Comparing Multiple Breath Washout Parameters in a Matched Cohort of Children With Cystic Fibrosis and Primary Ciliary Dyskinesia

ABSTRACT

Background

Cystic Fibrosis (CF) and primary ciliary dyskinesia (PCD) are congenital disorders characterized by impaired mucociliary clearance, resulting in chronic airway infection, inflammation, and progressive lung damage. CF lung disease involves defective CFTR function throughout the entire airway epithelium, whereas PCD originates from dysfunction of motile respiratory cilia located predominantly in proximal airways. Diagnostic tools such as FEV₁ lack sensitivity to detect early peripheral lung damage. Lung clearance index (LCI), obtained via multiple breath washout (MBW), has emerged as a promising marker for early detection of lung abnormalities.

Objectives

To compare LCI between age and FEV₁‐matched people with (pw) CF and PCD (without highly effective CFTR modulator treatment), hypothesizing more abnormal LCI in CF due to more peripheral airway involvement. Secondary objectives included comparing phase III slope parameters (S_cond x VT, S_acin x VT) to explore ventilation inhomogeneity and localization of lung disease.

Methods

This retrospective cross‐sectional study included pwCF and pwPCD aged 6–20 years with available MBW and spirometry data (2011–2024).

Results

PwCF (n23, median age 14.4 y) had similar FEV₁ z‐scores compared to 23 pwPCD (median age 14.4 y) (−0.1 vs. –1.1, p: 0.33). LCI values were more abnormal in the CF group compared to the PCD group (7.8 vs. 7.1; p: 0.04). FEF_25‐75%pred and phase III slope parameters were not significantly different, though a trend toward higher S_cond x VT in CF was noted (0.08 vs. 0.06; p: 0.11).

Conclusion

Despite similar FEV₁ abnormalities, reflecting proximal airway impairment, pwCF exhibit greater peripheral airway involvement than pwPCD, as indicated by higher LCI values.

