Oscillometry: old physiology with a bright future

Oscillometry in severe asthma: the state of the art and future perspectives

INTRODUCTION

Approximately 3-10% of people with asthma have severe asthma (SA). Patients with SA have greater impairment in daily life and much higher costs. Even if asthma affects the entire bronchial tree, small airways have been recognized as the major site of airflow limitation. There are several tools for studying small airway dysfunction (SAD), but certainly the most interesting is oscillometry. Despite several studies, the clinical usefulness of oscillometry in asthma is still in question. This paper aims to provide evidence supporting the use of oscillometry to improve the management of SA in clinical practice.

AREAS COVERED

In the ATLANTIS study, SAD was strongly evident across all severity. Various tools are available for evaluation of SAD, and certainly an integrated use of these can provide complete and detailed information. However, the most suitable method is oscillometry, implemented for clinical routine by using either small pressure impulses or small pressure sinusoidal waves.

EXPERT OPINION

Oscillometry, despite its different technological implementations is the best tool for determining the impact of SAD on asthma and its control. Oscillometry will also be increasingly useful for choosing the appropriate drug, and there is ample room for a more widespread diffusion in clinical practice.

Interpreting lung oscillometry results: Z-scores or fixed cut-off values?

Instead of using % predicted values or Z-scores to define and quantify respiratory abnormality in oscillometry, fixed cut-off values will likely give more accurate discriminationhttps://bit.ly/3vVmIyE

We read with great interest the article by Lianget al. [1] wherein the authors analysed the characteristics and diagnostic performance of oscillometry in adult patients with asthma (n=781), COPD (n=688), interstitial lung disease (n=274), bronchiectasis (n=109) and upper airway obstruction (n=40), and compared these with spirometry using data collected from a registry from across 13 hospital clinics in China. The authors concluded that compared to spirometry, respiratory oscillometry was more appropriate for evaluating, rather than diagnosing, respiratory diseases. The conclusion was based on the comparison of Z-scores for area under the curve (AUC), sensitivity and specificity between forced expiratory volume in 1 s (FEV₁) (spirometry) and resistance measured at 5 Hz (R₅) (oscillometry) (AUC 0.900, sensitivity 74% and specificity 95.4% versus 0.807, 62.4% and 90.3%, respectively, for all respiratory diseases, and 0.820, 76.2% and 75.2% versus 0.788, 66.7% and 81.5%, respectively, for obstructive airways diseases). This is clearly one of the largest studies to date that has examined the diagnostic performance of oscillometry in real-life clinical practice; however, we argue that the Z-score methodology used to compare diagnostic performance of oscillometry with spirometry is not appropriate and therefore misleads the interpretation.

Clinical significance and applications of oscillometry

Recently, “Technical standards for respiratory oscillometry” was published, which reviewed the physiological basis of oscillometric measures and detailed the technical factors related to equipment and test performance, quality assurance and reporting of results. Here we present a review of the clinical significance and applications of oscillometry. We briefly review the physiological principles of oscillometry and the basics of oscillometry interpretation, and then describe what is currently known about oscillometry in its role as a sensitive measure of airway resistance, bronchodilator responsiveness and bronchial challenge testing, and response to medical therapy, particularly in asthma and COPD. The technique may have unique advantages in situations where spirometry and other lung function tests are not suitable, such as in infants, neuromuscular disease, sleep apnoea and critical care. Other potential applications include detection of bronchiolitis obliterans, vocal cord dysfunction and the effects of environmental exposures. However, despite great promise as a useful clinical tool, we identify a number of areas in which more evidence of clinical utility is needed before oscillometry becomes routinely used for diagnosing or monitoring respiratory disease.

