COPDNCT04833725
Screening and Early Warning of Chronic Obstructive Pulmonary Disease Combined With Sleep Respiratory Disease Based on Medical Internet of Things
An observational study of COPD and Osa Syndrome, run by Peking University Third Hospital at 1 site in China.
Trial summary
Chronic obstructive pulmonary disease (COPD) is a common disease that endangers people's health, causing severe economic and treatment burdens. Sleep breathing disease, as a complication of COPD, increases the hospitalization rate and mortality of COPD. Read more about this study
- Who is it for?
- Ages 40 to 80 years
- Conditions the study is for
- COPD, Osa Syndrome
- What is being tested?
- wearable devices
- Type of treatment
- Medical device
- How many people
- 680 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2020to 31 Dec 2022
- 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 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with stable COPD from Peking University Third Hospital, Beijing Haidian Hospital, Beijing Zhongguancun Hospital, Beixiaguan Community Health Service Center and Huayuan Road Community Health Service Center.
Who may be able to join
- The diagnostic criteria for COPD are in line with the diagnostic guidelines for COPD in China from 40 to 80 years old.
Who cannot join
- Patients who cannot use IoT's mobile applications and cannot complete sleep monitoring and follow-up visits.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Peking University Third Hospital
Beijing
Yahong Chen, +8601082266699, chenyahong@vip.sina.com
About this study
Chronic obstructive pulmonary disease (COPD) is a common disease that endangers people's health, causing severe economic and treatment burdens. Sleep breathing disease, as a complication of COPD, increases the hospitalization rate and mortality of COPD. At present, community doctors have insufficient knowledge of COPD and its complications, and they also lack standardized screening and related disease management capabilities. This trail intends to use IoT medical technology to screen for COPD combined with sleep breathing diseases. It can establish a two-way referral channel between primary community hospitals and higher-level hospitals, which provides early warning services for COPD combined with sleep breathing diseases. This trial explores the impact of sleep breathing disease on COPD's acute exacerbation, which improves the understanding of COPD patients combined with sleep breathing diseases. It also improves COPD management and its complications control at the community-level and reduces COPD patients' potential risks and treatment burdens. It also explores tiered diagnosis and treatment models for COPD, promotes the construction of intelligent IoT infrastructure, and enhances standardized diagnosis and treatment of COPD at the grassroots level in China.
Read the detailed description
This study is a multi-center joint study, which mainly consists of two parts. First, a cross-sectional observational study was adopted to recruit patients with stable COPD in multiple centers. The COPD's diagnostic criteria follow diagnostic guidelines in China, and the patients were selected among 40-80 years old. Note that we excluded patients who cannot use IoT's mobile applications and cannot complete sleep monitoring and follow-up visits. All patients collect sleep monitoring information through wearable devices, together with demographic characteristics, pulmonary function tests, blood routines, biochemistry, electrocardiogram, chest radiograph, COPD assessment scale, modified British Medical Research Association dyspnea index, St. George's Quality of Life Questionnaire, Sleep Apnea Clinical Score, Berlin Questionnaire, Epworth Sleepiness Scale, Etc. This study estimates patient health status from the collected information, then diagnoses sleep apnea and calculates sleep apnea prevalence. Specifically, we build standards from the analysis of sleep monitoring information, and we form an OSA screening model by applying machine learning algorithms. Second, we establish a COPD cohort joined with sleep breathing disease, where we select COPD patients meeting the diagnostic criteria for sleep breathing disease. All patients use wearable devices and IoT technology for information collection and data management. We also build the early warning platform, and it allows flexible adjustment on the COPD plan according to individual differences and community differences. This tudy requires followed up visit once a month. By observing the number of hospitalizations, the incidence of acute exacerbations, and other secondary observation indicators of COPD patients, the early warning platform can analyze COPD's acute exacerbations combined with sleep respiratory disease. We develop the disease and prognosis model for COPD patients with SAO by applying machine learning algorithms on the previous platform.
