Screening and Early Warning of Chronic Obstructive Pulmonary Disease Combined With Sleep Respiratory Disease Based on Medical Internet of Things

NCT04833725 | Unknown

• 1 other identifier: 2020-2Z40917
• Study Type: observational
• Target: 680
• Locations: 1 country
• Sites: 1

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.

Conditions

COPD; Osa Syndrome

Outcome Measures

Primary Outcomes (2)

• 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.

  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

  12 months

Study Arms (1)

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

Interventions

wearable devices DEVICE

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.

COPD combined with OSA

Eligibility Criteria

• Age: 40 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• The diagnostic criteria for COPD are in line with the diagnostic guidelines for COPD in China from 40 to 80 years old.

You may not qualify if:

• Patients who cannot use IoT's mobile applications and cannot complete sleep monitoring and follow-up visits.

Sponsors & Collaborators

• Peking University Third Hospital: lead
• Beijing Municipal Health Commission: collaborator
• BOE Technology Group Co. Ltd.: collaborator

Study Sites (1)

Peking University Third Hospital

Beijing, China

RECRUITING

Related Publications (1)

• 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.

  PMID: 38418236 DERIVED

MeSH Terms

Conditions

Pulmonary Disease, Chronic Obstructive; Carnevale syndrome

Interventions

Wearable Electronic Devices

Condition Hierarchy (Ancestors)

Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Electrical Equipment and Supplies; Equipment and Supplies

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 2, 2021
• First Posted: April 6, 2021
• Study Start: January 1, 2020
• Primary Completion: December 31, 2021
• Study Completion: December 31, 2022
• Last Updated: April 6, 2021

Record last verified: 2021-04

Locations

CN (1)