NCT07267104

Mathematical Analysis of Signals and Clinical Parameters Provided by Non-invasive Home Ventilation Devices

SAGE-NIV: Surveillance and Artificial Intelligence Guidance for Exacerbations in COPD Patients With Home Non-Invasive Ventilation

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 75 (estimated)
• Sponsor: Corporacion Parc Tauli · Academic / Other
• Sex: All
• Age: 40 Years – 80 Years
• Healthy volunteers: Not accepted

Summary

This study will look at people with COPD who use a home breathing machine called non-invasive ventilation (NIV). NIV machines collect information about your breathing, such as air flow, pressure, and mask leaks. Researchers want to use a computer program, called artificial intelligence (AI), to study this information. The goal is to find early signs that your breathing may be getting worse. People with COPD who already use NIV at home may join this study. The study does not change your treatment. It only uses the breathing data already recorded by your NIV machine. The computer program will look for patterns in the data. These patterns may help doctors: Notice early warning signs of a COPD flare-up Find problems with how you and the machine work together Improve the way NIV is monitored at home The main goal is to create a tool that helps patients and doctors manage home NIV more easily and more safely.

Detailed description

This study proposes the development of an artificial intelligence (AI) system to monitor and analyse detailed non-invasive mechanical ventilation (NIV) data in COPD patients, with the aim of predicting clinical exacerbations and improving home management. Analysis of data from home NIV devices allows assessment of patient compliance, detection of leaks and asynchronies, and monitoring of upper airway events. However, the potential of these data to improve ventilation management in COPD patients has been limited, in part due to the lack of tools to process and interpret the detailed records. Transforming these data into an open format opens up the possibility of applying artificial intelligence to analyse large amounts of information and develop predictive models. The multi-centre, observational, longitudinal study design will include COPD patients on NIV therapy who meet adherence criteria. Detailed leak, pressure and flow time data, previously decrypted and converted into a data format readable by analysis software, will be analysed. The identified metrics will be evaluated by machine learning algorithms using techniques such as random forest and neural networks. Expected outcomes include the development of an automated predictive model to enable early detection of exacerbations and improved patient-ventilator synchronisation, moving towards more efficient and personalised telemonitoring in home NIV management.

Conditions

• COPD (Chronic Obstructive Pulmonary Disease)

Interventions

Type	Name	Description
OTHER	The intervention involves download data of ventilator with clinical dates of the patient and model ventilator and parameters in acute exacebartion fo COPD	Recruitment: * Collection of the clinical variables described in the previous section. * Download the data from the commercial ventilator mentioned in the 'Inclusion criteria' section. By default, the option 'all available detailed data' is selected in the menu corresponding to the built-in software. * Contact the coordinating centre to obtain an internal study code. * Send the contents of the folder corresponding to the recruited patient to the coordinating centre (using an encrypted system). Treatment and handling of data: * The clinical data collected after anonymisation will be stored on-line using the RedCap platform (https://www.project-redcap.org/). Data downloaded from the ventilator will be identified by a random code and stored on the encrypted Proton platform (https://proton.me/es-es) or similar. * Built-in software data: Once the file has been received, the 10 days prior to the admission, which will be the reason for recruitment

Timeline

• Start date: 2025-03-25
• Primary completion: 2025-04-19
• Completion: 2026-12-01
• First posted: 2025-12-05
• Last updated: 2025-12-05

Locations

1 site across 1 country: Spain