Defining airway dysfunction in asthma and COPD using oscillometry towards precision medicine

About the Project

Chronic airway diseases, including asthma and chronic obstructive pulmonary disease (COPD), are leading causes of morbidity and healthcare utilisation worldwide. Despite advances in therapeutics, substantial heterogeneity remains in disease presentation, progression, and treatment response. Current clinical assessment relies heavily on spirometry, which predominantly reflects large airway function and may fail to detect early or clinically relevant small airways dysfunction (SAD). This limitation contributes to suboptimal phenotyping, delayed intervention, and imprecise risk stratification.

Oscillometry is a non-invasive, tidal breathing technique that provides detailed assessment of airway resistance and reactance across the bronchial tree, including the small airways. Emerging evidence suggests that oscillometry-defined SAD is highly prevalent in both asthma and COPD and is associated with increased exacerbation risk, symptom burden, and poor disease control. Furthermore, oscillometric indices may capture pathophysiological changes not detectable by spirometry, offering a unique opportunity to refine disease phenotyping and enable precision medicine approaches.

This PhD project will leverage existing well-characterised clinical cohorts and registry infrastructure to investigate the role of oscillometry in asthma and COPD. The overarching aim is to determine whether oscillometry-derived parameters can improve disease phenotyping, predict clinically meaningful outcomes (including exacerbations and remission), and inform treatment response.

The project will comprise three interrelated work packages. First, the student will characterise oscillometry-defined SAD across asthma and COPD populations, exploring its prevalence, clinical correlates, and relationship with established physiological and inflammatory biomarkers (e.g., spirometry, FeNO, blood eosinophils). This will include evaluation of different oscillometric indices (e.g., Rrs5-20, AX, Xrs5) and their association with disease severity and comorbid traits.

Second, the project will focus on risk stratification. Using longitudinal clinical data, the student will assess whether oscillometry parameters independently predict key outcomes such as moderate and severe exacerbations, hospitalisation, and mortality. Advanced statistical modelling techniques, including multivariable regression, will be applied to develop and validate predictive models. Particular emphasis will be placed on integrating oscillometry with clinical and biomarker data to enhance predictive performance.

Third, the student will evaluate the role of oscillometry in monitoring treatment response and defining clinically meaningful change. This will include assessing changes in oscillometric indices following pharmacological interventions (e.g., inhaled therapies, biologics) and determining their relationship with improvements in symptoms, lung function, and clinical remission. The project will also explore potential minimal clinically important differences (MCIDs) for oscillometric measures, addressing a key gap in current clinical application.

The student will benefit from access to large, deeply phenotyped datasets and a multidisciplinary supervisory team with expertise in respiratory medicine, physiology, and data science. Training will include advanced statistical methods, data management, and scientific writing, with opportunities for national and international collaboration. The project is well aligned with ongoing initiatives in precision medicine and digital health and is expected to generate high-impact outputs, including peer-reviewed publications and contributions to clinical guidelines.

Overall, this project aims to establish oscillometry as a clinically relevant tool in the assessment and management of asthma and COPD, bridging the gap between physiological measurement and patient-centred outcomes.