COPD (Chronic Obstructive Pulmonary Disease)
Conditions
Keywords
COPD, Artificial Intelligent, exacerbations
Brief 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.
Interventions
OTHERThe 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
Sponsors
Corporacion Parc Tauli
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
40 Years to 80 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Mean expiratory constant time (seconds) | 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
| Measure | Time frame | Description |
|---|---|---|
| Mean respiratory rate (RR) rpm | 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. |
| Mean inspiratory time (seconds) | 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. |
| Mean Inspiratory time/ total time (s) | 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. |
| exacerbation previous year (n) | Baseline | Specified if the patient had an exacerbation or more the previous year, review of clinical history form previous year |
| FEV1 (%) | Baseline | FEV1 (%), of the last spirometry, last spirometry previous acute exacerbation |
| FVC % | Baseline | FVC% of last spirometry, FVC% of last spirometry previous of acute exacerbation |
| FEV1/FVC % | Baseline | FEV1/FVC % OF LAST SPIROMETRY, previous of acute exacerbation |
| Date of exacerbation (dd/mm/yyyy) | Baseline | date of admission |
| Age (years) | Baseline | age in the admission |
| Gender (male / female) | Baseline | gender of the patient |
Countries
Spain
Contacts
Primary ContactManel Lujan, Professor MD pHD
Backup ContactCristina Lalmolda Puyol, RT phD
Outcome results
None listed