1. Introduction

Cystic fibrosis (CF) and primary ciliary dyskinesia (PCD) are hereditary diseases characterized by progressive airway disease together with other systemic problems. In both CF and PCD mucociliary clearance is impaired leading to chronic endobronchial infection and inflammation, contributing to progressive obstructive lung disease and bronchiectasis [1]. This similarity is the main reason why treatments for PCD are largely modeled on those developed for CF [2]. Nevertheless, there are critical differences in underlying mechanisms in these conditions. In PCD, dysfunction of the motile respiratory cilia arises from genetically determined abnormalities in ciliary protein structure and function, leading to a significant impairment of the ciliary beating that is crucial for effective mucociliary clearance [3]. In CF, mutations in the CFTR gene, encoding a chloride and bicarbonate‐conducting transmembrane channel known as the cystic fibrosis transmembrane conductance regulator (CFTR) [4] cause disturbed ion and water transport across the respiratory epithelia with airway surface liquid dehydration and thickened mucus. Since cilia are primarily located in the more proximal airways [5], PCD is in theory a more proximal airway disease whereas in CF, the affected CFTR ion channel is expressed throughout the entire respiratory tract [6, 7]. However, airway disease distribution in CF and PCD is increasingly recognized as complex. Recent imaging studies have demonstrated distal airway involvement in PCD, including mucus plugging and tree‐in‐bud opacities [8, 9]. These observations suggest that, both diseases involve the distal airways, but differences in the extent and distribution of airway remodeling may exist between CF and PCD. Support for this concept comes from recent work from our group, in which explanted lungs from individuals with CF, PCD and control donors were studied at the time of lung transplantation using whole‐lung high‐resolution computed tomography (HRCT), micro‐CT, and paired histology [10]. Although PCD lungs exhibited severe bronchiectasis reflected by an increased airway to total lung volume (AV%) ratio, airway dilatation was predominantly confined to more proximal airway generations. In contrast, CF lungs showed more pronounced airway dilatation extending into the peripheral airway generations. These structural differences may contribute to differences in ventilation inhomogeneity between CF and PCD. Structural lung damage can be assessed using HRCT, which may reveal findings such as bronchiectasis, atelectasis, mucus plugging, bronchiolitis, and air trapping [11]. HRCT remains the gold standard for diagnosing bronchiectasis also in the context of CF and PCD, but it is not suitable for routine follow‐up due to the high radiation burden associated with repeated use [12]. For a long time, spirometry with the measurement of Forced Expiratory Volume in 1 second (FEV₁) was considered the gold standard for the follow‐up of lung function in pwCF and pwPCD. Although PCD was once considered a less severe lung disease compared to CF (in the pre‐modulator era), its impact on lung function can also be evident from an early age [13, 14]. Growing evidence suggests that FEV₁ may be insensitive to early functional lung impairment and structural airway changes [15, 16, 17] because FEV₁ primarily assesses central airway obstruction. Conversely, during the early years of PCD and CF, damage predominantly affects the smaller distal airways, leading to functional impairment of peripheral ventilation despite more preserved central airway function [15]. Lung Clearance Index (LCI), obtained through nitrogen multiple breath washout (MBW), has been studied as a more effective marker for detecting lung function decline in early disease. LCI is more frequently abnormal in pwCF and pwPCD compared to FEV₁ and is more sensitive in identifying patients with abnormal HRCT findings, particularly in the early stages of the disease [16]. MBW also offers additional advantages, as it is not effort‐dependent and is suitable for preschool aged children. LCI has thus proven to be a valuable follow up parameter of ventilation inhomogeneity (VI) for progressive lung disease in pwCF and pwPCD [18, 19, 20]. While LCI indicates that VI is present, it does not reveal where in the airway tree the problem originates. To gain insight into the location of airway involvement, the phase‐III slope parameters S_cond and S_acin can be used. These parameters are derived from the shape of the alveolar plateau during MBW. When ventilation is homogeneous, this plateau is almost flat; when different lung regions empty unevenly, it becomes tilted. The degree and pattern of this tilt provide information about the underlying site of VI. S_cond reflects inhomogeneity arising in the conducting airways, where gas transport is driven by convection. S_acin reflects inhomogeneity located in the acinar region, where diffusion–convection interaction dominates gas mixing [21]. In CF, S_cond tends to become abnormal before LCI rises [21]. Limited evidence suggests that CF lung disease originates in the conducting airways, implying that VI may arise from problems with convection [22]. Previous research from our group identified a marked loss of terminal bronchioles in end‐stage CF lungs, highlighting the role of small airway remodeling in disease progression. These findings underscore the significance of small airway disease and suggest that bronchiectasis may arise following small airway occlusion [23]. This study aimed to compare LCI between matched pwCF and pwPCD. Secondary aims included assessing differences in phase III slope parameters (S_cond and S_acin) and the spirometry derived parameter Forced Expiratory Flow 25%–75% (FEF_25–75%). We also included the peripheral flow measurement FEF_25–75% in our analysis since this parameter may be more sensitive than FEV₁ for detecting early lung function changes in small airways in CF [24]. We hypothesize that PCD primarily affects more proximal airways, whereas CF involves more distal regions, leading to higher LCI values in CF despite similar FEV₁ z‐scores. We further hypothesized that S_cond would be more impaired in CF due to greater distal airway involvement.

2. Materials and Methods

2.1. Study Design

We performed a single‐center, retrospective cross‐sectional study at the Pediatric Pulmonology Department of the University Hospital of Leuven (Belgium). Ethical approval was granted by the local ethics committee (S51800), and all participants provided written informed consent or assent.

2.2. Study Population

Participants were retrospectively identified from routine clinical follow‐up records between 2011 and 2024. The diagnosis of CF was based on abnormal sweat chloride levels (> 60 mmol/L) and the presence of two CF‐causing CFTR mutations. The diagnosis of PCD required confirmation of abnormal ciliary beating evaluated after ciliogenesis in culture, and all pwPCD also had a genetically confirmed diagnosis [25]. Initially, we identified a cohort of pediatric pwPCD who underwent MBW testing. From this cohort, we selected those whose test results met predefined criteria for acceptable quality [26]. PwCF were recruited from the same institution. Each eligible pwCF was matched to a pwPCD based on age and FEV₁ z‐score. No formal sample size calculation was performed; all eligible matched patients were included to maximize the study population within the strict inclusion criteria.

Inclusion criteria for both groups included an age between 6 and 20 years and a clinically stable condition at the time of testing. Exclusion criteria for both groups included pulmonary exacerbation requiring antibiotic therapy within 1 month prior to testing. PwCF treated with highly effective modulator therapy (HEMT) were excluded, including those with a gating mutation treated with ivacaftor; however, patients receiving ivacaftor‐lumacaftor or ivacaftor‐tezacaftor remained eligible. An overview of the patient selection criteria is shown in Figure 1.

