COPD
Conditions
Keywords
COPD, Intelligent Terminal Device, Algorithm model
Brief summary
Chronic obstructive pulmonary disease (COPD) is one of the most common respiratory diseases. Early detection and treatment are critical to prevent the deterioration of COPD. In this study, investigators aim to develop an algorithm that can detect and infer the severity level of COPD from physiological parameters and audio data which are collected by a wearable device. Investigators will complete the study in two stages: stage 1. A panel study to assess the ability to infer the severity of COPD by intelligent terminal devices; stage 2. Establish an algorithm that can detect and infer the severity level of COPD by intelligent terminal devices.
Detailed description
In this study, investigators aim to establish an algorithm that can detect and infer the severity level of COPD from physiological parameters, coughing sounds, and forceful blowing sounds data that are collected by wearable devices. This study is divided into two stages. Stage one: A panel study to assess the ability to infer the severity of COPD by intelligent terminal devices. 30 patients with stable COPD will be enrolled and will undergo pulmonary function tests, electrocardiogram, echocardiography measurement, blood gas analysis, six-minutes walking test (6MWT), and polysomnography. And they are required to fill in the questionnaires related to COPD every day. Physiological parameters including oxygen saturation, heart rate, sleep, and physical activity will be collected by a wearable device for 7-14 consecutive days. Coughing and forceful blowing sounds will be collected twice daily. The association between the severity of COPD and physiological parameters from the wearable device will be analyzed. Stage two: Establish an algorithm that can detect and infer the severity level of COPD by intelligent terminal devices. 200 patients with stable COPD and 200 non- COPD subjects will be enrolled. Questionnaires related to COPD will be collected, and subjects will undergo pulmonary function tests and electrocardiograms. Physiological parameters including oxygen saturation and heart rate will be continuously collected by a wearable device for about 3～7 days. Investigators will also collect coughing and forceful blowing sounds. A COPD diagnosis algorithm model based on physiological parameters and audio data of intelligent terminal devices will be established. The study protocol has been approved by the Peking University First Hospital Institutional Review Board (IRB) (2022-083). Any protocol modifications will be submitted for IRB review and approval.
Interventions
None listed
Sponsors
People's Hospital of Beijing Daxing District
Beijing Miyun Hospital
Civil Aviation General Hospital
Aerospace 731 Hospital
The Hospital of Shunyi District Beijing
Shichahai community health service center
Peking University Shougang Hospital
Beijing Jingmei Group General Hospital
Beijing Luhe Hospital
Beijing Jishuitan Hospital
Peking University First Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
Stage 1: Inclusion criteria: 1. Older than 18 years old, no gender limitation; 2. In COPD stable stage (if there is an acute exacerbation, patients should be enrolled 3 months after remission of the exacerbation); 3. Be able to carry out daily activities and wear wearable devices; 4. Have willing to participate in this study and comply with the study protocol, and can sign informed consent; 5. Possess mobile communication equipment, which can meet the requirement of installing wearable device APP, and have a recording function.
Exclusion criteria
1. Have been diagnosed with chronic respiratory diseases other than COPD, such as bronchial asthma, lung cancer, active tuberculosis, bronchiectasis, and diffuse lung diseases (interstitial pneumonia, occupational lung disease, sarcoidosis, etc.); 2. lobectomy and/or lung transplantation, pleural disease; 3. Complicated with serious underlying diseases, including severe mental illness, intellectually impaired diseases, neurological disease (resulting in limb movement disorder), malignant tumor (PS score \> 2), chronic liver disease (transaminase \> Normal high limit 3 times), heart failure (NYHA\> Grade 3), autoimmune disease, chronic kidney disease (CKD-5), unstable coronary heart disease, arrhythmias (atrial fibrillation, atrial flutter, severe ventricular arrhythmia), congenital heart disease, pulmonary hypertension, etc., or life expectancy of less than 6 months; 4. Malnutrition (BMI\<18 kg/m2); 5. Bilateral wrist and hand edema, wrist soft tissue injury, can not wear a watch/bracelet because of the incompleted skin; 6. Double upper limb pigmentation or abnormal blood supply (occlusion, thrombosis, trauma, etc.) Stage 2 COPD group: Patients with stable COPD（same inclusion and
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Stage 1: Association between the severity of COPD airflow restriction and data collected by wearable devices | 2 months | Association between the severity of COPD airflow restriction and data collected by wearable devices |
| Stage 2：Establish an algorithm that can detect and infer the severity level of COPD by intelligent terminal devices | 5 months | Establish an algorithm that can detect and infer the severity level of COPD by intelligent terminal devices |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Stage 1: The compliance of subjects with wearable devices | 2 months | The compliance of subjects with wearable devices is defined as the percentage of the actual completion time of data collection to the minimum required time (10 hours X 7 days=70 hours). |
| Stage 1: Association between the severity of COPD airflow restriction, CAT score, mMRC score, echocardiography, blood gas analysis, six-minutes walking distance, polysomnography，and data collected by wearable devices | 2 months | Association between the severity of COPD airflow restriction, CAT score, mMRC score, echocardiography, blood gas analysis, six-minutes walking distance, polysomnography，and data collected by wearable devices |
| Stage 2: Association between the severity of COPD airflow restriction, CAT score, mMRC score，and data collected by wearable devices | 5 months | Association between the severity of COPD airflow restriction, CAT score, mMRC score，and data collected by wearable devices |
| Stage 2: number of adverse events | 5 months | The number of adverse events |
Countries
China
Outcome results
None listed