A smartphone-based algorithm comprising cough analysis and patient-reported symptoms identifies acute exacerbations of asthma: a prospective, double blind, diagnostic accuracy study

Abstract

Objective: Early and accurate recognition of asthma exacerbations reduces the duration and risk of hospitalization. Current diagnostic methods depend upon patient recognition of symptoms, expert clinical examination, or measures of lung function. Here, we aimed to develop and test the accuracy of a smartphone-based diagnostic algorithm that analyses five cough events and five patient-reported features (age, fever, acute or productive cough and wheeze) to detect asthma exacerbations.Methods: We conducted a double-blind, prospective, diagnostic accuracy study comparing the algorithm with expert clinical opinion and formal lung function testing. Results: One hundred nineteen participants >12 years with a physician-diagnosed history of asthma were recruited from a hospital in Perth, Western Australia: 46 with clinically confirmed asthma exacerbations, 73 with controlled asthma. The groups were similar in median age (54yr versus 60yr, p=0.72) and sex (female 76% versus 70%, p=0.5). The algorithm’s positive percent agreement (PPA) with the expert clinical diagnosis of asthma exacerbations was 89% [95% CI: 76%, 96%]. The negative percent agreement (NPA) was 84% [95% CI: 73%, 91%]. The algorithm’s performance for asthma exacerbations diagnosis exceeded its performance as a detector of patient-reported wheeze (sensitivity, 63.7%). Patient-reported wheeze in isolation was an insensitive marker of asthma exacerbations (PPA=53.8%, NPA=49%). Conclusions: Our diagnostic algorithm accurately detected the presence of an asthma exacerbation as a point-of-care test without requiring clinical examination or lung function testing. This method could improve the accuracy of telehealth consultations and might be helpful in Asthma Action Plans and patient-initiated therapy.

Keywords:

• Diagnostics
• control/management
• quality of life

Acknowledgements

The authors wish to thank the many patients and their families who have given their time and enthusiasm for this work.

Disclosure statement

PP, SC and UA are or have been scientific advisors of ResApp Health (RAP). PP and UA are shareholders in RAP. UA was RAP’s Chief Scientist. RAP is an Australian publicly listed company commercializing the technology under license from the University of Queensland. UA is a named inventor of the UQ technology. NB received consulting fees for statistical analysis. JB declares no competing interests.

Funding

ResApp Health provided funding to support research nurse salaries and technical support in algorithmic development to the Breathe Easy Program at JHC and UQ. In addition, Joondalup Health Campus provided office space, IT services and consumables in kind.

Data availability statement

The underlying codes are the property of ResApp Health and are not available. The datasets supporting the conclusion of this article are available on reasonable request from PP. The cough recordings are not available but may be uploaded as an educational tool in the future.