Contemporary N2 and SF6 multiple breath washout in infants and toddlers with cystic fibrosis

Impact of Reanalysis of Nitrogen Multiple-Breath Washout on its Relationship with Chest Magnetic Resonance Imaging Findings in Clinically Stable and Pulmonary Exacerbated Children with Cystic Fibrosis

RATIONALE

Multiple-breath washout (MBW)-derived lung clearance index (LCI) detects lung disease in children with cystic fibrosis (CF). Correction of a cross-talk error in the software of the MBW device Exhalyzer D in a new software version has generated significant interest regarding its impact on previous MBW findings. Since LCI and chest magnetic resonance imaging (MRI) correlated before in CF children, this study aims to reassess previous MBW data after correction.

PATIENTS/METHODS

Reanalysis of the main findings from a previously published study comparing MBW and MRI in a pediatric CF cohort by reassessment of nitrogen (N2) MBW of 61 stable children with CF, 75 age-matched healthy controls (HC), and 15 CF children with pulmonary exacerbation (PEx) in the corrected software version.

RESULTS

The corrected LCI (N2LCIcor) decreased in the entire cohort (-17.0 (11.2)%), HC (-8.5 (8.2)%), stable CF children (-22.2 (11.1)%), and within the PEx group at baseline, at PEx and after antibiotic therapy (-21.5 (7.3)%; -22.5 (6.1)%; -21.4 (6.6)%; all P<0.01). N2LCIcor and N2LCIpre correlated with chest MRI scores in stable CF (r=0.70 to 0.84; all P<0.01) without a significant difference between N2LCIcor and N2LCIpre. Change in LCI from baseline to PEx and from PEx to after therapy decreased from N2LCIpre to N2LCIcor, but these changes remained significant (all P=0.001).

DISCUSSION/CONCLUSIONS

Our results indicate that N2LCIcor is significantly lower than N2LCIpre, but key results published in the original study demonstrating N2MBW and MRI as complementary methods for clinical surveillance in children with CF remain unaffected.

Reanalysis of N2-lung clearance index and the comparison to SF6-lung clearance index and magnetic resonance imaging

Since the finding and correction of an error in previous spiroware software versions commonly used with the Exhalyzer D for multiple-breath washout (MBW) analysis, there has been an ongoing discussion about its impact on MBW results. In this study, we reanalyzed previously published findings with the corrected spiroware version 3.3.1. In total, 31 infants and preschool children with cystic fibrosis (CF) (mean age 2.3 ± 0.8 years) and 20 healthy controls (mean age 2.3 ± 1.1 years) underwent consecutive sulfure hexafluoride (SF6)- and nitrogen (N2)-MBW. In addition, children with CF underwent chest magnetic resonance imaging (MRI) on the same day. After reanalysis of MBW data, the corrected N2-lung clearance index (LCI) decreased by 10-15% in both groups (P = 0.001) but remained significantly higher than the SF6-LCI (P<0.01). Diagnostic agreement between the MBW results remained moderate with a persistent correlation between SF6- and N2-MBW. The corrected upper limit of normal of the N2-LCI changed classification of nine children with CF, eight of which were within the normal range after correction. The correlation between the different LCI values and the chest MRI scores remained significant with strongest correlation with the MRI perfusion score. Consequently, the corrected N2-LCI is significantly lower than the previous N2-LCI, but key results published before are not affected by the reanalysis.

Nationwide lung function monitoring from infancy in newborn-screened children with cystic fibrosis

Background

Cystic fibrosis (CF) lung disease starts in infancy and can be assessed for structural lung abnormalities using computed tomography or magnetic resonance scans, or for lung function impairment using multiple breath washout (MBW). However, in infancy these two methods are not well correlated. Trajectories of CF lung disease assessed by MBW in infants and toddlers remain poorly described, which is why we aimed to 1) describe the trajectory of lung function, 2) explore risk factors for progression and 3) explore the real-life effect of lumacaftor/ivacaftor.