FIGURE 1.

Flow chart of patient selection criteria. This figure shows the patient selection for pwCF and pwPCD, from initial screening to final inclusion. Only pediatric patients with valid MBW and spirometry, no exacerbations, and no other exclusions were retained. Eventually, 23 matched pairs (one pwCF and one pwPCD) were selected based on age and FEV₁ z‐score. pwCF: people with CF, pwPCD: people with PCD, MBW: multiple breath washout, ETI: elexacaftor–tezacaftor–ivacaftor, FEV₁: forced expiratory volume in 1 s.

2.3. Measurements

Nitrogen MBW testing was performed by trained technicians using the ECO MEDICS AG EXHALYZER D device according to standard operating procedures. Data were collected using Spiroware 3.3 software, and raw data from measurements performed before the availability of Spiroware 3.3 were reanalyzed. MBW outcome parameters collected included FRC, FRC CV%, LCI_2.5%, LCI_2.5% z‐score, LCI_2.5% CV%, S_cond x VT, and S_acin x VT. For LCI values, a CV% of up to 5% was allowed for inclusion. LCI values were expressed as z‐scores based on Global Lung Initiative (GLI) reference equations to allow standardized interpretation relative to the normal population distribution. Reference values for FRC and LCI are provided in Table S1 and are based on the GLI guidelines [27]. LCI was calculated at the 2.5% washout endpoint (LCI₂.₅), defined as the cumulative expired volume required to reduce tracer gas concentration to 2.5% of its starting value. This endpoint reflects later phases of washout and has been shown to provide greater sensitivity to ventilation inhomogeneity than a 5% washout endpoint (LCI₅), which has been proposed as a shorter but less sensitive alternative [28].

MBW values were averaged from acceptable trials, with quality ensured by curve inspection and verification of within‐session repeatability. Spirometry measurements were performed by trained technicians according to ATS/ERS guidelines [29]. For each patient, the spirometry test performed closest in time to the MBW test was selected. For each patient, FEV1%pred, FEV₁ z‐score, FEF_25‐75% values were collected. All z‐scores were obtained using the Global Lung Function Initiative calculator [30]. No sensitivity or subgroup analyses were performed.

2.4. Statistical Analysis

Matching of the groups was based on age and FEV₁ z‐score. Normality of the differences between the two groups was assessed using the Shapiro‐Wilk or Kolmogorov‐Smirnov test prior to analysis. For baseline parameters FEV₁ and age, a paired t‐test and Wilcoxon signed ranks test were used. For normally distributed outcome parameters, an unpaired t‐test was used; if non‐normally distributed, the Mann Whitney test was applied. These approaches are equivalent to linear models incorporating a fixed group effect and within‐pair correlation. There was no missing data of the primary and secondary outcome parameters. Statistical analyses were conducted using SPSS software (version 29.0.2.0).

3. Results

3.1. Subjects

The final analytic cohort included 23 pairs of a pwCF, matched with pwPCD (Figure 1). Baseline characteristics are provided in Table 1. As anticipated, age (p: 0.08) and FEV₁%pred (p: 0.28), as well as FEV₁ z‐score (p: 0.33), were not significantly different between the PCD and CF groups. FEF_25‐75%pred was also not significantly different in both groups (p: 0.56).

TABLE 1.

Clinical and genetic characteristics of pwCF and pwPCD.

	pwCF (n 23)	pwPCD (n 23)
Sex Male (n, %)	7 (30%)	11 (48%)
Ethnicity Caucasian (n, %)	23 (100%)	22 (96%)
Diagnosis
Neonatal screening (n, %)	9 (39%)
Suggestive clinical symptoms (n, %)	14 (61%)	23 (100%)
Age at diagnosis (y)	0.2 (0–4)	4.1 (0–12)
Age at MBW (y)	14.36 (9.02–20.45)	14.29 (8.71–20.25)
Mutation type
Homozygous DF508 (n, %)	18 (78%)
Heterozygous with DF508 (n, %)	3 (13%)
Heterozygous without DF508 (n, %)	2 (9%)
DNAH5 (n, %)		9 (39%)
DNAH11 (n, %)		4 (17%)
DNAAF1 (n, %)		3 (13%)
HYDIN (n, %)		2 (9%)
CCDC103 (n, %)		1 (4%)
DNAI1 (n, %)		1 (4%)
RSPH9 (n, %)		1 (4%)
RSPH4A (n, %)		1 (4%)
CCDC40 (n, %)		1 (4%)
Modulator treatment	12 (52%)

Abbreviations: pwCF, people with CF; pwPCD, people with PCD; n, number; MBW, multiple breath washout.

