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NCT Number: NCT07267104

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

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.

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Key information

Conditions

COPD (Chronic Obstructive Pulmonary Disease) Chronic Disease Disease Attributes Lung Diseases Lung Diseases, Obstructive Pathologic Processes Pathological Conditions, Signs and Symptoms Pulmonary Disease, Chronic Obstructive Respiratory Tract Diseases

Age range

40 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Corporation Parc Tauli de Sabadell

Sabadell, Barcelona, Spain

Location status: Recruiting

Location contact

Manel Luján Dr Luján, Professor MD pHD

CONTACT

[email protected]

+34 937231010

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

The intervention involves download data of ventilator with clinical dates of the patient and model ventilator and parameters in acute exacebartion fo COPD

Other

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

Primary outcomes

  1. Mean expiratory constant time (seconds)

    Time frame: 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 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.

Secondary outcomes

  1. Mean respiratory rate (RR) rpm

    Time frame: 10 days prior to the admission, which will be the reason for recruitment, and the 10 days that will act as a control

    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.

  2. Mean inspiratory time (seconds)

    Time frame: 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 (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.

  3. Mean Inspiratory time/ total time (s)

    Time frame: 10 days prior to the admission, which will be the reason for recruitment, and the 10 days that will act as a control

    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.

  4. exacerbation previous year (n)

    Time frame: Baseline

    Specified if the patient had an exacerbation or more the previous year, review of clinical history form previous year

  5. FEV1 (%)

    Time frame: Baseline

    FEV1 (%), of the last spirometry, last spirometry previous acute exacerbation

  6. FVC %

    Time frame: Baseline

    FVC% of last spirometry, FVC% of last spirometry previous of acute exacerbation

  7. FEV1/FVC %

    Time frame: Baseline

    FEV1/FVC % OF LAST SPIROMETRY, previous of acute exacerbation

  8. Date of exacerbation (dd/mm/yyyy)

    Time frame: Baseline

    date of admission

  9. Age (years)

    Time frame: Baseline

    age in the admission

  10. Gender (male / female)

    Time frame: Baseline

    gender of the patient

Study contacts

Contact information is provided by the study sponsor or research team.

Cristina Lalmolda Puyol, RT phD

CONTACT

[email protected]

+34 692186820

Manel Lujan, Professor MD pHD

CONTACT

[email protected]

+34 937231010

Sponsors and collaborators

Lead sponsor

Corporacion Parc Tauli

Other

Registry information

Official study title

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

Acronym: SAGE-NIV

Important dates

Study start
2025
Primary completion
2025
Study completion
2026
First posted
Dec 5, 2025
Registry last updated
Dec 5, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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