The use of Symmetric Projection Attractor Reconstruction (SPAR) as a novel assessment tool in Asthma

Optimal asthma care relies on accurate and detailed assessment of the patient's clinical condition. A cornerstone of assessment is pulmonary function testing. Such testing frequently involves measuring airflow at the mouth during maximal breathing efforts. Although widely employed, such measurements are relatively insensitive and often relate poorly to overall disease severity. Lack of sensitivity leads to difficulty diagnosing early lung disease or early intervention following deterioration, which may ultimately affect prognosis. Existing techniques can also be challenging to perform by patients resulting in inaccurate or difficult to interpret measures and potential misdiagnosis.

Symmetric Projection Attractor Reconstruction (SPAR) is a novel analysis tool to quantify morphology changes from physiological waveforms such as respiratory flow and transforms high-fidelity waveform data into a simpler, quantifiable image (termed an attractor). Small changes in the respiratory flow waveform shape and variability reflecting the impact of changes in lung mechanics that occur in asthma are picked up as bigger changes in the attractor. This means that pathophysiological changes which may be missed with conventional testing (eg simple metrics used in PFT) can be quantified, making better use of all the available data. This could lead to more sensitive identification of pathological change and/or deterioration. As the system tracks through time series waveform data, overlapping attractors are formed, allowing us to map physiological changes over time. Small changes in the waveform's morphology and variability, will result in specific changes in the attractor's shape and colour, respectively. Every attractor bears a unique relationship with the input waveform data that created it. Therefore, different attractor morphology and variability features exist for different types of waveform which reflect the underlying phenotype.

The aim of the proposed study is to show proof of concept for SPAR analysis as a clinical decision support tool in patients with asthma. This is a detailed, observational clinical physiological study with the aim of assessing the use of SPAR in determining asthma severity and response to induced change in airway calibre. Respiratory flow waveforms be recorded in a large cohort of healthy subjects (n=81) and asthmatic patients across a range disease severities (n=152, n=76 mild, n=76 moderate/severe) and under conditions of reduced or increased airway calibre. Initial healthy subject data will be processed through pilot study derived respiratory waveform SPAR coding to generate corresponding, time dependent attactors. This will enable key SPAR features to be identified and in silico modelling to associate SPAR features with physiological meaning. As healthy subject and patient data are collected, specific respiratory coding will be developed to quantify specific respiratory features and machine learning algorithms developed, optimised and tested to classify different disease phenotypes and the response to brochoconstriction and bronchodilation, reflecting acute change in disease control.

The most significant outcome of this project will be a new approach for diagnosis and monitoring of asthma which has the potential of realising an appreciable improvement in quality of life for a significant proportion of the population. The study involves a multidisciplinary research group of biomedical scientists, clinicians and mathematicians thus avoiding potentially outdated technologies or suboptimal implementation of novel approaches. SPAR has the potential to be a diagnostic tool in patients with asthma, particularly in difficult to assess groups such as children, as measures are derived from resting tidal breathing and do not involve specific respiratory manoeuvres as well as an individualised monitoring/alert tool allowing early intervention at the onset of deterioration, thereby improving outcomes.