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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.
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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.
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Secondary ID [1]
295999
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None
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Universal Trial Number (UTN)
U1111-1219-9446
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Trial acronym
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Linked study record
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Health condition
Health condition(s) or problem(s) studied:
Pneumonia
309531
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Asthma
309532
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Croup
309533
0
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Bronchiolitis
309534
0
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Lower Respiratory Infection
309535
0
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COPD
309536
0
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Emphysema
309537
0
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Bronchiectasis
309538
0
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Interstitial Lung Disease
309539
0
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Respiratory Failure
309540
0
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Upper respiratory Disease
309541
0
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Condition category
Condition code
Respiratory
308359
308359
0
0
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Asthma
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Respiratory
308360
308360
0
0
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Chronic obstructive pulmonary disease
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Respiratory
308361
308361
0
0
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Other respiratory disorders / diseases
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Cardiovascular
308379
308379
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0
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Other cardiovascular diseases
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Intervention/exposure
Study type
Interventional
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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.
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Intervention code [1]
312339
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Diagnosis / Prognosis
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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.
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Control group
Active
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Outcomes
Primary outcome [1]
307345
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The primary outcome is the PPA and NPA between the Digital Diagnosis and the consensus expert clinical diagnosis for each Respiratory Condition.
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Assessment method [1]
307345
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Timepoint [1]
307345
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The diagnosis will be available within one minute of recording.
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Secondary outcome [1]
351554
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No secondary outcome
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Assessment method [1]
351554
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Timepoint [1]
351554
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N/A
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Eligibility
Key inclusion criteria
Patients with a history of a chronic respiratory condition or who have symptoms of an acute respiratory disease.
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Minimum age
1
Months
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Maximum age
No limit
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Sex
Both males and females
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Can healthy volunteers participate?
No
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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
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Study design
Purpose of the study
Diagnosis
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Allocation to intervention
Non-randomised trial
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Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
N/A
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Methods used to generate the sequence in which subjects will be randomised (sequence generation)
N/A
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Masking / blinding
Open (masking not used)
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Who is / are masked / blinded?
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Intervention assignment
Single group
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Other design features
N/A
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Phase
Not Applicable
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Type of endpoint/s
Efficacy
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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)
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Recruitment
Recruitment status
Recruiting
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Date of first participant enrolment
Anticipated
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Actual
23/03/2015
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Date of last participant enrolment
Anticipated
31/12/2021
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Actual
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Date of last data collection
Anticipated
31/12/2021
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Actual
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Sample size
Target
5000
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Accrual to date
4136
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Final
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Recruitment in Australia
Recruitment state(s)
WA
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Recruitment hospital [1]
11799
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Joondalup Health Campus - Joondalup
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Recruitment hospital [2]
11800
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Perth Children's Hospital - Nedlands
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Recruitment postcode(s) [1]
23926
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6027 - Joondalup
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Recruitment postcode(s) [2]
23927
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6009 - Nedlands
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Funding & Sponsors
Funding source category [1]
300594
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Hospital
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Name [1]
300594
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Joondalup Health Campus
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Address [1]
300594
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Grand Blvd &, Shenton Ave,
Joondalup WA 6027
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Country [1]
300594
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Australia
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Funding source category [2]
300605
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Commercial sector/Industry
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Name [2]
300605
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ResApp Health
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Address [2]
300605
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44 St Georges Terrace,
Perth WA 6000
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Country [2]
300605
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Australia
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Primary sponsor type
Commercial sector/Industry
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Name
ResApp Health
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Address
44 St Georges Terrace,
Perth WA 6000
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Country
Australia
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Secondary sponsor category [1]
300093
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None
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Name [1]
300093
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Address [1]
300093
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Country [1]
300093
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Ethics approval
Ethics application status
Approved
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Ethics committee name [1]
301379
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Joondalup Health Campus Human Research Ethics Committee
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Ethics committee address [1]
301379
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Grand Blvd &, Shenton Ave, Joondalup WA 6027
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Ethics committee country [1]
301379
0
Australia
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Date submitted for ethics approval [1]
301379
0
29/01/2015
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Approval date [1]
301379
0
06/03/2015
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Ethics approval number [1]
301379
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1501
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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.
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Trial website
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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.
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Public notes
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Contacts
Principal investigator
Name
86814
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A/Prof Paul Porter
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Address
86814
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Suite 204
Medical Centre
Joondalup Health Campus
Grand Blvd &, Shenton Ave,
Joondalup WA 6027
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Country
86814
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Australia
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Phone
86814
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+61 08 94009919
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Fax
86814
0
+61 08 94009919
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Email
86814
0
[email protected]
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Contact person for public queries
Name
86815
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Paul Porter
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Address
86815
0
Suite 204
Medical Centre
Joondalup Health Campus
Grand Blvd &, Shenton Ave,
Joondalup WA 6027
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Country
86815
0
Australia
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Phone
86815
0
+61 08 94009919
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Fax
86815
0
+61 08 94009919
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Email
86815
0
[email protected]
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Contact person for scientific queries
Name
86816
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Paul Porter
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Address
86816
0
Suite 204
Medical Centre
Joondalup Health Campus
Grand Blvd &, Shenton Ave,
Joondalup WA 6027
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Country
86816
0
Australia
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Phone
86816
0
+61 08 94009919
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Fax
86816
0
+61 08 94009919
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Email
86816
0
[email protected]
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Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
No
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No/undecided IPD sharing reason/comment
Not available for this study due to deidentified data.
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What supporting documents are/will be available?
No Supporting Document Provided
Doc. No.
Type
Citation
Link
Email
Other Details
Attachment
13715
Study protocol
375939-(Uploaded-24-05-2021-13-28-32)-Study-related document.docx
13716
Ethical 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
Source
Title
Year of Publication
DOI
Embase
A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children.
2019
https://dx.doi.org/10.1186/s12931-019-1046-6
Embase
Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis.
2021
https://dx.doi.org/10.1038/s41746-021-00472-x
Embase
Stratifying asthma severity in children using cough sound analytic technology.
2021
https://dx.doi.org/10.1080/02770903.2019.1684516
Dimensions AI
Implementation of a novel digital diagnostic tool to support the assessment of respiratory disease in a COVID-19 fever clinic
2022
https://doi.org/10.1136/bmjinnov-2021-000673
N.B. These documents automatically identified may not have been verified by the study sponsor.
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