Methods

This was a nationwide observational cohort study (2018-2021) using data collected as part of the routine clinical surveillance programme (including MBW and monthly endo-laryngeal suction sampling for bacterial pathogens) in children born after implementation of newborn screening for CF (May 2016). Lumacaftor/ivacaftor commenced from age 2 years in children homozygous for F508del. Ventilation distribution efficiency (VDE), recently described to have advantages over lung clearance index (LCI), was reported as the primary MBW outcome after z-score calculations based on published reference data. Mixed effect linear regression models were the main statistical analyses performed in this study.

Results

59 children, aged 2-45 months, contributed with 211 MBW occasions (median (interquartile range (IQR)) 3 (2-5) MBW occasions per child) with a median (IQR) follow-up time of 10.8 (5.2-22.3) months. An overall mean annual deterioration rate of -0.50 (95% CI -0.78- -0.22) z-VDE was observed, starting from an estimated mean z-VDE of -1.68 (95% CI -2.15- -1.22) at age 0.0 years (intercept). Pseudomonas aeruginosa "ever" (n=14, MBWs 50) had a significantly worse z-VDE trajectory versus P. aeruginosa "never" (mean difference 0.53 (95% CI 0.16-0.89) per year; p=0.0047) and lumacaftor/ivacaftor treatment (n=22, MBWs 46) significantly improved the trajectory of z-VDE (mean difference 1.72 (95% CI 0.79-2.66) per year; p=0.0004), leading to a stable mean z-VDE trajectory after start of treatment.

Conclusions

Infants and toddlers with CF demonstrated progressive deterioration in z-VDE over the first years of life. P. aeruginosa isolation "ever" was associated with an accelerated deterioration in lung function, while lumacaftor/ivacaftor therapy significantly improved and stabilised the trajectory.

Turning lung clearance index on its head. Reference data for SF6 multiple-breath washout derived ventilation distribution efficiency

Cystic fibrosis (CF) lung disease is characterized by increased ventilation inhomogeneity (VI), as measured by multiple-breath washout (MBW). Lung clearance index (LCI) is the most reported VI outcome. This study aimed to evaluate historically published reference equations for sulfur hexafluoride (SF6) MBW outcomes, to data collected using updated commercial SF6MBW equipment, and to produce device-specific equations if necessary. SF6MBW was performed in 327 healthy children aged 0.1-18.4 yr [151 (46%) girls], 191 (58.4%) <3 yr. z-Scores were calculated from published reference equations (FRC and LCI) and multivariate linear regression was performed to produce device-specific reference equations. Due to increasing residual standard deviations with increasing LCI values, investigation of methods for improvement were investigated, based on the relationship between VI and dead space ventilation (VD/VT; dead space volume/tidal volume) in a cohort of 59 healthy children, 26 children with CF (n = 138 test occasions), and 49 adults with lung disease. Historical SF6MBW reference equations were unsuitable for EXHALYZER D data. In contrast to LCI and log10(LCI), 1/LCI (ventilation distribution efficiency; VDE) was linearly related to VD/VT, with z-scores linearly related to its absolute values. Reference equations were reported for VDE and log10(FRC). Significant predictors for VDE and log10(FRC), respectively, were log10(age) and sex, and log10(height), sex, and posture. VDE is potentially a better index of VI than LCI, particularly in more advanced CF lung disease and also for longitudinal monitoring. Further confirmatory clinical studies, particularly longitudinal imaging studies of structural or ventilatory changes, are warranted.NEW & NOTEWORTHY Lung clearance index (LCI) is the most used outcome from the multiple-breath washout test. As known for decades, the LCI is not linearly related to dead space ventilation, giving difficulties interpreting changes over time and in clinical trials. We present a new and improved outcome based on LCI, the ventilation distribution efficiency (VDE), which solves this problem by being linearly related to dead space ventilation. A pediatric age range reference equation for VDE is presented.

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.