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Trial registered on ANZCTR


Registration number
ACTRN12618001521213
Ethics application status
Approved
Date submitted
5/09/2018
Date registered
11/09/2018
Date last updated
22/10/2021
Date data sharing statement initially provided
30/04/2019
Type of registration
Retrospectively registered

Titles & IDs
Public title
The Breathe Easy Study. Developing diagnostic tests for respiratory disease using sound measurement and machine learning techniques.
Scientific title
The Breathe Easy Study: Developing digital diagnostic tests for respiratory diseases using non-contact sound recordings in children and adults. Initial technical training and development phase and secondary blinded prospective diagnostic accuracy studies.
Secondary ID [1] 295999 0
None
Universal Trial Number (UTN)
U1111-1219-9446
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Pneumonia 309531 0
Asthma 309532 0
Croup 309533 0
Bronchiolitis 309534 0
Lower Respiratory Infection 309535 0
COPD 309536 0
Emphysema 309537 0
Bronchiectasis 309538 0
Interstitial Lung Disease 309539 0
Respiratory Failure 309540 0
Upper respiratory Disease 309541 0
Condition category
Condition code
Respiratory 308359 308359 0 0
Asthma
Respiratory 308360 308360 0 0
Chronic obstructive pulmonary disease
Respiratory 308361 308361 0 0
Other respiratory disorders / diseases
Cardiovascular 308379 308379 0 0
Other cardiovascular diseases

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
Patients with defined respiratory conditions (including Asthma, COPD, Bronchiolitis, Croup, Interstitial Lung Disease, Pneumonia, Upper and Lower Respiratory infections, Bronchiectasis, Emphysema), confirmed by expert review, will provide one series of (5-10) coughs, recorded by a smartphone for analysis and technical development of an algorithm to produce a digital diagnostic test. This procedure takes 30 seconds when the patient can provide a voluntary cough and up to 5 minutes if spontaneous. Children (who are able to cough on demand or are doing so spontaneously) and adults will be approached.
This development phase will develop tests for each of the conditions examined as well as measures of severity and complications.
Once the algorithms have been developed and the diagnostic tests refined to appropriate accuracy levels [Predicted Positive and Negative Percent Agreement with expert consenus clinical adjudication to be greater than 80% using a total (n=number needed to have lower limit 95% CI above agreed regulatory body requirements] enrolled cohort with mixed respiratory diseases) further subjects will be enrolled for formal diagnostic accuracy studies with the teams determining clinical results by expert consensus adjudication panel using all available information from clinical notes, digital diagnosis and statistical analysis being blinded from each other. The enrollment and data collection (including cough recording) will be identical to the development phase however the clinical diagnostic teams adjudication will be blinded from the App algorithm developer and the algorithms will be run by a third party. Clinical diagnosis and algorithm diagnosis will be provided separately to a statistical group from Curtin University for analysis.
Development of the diagnostic algorithms and the subsequent testing for accuracy is a continuum and ongoing process and is considered the same development program.
Intervention code [1] 312339 0
Diagnosis / Prognosis
Comparator / control treatment
Each individual condition will have the digital diagnostic output compared to a consensus expert clinical diagnosis. This is a non-reference standard with Positive Percent Agreement and Negative Percent Agreement (PPA and NPA) being the appropriate statistical values to be described. The clinical diagnostic team have access to all clinical details including all investigations.
For Paediatric trials two Paediatricians will adjudicate with a third Paediatrician being asked to adjudicate if there are disagreements with the majority diagnosis accepted.
For adult trials the clinical diagnosis will be based upon formal lung function tests when available, consensus clinical diagnosis between at least two clinicians and a full review of clinical notes and investigations.
Control group
Active

Outcomes
Primary outcome [1] 307345 0
The primary outcome is the PPA and NPA between the Digital Diagnosis and the consensus expert clinical diagnosis for each Respiratory Condition.
Timepoint [1] 307345 0
The diagnosis will be available within one minute of recording.
Secondary outcome [1] 351554 0
No secondary outcome
Timepoint [1] 351554 0
N/A

Eligibility
Key inclusion criteria
Patients with a history of a chronic respiratory condition or who have symptoms of an acute respiratory disease.
Minimum age
1 Months
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
Inability to Provide a cough either spontaneously or voluntarily.
Unable to provide informed consent or assent
Severe respiratory distress including the use of CPAP or BiPAP
Abdominal or eye surgery within 3 months

Study design
Purpose of the study
Diagnosis
Allocation to intervention
Non-randomised trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
N/A
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
N/A
Masking / blinding
Open (masking not used)
Who is / are masked / blinded?