3.2. MBW Parameters in CF Versus PCD

LCI_2.5% values were more abnormal in pwCF (7.80, range 6.06–15.35), compared to pwPCD (7.12, range 5.69–9.08, p: 0.04, Table 2 and Figure 2), also when expressed as LCI z‐scores (p: 0.02). According to the GLI guidelines [27], our results translate into an abnormal LCI_2.5% value (above the ULN 1.96 z‐score) for 14/23 patients in our CF group compared to 11/23 patients in our PCD group. For the phase III slope parameters, no significant difference was seen in S_cond nor S_acin between the two groups (p: 0.08 and p: 0.19, respectively), although there was a trend for higher S_cond values in CF. Moreover, there was a high Coefficient of Variation (CV%) for these parameters in both groups. For S_cond, the median CV% was 23.1% in the PCD group and 8.4% in the CF group; for S_acin, it was 19.3% and 17.2%, respectively.

TABLE 2.

Spirometry and multiple breath washout parameters of pwCF versus pwPCD.

	pwCF (n 23)	pwPCD (n 23)	pwCF versus pwPCD
Age at MBW (y)	14.36 (9.02–20.45)	14.44 (8.71–20.25)	p: 0.08
FEV1 (% pred)	93% (64–110)	89% (71–115)	p: 0.28
FEV1 (z‐score)	−0.09 ((−3.31) to 4.42)	−1.10 ((−2.73) to 1.20)	p: 0.33
FEF25‐75 (% pred)	77 (40–125)	76 (42–126)	p: 0.56
FVC (% pred)	100 (64–123)	96 (81–116)	p: 0.19
LCI2.5%	7.80 (6.06–15.35)	7.12 (5.69–9.08)	p: 0.04
LCI2.5% (z‐score)	4.38 ((−0.77) to 26.86)	2.40 ((−1.86) to 8.21)	p: 0.02
ScondxVT	0.08 (0.01–0.2)	0.06 (0.02–0.12)	p: 0.11
SacinxVT	0.08 (0–0.3)	0.08 (0.02–0.13)	p: 0.37
Scond CV (%)	8.4 (0.9–76.0)	23.1 (2.7–104.6)
Sacin CV (%)	17.2 (1.2–100.0)	19.3 (1.8–91.0)

Abbreviations: pwCF, people with CF; pwPCD, people with PCD; MBW, multiple breath washout; y, years; FEV1, forced expiratory volume in one second; % pred, percentage predicted; FEF_25‐75, forced expiratory flow between 25%–75%; LCI, lung clearance index; S_cond, conductive ventilation heterogeneity; VT, tidal volume; S_acin, acinar ventilation heterogeneity; CV%, coefficient of variance.

FIGURE 2.

LCI is higher in pwCF compared to pwPCD. (A) Boxplot comparing LCI with spaghetti plot connecting values in matched subjects (n: 23). (B) Boxplots comparing S_cond and S_acin in pwCF and pwPCD (n: 23). The boxes represent the interquartile range (IQR). Whiskers extend to the minimum and maximum non‐outlier values, and outliers are shown as individual points. CF: cystic fibrosis, PCD: primary ciliary dyskinesia, pwCF: people with CF, pwPCD: people with PCD, LCI: lung clearance index, S_cond: conductive ventilation heterogeneity, VT: tidal volume, S_acin: acinar ventilation heterogeneity.

