SAGE-NIV: Surveillance and Artificial Intelligence Guidance for Exacerbations in COPD Patients With Home Non-Invasive Ventilation
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 75
- 试验地点
- 1
- 主要终点
- Mean expiratory constant time (seconds)
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 40 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
结局指标
主要结局
Mean expiratory constant time (seconds)
时间窗: 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.
次要结局
- Date of exacerbation (dd/mm/yyyy)(Baseline)
- 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)
- 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/ 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)
- exacerbation previous year (n)(Baseline)
- FEV1 (%)(Baseline)
- FVC %(Baseline)
- FEV1/FVC %(Baseline)
- Age (years)(Baseline)
- Gender (male / female)(Baseline)
研究者
Cristina Lalmolda-Puyol
NIV coordinator Neumology Service
Corporacion Parc Tauli