Feasibility Study of Forced Oscillometry in the Prediction of Chronic Respiratory Diseases Using Machine Learning Approaches
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 50
- Locations
- 1
- Primary Endpoint
- Oscillometric breathing pattern
Study Overview
Brief Summary
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.
Detailed Description
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.
Study Design
- Study Type
- Observational
- Observational Model
- Other
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 18 Years to 99 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •18 - 90 years
- •Spirometry available
- •Confirmed clinical diagnosis of COPD, asthma, interstitial lung disease according to national or international guidelines
Exclusion Criteria
- •Acute respiratory infection
Arms & Interventions
Oscillometry
Compare oscillometry results with spirometryClick to apply
Intervention: 1 (Other)
Outcomes
Primary Outcomes
Oscillometric breathing pattern
Time Frame: 1 year
Analyze results obtained
Secondary Outcomes
- Respiratory pattern spirometry(1 year)