Bronchiectasis is a common clinical problem with great variance in its aetiology. treatment and prognosis. Lung function assessment using spirometry, and in particular FEV1 is used to objectively assess the degree of airflow impairment present. This however has limitations in its use, as it is frequently normal in mild to moderate disease and in many clinical trials does not show evidence of responsiveness to interventions. The lung clearance index (LCI), derived from multiple breath washout (MBW) testing has been shown in CF to have greater sensitivity in assessing lung function by determining ventilation inhomogeneity than indices of airflow obstruction. The aims of this thesis were to assess the reliability, validity and sensitivity of Lel as a marker of lung disease in bronchiectasis. This project has shown that LCI has good intervisit repeatability and is a feasible test for use in the clinical setting assessing 30 patients who are clinically stable on 2 occasions. 2 weeks apart. LCI has also been shown to have superior sensitivity to FEV1 when comparing radiological changes using the gold standard of high resolution eT scanning in 60 patients at a single visit when Clinically stable. However, pilot data in a responsiveness study in 9 patients receiving treatment for an infective exacerbation using intravenous antibiotics showed no statistically significant changes in LCI as a marker of responsiveness. This requires a larger. multicentre driven approach to assess the true responsiveness of LCI and other treatment interventions. Overall these results show that LCI by MBW is a reliable, sensitive and feasible measure of lung function in bronchiectasis. The results and the lessons learned in this area can guide future studies to use LCI as a key outcome measure in the management of bronchiectasis both clinically and in the research environment.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:677281 |
| Date | January 2015 |
| Creators | Rowan, Stephen |
| Publisher | Queen's University Belfast |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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