression analyses, LCI2.5% was negatively associated with height (coefficient −0.005, 95% CI −0.0087 to −0.0019; p=0.002), weight (coefficient −0.0049, 95% CI −0.0090 to −0.0008; p=0.020) and age (coefficient −0.0225, 95% CI −0.0421 to −0.0030; p=0.024), and positively associated with VD/VT (coefficient 0.0258, 95% CI 0.0093–0.0422; p=0.002). No association was found between LCI2.5% and sex or VT/FRC (p>0.05).
Similarly, LCI5% and moment ratio outcomes were negatively associated with height, weight and age (supplementary table S1). In addition, VD/VT and VT/FRC were both negatively associated with age (supplementary figure S2), while LCI5% and M1/M0 were positively associated with VD/VT and VT/FRC (supplementary table S1 and supplementary figure S2). There was no evidence for association of LCI5% and moment ratios with sex (p>0.05) (supplementary table S1).
In a multivariable regression model age, VD/VT and VT/FRC were independently associated with LCI and moment ratio outcomes (supplementary table S2). However, the regression coefficients for each of these independent predictors were small (r≤0.10). For LCI2.5% the coefficient for age was 0.04, indicating that, after adjusting for VD/VT and VT/FRC, LCI2.5% would increase by 0.04 units·year–1 (95% CI 0.01–0.07). This equates to a maximum 0.5 unit change in LCI2.5% over 12 years (from 6 years to 18 years of age). Thus, in our judgement, the age related changes in LCI and moment ratios are negligible. Therefore, we report fixed ULN during this age interval (figure 2).
Relationship between age and lung clearance index (LCI) at (a) 2.5% (LCI2.5%), (b) 5% (LCI5%), (c) moment ratio one (M1/M0) and (d) moment ratio two (M2/M0). Dashed lines indicate the upper limit of normal (ULN)
The mean LLN and ULN for LCI and moment ratios are provided in table 2. The ULN for LCI2.5% was 7.91 and for LCI5% was 5.73. For M1/M0 the ULN was 1.75 and for M2/M0 was 6.15 (figure 2).
Variability in multiple breath nitrogen washout (N2MBW) outcomes from 180 healthy children
Functional residual capacity
FRC values were right skewed and therefore natural log (ln) transformed FRC values and predictors were used in the models. Ln(FRC) was positively associated with ln(height), ln(weight), ln(VT) and ln(age), and negatively associated with ln(VD/VT) in the univariate analysis (figure 3 and supplementary table 1). There was no evidence of association with sex (p=0.68) (supplementary table 1). In a multivariable model, ln(FRC) was independently associated with ln(height) and ln(weight). There were no differences in FRC z-scores between centres (p=0.81). The full FRC prediction equation, including the intercept for the model, is provided in the supplementary material. See also equations 1 and 2 (where FRC is expressed in L, height in cm and weight in kg).
Relationship between functional residual capacity (FRC) and height expressed (a) linearly or (b) as natural logarithms
1
2Discussion
Normative N2MBW data for a paediatric Caucasian population between the ages of 6 and 18 years is reported using commercially available equipment and software. While a significant age dependency is observed for LCI and moment ratio outcomes, the magnitude of this effect is small during the age interval of 12 years. Therefore, fixed ULN for LCI and moment ratios can be used in this age group. FRC was independently predicted by both height and weight, and FRC predicted values and a z-score equation are provided.
Comparison with the literature
The LCI2.5% values in our cohort are slightly higher than previously reported for this age group [16, 17, 24]. However, it is difficult to perform a direct comparison with previous studies due to differences in equipment and software algorithms. Lum et al. [17] reported an ULN for LCI2.5% of 7.53 in healthy children who performed MBW measurements using a mass spectrometer with sulfur hexafluoride (SF6) as the tracer gas. However, several studies have reported higher LCI2.5% values in N2MBW compared with SF6 MBW [18, 33, 34]. These differences may be explained by different distribution of a resident gas compared with an exogenous gas in the lung tissue and/or by the contribution of nitrogen diffusion from the lung tissue during the washout [18, 20]. Fuchs et al. [16] reported an ULN for LCI2.5% of 7.0 in healthy children who performed SF6 MBW using a device which utilises a similar ultrasonic flowmeter measurement principle. Recent data show that this device provides lower LCI2.5% values compared to the device used in our study [19, 35]. In addition, Houltz et al. [24] reported an ULN for LCI2.5% of 7.09 in healthy children who performed N2MBW using the same equipment used in our study; however, they analysed their data using custom-made software and as such a direct comparison between the two datasets is not possible. These findings highlight the need for equipment and software specific normative data for MBW data.
