Mathematical Analysis of Signals and Clinical Parameters Provided by Non-invasive Home Ventilation Devices
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age40-80
SponsorCorporacion Parc Tauli
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial population
COPD with chronic NIV in acute exacerbation
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
The intervention involves download data of ventilator with clinical dates of the patient and model ventilator and parameters in acute exacebartion fo COPD
Other interventionRecruitment: * 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
Treatment groups
75 Participants
are divided into 1 treatment groupGroup A: study cohort with COPD and NIV patients for at least 6 months1 intervention
Trial outcomes
Primary outcomes
1
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.
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 controlSecondary outcomes
1
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.
Time frame
10 days prior to the admission, which will be the reason for recruitment, and the 10 days that will act as a control2
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.
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 control3
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.
Time frame
10 days prior to the admission, which will be the reason for recruitment, and the 10 days that will act as a control4
exacerbation previous year (n)
Specified if the patient had an exacerbation or more the previous year, review of clinical history form previous year
Time frame
BaselineSponsors and contacts
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Source ClinicalTrials.gov