- Conditions
- COPD, Osa Syndrome
- Being tested
- wearable devices
- Medical subject headings
- Pulmonary Disease, Chronic Obstructive, Carnevale syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
wearable devicesDevice
All patients use wearable devices and IoT technology for information collection and data management. Specifically, we build standards from the analysis of sleep monitoring information, and we form an OSA screening model by applying machine learning algorithms.
| Study group | Role | What they receive |
|---|---|---|
| COPD combined with OSA | All patients collect sleep monitoring information through wearable devices, together with demographic characteristics, pulmonary function tests, blood routines, biochemistry, electrocardiogram, chest radiograph, COPD assessment scale, modified British Medical Research Association dyspnea index, St. George's Quality of Life Questionnaire, Sleep Apnea Clinical Score, Berlin Questionnaire, Epworth Sleepiness Scale, Etc. This study estimates patient health status from the collected information, then diagnoses sleep apnea and calculates sleep apnea prevalence. Receives: Device: wearable devices |
What is being measured
The primary outcome is the main question the study is designed to answer.
Build the screening model of COPD combined with OSA - 12 months
We build the screening model of COPD combined with OSA by applying machine learning techniques to the monitoring information. And we evaluate its effectiveness on the patient status estimation, where the morbidity of COPD with OSA is measured through the screening model.
Measured: 12 months
Build the prognosis model of COPD combined with OSA - 12 months
We build the prognosis model of COPD combined with OSA and integrate it into the early warning platform. It observes the incidence rate of acute exacerbation COPD and other indexes
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Apr 2021
- Study start1 Jan 2020
- Primary completion31 Dec 2021 (estimated)
- Study completion31 Dec 2022 (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 Peking University Third 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
- 680 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University Third Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Beijing Municipal Health Commission, BOE Technology Group Co., Ltd.
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04833725
- Sponsor's protocol ID
- 2020-2Z40917
- Status last verified
- Apr 2021
- First submitted
- 2 Apr 2021
- Last updated
- 6 Apr 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Pan Z, Liao S, Sun W, Zhou H, Lin S, Chen D, Jiang S, Long H, Fan J, Deng F, Zhang W, Chen B, Wang J, Huang Y, Li J, Chen Y. Screening and early warning system for chronic obstructive pulmonary disease with obstructive sleep apnoea based on the medical Internet of Things in three levels of healthcare: protocol for a prospective, multicentre, observational cohort study. BMJ Open. 2024 Feb 28;14(2):e075257. doi: 10.1136/bmjopen-2023-075257.
PubMed 38418236
Study at a glance
From the registry record, last updated by the sponsor on 6 Apr 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Screening and Early Warning of Chronic Obstructive Pulmonary Disease Combined With Sleep Respiratory Disease Based on Medical Internet of Things
- ClinicalTrials.gov ID
- NCT04833725
- Acronym / sponsor ID
- 2020-2Z40917
- Conditions
- COPD, Osa Syndrome
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 680 participants (estimated)
- Lead sponsor
- Peking University Third HospitalUniversity, hospital or other
- Collaborators
- Beijing Municipal Health Commission, BOE Technology Group Co., Ltd.
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 31 Dec 2021 (estimated)
- Study completion
- 31 Dec 2022 (estimated)
- First posted
- 6 Apr 2021 (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.
Chronic obstructive pulmonary disease (COPD) is a common disease that endangers people's health, causing severe economic and treatment burdens. Sleep breathing disease, as a complication of COPD, increases the hospitalization rate and mortality of COPD. At present, community doctors have insufficient knowledge of COPD and its complications, and they also lack standardized screening and related disease management capabilities. This trail intends to use IoT medical technology to screen for COPD combined with sleep breathing diseases. It can establish a two-way referral channel between primary community hospitals and higher-level hospitals, which provides early warning services for COPD combined with sleep breathing diseases. This trial explores the impact of sleep breathing disease on COPD's acute exacerbation, which improves the understanding of COPD patients combined with sleep breathing diseases. It also improves COPD management and its complications control at the community-level and reduces COPD patients' potential risks and treatment burdens. It also explores tiered diagnosis and treatment models for COPD, promotes the construction of intelligent IoT infrastructure, and enhances standardized diagnosis and treatment of COPD at the grassroots level in China.
