Date Published: February 13, 2019
Publisher: Public Library of Science
Author(s): Craig A. Williams, Kyle C. A. Wedgwood, Hossein Mohammadi, Katie Prouse, Owen W. Tomlinson, Krasimira Tsaneva-Atanasova, Alessandro Moura Zagatto.
Cystic fibrosis (CF) is a debilitating chronic condition, which requires complex and expensive disease management. Exercise has now been recognised as a critical factor in improving health and quality of life in patients with CF. Hence, cardiopulmonary exercise testing (CPET) is used to determine aerobic fitness of young patients as part of the clinical management of CF. However, at present there is a lack of conclusive evidence for one limiting system of aerobic fitness for CF patients at individual patient level. Here, we perform detailed data analysis that allows us to identify important systems-level factors that affect aerobic fitness. We use patients’ data and principal component analysis to confirm the dependence of CPET performance on variables associated with ventilation and metabolic rates of oxygen consumption. We find that the time at which participants cross the gas exchange threshold (GET) is well correlated with their overall performance. Furthermore, we propose a predictive modelling framework that captures the relationship between ventilatory dynamics, lung capacity and function and performance in CPET within a group of children and adolescents with CF. Specifically, we show that using Gaussian processes (GP) we can predict GET at the individual patient level with reasonable accuracy given the small sample size of the available group of patients. We conclude by presenting an example and future perspectives for improving and extending the proposed framework. The modelling and analysis have the potential to pave the way to designing personalised exercise programmes that are tailored to specific individual needs relative to patient’s treatment therapies.
Cystic fibrosis (CF) is the most common life shortening genetic disease in the Caucasian population, affecting nearly 11,000 individuals in the United Kingdom (UK)  and predominantly manifests itself throughout the respiratory, digestive and reproductive systems of the human body. The genetic mutation responsible for CF results in reduced trans-epithelial chloride transport, and increased sodium and water absorption, thus reducing the hydration status of the mucosal lining of the airway and digestive systems. The resultant viscous mucus is liable to increased infection and further inflammation and a progressive decline in lung function . Currently there is no cure for CF and therefore CF is a diseased that is managed with appropriate medication, nutrition, physiotherapy and exercise. Enhanced aerobic fitness as represented by maximal oxygen consumption (V˙O2max) is associated with lower risk of hospitalisation, increased exercise tolerance, reduced residual volume, increased endurance of the respiratory muscles, enhanced sputum expectoration and decreased rate of decline in pulmonary function [3–7]. Furthermore, individuals with CF possessing a higher V˙O2max are shown to have a reduced mortality risk, as Nixon et al.  reported that adults with a V˙O2max greater than 82% of their predicted value had an 83% 8-year survival rate, compared to just 28% 8-year survival rate for patients with a V˙O2max less than 58% of their predicted value . This enhanced survival has also been reported in a paediatric cohort, with a 100% survival rate after 7 years in children with CF with a V˙O2peak over 45 ml.kg-1.min-1 .
This study was a retrospective analysis of existing CPET data from 15 children and adolescents with CF. Original ethics approvals was approved by South West NHS Research Ethics Committees [10/H0107/78; 13/SW/0166; 14/SW/0061] and written informed consent and assent was obtained from parent(s)/guardian(s) and participants, respectively in accordance with the Declaration of Helsinki.
Our primary long-term modelling aim is to eventually use the models to evaluate how CF impairs exercise tolerance relative to increasing ventilatory and metabolic demands. Our predictive models could also be used to evaluate therapies and their effect on exercise performance. Ultimately, we hope that this will form a series of steps to design better exercise treatment that is tailored to specific individual needs relative to patient’s treatment therapies, a treatment modality that is affordable, and personalised . It is interesting to note that in spite of the small number of data points used to train our GP emulator, the accuracy as computed by the leave-one-out cross-validation mean square error is high. This observation implies that the relationship between GET and our chosen input variables is ‘smooth’ (in that there are no large, sudden changes in GET as our input variables vary) . In turn, this provides further evidence that our chosen input variables are good predictors of GET for this patient group.
A limitation with the current study is the utilisation of a relatively small sample size, and this is most likely contributing towards aforementioned errors in prediction. Future studies should seek to utilise CPET collected annually in CF centres, to develop larger, multi-centre, samples whereby a uniform exercise protocol is utilised. Given that utilisation of CPET is now recommended and endorsed for regular use by international medical societies , and individual CF centres are reporting upon experiences of using CPET , large-scale utilisation of such data is a feasible target.
In conclusion, our proposed stimulator allows for the reproduction of the physiological observations of ventilation and metabolic rates of O2 consumption acquired during a CPET in relation to people with CF. The modelling framework was able to successfully replicate the relationship between ventilatory dynamics, lung capacity and function and performance in CPET within a group of children and adolescents with CF. In particular, by using the Gaussian processes (GP) the GET, a well-known physiological threshold marker of exercise intensity, as well as other important measurements such as breathing frequency and tidal volume, at the individual patient level were accommodated into the model. The stimulator has the potential to be suitable for future applications of the investigations of drug therapies or other physical interventions on exercise performance.