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

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.

Study Overview

• Status: Recruiting
• Conditions: COPD; Osa Syndrome
• Intervention / Treatment: Device: wearable devices

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 Type: Observational
• Enrollment (Anticipated): 680

Contacts and Locations

Study Locations

• China
  • Beijing, China
    • Recruiting
    • Peking University Third Hospital
    • Contact: Yahong Chen; Phone Number: +8601082266699; Email: chenyahong@vip.sina.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 40 years to 80 years (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• 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.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Peking University Third Hospital
• Collaborators: Beijing Municipal Health Commission; BOE Technology Group Co. Ltd.

Study record dates

Study Major Dates

• Study Start (ACTUAL): January 1, 2020
• Primary Completion (ANTICIPATED): December 31, 2021
• Study Completion (ANTICIPATED): December 31, 2022

Study Registration Dates

• First Submitted: April 2, 2021
• First Submitted That Met QC Criteria: April 2, 2021
• First Posted (ACTUAL): April 6, 2021

Study Record Updates

• Last Update Posted (ACTUAL): April 6, 2021
• Last Update Submitted That Met QC Criteria: April 2, 2021
• Last Verified: April 1, 2021

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Lung Diseases; Lung Diseases, Obstructive; Pulmonary Disease, Chronic Obstructive; Respiration Disorders; Respiratory Tract Diseases
• Other Study ID Numbers: 2020-2Z40917

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No