COPDNCT06082791
A Clinical Validation Study to Evaluate the Diagnostic Accuracy of a Cough-based Auxiliary Diagnosis Algorithm for COPD
An observational study of COPD and Cough, run by Ruijin Hospital at 1 site in China.
Trial summary
Cough is one of the most common symptoms in COPD patients, and its different characteristics in amplitude, frequency and waveform can be analyzed by intelligent algorithms for the auxiliary diagnosis of COPD. Read more about this study
- Who is it for?
- Ages 40 years and olderHealthy volunteers can take part
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2023to 30 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: three cohorts are included COPD respiratory disease without cold healthy people
Who may be able to join
- patients with stable COPD were recruited for this study. Inclusion criteria were age ≥40 years and a diagnosis of COPD
Who cannot join
- Exclusion criteria were acute exacerbations of respiratory symptoms within the past 2 weeks or significant comorbid cardiovascular, neurological, musculoskeletal, immunological, or infectious diseases.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality, 200000
Jiemin Qu, phd, 15390460355, 15390460355@163.com
About this study
Official title: A Clinical Validation Study to Evaluate the Diagnostic Accuracy of a Cough-based Auxiliary Diagnosis Algorithm for Chronic Obstructive Pulmonary Disease (COPD)
Cough is one of the most common symptoms in COPD patients, and its different characteristics in amplitude, frequency and waveform can be analyzed by intelligent algorithms for the auxiliary diagnosis of COPD. The current study is to conduct external clinical validation of COPD auxiliary diagnosis algorithm and lung function prediction algorithm, and preliminatively verify the performance of the algorithm, with the ultimate goal of developing a portable and rapid COPD auxiliary diagnosis and condition assessment tool that can be used at home.
- Medical subject headings
- Pulmonary Disease, Chronic Obstructive, Cough
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| COPD | Not described | |
| other respiratory disease without COPD | Not described | |
| healthy people without respiratory disease | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Positive Agreement Rate (PPA)
Positive Agreement Rate (PPA) of auxiliary Diagnosis Algorithm for COPD
Measured: 12.30.2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Oct 2023
- Study start1 Jul 2023
- Primary completion30 Dec 2023 (estimated)
- Study completion30 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for COPDStudies of the same condition, open and under way.
- More trials run by Ruijin HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ruijin Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06082791
- Sponsor's protocol ID
- 2023-199
- Status last verified
- Sep 2023
- First submitted
- 8 Oct 2023
- Last updated
- 13 Oct 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Publications
Zhou J, Huang J, Wang Q, Yan J, Cao H, Huang L, Chen S, Ruan X, Zhu W, Mao J, Liu Y, Bu Z, Yang M, Wang Q, Zhou Y, Fan E, Tong L, Sun X, Zhao D, Wang P, Zhou M, Qu J. A cough sound-based deep learning algorithm for accessible prompt detection of chronic obstructive pulmonary disease with smartphones. NPJ Prim Care Respir Med. 2026 Mar 28;36(1):32. doi: 10.1038/s41533-026-00486-6.
PubMed 41896558
Study at a glance
From the registry record, last updated by the sponsor on 13 Oct 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Clinical Validation Study to Evaluate the Diagnostic Accuracy of a Cough-based Auxiliary Diagnosis Algorithm for Chronic Obstructive Pulmonary Disease (COPD)
- ClinicalTrials.gov ID
- NCT06082791
- Acronym / sponsor ID
- 2023-199
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Ruijin HospitalUniversity, hospital or other
- Study start
- 1 Jul 2023 (actual)
- Primary completion
- 30 Dec 2023 (estimated)
- Study completion
- 30 Dec 2024 (estimated)
- First posted
- 13 Oct 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Cough is one of the most common symptoms in COPD patients, and its different characteristics in amplitude, frequency and waveform can be analyzed by intelligent algorithms for the auxiliary diagnosis of COPD. The current study is to conduct external clinical validation of COPD auxiliary diagnosis algorithm and lung function prediction algorithm, and preliminatively verify the performance of the algorithm, with the ultimate goal of developing a portable and rapid COPD auxiliary diagnosis and condition assessment tool that can be used at home.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| COPD | – | – |
| other respiratory disease without COPD | – | – |
| healthy people without respiratory disease | – | – |
Who may qualify
- Ages
- 40 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- three cohorts are included COPD respiratory disease without cold healthy people
Key inclusion criteria (1 listed)
- patients with stable COPD were recruited for this study. Inclusion criteria were age ≥40 years and a diagnosis of COPD
Key exclusion criteria (1 listed)
- Exclusion criteria were acute exacerbations of respiratory symptoms within the past 2 weeks or significant comorbid cardiovascular, neurological, musculoskeletal, immunological, or infectious diseases.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Positive Agreement Rate (PPA)
Time frame: 12.30.2024
Positive Agreement Rate (PPA) of auxiliary Diagnosis Algorithm for COPD
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Oct 2023.
- Derived from this study: PubMed 41896558Zhou J, Huang J, Wang Q, Yan J, Cao H, Huang L, Chen S, Ruan X, Zhu W, Mao J, Liu Y, Bu Z, Yang M, Wang Q, Zhou Y, Fan E, Tong L, Sun X, Zhao D, Wang P, Zhou M, Qu J. A cough sound-based deep learning algorithm for accessible prompt detection of chronic obstructive pulmonary disease with smartphones. NPJ Prim Care Respir Med. 2026 Mar 28;36(1):32. doi: 10.1038/s41533-026-00486-6.
- Individual participant dataSharing planned: Yes
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COPD and Cough, run by Ruijin Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 13 Oct 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jiemin QU, pHD (15390460355, 15390460355@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Positive Agreement Rate (PPA) (time frame: 12.30.2024). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06082791, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Oct 2023.