This paper provides a current review of the interpretation, clinical significance and application of oscillometry in respiratory medicine, with special emphasis on limitations of evidence and suggestions for future research.https://bit.ly/3GQPViA

High Frequency-Low Amplitude Oscillometry: Continuous Unobtrusive Monitoring of Respiratory Function on PAP Machines

Oscillometry or Forced Oscillation Technique, traditionally used in intermittent clinical measurements, has recently gained substantial attention from its application as a continuous monitoring tool for large and small airways. However, low frequency (< 8 Hz) continuous oscillometry faces high breathing noise, and hence requires high oscillation amplitudes to maintain an acceptable signal-to-noise ratio. Therefore, PAP machines that utilize low frequency oscillometry do so intermittently to distinguish airway patency several seconds after a breathing pause has occurred. We hypothesized that high frequency and low amplitude (HFLA) oscillometry may be as sensitive and applicable for monitoring upper airway patency to distinguish between central and obstructive apnea and hypopnea events, and for monitoring respiratory impedance. An inline oscillometry prototype device was developed and connected to commercial PAP machines to test whether oscillometry at 17, 43, and 79 Hz are as sensitive to airway patency as oscillometry at 4 Hz. Analysis of 11 patients with 171 apneas and hypopneas showed that all frequency oscillometry inputs were equally sensitive in distinguishing between central and obstructive apneas, while 17 Hz and 43 Hz oscillometry were most sensitive in distinguishing between central and obstructive hypopneas. Observations during normal breathing also showed the same periodicity and cross-correlation between impedance measurements from HFLA oscillometry compared to 4 Hz. Our findings provide an unobtrusive means of distinguishing airway patency during sleep and a means of continuous monitoring of respiratory function, with the potential for detection and prediction of developing respiratory diseases and significantly richer context for data analytics.

Within-session variability as quality control for oscillometry in health and disease

Oscillometry is increasingly adopted in respiratory clinics, but many recommendations regarding measurement settings and quality control remain subjective. The aim of this study was to investigate the optimal number of measurements and acceptable within-session coefficient of variation (CoV) in health, asthma and COPD. 15 healthy, 15 asthma and 15 COPD adult participants were recruited. Eight consecutive 30-s measurements were made using an oscillometry device, from which resistance at 5 Hz (R rs5 ) was examined. The effect of progressively including a greater number of measurements on R rs5 and its within-session CoV was investigated. Data were analysed using one-way repeated-measures ANOVA with Bonferroni post hoc test. The CoV(R rs5 ) of the first three measurements was 6.7±4.7%, 9.7±5.7% and 12.6±11.2% in healthy, asthma and COPD participants, respectively. Both mean R rs5 and CoV(R rs5 ) were not statistically different when progressively including four to eight measurements. Selecting the three closest R rs5 values over an increasing number of measurements progressively decreased the CoV(R rs5 ). In order for ≥95% of participants to fall within a target CoV(R rs5 ) of 10%, four or more, five and six measurements were needed in health, asthma and COPD, respectively. Within-session variability of oscillometry is increased in disease. Furthermore, the higher number of measurements required to achieve a set target for asthma and COPD patients may not be practical in a clinical setting. Provided technical acceptability of measurements is established, i.e. by removing artefacts and outliers, then a CoV of 10% is a marker of quality in most patients, but we suggest higher CoVs up to 15-20% should still be reportable.

Small airway dysfunction and poor asthma control: a dangerous liaison

Asthma is a common chronic condition, affecting approximately 339 million people worldwide. The main goal of the current asthma treatment guidelines is to achieve clinical control, encompassing both the patient symptoms and limitations and the future risk of adverse asthma outcomes. Despite randomized controlled trials showing that asthma control is an achievable target, a substantial proportion of asthmatics remain poorly controlled in real life. The involvement of peripheral small airways has recently gained greater recognition in asthma, and many studies suggest that the persistent inflammation at these sites leads to small airway dysfunction (SAD), strongly contributing to a worse asthma control. Overall, the impulse oscillometry (IOS), introduced in the recent years, seems to be able to sensitively assess small airways, while conventional spirometry does not. Therefore, IOS may be of great help in characterizing SAD and guiding therapy choice. The aim of this article is to review the literature on SAD and its influence on asthma control, emphasizing the most recent evidence.

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.