Stratifying asthma severity in children using cough sound analytic technology

Abstract

Introduction: Asthma is a common childhood respiratory disorder characterized by wheeze, cough and respiratory distress responsive to bronchodilator therapy. Asthma severity can be determined by subjective, manual scoring systems such as the Pulmonary Score (PS). These systems require significant medical training and expertise to rate clinical findings such as wheeze characteristics, and work of breathing. In this study, we report the development of an objective method of assessing acute asthma severity based on the automated analysis of cough sounds.

Methods: We collected a cough sound dataset from 224 children; 103 without acute asthma and 121 with acute asthma. Using this database coupled with clinical diagnoses and PS determined by a clinical panel, we developed a machine classifier algorithm to characterize the severity of airway constriction. The performance of our algorithm was then evaluated against the PS from a separate set of patients, independent of the training set.

Results: The cough-only model discriminated no/mild disease (PS 0–1) from severe disease (PS 5,6) but required a modified respiratory rate calculation to separate very severe disease (PS > 6). Asymptomatic children (PS 0) were separated from moderate asthma (PS 2–4) by the cough-only model without the need for clinical inputs.

Conclusions: The PS provides information in managing childhood asthma but is not readily usable by non-medical personnel. Our method offers an objective measurement of asthma severity which does not rely on clinician-dependent inputs. It holds potential for use in clinical settings including improving the performance of existing asthma-rating scales and in community-management programs.

Abbreviations
AM	=	accessory muscle
BI	=	breathing index
CI	=	confidence interval
FEV1	=	forced expiratory volume in one second
LR	=	logistic regression
PEFR	=	peak expiratory flow rate
PS	=	pulmonary score
RR	=	respiratory rate
SD	=	standard deviation
SE	=	standard error
WA	=	Western Australia

Keywords:

• Algorithm development
• wheeze
• pulmonary score
• sound-analysis

Acknowledgements

The authors wish to acknowledge Ms. Brooke Schneider (research nurse) and Ms. Jacqueline Noonan (research nurse).

Declaration of interest

UA and PP are scientific advisors and shareholders in ResApp Health (RAP). UA held the Chief Scientist/consultant positions at ResApp (June 2017-May 2019). RAP is commercializing the technology under license from the University of Queensland, where UA is employed. UA and VS are named inventors of related UQ technology. JT, TW, JC and VS are shareholders in RAP. UA currently holds a consultancy at RAP through UQ. JB declares no conflict.

Ethics approval and consent to participate

Ethical approval was obtained from the Human Research Ethics Committees of Princess Margaret Hospital (2015030EP), Joondalup Health Campus (1501), Curtin University (HRE2018-0016) and The University of Queensland (2015000395/2016.01.179). All participants, parents, or guardians signed a consent to participate form.

Code, data and materials availability

The underlying codes are the property of ResApp Health and are not available. The cough recordings are not available but will be uploaded as an educational tool in the future.

Author contributions

VS and UA designed the study and conducted the mathematical algorithm development work. PP and UA designed the Breathe Easy study. PP led the clinical team, coordinated the clinical analysis and recording data collection. UA led the algorithm development team. VS, UA and PP produced the first draft with all authors adding and revising the manuscript.

Additional information

Funding

ResApp Health provided funding to support the Breathe Easy Program at JHC and UQ. UQ and Joondalup Health Campus provided office space, IT services and consumables in kind.