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

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

• Другое 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).

Первичные конечные точки

• Mean expiratory constant time (seconds) [Срок оценки: the 10 days prior to the admission, which will be the reason for recruitment, and the 10 days that will act as a control]

Вторичные конечные точки (10)

• Mean respiratory rate (RR) rpm [Срок оценки: 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) [Срок оценки: 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) [Срок оценки: 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) [Срок оценки: Baseline]
• FEV1 (%) [Срок оценки: Baseline]
• FVC % [Срок оценки: Baseline]
• FEV1/FVC % [Срок оценки: Baseline]
• Date of exacerbation (dd/mm/yyyy) [Срок оценки: Baseline]
• Age (years) [Срок оценки: Baseline]
• Gender (male / female) [Срок оценки: Baseline]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Испания · 1 центр

• Corporation Parc Tauli de Sabadell — Sabadell

Идентификаторы

NCT: NCT07267104 · SAGE-NIV · SEPAR PII-NIV

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