Feasibility Study of Forced Oscillometry in the Prediction of Chronic Respiratory Diseases Using Machine Learning Approaches
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 50
- 试验地点
- 1
- 主要终点
- Oscillometric breathing pattern
研究概览
简要总结
Unicentric retrospective study designed to analyses the performance of various machine learning approaches to predict patterns of chronic respiratory diseases such as asthma, based mainly on clinical information and respiratory spirometry/oscillometry.
详细描述
Impulse oscillometry is a technique that allows evaluation of pulmonary mechanics through the application of sound waves of different frequencies, collecting the oscillations produced in the patient in response. The use of mathematical algorithms in the interpretation of oscillometry improves the evaluation of pulmonary function. The aim of the present study is to evaluate machine learning approaches to recognize respiratory patterns of different diseases.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 99 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •18 - 90 years
- •Spirometry available
- •Confirmed clinical diagnosis of COPD, asthma, interstitial lung disease according to national or international guidelines
排除标准
- •Acute respiratory infection
研究组 & 干预措施
Oscillometry
Compare oscillometry results with spirometryClick to apply
干预措施: 1 (Other)
结局指标
主要结局
Oscillometric breathing pattern
时间窗: 1 year
Analyze results obtained
次要结局
- Respiratory pattern spirometry(1 year)