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

Overview

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 Type

  • Study Type: Observational
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: December 31, 2021

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.

Interventions

  • 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.

Arms, Groups and Cohorts

  • 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.

Clinical Trial Outcome Measures

Primary Measures

  • Build the screening model of COPD combined with OSA – 12 months
    • Time Frame: 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
    • Time Frame: 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

Participating in This Clinical Trial

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.

Gender Eligibility: All

Minimum Age: 40 Years

Maximum Age: 80 Years

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • Peking University Third Hospital
  • Collaborator
    • Beijing Municipal Health Commission
  • Provider of Information About this Clinical Study
    • Sponsor

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