Screening and Early Warning of Chronic Obstructive Pulmonary Disease Combined With Sleep Respiratory Disease Based on Medical Internet of Things
Study Details
Study Description
Brief 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. 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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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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
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| 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. |
Device: wearable devices
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.
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Outcome Measures
Primary Outcome Measures
- Build the screening model of COPD combined with OSA - 12 months [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 [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
Eligibility Criteria
Criteria
Inclusion Criteria:
The diagnostic criteria for COPD are in line with the diagnostic guidelines for COPD in China from 40 to 80 years old.
Exclusion Criteria:
Patients who cannot use IoT's mobile applications and cannot complete sleep monitoring and follow-up visits.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Peking University Third Hospital | Beijing | China |
Sponsors and Collaborators
- Peking University Third Hospital
- Beijing Municipal Health Commission
- BOE Technology Group Co. Ltd.
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 2020-2Z40917