(MRC DTP) - Detecting and modelling the progression of early cystic fibrosis lung disease from multiple breath washin and washout

This project will use mathematical modelling to help improve a clinical lung-function test for cystic fibrosis (CF), an inherited condition that results in progressive lung damage from birth and an early death. The project relates to a major NIHR-funded study (LCI-SEARCH), involving a sensitive method of tracking physiological changes in the lungs of more than 100 adults and children with CF, using multiple-breath wash-in of an inert tracer gas (SF6) followed by wash-out during tidal breathing. The methodology incorporates a novel closed-circuit (rebreathing) wash-in method, using a portable device with a quick wash-in time. Although conventionally discarded during analysis, the wash-in part of the test is affected by the same disease processes assessed during wash-out. The comprehensive longitudinal dataset generated in LCI-SEARCH, the novel method of wash-in and the mechanistic mathematical models to be developed in this project together offer a unique opportunity to develop new indices of gas mixing efficiency, offering improved interpretation and
prediction of airway function.

The information provided by wash-in may offer greater sensitivity than wash-out, and will offer considerable advantages in terms of improved test time. In addition, the system measures other expired gas species (such as CO2) that will allow us to explore the differential effects of disease and age-related changes on measures such as the alveolar slopes of expired gases. Measures related to alveolar slope progression currently provide greater sensitivity than summary measures of wash-out efficiency, such as lung clearance index.
However these indices are complex to generate and not reproducible enough for clinical interpretation. We plan to exploit observed differences between CO2 and SF6 slopes to generate wash-out indices of greater sensitivity and to use these data to inform mathematical models of lung ventilation that will allow us to infer the processes underlying the gas-mixing abnormalities.

Building on an existing methodology, mathematical models will be based on principles of fluid mechanics and gas transport in complex geometrical domains, aiming to provide a tractable representation of measured data. The objectives are to generate measures of lung physiology that offer both enhanced sensitivity and reduced test time compared to those currently available, which could offer substantial advantage to the clinical scalability of the technology and methodology currently in use. The student will gain clinical exposure at the
Manchester Adult CF Centre and will benefit from additional external supervision from Prof Peter Robbins, Head of Dept. Physiology, University of Oxford.