4. Discussion

In this retrospective study, we aimed to compare LCI and other MBW parameters in matched pwCF and pwPCD with comparable FEV₁ abnormality, reflecting similar proximal airway disease. Our primary finding was that pwCF demonstrated significantly higher LCI values compared to pwPCD, despite similar values for FEV₁. These findings suggest a higher degree of ventilation inhomogeneity in CF, potentially indicating more pronounced involvement of distal airways, supporting our hypothesis based on previous research by our group, that airway involvement in CF extends more into the distal airways compared to PCD [10]. LCI is particularly sensitive to ventilation inhomogeneity arising from disease affecting the peripheral airways, and more extensive dilatation in distal airway generations may therefore contribute to higher LCI values in CF, even in the presence of significant mucus plugging in PCD. Although LCI was lower in PCD, 11/23 children had abnormal values, confirming distal airway abnormalities. This observed difference in LCI between pwCF and pwPCD may be clinically meaningful and not merely statistically significant. Perrem et al. showed that within‐subject changes in LCI of approximately 10%–15% exceed normal measurement variability and are more often associated with clinical deterioration in children with cystic fibrosis [31]. In this context, the difference observed in our study (LCI 7.80 vs. 7.12), corresponding to a relative increase of ~9.5%, approaches the lower threshold of clinically relevant change described in the literature. Even in pediatric populations with mild disease and preserved spirometry, such modest LCI elevations may reflect early small airway dysfunction not captured by conventional lung function measures.

From a clinical perspective, differentiating the extent of distal airway involvement between CF and PCD is relevant because it may inform both disease monitoring and therapeutic strategies. In CF, systemically administered CFTR modulators have the potential to influence disease processes throughout the airway tree, including the distal airways. In contrast, current treatment options in PCD largely rely on inhaled mucolytics and airway clearance techniques, which predominantly target the large and medium‐sized airways. Improved understanding of distal airway involvement may therefore help interpret functional outcomes such as LCI and highlight the need for future disease‐modifying therapies in PCD. Our secondary objective was to compare additional MBW parameters of normalized phase III slope parameters, such as S_cond and S_acin, as they may help identify the localization of the physiological mechanisms underlying ventilation inhomogeneity. No significant differences were detected between pwCF and pwPCD for these secondary parameters. We only observed a trend towards increased S_cond values in CF patients, suggesting possibly greater convection‐dependent ventilation inhomogeneity. However, variability was high in our group, which is known from other studies, and thus the study population may have been too small [32]. Moreover, there is very limited data on reference values [21]. Several studies have evaluated MBW indices in CF and PCD [33]. Recently, all‐age reference equations for LCI and functional residual capacity (FRC) were established using the Global Lung Function Initiative methodology [27]. However, reference values for phase III slope parameters are still lacking, limiting comparisons across studies and populations. Previous work has consistently shown that MBW‐derived LCI is frequently abnormal in PCD, often preceding abnormalities in conventional spirometric indices. A prior study from our group demonstrated that in pwPCD, LCI is more frequently abnormal than FEV₁ and correlates more strongly with structural lung disease on CT [16]. Similarly, the 2022 systematic review by Zafar et al. concluded that LCI in PCD often exceeds the normal range earlier than FEV₁ and FEF_25–75% and shows a stronger correlation with HRCT findings [17]. Comparative data between CF and PCD are more limited. In a small pediatric cohort of pwPCD and pwCF with a similar age range (3–12 years), no significant differences in FEV₁ were observed between groups, while LCI was elevated in both disease groups compared to healthy controls [15]. In that study, FEF_25–75% was significantly lower in the PCD group, despite non‐significant differences in LCI values between pwPCD and pwCF. In contrast, in the present study—where patients were matched for FEV₁ z‐score—no significant differences in FEF_25–75% between pwPCD and pwCF were observed. This difference in findings highlights the influence of study design and matching criteria when comparing spirometric outcomes between disease groups.