Intervention assignment
Single group
Other design features
N/A
Phase
Not Applicable
Type of endpoint/s
Efficacy
Statistical methods / analysis
Each respiratory condition will be reported as PPA, PNA, LR+ and -, PPV and NPV with 95% confidence intervals. Further age grouping 0-2 years, 2-12 years and adult as well as all age (children)

Recruitment
Recruitment status
Recruiting
Date of first participant enrolment
Anticipated
Actual
23/03/2015
Date of last participant enrolment
Anticipated
31/12/2021
Actual
Date of last data collection
Anticipated
31/12/2021
Actual
Sample size
Target
5000
Accrual to date
4136
Final
Recruitment in Australia
Recruitment state(s)
WA
Recruitment hospital [1] 11799 0
Joondalup Health Campus - Joondalup
Recruitment hospital [2] 11800 0
Perth Children's Hospital - Nedlands
Recruitment postcode(s) [1] 23926 0
6027 - Joondalup
Recruitment postcode(s) [2] 23927 0
6009 - Nedlands

Funding & Sponsors
Funding source category [1] 300594 0
Hospital
Name [1] 300594 0
Joondalup Health Campus
Country [1] 300594 0
Australia
Funding source category [2] 300605 0
Commercial sector/Industry
Name [2] 300605 0
ResApp Health
Country [2] 300605 0
Australia
Primary sponsor type
Commercial sector/Industry
Name
ResApp Health
Address
44 St Georges Terrace,
Perth WA 6000
Country
Australia
Secondary sponsor category [1] 300093 0
None
Name [1] 300093 0
Address [1] 300093 0
Country [1] 300093 0

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 301379 0
Joondalup Health Campus Human Research Ethics Committee
Ethics committee address [1] 301379 0
Grand Blvd &, Shenton Ave,
Joondalup WA 6027
Ethics committee country [1] 301379 0
Australia
Date submitted for ethics approval [1] 301379 0
29/01/2015
Approval date [1] 301379 0
06/03/2015
Ethics approval number [1] 301379 0
1501

Summary
Brief summary
This study is designed to develop accurate digital diagnostic tests, used on a smart device, for common respiratory illnesses in children and adults including asthma, croup, bronchiolitis, COPD and pneumonia. These tests can then be used in resource-poor communities, emergency departments or via telehealth applications.
The aim is to develop tests that are as accurate as an expert clinical assessment but do not need a clinical examination or other investigations such as x-rays to be performed.
Trial website
Trial related presentations / publications
R. V. Sharan, U. R. Abeyratne, V. R. Swarnkar, and P. Porter, "Cough sound analysis for diagnosing croup in pediatric patients using biologically inspired features," in 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), Jeju Island, South Korea, 2017.

R. V. Sharan, U. R. Abeyratne, V. R. Swarnkar, and P. Porter, Automatic Croup Diagnosis Using Cough Sound Recognition. IEEE Transactions on Biomedical Engineering. Accepted for publication.
Public notes

Contacts
Principal investigator
Name 86814 0
A/Prof Paul Porter
Address 86814 0
Suite 204
Medical Centre
Joondalup Health Campus
Grand Blvd &, Shenton Ave,
Joondalup WA 6027
Country 86814 0
Australia
Phone 86814 0
+61 08 94009919
Fax 86814 0
+61 08 94009919
Email 86814 0
[email protected]
Contact person for public queries
Name 86815 0
Paul Porter
Address 86815 0
Suite 204
Medical Centre
Joondalup Health Campus
Grand Blvd &, Shenton Ave,
Joondalup WA 6027
Country 86815 0
Australia
Phone 86815 0
+61 08 94009919
Fax 86815 0
+61 08 94009919
Email 86815 0
[email protected]
Contact person for scientific queries
Name 86816 0
Paul Porter
Address 86816 0
Suite 204
Medical Centre
Joondalup Health Campus
Grand Blvd &, Shenton Ave,
Joondalup WA 6027
Country 86816 0
Australia
Phone 86816 0
+61 08 94009919
Fax 86816 0
+61 08 94009919
Email 86816 0
[email protected]

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
No
No/undecided IPD sharing reason/comment
Not available for this study due to deidentified data.


What supporting documents are/will be available?

Doc. No.TypeCitationLinkEmailOther DetailsAttachment
13715Study protocol    375939-(Uploaded-24-05-2021-13-28-32)-Study-related document.docx
13716Ethical approval    375939-(Uploaded-24-05-2021-13-24-31)-Study-related document.pdf



Results publications and other study-related documents

Documents added manually
No documents have been uploaded by study researchers.

Documents added automatically
SourceTitleYear of PublicationDOI
EmbaseA prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children.2019https://dx.doi.org/10.1186/s12931-019-1046-6
EmbaseIdentifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis.2021https://dx.doi.org/10.1038/s41746-021-00472-x
EmbaseStratifying asthma severity in children using cough sound analytic technology.2021https://dx.doi.org/10.1080/02770903.2019.1684516
Dimensions AIImplementation of a novel digital diagnostic tool to support the assessment of respiratory disease in a COVID-19 fever clinic2022https://doi.org/10.1136/bmjinnov-2021-000673
N.B. These documents automatically identified may not have been verified by the study sponsor.