Hospital de la Santa Creu i Sant Pau
Barcelona, 08041, Spain
Location status: Recruiting
Recruiting
NCT Number: NCT07447596
Oscillometry and Machine Learning Approaches
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.
Recruiting
Interested in participating?
Request InfoKey information
Conditions
Age range
18 year–99 year
Sex eligibility
All sexes
Study type
Observational
Primary location
About this study
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.
Who can participate
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
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
Treatment and study plan
1
OtherCompare oscillometry results with spirometryClick to apply
Primary outcomes
-
Oscillometric breathing pattern
Time frame: 1 year
Analyze results obtained
Secondary outcomes
-
Respiratory pattern spirometry
Time frame: 1 year
Forced expiratory volume in 1 second
Study contacts
Contact information is provided by the study sponsor or research team.
Astrid Crespo, PhD
CONTACT
Astrid Crespo-Lessmann, PhD
CONTACT
Sponsors and collaborators
Lead sponsor
Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau
Other
Registry information
Official study title
Feasibility Study of Forced Oscillometry in the Prediction of Chronic Respiratory Diseases Using Machine Learning Approaches
Important dates
- Study start
- 2025
- Primary completion
- 2026
- Study completion
- 2026
- First posted
- Mar 3, 2026
- Registry last updated
- Mar 3, 2026
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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