A major strength of this study is the high‐quality dataset, achieved through strict inclusion criteria and careful matching of participants. This approach minimizes confounding factors and enhances the comparability between the CF and PCD group. We opted not to include pwCF receiving modulator therapy with elexacaftor/tezacaftor/ivacaftor (ETI) or ivacaftor for gating mutations, as these regimens significantly alter the clinical trajectory of CF (including spirometry and MBW parameters) by improving CFTR function and thus mucociliary clearance [29]. Including patients on ETI would introduce a substantial confounder, potentially obscuring the natural disease processes we aimed to investigate. However, our cohort does include individuals treated with ivacaftor–lumacaftor and ivacaftor–tezacaftor. These therapies may also exert a modest favorable effect on FEV₁ [34], implying that our population does not fully represent the true pre‐modulator era, but is a clear presentation of a pre‐highly effective modulator era. Further studies will have to reveal physiologic changes in pwCF treated with modulators from an early age. However, strict selection criteria also resulted in a relatively small sample size and may limit the generalizability of our findings. Looking at our pwPCD group, PCD is a highly heterogeneous disease. Results should therefore be interpreted with caution, particularly in light of genotype‐related differences in disease severity [35, 36]. In our relatively small cohort, only one individual carried a CCDC40 mutation, a genotype known to be associated with a more severe disease course. However, CCDC40 represents only 8%–10% in European PCD populations and is mostly present in Scandinavian cohorts [37], whereas our cohort included a substantial proportion of patients with DNAH5 and DNAH11 mutations, which together account for a large fraction of PCD cases encountered in routine clinical practice. Also, for our pwPCD, we acknowledge intra‐individual variability in lung function measurements has been reported previously, particularly in PCD, and may complicate the interpretation of cross‐sectional spirometry outcomes. Recent work has highlighted that fluctuations in spirometry in PCD can occur even in clinically stable patients, underscoring the importance of considering measurement variability when interpreting lung function data [38]. To address this, we specifically evaluated intra‐individual variability within our cohort, restricting analyses to measurements obtained during clinically stable conditions. Intra‐individual FEV₁ variability was comparable between both groups (pwPCD: mean 5.7%; pwCF: mean 5.6%). Furthermore, all spirometry measurements were performed using the same device and by one of three trained team members following identical instructions, supporting the reproducibility and robustness of our lung function data. Additionally, S_cond and S_acin values are known to have considerable variability, which may have reduced the sensitivity to detect significant differences between groups. It should also be noted that imaging data were not included, limiting our ability to directly compare functional measurements with structural abnormalities on HRCT. However, LCI may be underestimated by complete mucus obstruction in large airways leading to atelectasis, which can be seen in PCD. Therefore an exploratory evaluation of HRCT abnormalities in the PCD cohort showed middle lobe atelectasis in 7/23 pwPCD, however, not associated with decreased FVC or LCI (data not shown). Also, in the scope of this study we did not include microbiological data. In summary, our findings indicate that the relation of FEV₁ and LCI is different in CF and PCD, whereas differences in phase III slope parameters were less pronounced. Despite the limitations in sample size and the variability of certain MBW indices, this study offers valuable insight into the differential distal airway involvement in CF and PCD.

5. Conclusion

Within pwCF and pwPCD with similar FEV₁ z‐score, reflecting similar proximal airway disease, LCI values were significantly higher in pwCF. These data support the hypothesis that in PCD less distal airway involvement is present compared to patients with CF with a similar proximal airway impairment. Our results propose LCI as a marker for differentiating the extent of distal airway involvement in CF and PCD. Future studies that include larger cohorts, pwCF on modulators and concurrent imaging data will help clarify the value of LCI and refine our understanding of the distinct pulmonary pathophysiology in PCD and CF.

Author Contributions

Lise Vanvuchelen: conceptualization, investigation, writing – original draft, writing – review and editing, visualization, methodology, formal analysis. Astrid Vermaut: conceptualization, investigation, writing – original draft, methodology, visualization, writing – review and editing, formal analysis, funding acquisition. Marijke Proesmans: conceptualization, writing – review and editing, supervision, methodology. François Vermeulen: conceptualization, writing – review and editing, supervision, methodology. Mieke Boon: methodology, conceptualization, investigation, funding acquisition, writing – original draft, writing – review and editing, formal analysis, supervision.

Ethics Statement

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee Research UZ/KU Leuven (S51800).

Conflicts of Interest

A.V., L.V. and M.B. are supported by the King‐Baudouin Foundation: Fund Alphonse & Jean Forton and Belgische Mucovereniging (2020‐J1810150‐217926 and 2023‐J1810150‐ 232722). The remaining authors have no potential conflicts of interest to disclose.

Supporting information

Table S1: Reference values for LCI and FRC based on Global Lung Function Initiative (GLI) equations.

Vanvuchelen L., Vermaut A., Proesmans M., Vermeulen F., and Boon M., “Comparing Multiple Breath Washout Parameters in a Matched Cohort of Children With Cystic Fibrosis and Primary Ciliary Dyskinesia,” Pediatric Pulmonology 61 (2026): e71735. 10.1002/ppul.71735.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.