Oscillometry for acute asthma in the pediatric emergency department: A feasibility study

Defining Treatment Response for Clinical Trials of Pediatric Acute Asthma

BACKGROUND

An agreed-upon definition of treatment response for clinical trials of pediatric acute asthma does not exist, limiting meaningful comparisons among therapeutic interventions and advances in asthma management.

OBJECTIVE

To develop a consensus definition of treatment response for clinical trials of pediatric acute asthma.

METHODS

A multidisciplinary panel of 22 experts participated in a Web-based modified Delphi process to achieve consensus on a definition of treatment response. Round 1 consisted of closed- and open-ended questions in which panelists ranked measures of treatment response developed by literature review, suggested additional measures, and explained their responses. In rounds 2 and 3, panelists reviewed summary statistics of the panel's rating from prior rounds and reconsidered their rankings. In round 3, pairwise ranking was performed to determine the ranked importance of components. Consensus was defined as 70% or greater agreement among panelists choosing Likert-scale values of 1 to 6 (extremely unimportant to extremely important) and an interquartile range less than 2.

RESULTS

Drawing on results from the expert panel, we developed a definition of treatment response that includes Clinical Severity Score, need for additional therapies, and hospitalization. Clinical Severity Score encompassed most ranked criteria (eg, respiratory distress, wheeze) for a treatment response definition. Panelists recommended that a valid and pragmatic severity score be used consistently across institutions. Panelists also achieved consensus on the top 10 criteria that appropriately classify need for hospitalization.

CONCLUSIONS

This consensus definition of treatment response can be used in clinical trials of children with acute asthma to standardize outcome measurement and report meaningful outcomes.

[Value of fractional exhaled nitric oxide combined with impulse oscillometry in the diagnosis of asthma in preschool children]

OBJECTIVES

To evaluate the value of fractional exhaled nitric oxide (FeNO) combined with impulse oscillometry (IOS) in the diagnosis of asthma in preschool children, and to establish the optimal predictive model.

METHODS

A retrospective analysis was performed on 156 children with wheezing, aged 3-5 years, who were admitted from September 2019 to December 2020. These children were divided into an asthma group with 52 children and a non-asthma group with 104 children. The two groups were compared in terms of IOS parameters, FeNO, and clinical data. The multivariate logistic regression analysis was used to establish the optimal predictive model.

RESULTS

Compared with the non-asthma group, the asthma group had significantly higher total respiratory system impedance at 5 Hz (Z5), resistance of respiratory system at 5 Hz and 20 Hz (R5 and R20, respectively), resonance frequency, reactance area (AX), and FeNO and a significantly lower reactance difference at 5 Hz (P<0.05). The receiver operating characteristic (ROC) curve analysis showed that Z5, R5, R20, and FeNO had a certain value in the diagnosis of asthma (P<0.05). The multivariate logistic regression analysis established the optimal predictive model of R20+AX+FeNO, with an area under the ROC curve of 0.858 (P<0.05), a sensitivity of 78.8%, and a specificity of 76.9%.

CONCLUSIONS

FeNO combined with IOS is helpful for the diagnosis of asthma in preschool children, and the model of R20+AX+FeNO has a certain value in the diagnosis of asthma in these children.

Pseudorandom Noise Forced Oscillation Technique to Assess Lung Function in Prematurely Born Children

The forced oscillation technique (FOT) is a non-volitional assessment that is used during tidal breathing. A variant of FOT uses a pseudorandom noise (PRN) signal which we postulated might have utility in assessing lung function in prematurely born children. We, therefore, undertook a systematic review to evaluate the evidence regarding PRN FOT. A comprehensive search of the literature was conducted by using the following databases: Medline, Embase, Web of Science and CINAHL. Observational studies, case series/reports and randomized-controlled trials were eligible for inclusion. Article abstracts and full texts were screened independently by two reviewers, with disagreements resolved by discussion or a third reviewer if necessary. Five studies were included (n = 587 preterm children). Three compared PRN FOT with spirometry, and two compare it to the interrupter technique. Most studies failed to report comprehensive methodology of the frequency spectra used to generate the PRN signal. There was evidence that poorer lung function, as assessed by PRN FOT, was associated with a greater burden of respiratory symptoms, but there was insufficient evidence to determine whether PRN FOT performed better than other lung-function tests. Detailed methodological documentation, in accordance with ERS guidance, is needed to assess the benefits of PRN FOT prior to routine clinical incorporation to assess prematurely born children.

Oscillometry of the respiratory system: a translational opportunity not to be missed

Airway oscillometry has become the de facto standard for quality assessment of lung physiology in laboratory animals and has demonstrated its usefulness in understanding diseases of small airways. Nowadays, it is seeing extensive use in daily clinical practice and research; however, a question that remains unanswered is how well physiological findings in animals and humans correlate? Methodological and device differences are obvious between animal and human studies. However, all devices deliver an oscillated airflow test signal and output respiratory impedance. In addition, despite analysis differences, there are ways to interpret animal and human oscillometry data to allow suitable comparisons. The potential with oscillometry is its ability to reveal universal features of the respiratory system across species, making translational extrapolation likely to be predictive. This means that oscillometry can thus help determine if an animal model displays the same physiological characteristics as the human disease. Perhaps more importantly, it can also be useful to determine whether an intervention is effective as well as to understand if it affects the desired region of the respiratory system, e.g., the periphery of the lung. Finally, findings in humans can also inform preclinical scientists and give indications as to what type of physiological changes should be observed in animal models to make them relevant as models of human disease. The present article will attempt to demonstrate the potential of oscillometry in respiratory research, an area where the development of novel therapies is plagued with a failure rate higher than in other disease areas.

Predicting Severe Asthma Exacerbations in Children: Blueprint for Today and Tomorrow

Severe asthma exacerbations are the primary cause of morbidity and mortality in children with asthma. Accurate prediction of children at risk for severe exacerbations, defined as those requiring systemic corticosteroids, emergency department visit, and/or hospitalization, would considerably reduce health care utilization and improve symptoms and quality of life. Substantial progress has been made in identifying high-risk exacerbation-prone children. Known risk factors for exacerbations include demographic characteristics (ie, low income, minority race/ethnicity), poor asthma control, environmental exposures (ie, aeroallergen exposure/sensitization, concomitant viral infection), inflammatory biomarkers, genetic polymorphisms, and markers from other "omic" technologies. The strongest risk factor for a future severe exacerbation remains having had one in the previous year. Combining risk factors into composite scores and use of advanced predictive analytic techniques such as machine learning are recent methods used to achieve stronger prediction of severe exacerbations. However, these methods are limited in prediction efficiency and are currently unable to predict children at risk for impending (within days) severe exacerbations. Thus, we provide a commentary on strategies that have potential to allow for accurate and reliable prediction of children at risk for impending exacerbations. These approaches include implementation of passive, real-time monitoring of impending exacerbation predictors, use of population health strategies, prediction of severe exacerbation responders versus nonresponders to conventional exacerbation management, and considerations for preschool-age children who can be especially high risk. Rigorous prediction and prevention of severe asthma exacerbations is needed to advance asthma management and improve the associated morbidity and mortality.