Detailed description
This study is a multi-center joint study, which mainly consists of two parts. First, a cross-sectional observational study was adopted to recruit patients with stable COPD in multiple centers. The COPD's diagnostic criteria follow diagnostic guidelines in China, and the patients were selected among 40-80 years old. Note that we excluded patients who cannot use IoT's mobile applications and cannot complete sleep monitoring and follow-up visits. All patients collect sleep monitoring information through wearable devices, together with demographic characteristics, pulmonary function tests, blood routines, biochemistry, electrocardiogram, chest radiograph, COPD assessment scale, modified British Medical Research Association dyspnea index, St. George's Quality of Life Questionnaire, Sleep Apnea Clinical Score, Berlin Questionnaire, Epworth Sleepiness Scale, Etc. This study estimates patient health status from the collected information, then diagnoses sleep apnea and calculates sleep apnea prevalence. Specifically, we build standards from the analysis of sleep monitoring information, and we form an OSA screening model by applying machine learning algorithms. Second, we establish a COPD cohort joined with sleep breathing disease, where we select COPD patients meeting the diagnostic criteria for sleep breathing disease. All patients use wearable devices and IoT technology for information collection and data management. We also build the early warning platform, and it allows flexible adjustment on the COPD plan according to individual differences and community differences. This tudy requires followed up visit once a month. By observing the number of hospitalizations, the incidence of acute exacerbations, and other secondary observation indicators of COPD patients, the early warning platform can analyze COPD's acute exacerbations combined with sleep respiratory disease. We develop the disease and prognosis model for COPD patients with SAO by applying machine learning algorithms on the previous platform.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| COPD combined with OSA | – | Device: wearable devicesAll patients collect sleep monitoring information through wearable devices, together with demographic characteristics, pulmonary function tests, blood routines, biochemistry, electrocardiogram, chest radiograph, COPD assessment scale, modified British Medical Research Association dyspnea index, St. George's Quality of Life Questionnaire, Sleep Apnea Clinical Score, Berlin Questionnaire, Epworth Sleepiness Scale, Etc. This study estimates patient health status from the collected information, then diagnoses sleep apnea and calculates sleep apnea prevalence. |
Who may qualify
- Ages
- 40 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with stable COPD from Peking University Third Hospital, Beijing Haidian Hospital, Beijing Zhongguancun Hospital, Beixiaguan Community Health Service Center and Huayuan Road Community Health Service Center.
Key inclusion criteria (1 listed)
- The diagnostic criteria for COPD are in line with the diagnostic guidelines for COPD in China from 40 to 80 years old.
Key exclusion criteria (1 listed)
- Patients who cannot use IoT's mobile applications and cannot complete sleep monitoring and follow-up visits.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| wearable devicesAll patients use wearable devices and IoT technology for information collection and data management. Specifically, we build standards from the analysis of sleep monitoring information, and we form an OSA screening model by applying machine learning algorithms. | Device | COPD combined with OSA |
Primary outcome
Build the screening model of COPD combined with OSA - 12 months
Time frame: 12 months
We build the screening model of COPD combined with OSA by applying machine learning techniques to the monitoring information. And we evaluate its effectiveness on the patient status estimation, where the morbidity of COPD with OSA is measured through the screening model.
Build the prognosis model of COPD combined with OSA - 12 months
Time frame: 12 months
We build the prognosis model of COPD combined with OSA and integrate it into the early warning platform. It observes the incidence rate of acute exacerbation COPD and other indexes
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Apr 2021.
- Derived from this study: PubMed 38418236Pan Z, Liao S, Sun W, Zhou H, Lin S, Chen D, Jiang S, Long H, Fan J, Deng F, Zhang W, Chen B, Wang J, Huang Y, Li J, Chen Y. Screening and early warning system for chronic obstructive pulmonary disease with obstructive sleep apnoea based on the medical Internet of Things in three levels of healthcare: protocol for a prospective, multicentre, observational cohort study. BMJ Open. 2024 Feb 28;14(2):e075257. doi: 10.1136/bmjopen-2023-075257.
- Individual participant dataSharing planned: Undecided
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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 Osa Syndrome, run by Peking University Third 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 6 Apr 2021. 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?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Build the screening model of COPD combined with OSA - 12 months (time frame: 12 months). The study lists 2 primary outcomes in all. 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 NCT04833725, 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 6 Apr 2021.