Ignite Creation Date:
2025-12-24 @ 10:46 PM
Ignite Modification Date:
2025-12-28 @ 10:11 AM
Study NCT ID:
NCT05551169
Status:
COMPLETED
Last Update Posted:
2024-01-05
First Post:
2022-09-17
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Detect and Infer the Severity of COPD by Intelligent Terminal Device
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D029424', 'term': 'Pulmonary Disease, Chronic Obstructive'}], 'ancestors': [{'id': 'D008173', 'term': 'Lung Diseases, Obstructive'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D002908', 'term': 'Chronic Disease'}, {'id': 'D020969', 'term': 'Disease Attributes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 432}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2022-06-21', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-01', 'completionDateStruct': {'date': '2023-08-11', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-01-02', 'studyFirstSubmitDate': '2022-09-17', 'studyFirstSubmitQcDate': '2022-09-19', 'lastUpdatePostDateStruct': {'date': '2024-01-05', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-09-22', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-08-11', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Stage 1: Association between the severity of COPD airflow restriction and data collected by wearable devices', 'timeFrame': '2 months', 'description': 'Association between the severity of COPD airflow restriction and data collected by wearable devices'}, {'measure': 'Stage 2：Establish an algorithm that can detect and infer the severity level of COPD by intelligent terminal devices', 'timeFrame': '5 months', 'description': 'Establish an algorithm that can detect and infer the severity level of COPD by intelligent terminal devices'}], 'secondaryOutcomes': [{'measure': 'Stage 1: The compliance of subjects with wearable devices', 'timeFrame': '2 months', 'description': '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).'}, {'measure': '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', 'timeFrame': '2 months', 'description': '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'}, {'measure': 'Stage 2: Association between the severity of COPD airflow restriction, CAT score, mMRC score，and data collected by wearable devices', 'timeFrame': '5 months', 'description': 'Association between the severity of COPD airflow restriction, CAT score, mMRC score，and data collected by wearable devices'}, {'measure': 'Stage 2: number of adverse events', 'timeFrame': '5 months', 'description': 'The number of adverse events'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['COPD', 'Intelligent Terminal Device', 'Algorithm model'], 'conditions': ['COPD']}, 'referencesModule': {'references': [{'pmid': '40093702', 'type': 'DERIVED', 'citation': 'Zhang C, Yu K, Jin Z, Bao Y, Zhang C, Liao J, Wang G. Intelligent wearable devices with audio collection capabilities to assess chronic obstructive pulmonary disease severity. Digit Health. 2025 Mar 13;11:20552076251320730. doi: 10.1177/20552076251320730. eCollection 2025 Jan-Dec.'}]}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': '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.\n\nThis 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.\n\nStage 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.\n\nThe 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Stage I: patients with stable COPD Stage 2: patients with stable COPD and non-COPD subjects', 'healthyVolunteers': True, 'eligibilityCriteria': 'Stage 1:\n\nInclusion criteria:\n\n1. Older than 18 years old, no gender limitation;\n2. In COPD stable stage (if there is an acute exacerbation, patients should be enrolled 3 months after remission of the exacerbation);\n3. Be able to carry out daily activities and wear wearable devices;\n4. Have willing to participate in this study and comply with the study protocol, and can sign informed consent;\n5. Possess mobile communication equipment, which can meet the requirement of installing wearable device APP, and have a recording function.\n\nExclusion criteria:\n\n1. 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.);\n2. lobectomy and/or lung transplantation, pleural disease;\n3. 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;\n4. Malnutrition (BMI\\<18 kg/m2);\n5. Bilateral wrist and hand edema, wrist soft tissue injury, can not wear a watch/bracelet because of the incompleted skin;\n6. Double upper limb pigmentation or abnormal blood supply (occlusion, thrombosis, trauma, etc.) Stage 2 COPD group: Patients with stable COPD（same inclusion and exclusion criteria as Stage 1） Control group: healthy non-COPD population whose lung function must meet: FEV1/ FVC \\> 0.7, FEV1 \\> Pred 80% after bronchial dilation test when they are enrolled;\n\nInclusion criteria:\n\n1. Older than 18 years old;\n2. Be able to carry out daily activities and wear wearable devices;\n3. Have willing to participate in this study and comply with the study protocol, and can sign informed consent;\n4. Possess mobile communication devices, which can meet the requirements of installing wearable devices APP, and have a recording function.\n\nExclusion criteria: COPD and other serious chronic diseases (same exclusion criteria as COPD group).'}, 'identificationModule': {'nctId': 'NCT05551169', 'briefTitle': 'Detect and Infer the Severity of COPD by Intelligent Terminal Device', 'organization': {'class': 'OTHER', 'fullName': 'Peking University First Hospital'}, 'officialTitle': 'Establishment of an Algorithm That Can Detect and Infer the Severity Level of COPD by Intelligent Terminal Device', 'orgStudyIdInfo': {'id': '2022083-0624'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Patients with stable COPD in Stage1', 'description': 'no intervention'}, {'label': 'Patients with stable COPD in Stage2', 'description': 'no intervention'}, {'label': 'Non-COPD subjects in Stage2', 'description': 'no intervention'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Aerospace 731 Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Beijing Jingmei Group General Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Beijing Jishuitan Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Beijing Luhe Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Beijing Miyun Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Civil Aviation General Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Peking University Shougang Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': "People's Hospital of Beijing Daxing District", 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Shichahai community health service center', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'The Hospital of Shunyi District Beijing', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}], 'overallOfficials': [{'name': 'Guangfa Wang, MD', 'role': 'STUDY_CHAIR', 'affiliation': 'Peking University First Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Peking University First Hospital', 'class': 'OTHER'}, 'collaborators': [{'name': "People's Hospital of Beijing Daxing District", 'class': 'OTHER'}, {'name': 'Beijing Miyun Hospital', 'class': 'UNKNOWN'}, {'name': 'Civil Aviation General Hospital', 'class': 'OTHER'}, {'name': 'Aerospace 731 Hospital', 'class': 'OTHER'}, {'name': 'The Hospital of Shunyi District Beijing', 'class': 'UNKNOWN'}, {'name': 'Shichahai community health service center', 'class': 'UNKNOWN'}, {'name': 'Peking University Shougang Hospital', 'class': 'OTHER'}, {'name': 'Beijing Jingmei Group General Hospital', 'class': 'UNKNOWN'}, {'name': 'Beijing Luhe Hospital', 'class': 'OTHER'}, {'name': 'Beijing Jishuitan Hospital', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Prof. & MD.', 'investigatorFullName': 'Guangfa Wang', 'investigatorAffiliation': 'Peking University First Hospital'}}}}