Assessment of inhomogeneity in gas exchange using a novel methodology

Abstract

Accurate measurement of respiratory disease activity within the lung poses a significant challenge, and the inability to achieve it hampers early disease detection, effective disease management and the objective assessment of therapeutic responses. In this thesis, the utility of computed cardiopulmonography (CCP) is explored as a novel, non-invasive way to measure lung inhomogeneity across three distinct cohorts: Healthy (control) study participants, T2-high asthma patients and survivors of severe COVID-19. Standardised normal values for CCP were established by analysis of 98 healthy people, who acted as the control group. Deviations from these baseline values were used to formulate prediction equations for each CCP parameter based on age, height, body mass index and sex. These equations were used to compute Z-scores for the asthma and post-COVID-19 cohorts. The analysis revealed that many patients exhibited abnormal Z-scores for CCP parameters. These findings highlighted deviations from the established normal ranges and demonstrated abnormalities in disease conditions. In the T2-high asthma cohort, the effect of biological treatments on lung inhomogeneity was examined. Ninety-one patients were studied before treatment, and 65 patients were studied before and after they began biological therapies. The study was focused on σlnCL, a metric of lung inhomogeneity, in which it correlated with the level of systemic inflammation as measured by the peripheral blood eosinophil count both at baseline and in relation to treatment effects by biologics. The use of CCP enabled differentiation between responders and non-responders based on changes in σlnCL. Responders in terms of early change in σlnCL were the ones showing improvements in lung function/symptoms and increased change of having remission at one year. This finding indicates that CCP could potentially provide an early treatment response signal that might be useful to guide treatment strategies. The COVID-19 cohort comprised 66 participants recovering from COVID-19 infection of varying clinical severity. The study aimed to assess whether CCP could identify abnormal lung function in recovered patients. Among the most severe cases, significant increases in anatomical dead space and decreases in functional residual capacity were observed. Despite the lack of pre-infection measurements, these findings highlight the potential use of CCP to assess aspects of lung function not easily measured by conventional tests. In conclusion, the work performed for this thesis has demonstrated that CCP offers a valuable tool by which to monitor disease activity in asthma and post-COVID-19 recovery. It underscores the importance of using advanced, non-invasive techniques to enhance our understanding and management of respiratory diseases. Additionally, this thesis represents the first attempt to establish normal values for CCP parameters through the use of Z-scores.