Mathematical Analysis of Signals and Clinical Parameters Provided by Non-invasive Home Ventilation Devices (SAGE-NIV)

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

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.

Study Overview

• Status: Recruiting
• Conditions: COPD (Chronic Obstructive Pulmonary Disease)
• Intervention / Treatment: Other: The intervention involves download data of ventilator with clinical dates of the patient and model ventilator and parameters in acute exacebartion fo COPD

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.

• Study Type: Observational
• Enrollment (Estimated): 75

Contacts and Locations

• Study Contact: Name: Manel Lujan, Professor MD pHD; Phone Number: +34 937231010; Email: mlujan@tauli.cat
• Study Contact Backup: Name: Cristina Lalmolda Puyol, RT phD; Phone Number: +34 692186820; Email: clalmolda@tauli.cat

Study Locations

• Spain
  • Barcelona
    • Sabadell, Barcelona, Spain
      • Recruiting
      • Corporation Parc Tauli de Sabadell
      • Contact: Manel Luján Dr Luján, Professor MD pHD; Phone Number: +34 937231010; Email: mlujan@tauli.cat

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

COPD with chronic NIV in acute exacerbation

Description

Inclusion Criteria:

• Age between 40 and 80 years.
• COPD diagnosed by pulmonary function tests.
• Home NIV therapy with good adherence (minimum daily compliance > 5 hours) for at least 6 months.
• Users of the ResMed LUMIS 150 ventilator. This is due to the presence of the decoding tool and a larger storage capacity (more than 100 nights) in the removable device of the ventilator.
• Acute exacerbation requiring hospital admission or home care.

Exclusion Criteria:

• Lack of informed consent.
• Previous clinical instability defined by the need for antibiotics and/or systemic corticosteroids in the two months prior to the inclusion exacerbation, excluding the 48 hours prior to admission, as this was considered part of the inclusion clinical picture.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

study cohort with COPD and NIV patients for at least 6 months; Inclusion criteria:Age between 40 and 80 years.COPD diagnosed by pulmonary function tests.Home NIV therapy with good adherence (minimum daily compliance > 5 hours) for at least 6 months.Users of the ResMed LUMIS 150 ventilator. This is due to the presence of the decoding tool and a larger storage capacity (more than 100 nights) in the removable device of the ventilator.Acute exacerbation requiring hospital admission or home care.; Exclusion criteria:Lack of informed consent.Previous clinical instability defined by the need for antibiotics and/or systemic corticosteroids in the two months prior to the inclusion exacerbation, excluding the 48 hours prior to admission, as this was considered part of the inclusion clinical picture. Ethical aspects: Patients will receive written information about the study and will also receive verbal explanations to clarify any doubts. Participation is voluntary and the patient may withdraw from the study at any time. No inv

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Mean expiratory constant time (seconds)	Mean expiratory constant time based on signal reconstruction and development of metrics basics on the data of traces of the patient ventilator detailed registered. They are converted to an open format using the tool provided and then uploaded to the protected data cloud. Signal reconstruction: based on the matrix , a programme has already been developed in Matlab® to reconstruct the signal from the built-in software. The events (arrows) are exactly the same in the built-in software and in the metrics development program. Three channels are imported: leakage, pressure and flow. Individual metrics For the expiratory part, peak expiratory, distance to peak expiratory, time constant, trend changes (points with first derivative = 0), etc. All mathematical development is implemented in in Matlab to facilitate automation.	the 10 days prior to the admission, which will be the reason for recruitment, and the 10 days that will act as a control

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Mean respiratory rate (RR) rpm	RR based on the same signal reconstruction based on the matrix with a programme has already been developed in Matlab® to reconstruct the signal from the built-in software ventilator Some of the metrics to be defined are: for inspiration, peak flow, distance to peak flow, number of peaks, inspiratory time constant, etc. For the expiratory part, peak expiratory, distance to peak expiratory, time constant, trend changes (points with first derivative = 0), etc. All mathematical development is implemented in Matlab to facilitate automation.	10 days prior to the admission, which will be the reason for recruitment, and the 10 days that will act as a control
Mean inspiratory time (seconds)	Mean inspiratory time (seconds) obtained by the same signal reconstruction. based on the same signal reconstruction based on the matrix with a programme has already been developed in Matlab® to reconstruct the signal from the built-in software ventilator Some of the metrics to be defined are: for inspiration, peak flow, distance to peak flow, number of peaks, inspiratory time constant, etc. For the expiratory part, peak expiratory, distance to peak expiratory, time constant, trend changes (points with first derivative = 0), etc. All mathematical development is implemented in Matlab to facilitate automation.	the 10 days prior to the admission, which will be the reason for recruitment, and the 10 days that will act as a control
Mean Inspiratory time/ total time (s)	Mean of this realtion based on the same signal reconstruction based on the matrix with a programme has already been developed in Matlab® to reconstruct the signal from the built-in software ventilator Some of the metrics to be defined are: for inspiration, peak flow, distance to peak flow, number of peaks, inspiratory time constant, etc. For the expiratory part, peak expiratory, distance to peak expiratory, time constant, trend changes (points with first derivative = 0), etc. All mathematical development is implemented in Matlab to facilitate automation.	10 days prior to the admission, which will be the reason for recruitment, and the 10 days that will act as a control
exacerbation previous year (n)	Specified if the patient had an exacerbation or more the previous year, review of clinical history form previous year	Baseline
FEV1 (%)	FEV1 (%), of the last spirometry, last spirometry previous acute exacerbation	Baseline
FVC %	FVC% of last spirometry, FVC% of last spirometry previous of acute exacerbation	Baseline
FEV1/FVC %	FEV1/FVC % OF LAST SPIROMETRY, previous of acute exacerbation	Baseline
Date of exacerbation (dd/mm/yyyy)	date of admission	Baseline
Age (years)	age in the admission	Baseline
Gender (male / female)	gender of the patient	Baseline

Collaborators and Investigators

• Sponsor: Corporacion Parc Tauli

Study record dates

Study Major Dates

• Study Start (Actual): March 25, 2025
• Primary Completion (Actual): April 19, 2025
• Study Completion (Estimated): December 1, 2026

Study Registration Dates

• First Submitted: November 17, 2025
• First Submitted That Met QC Criteria: November 24, 2025
• First Posted (Actual): December 5, 2025

Study Record Updates

• Last Update Posted (Actual): December 5, 2025
• Last Update Submitted That Met QC Criteria: November 24, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: COPD; exacerbations; Artificial Intelligent
• Additional Relevant MeSH Terms: Pathologic Processes; Chronic Disease; Disease Attributes; Respiratory Tract Diseases; Lung Diseases; Lung Diseases, Obstructive; Pathological Conditions, Signs and Symptoms; Pulmonary Disease, Chronic Obstructive
• Other Study ID Numbers: SAGE-NIV; SEPAR PII-NIV (Other Grant/Funding Number: Sociedad española de neumología y cirugía torácica)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Redcap and drive account
• IPD Sharing Time Frame: Available since 2025, March to December 2026
• IPD Sharing Access Criteria: Each PI of every center involve in the project
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; ICF; CSR

Drug and device information, study documents

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