Centre differences
Slight differences in height and weight between centres were not surprising because the age distribution of the study participants differed between these centres. Previous multi-centre studies have reported differences in MBW indices using the same measuring equipment and protocol [17, 36]. In order to minimise the risk for between-centre differences, the analysis was performed using the same software version, system settings and equipment-related VD. We did not find any differences in LCI and moment ratio outcomes between centres. Small but statistically significant differences were found in FRC between centres; however, FRC z-scores were not different between centres. Therefore we hypothesise that any differences in FRC between centres were simply due to differences in demographic data.
Predictors of MBW outcomes
Previous studies have reported that LCI2.5% is age dependent from birth to adulthood [17, 25]; however, it is unclear whether this age dependence continues during school age and adolescence. Lum et al. [17] reported that despite a small continuation of decline in LCI2.5% throughout the entire paediatric range, changes were minimal once the child reached 6 years of age. The authors reported that fixed ULN could be used for LCI2.5% between the ages of 6 and 19 years. We report small but statistically significant associations between age and LCI as well as between age and moment ratios. However, the changes in LCI and moment ratios with age during the school age period are minimal, as shown in figure 2. Therefore we believe that ULN for LCI and moment ratios are appropriate throughout this age range.
We found that both height and weight independently predicted FRC in our cohort. This contrasts with data from Lum et al. [17], who found that height, age and sex were independent predictors of FRC in their cohort. It should be noted, however, that the age range of their population extended from infancy to adolescence. During the period from early childhood to adolescence, pubertal changes can result in higher variability in weight with increasing height. Therefore, pubertal changes during this period likely influenced the association between FRC and weight, which appears to be independent of sex. Only seven out of 180 subjects in our study were classified as obese (body mass index (BMI) z-scores ≥2) and, therefore, it is possible that predicted FRC values could be underestimated in obese individuals.
Technical and physiological factors
In order to further understand the technical and physiological factors that may influence ventilation distribution outcome during childhood and adolescence, we investigated whether equipment-related VD and breathing pattern influenced results. It has been reported previously that VD/VT decreases with age during childhood [37] and that VD/VT is positively associated with LCI outcomes [30]. We also found that VD/VT was positively associated with LCI and moment ratio outcomes. Users can ensure that the effect of VD on MBW outcomes is minimised by using the appropriate VD reducers recommended by the manufacturer. In addition, manufacturers of MBW devices should aim to further reduce equipment-related VD to avoid over-estimation of ventilation distribution outcomes in young children [13].
It has previously been shown that breathing pattern can influence MBW outcomes. Fixed 1 L breathing protocols highly influenced LCI outcomes in children compared with relaxed breathing [29]. We found that the VT/FRC ratio was negatively associated with age and positively associated with ventilation distribution outcomes. These findings indicate that any small age dependence of ventilation distribution outcomes in our study were likely to be influenced by age dependent changes in VD/VT and VT/FRC during the period from early childhood to adolescence.
Strengths and limitations
This study comprises the largest sample of healthy Caucasian children collected using a commercially available MBW device. All four centres have extensive experience in MBW testing and collected MBW data using the same equipment and measurement protocol. In addition, all the measurements were quality controlled and analysed using the same software version, equipment VD and system settings. However, the sample size is still relatively small for the generation of reference values. In addition, the quality control has been performed by a single operator, which could have produced a bias in the study. We did not report phase III slope indices as these estimates require additional breath-by-breath quality control, which is not yet standardised. Furthermore, we did not include preschool children in this cohort for several reasons, including the limited number of preschool visits available in our data set, lack of preschool MBW standardisation at the time of measurement and differences in interfaces used for testing at this age (i.e. facemask versus mouthpiece).
Recommendations for the use of reference values
MBW outcomes are considered to be device and tracer gas specific [18–20, 38] and these reference values have been generated from N2MBW data collected on the Eco Medics Exhalyzer D device. It is possible that the ULN we report for LCI and moment ratios are appropriate for other MBW systems but further work is needed to address this. Our data were analysed using Spiroware version 3.2.1, which uses different signal processing algorithms compared with previous versions (as described in the supplementary material) and which has a known influence on outcomes [21, 38]. Ideally, the most robust approach for Spiroware users would be to reload their raw data (A-files) collected in previous software versions into the new software version in order to re-analyse them. If this is not feasible, the users should recruit age-matched healthy controls at their own centre. In addition, as we only included subjects of Caucasian origin and it is unclear whether MBW indices differ with ethnicity at this age range [17, 39], we cannot generalise these data to other ethnic groups. Future studies that include children of other ethnicities are needed.
