Not provided
Not provided
| ID | Type | Description | Link |
|---|---|---|---|
| SEPAR PII-NIV | Other Grant/Funding Number | Sociedad española de neumología y cirugía torácica |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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.
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.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| study cohort with COPD and NIV patients for at least 6 months |
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 |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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:
Treatment and handling of data:
Once the file has been received, the 10 days prior to the admission, which will be the reason for recruitment |
| 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 |
| 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. |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
COPD with chronic NIV in acute exacerbation
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Manel Lujan, Professor MD pHD | Contact | +34 937231010 | mlujan@tauli.cat | |
| Cristina Lalmolda Puyol, RT phD | Contact | +34 692186820 | clalmolda@tauli.cat |
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Corporation Parc Tauli de Sabadell | Recruiting | Sabadell | Barcelona | Spain |
Redcap and drive account
Available since 2025, March to December 2026
Each PI of every center involve in the project
Not provided
Not provided
Not provided
Not provided
Not provided
|
| 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 |
| ID | Term |
|---|---|
| D029424 | Pulmonary Disease, Chronic Obstructive |
| ID | Term |
|---|---|
| D008173 | Lung Diseases, Obstructive |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
Not provided
Not provided