Conclusion
This study provides reference values for N2MBW outcomes in Caucasian school aged children measured on the commercially available Eco Medics AG ultrasonic flowmeter device. Definition of the ULN over a wide age range will allow appropriate interpretation of MBW outcomes in the paediatric clinical setting.
Supplementary material
Supplementary Material
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Supplementary material ERJ-01302-2019.SUPPLEMENT
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Acknowledgements
The authors appreciate the contributions of Sandra Lüscher, Monika Graf, Linda Beul, Gisela Wirz, Sharon Krattinger, Nadja Kranz, Marisa Guidi and Insa Korten (Division of Respiratory Medicine, Dept of Pediatrics, Inselspital and University of Bern, Bern, Switzerland), of Cornelia Joachim (Division of Pediatric Pulmonology and Allergy and Cystic Fibrosis Center, Dept of Pediatrics, University of Heidelberg, Heidelberg, Germany) and of Caroline McGirr, Jasmine Grdosic and Chloe Batchelor (Telethon Kids Institute, Subiaco, Australia) to data collection. They also would like to thank the children and their families that participated in the study.
Footnotes
After publication of this study, an error in the cross-sensitivity correction of the oxygen and carbon dioxide sensors in the Exhalyzer D device has been identified that leads to an overestimation of multiple breath washout outcomes. Please refer to the correction notice published in the August 2022 issue of the European Respiratory Journal for further details.
This article has an editorial commentary: https://doi.org/10.1183/13993003.00485-2020.
This article has supplementary material available from erj.ersjournals.com
Conflict of interest: P. Anagnostopoulou has nothing to disclose.
Conflict of interest: P. Latzin reports personal fees from Gilead, Novartis, Polyphor, Santhera, Schwabe, Vertex, Vifor and Zambon, as well as grants from Vertex, outside the submitted work.
Conflict of interest: R. Jensen has nothing to disclose.
Conflict of interest: M. Stahl reports personal fees from Vertex Pharmaceuticals, outside the submitted work.
Conflict of interest: A. Harper has nothing to disclose.
Conflict of interest: S. Yammine has nothing to disclose.
Conflict of interest: J. Usemann has nothing to disclose.
Conflict of interest: R.E. Foong has nothing to disclose.
Conflict of interest: B. Spycher has nothing to disclose.
Conflict of interest: G.L. Hall reports grants from the NHMRC and the Australia and USA CF Foundation, and non-financial support (loan of equipment) from ndd, during the conduct of the study.
Conflict of interest: F. Singer reports personal fees from Vertex and Novartis, outside the submitted work.
Conflict of interest: S. Stanojevic has nothing to disclose.
Conflict of interest: M.A. Mall reports grants from the German Federal Ministry of Education and Research (82DZL004A1) and the Einstein Foundation Berlin (EP-2017-393), during the conduct of the study, and personal fees advisory board work, consultancy and lectures from Boehringer Ingelheim and Vertex Pharmaceuticals, outside the submitted work.
Conflict of interest: F. Ratjen reports grants and personal fees for consultancy from Vertex, plus personal fees for consultancy from Novartis, Bayer, Roche and Genetech, outside the submitted work.
Conflict of interest: K.A. Ramsey has nothing to disclose.
Support statement: This work was funded by the Fondation Botnar, the Swiss National Foundation (grant 324730_144280/1), the Swiss Cystic Fibrosis Society (CHCF) NHMRC (AP1020555), the Cystic Fibrosis Foundation, and the Cystic Fibrosis Foundation Therapeutics (HALL14A0), the German Ministry for Education and Research (82DZL00401, 82DZL004A1 to M.A. Mall), and the German Cystic Fibrosis Association Mukoviszidose e.V. (grant 15/01 to M. Stahl). K.A. Ramsey is supported by a grant from the Swiss National Science Foundation (18-061). M.A. Mall is supported by a grant from the Einstein Foundation Berlin (EP-2017-393). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funding information for this article has been deposited with the Crossref Funder Registry.
- Received July 2, 2019.
- Accepted December 10, 2019.
- Copyright ©ERS 2020
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